How the gang of three, leptin, ghrelin and amylin, influence obesity

Obesity is commonly defined as an excessive amount of body fat relative to lean mass.1 Individuals who are classified as obese typically suffer from additional health problems which either do not afflict or have dramatically lower rates of affliction in those who are not obese. Unfortunately in the United States excess weight gain, which commonly leads to obesity, has reached what some are calling epidemic levels with more than 60% of American adults either overweight or obese.1 In addition to the dramatic rise in adult obesity even more concerning is the even more rapid rise in childhood obesity.1

One of the more practical rationalities explaining this dramatic increase in obesity is a change in energy homeostasis between energy intake versus energy expenditure. Energy intake is almost entirely derived from food consumption where energy expenditure is derived from biological thermogenesis which includes basal metabolism, adaptive thermogenesis and physical activity.2 Based on this information it makes sense to argue that an increase in food consumption joined with a corresponding decrease in physical activity due to modern conveniences creates an imbalance resulting in weight gain and obesity.

This explanation can address most of the new increases in adult obesity, but it seems to breakdown when applied to children and infants. Surprisingly, in addition to children and adults, some research demonstrates that even infants have not escaped this obesity epidemic.3 Despite changing food consumption and exercise patterns in adults and young children to teens, little has changed in the diet of infants, either breast milk from mother or bottle formula. One possible explanation is that obese mothers are influencing these changes in their infants, which does have some backing from empirical evidence,3 but at the moment it appears to be a stretch to presume such an explanation to be the principle reason behind this increase.

This change in obesity rate with no definitive explanation stemming from energy intake has lead others to question if the above theory regarding disruption in energy balance is the most influential element behind the obesity rate increase. Make no mistake that an increase in energy imbalance in favor of intake is a significant component in increased obesity rate, but due to the infant ‘question’ it may not be the only significant component. Two other popular explanations have emerged to explain this gap: toxic compounds in the environment and increased viral infections.

These two additional explanations have generated certain levels of empirical backing which add to their legitimacy. However, these elements will not be addressed in this particular blog post, but in a separate post in the future. This blog post will focus on the biochemistry behind how the increasing energy imbalance induces such a significant level of weight gain when under normal circumstances adult body weight is relatively constant despite large variations in daily food intake and energy expenditure.4 Clearly the reaction of the body to hormones and other agents, which govern appetite and energy expenditure change in obese individuals versus non-obese individuals.

Among the hormones that direct energy homeostasis, leptin is widely viewed as one of the most important, if not the most important. Leptin is secreted by adipocytes at a rate which generates serum concentrations in similar proportion to existing adipose tissue.5-7 The principle role of leptin is to provide a measurement of energy to the central nervous system to determine what types of adjustments are required to properly maintain energy homeostasis.8,9 Under normal conditions higher than normal leptin concentrations result in limited to no appetite whereas lower than normal leptin concentrations result in increased appetite as well as reduced energy expenditure and an initialization of the starvation response.10

Most work involving leptin utilize mice as the model organism all which have six distinct leptin receptors (LR) that have been designated a through f and are sub-divided into three classes: short, long and secreted.10 The secreted receptor (LRe) is created via products of alternatively spliced mRNA or proteolytic cleavage of membrane bound LRs.10,11 Secreted forms only contain extracellular domains which bind circulating leptin acting almost like a leptin inhibitor of sorts.10,11 Short receptor (LRa, LRc, LRd and LRf) function is not fully clear, but they are thought to aid leptin transport across the blood brain barrier.11,12 Finally long receptor (LRb) is crucial for leptin action as it is the principle binding receptor, which initiates leptin influence on a given neuron.10

While leptin does act in various portions of the brain, the principle region of action is in the hypothalamus, acting on thyroid and growth hormones along with sex steroids13,14 largely due to high LRb mRNA expression and associated receptor expression.13,15-16 On a side note in addition to appetite, leptin also regulates glucose homeostasis independently of adiposity and regulates glycemia.17-20

The chief area of action for leptin in the hypothalamus is the arcuate nucleus (ARC). In the ARC LRb acts on two different types of neurons: first neuropeptide Y (NPY) and agouti-related peptide (AgRP) synthesizers and second pro-opiomelanocortin (POMC) synthesizers.13,16 For signaling POMC is processed to a-melanocyte-stimulating hormone (aMSH) which binds to melanocortin-3 or 4 receptors (MC3R or MC4R) inducing appetite suppression.21-24 NPY is an orexigenic (promotes food consumption) hormone which incorporates a negative feedback mechanism suppressing growth and reproductive elements as a means to reduce energy expenditure25-27 whereas AgRP is an inverse agonist for MC4R, which also decreases cAMP production.28 Basically both NPY and AgRP act to increase appetite and reduce energy expenditure in times when the body believes a low energy state exists. Leptin binding to LRb inhibits NPY and AgRP secretion16,29 and promotes aMSH secretion in a dose-dependent fashion30 due to promotion of POMC. So overall leptin actively influences appetite by inhibiting appetite promoting NPY and driving POMC synthesis.

When individuals become obese leptin levels do not decrease. In obese individuals leptin levels are actually still represented in somewhat proper proportion to corresponding adipose tissue.7,31-32 The inability of higher leptin concentrations to reduce weight or appetite has lead to the conclusion that obese patients develop some form of leptin resistance.

A vast majority of the studies associated with leptin resistance have taken place in mice, but is also believed to properly translate to humans as well. For the purpose of experimentation mice are commonly divided into a control group and a secondary group that becomes obese due to increased food consumption. This secondary group is commonly referred to as diet-induced obesity (DIO) mice because the obesity is developed through food consumption not genetic knockout or mutation.

In DIO mice the development of leptin resistance has been hypothesized to develop over three stages: first, mice gain weight increasing the amount of adipose tissue and corresponding levels of leptin, but appear to maintain a normal response to leptin. Second, associated leptin interacting peripheral neuronal areas suffer from diminished capacity to respond to leptin. Third, associated leptin interacting central neuronal areas suffer from diminished capacity to respond to leptin.33-35

Evidence for leptin resistance has built considerably over the years. For example between non-obese and DIO mice there are no changes relative to NPY, AgRP and POMC mRNA expression with no leptin exposure.29,36 After leptin is injected decreases NPY and AgRP mRNA expression occur in control mice, but did not change significantly in DIO mice.29 Also DIO mice appear to have less activation of MC4R compared to control mice,29 which makes little sense when considering that DIO mice have higher concentrations of leptin. Some believe that the lack of MC4R activation coupled with no additional reduction in AgRP expression drives leptin resistance downstreatm in the melanocortin system.29

With this information it makes sense to conclude that the leptin resistance is being driven by the leptin concentration itself, not by another element. Studies have appeared to rule out reduced LRb expression as a reason for resistance because LRb mRNA expression typically does not demonstrate a significant difference between control and DIO mice.29,37 While there are many other hypotheses regarding the mechanism of action, the two that receive the most study are the failure of extracellular leptin to reach its appropriate receptors in the brain and the failure of the leptin activated LRb signaling mechanism.38-41

After leptin binds to LRb a number of secondary steps occur. LRb binding initiates the activation of intracellular Jak2 tyrosine kinase. Activation of Jak2 triggers autophosphorylation of the four tyrosine residues on Jak2 as well as phosphorylation of the three tyrosine residues on the intracellular tail of LRb: 985, 1077 and 1138.10 With respect to positive action LRb residue 1138 is the most important as its phosphorylation recruits signal transducer and activator of transcription (STAT) 3, for activation which is directly responsible for transcription of POMC.10 Residue 1077 recruits transcriptional activation of STAT5, which is thought to increase energy expenditure.10

However, to ensure that the effect of leptin does not permeate to a dangerous level, leptin has an inherent negative feedback element. While leptin activation of Jak2 leads to phosphorylation on residue 1138 activating STAT3 leading to the transcription of POMC, STAT3 also results in the transcription of inhibitory suppressor of cytokine signaling 3 (SOCS3).10 SOCS3 acts as the principle negative feedback inhibitor of LRb by binding to residue 985 inhibiting STAT3 recruitment and possibly activation.42,43 Tyrosine phosphatase SHP-2, which is recruited by the phosphorylation of residue 985, is also thought to be a secondary inhibitor, but SOCS3 is considered more important (if not simply based on more confirmed function).42-47 The negative inhibitory ability of SOCS-3 is supported by SOCS-3 deficiencies resulting in significant increased leptin action.29,45 A third LRb inhibitor is tyrosine phosphatase PTP1B, but because this element appears to operate independently of leptin interaction it will not be discussed further.

The rationality behind a feedback mechanism seems straightforward. For obese individuals it may be appropriate that the body limit food consumption and supplement itself through the breakdown adipose tissue, but this process does not supply all necessary nutrients for long-term survival. Therefore, the leptin inhibition feedback mechanism seems designed so that the obese do not overeat, but do eat once and a while in attempt to acquire these essential nutrients. Also the feedback mechanism could be related to necessary changes in behavior which demand greater levels of short-term energy largely derived from food consumption. However, the feedback mechanism may overcompensate in its role in obese individuals.

Relating back to the second theory behind leptin resistance focusing on the activation of the signaling mechanism after the initial binding event, leptin resistance may revolve around an inhibitory desensitization event (similar to nicotinic acetylcholine receptors). For this theory an initial leptin binding event starts the positive secondary mechanism and extended binding begins the inhibitory mechanism. Thus, due to anticipated increases in leptin binding rate and absolute binding times as adipose tissue increases due to an individual becoming obese, leptin response rates at higher leptin concentrations will decrease due to desensitization of LRbs.

Higher baseline SOCS-3 mRNA in the ARC in DIO mice29,48 support the idea of desensitization as if leptin was losing affinity for LRb then SOCS-3 levels would not change significantly. However, what is the cause of this increase? One possibility could be suppose phosphorylation of residue 1138 occurs at a faster rate than phosphorylation of residue 985 (this makes sense if under normal circumstances a sustainable positive effect is seen), but the effect or SOCS-3 has a longer residence time than POMC (recall that POMC is cleaved into aMSH); therefore, the longer leptin stays bound to LRb the more inhibition begins to overtake leptin action. Under this scenario, the longer leptin stays bound to LRb the greater the inhibitory effect on residue 1138 reducing STAT3 activity. Overall the higher concentrations of leptin in obese individuals do not allow for a sufficient period of time for the LRb receptor to “deactivate”, thus leptin action is significantly more inhibited.

Another possibility may be that leptin could bind to the short receptors (LRa, c, d and f), possibly present on the neuron, which could trigger an inhibitory mechanism separate from the initial mechanism associated with LRb. The theory here is that the long receptors have a higher expression rate in neurons which allow for more binding opportunities with leptin, but the short receptors have a higher binding affinity for leptin. As long as there is free leptin the high frequency short-term binding to LRb wins out over the low frequency long-term binding to short LRs, thus leptin represses appetite. However, suppose in situations of high leptin concentrations (obese individuals) more LRe is synthesized and secreted (as a secondary mechanism of inhibition) which binds a lot of the free leptin changing the pattern of influence so that long-term binding short LRs start to have more influence than LRb which leads to leptin resistance. Overall this theory seems more unlikely relative to desensitization as increasing leptin concentrations, no significant reduction in LRb expression and increased SOCS-3 expression all support a form of desensitization and this secondary theory relies on some unknown functionality of certain leptin receptors.

One interesting side note on this element is that STAT3 recruitment decreases profoundly in the ARC, but small recruitment decrease is seen in the ventromedial hypothalamus (VMH) or the dorsomedial hypothalamus (DMH).48 One reason for this suggestion is that cytokine signaling-3 (SOCS-3) expression is also localized to the ARC over other regions49,50 as SOCS-3 reduces the effectiveness of leptin action, probably through inhibition in the secondary pathway. Another reason is that leptin concentration in the VMH or the DMH is not high enough to induce the desensitization element associated with leptin resistance and resulting prolonged decreased STAT3 recruitment. Finally a third possibility may be a more prominent associated helper effect of amylin in the VMH over the ARC.

One of the principle reasons for regionalized increase in SOCS-3 (the first explanation above) could simply be a volume difference due to ARC being localized in a region more conducive to transport of leptin across the blood brain barrier.51 This argument is further supported by a delay in the time course for LRb signaling in non-ARC neurons relative to ARC neurons when exposed to leptin peripherally injected, but this delay does not exist when leptin is centrally injected (central injection skips passage through the blood brain barrier).51

Recall that the concentration of leptin is tied to adipose tissue, thus when exploring the nature of obesity and the role of leptin it is appropriate to investigate the role of saturated fat. Experience with cell culture studies have shown that increasing levels of saturated fat induces increased insulin resistance both in signaling and gene expression due to inhibition of the receptor, receptor substrate 1 and 2 tyrosine phosphorylation and activation of insulin antagonists phosphoinositide-3 (PPI3) kinase and Akt.52-54 There are some conflicting studies regarding how dangerous saturated fat is with regard to catalyzing obesity in different individuals.55,56 A reason behind these opposing results is probably drawn from the complexity of gene response with regards to saturated fat.

For instance one study found that high dietary saturated fat intake significantly increased the risk of obesity by 32% in those carrying 2 or more risk alleles vs. those carrying 0 or 1 risk alleles based on the relationship between STAT3 and saturated fat.52 This study determined that a vast majority of individuals are presumed to have 2 risk alleles making them more susceptible to obesity due to saturated fat consumption.52 The interaction between STAT3 and saturated fat is thought to involve saturated fat activation of toll-like receptor-4 (TLR4) and cross-talk between that activation and the JAK-STAT3 pathway.52,57 This interaction may explain why mice feed diets higher in saturated fat appear to have more aggravated cases of leptin resistance. For example some research demonstrates that for diets equally high in total fat the one comprised predominately of poly-unsaturated fat does not significantly change NPY or AgRP mRNA levels in the ARC unlike diets comprised predominately of poly-saturated fat.58 Based on this result leptin levels should be inhibited less for unsaturated fat as opposed to saturated fat.

However, an unexpected side result is that POMC mRNA expression does not change regardless of principle fat component of the diet.58 This result is unexpected because with more leptin resistance there should be some corresponding drop in POMC mRNA expression. One explanation for this result could be that there was a decrease, but the decrease was not observed because the time frame of the experiment was not long enough. Overall ARC-based NPY and AgRP also seem tied to saturated fat content largely stemming from the increases in leptin concentration derived from saturated fat instead of just total fat content.

Another issue regarding leptin resistance involves how it influences overall appetite. For the purpose of this discussion take two individuals one who is not obese and one who is obese. The overall influence of leptin resistance can be viewed in two ways. First, when an individual moves from not obese to obese leptin loses effectiveness, but not in a negative fashion; the overall leptin effect is higher than that seen in the non-obese individual, but the leptin influence ratio decreases. For example a non-obese individual with 100 ug of leptin would eat 900 calories instead of 1000 calories whereas an obese individual with 200 ug of leptin would eat 850 calories instead of 1000 calories. Second, when an individual moves from not obese to obese leptin effectiveness becomes negative both overall and as a ratio. In this scenario an individual with 200 ug of leptin would eat 930 calories. The second scenario seems supported by conclusions that food intake is increased in obese individuals ranging from 14 to 17%58 as well as decreased STAT3 phosphorylation and other LRb signaling.10 Thus if this result accurately depicts the second scenario leptin resistance must be fairly significant, creating a positive feedback loop expanding the probability that an individual remains obese.

One question from this second scenario is that even while food intake increases in obese individuals, in the short-term NPY and AgRP mRNA (and protein) levels drop.58 There does not appear to be any significant information pertaining to whether or not these mRNA levels rebound in the presence of long-term leptin resistance. If the NPY/AgRP values do rebound then it would be significant evidence that leptin was the defining element connecting NPY/AgRP and saturated fat. If not, then there most likely needs to be a secondary element which links NPY/AgRP decrease and saturated fat apart from leptin. If this is the case the most promising element for this secondary linkage is STAT3 and its TLR4 saturated fat link.

Recent information has determined that leptin may not only regulate appetite, but may also interact with dopamine neurons stimulating the reward pathway in the brain.59-62 Leptin is now thought to act in consort with the mesolimbic dopamine system which is made up of dopamine releasing neurons in the ventral tegmental area (VTA) which innervate into the striatum, amygdala and prefrontal cortex.59 In addition to some neurons expressing LRb in the VTA, other studies have identified LRb expressing neurons in the lateral hypothalamic area (LHA) which project to the VTA.10 Therefore, it appears that there are two separate leptin based systems which could influence the dopamine based reward pathway in some context. This effect would especially be prominent if leptin limited the pleasure received from consuming food because then the development of leptin resistance would neutralize this limiting characteristic increasing the probability of greater food consumption in obese individuals.

With regards to leptin resistance and its overall influence on obesity it may have an important partner in amylin. Amylin is a 37 amino acid peptide hormone commonly co-secreted with insulin from pancreatic b-cells,64 which principally binds to receptors in the hindbrain area postrema (AP).65,66 The importance of amylin relative to leptin and obesity is a number of experiments have demonstrated that increasing concentrations of amylin in both non-obese and obese individuals induces weight loss with an increased reduction when combined with leptin.66,67 One lingering question is whether or not this weight loss can be principally attributed to amylin directly or if amylin reduces leptin resistance allowing leptin to be more effective.

One piece of evidence which supports the latter explanation and ties to the issue of leptin resistance is that amylin-augmented weight loss in DIO rats was observed with an increase in POMC expression in the ARC.68,69 This increase in POMC expression implies that weight loss seen in obese individuals due to increased levels of amylin would eventually be realized as reduced food intake due to aMSH binding, an increase probably stemming from leptin derived POMC increases.

While leptin activity in the ARC is important it appears, not surprisingly, that it not the exclusive operating area for leptin influence with regards to appetite control. Selective lesioning of leptin receptors on ARC POMC neurons does not completely replicate the development of obesity which accompanies general leptin deficiency (from knockouts).48 Also leptin resistance demonstrates a decrease in STAT3 signaling throughout the VMH and AP which support the notion of leptin resistance beyond the ARC.70,71 Therefore, the mechanism behind amylin induced reduction of leptin resistance could stem from amylin increasing signal effectiveness in the VMH and AP.66

Action in the VMH is supported by an approximate 43% increase in leptin signaling (derived from STAT3 comparisons) in the VMH without any direct increase in the ARC.66,72 An increase in STAT3 concentration coincides with other studies.67 The important targets for leptin in the VMH appear to be glucose-sensitive neurons and transcription factor SF-1.73-76 Transcription factor SF-1 could be important because it induces DIO in mice that lack it.77 One reason explaining this result is that co-localization studies identify SF-1 VMH neurons linked to leptin-induced activation of STAT3.66

No changes in leptin response were seen in the caudal nucleus of the solitary tract (NTS) with direct amylin treatment.66 Nor did amylin influence leptin, LRb or SOCS-3 concentrations via gene expression changes.66 Once again the lack of amylin influence on leptin, LRb, SOCS-3 could be a timecourse issue due to measurements not being long enough or taken at the appropriate time, but overall this is unlikely. Moreover it is unlikely that the AP is a site of cooperative action between leptin and amylin due to the low level of overall expression of leptin receptors and low leptin-derived STAT3 expression.66,78 Also amylin receptors are limited in the ARC and amylin administration does not activate c-Fos in the hypothalamus.79,80 Finally amylin functions through calcitonin receptor binding using receptor activity-modulating protein (RAMP) via a second messenger cGMP81,82 not through Jak kinases lke leptin. Therefore, if the principle area of action for amylin is the AP its action must propogate from the AP to an area more conducive for leptin action like the VMH.

Based on all of the results amylin influence seems to be localized to its respective receptors in the AP and through neuronal connections (probably polysynaptic) act upstream on the VMH enhancing leptin response through the NTS and the lateral parabrachial nucleus (lPBN).66,79,84,85 This conclusion is supported by amylin binding in the AP activating the NTS, IPBN and central nucleus in the amygdala to inhibit fasting derived activation of the lateral hypothalamic (LH) area65 where LH afferents are thought to influence VMH signaling.86,87 Also lesions on the lPBN blocked amylin-induced c-Fos expression.83 There is also some evidence to suggest that leptin binding is also increased in the DMN.66 In addition leptin may have a feedback effect on amylin in that amylin effectiveness is reduced in LRb knockouts.88

So how does amylin binding aid leptin? Amylin knockout mice exhibited overall reduced leptin signaling in response to exogenous leptin, largely patterned with a reduced residence time of action.66 This reduction in operational time (initially the leptin was able to function and later become less effective) is thought to involve a reduction in LRb expression.66 A smaller amount of LRb would result in faster desensitization of available receptors and loss of leptin activity.

The reverse of this thought process makes sense to why amylin may ‘rescue’ leptin action in obese individuals. Recall that STAT3 expression does not appear significantly reduced in non-ARC areas of leptin action like the VMH (this is probably the reason that most obese individuals do not incessantly eat). Increased amylin concentrations could increase the amount of LRb expression creating more leptin binding targets. More leptin binding targets would reduce the overall binding time a given LRb had with leptin generating more ‘deactivation’ time which could reduce the rate of desensitization. This theory is further supported by other data demonstrating a 70% increase in ARC leptin binding with amylin/leptin sustained infusion.67 However, there is little information regarding how long-term the weight loss produced by amylin augmentation may be. Overall it is difficult to conclude that amylin by itself can result in enough weight loss to reduce leptin concentration enough to eliminate leptin resistance in combination with greater LRb expression.

Overall leptin and amylin appear to have cooperative effect in managing energy intake and expenditure in humans. While leptin appears to have a greater overall influence, especially in non-obese individuals due to a lack of resistance, both molecules are able to reduce weight in non-obese and obese individuals individually, but their effects are significantly augmented when both are present. Like leptin, amlyin is also thought to increase energy expenditure.69,89,90 The nature of leptin resistance and the general decrease in weight in response to increased amylin levels due to injection or other means of administration leads to two conclusions. First, it seems reasonable to suggest that amylin concentrations in obese individuals could also be negatively affected either in a manner of overall availability or its overall effectiveness (similar to leptin). Second, amylin as an outside therapeutic target (endogenous administration) does not appear to have the necessary influence for use as a stand-alone treatment for obesity because while weight is lost, the overall obesity does not appear reversed.

Ghrelin is another element which may play a meaningful role in energy regulation affecting the probability of becoming obese. The principle role of ghrelin is to stimulate growth hormone release91-93 it is also thought to play a role in regulation of body weight.94,95 Its role in regulation comes from the response of increasing ghrelin concentrations stimulating appetite with a corresponding decrease in levels after eating, more than likely induced by insulin and leptin.96-99 Ghrelin exerts its effect by binding to NPY/AgRP neurons. The reason behind this appetite stimulation aspect of ghrelin could stem from its relationship with growth hormones as more available energy is typically advantageous to augmenting the effectiveness of growth hormones.

A vast majority of synthesized ghrelin is produced in the stomach so to act in the brain it needs to cross the blood brain barrier, similar to leptin. Studies have shown that due to the non-saturated, saturated transport structure used by ghrelin normal individuals typically have an inverse relationship between weight and ghrelin transport.100 Unlike what is believed for leptin, not surprising given the general opposition of their roles, triglycerides promote ghrelin transport across the blood brain barrier rather than inhibit.100 The ghrelin promotion effect makes sense if one believes that triglycerides are a signal to the brain for starvation.101 However, the loss of transport in obese mice is not accounted for by triglycerides leading others to conclude that there must be a second secreted molecule which drives this transport inefficiency. One possibility may be obestatin.

From a synthesis standpoint leptin inhibition of ghrelin seems to make sense both in the opposing effects on appetite as well as obese individuals having lower baseline concentrations of ghrelin than non-obese individuals.102,103 However, the interesting element to this lower baseline is that it does not appear to be uniform (steady-state). Instead the relationship between ghrelin concentrations in obese and non-obese individuals differed based on time of day with daylight levels being somewhat similar with obese individuals having much lower concentrations at night.94

Currently the reason behind the significant change in ghrelin concentration at night is unknown, but a consequence of this change may relate to obesity from a standpoint of energy expenditure more than energy intake. Despite significant increases in ghrelin concentration during the night in healthy individuals, there is little food consumption or need for food consumption to correspond to this increase; therefore, these concentration increases probably have more to do with growth hormone regulation than energy intake. Growth hormone activation typically results in greater energy expenditure. However, in obese individuals if nocturnal ghrelin concentrations are not increasing then there is less growth hormone activity which can result in stunted growth, less efficient cell repair and less overall energy consumption. Thus this change in ghrelin concentration could be another positive feedback associated with obesity in that the more obese an individual is the less energy he/she expends during the night through the use of growth hormones, thus increasing the probability of maintaining that obesity.

One final issue is that there is some question whether or not leptin transport across the blood-brain barrier is obstructed in obese individuals compared to non-obese individuals. If leptin transport activity is negatively influenced it could offer an alternative explanation to why greater concentrations of leptin may induce less leptin activity. Unfortunately there are two immediate problems with this theory. First, the methodology explaining how obesity negatively influences leptin transport is unknown and little viable explanations exist to why obesity would negatively affect transport. The best explanation could be that triglycerides, which typically increase in concentration as an individual becomes more obese, reduce leptin transport, but the demanded volume of reduction seems too high from an intuitive standpoint. Second, decreases in leptin transport are counter to the demonstrated increases in SOCS-3 concentrations in DIO mice. Therefore, even if leptin transport across the blood-brain barrier is reduced in obese individuals, which is still questionable,71,104-106 it is difficult to suggest that this reduction is significant enough to act as a meaningful explanation for ‘leptin resistance’.

Overall the process of moving from a non-obese individual to an obese individual from an energy balance perspective appears to lean heavily on hormone leptin. Leptin functions not only to influence appetite, but also reward pathways inducing a secondary biochemical means to affect food consumption and a possible interaction with saturated fat which may change leptin secretion patterns. The biggest energy balance element driving maintenance of obesity is the development of leptin resistance which probably follows a desensitization pathway with leptin saturation relative to LRb in the ARC. In addition to leptin, amylin and ghrelin also play important roles in appetite regulation and energy expenditure.

At the moment there does not appear to be a ‘silver’ bullet treatment for obesity on a biochemical level. However, there are opportunities for co-therapies to be successful. For example as previously mentioned it does not appear that increasing amylin concentration will be sufficient to overcome leptin resistance in obese individuals; however, co-therapy using an amylin mimic and an aMSH agnoist could work together to overcome a significant potion of the leptin resistance. Further study of obestatin could lead to a better understanding of its role, if any, in ghrelin function and whether or not it would be useful in treatment. Another option may be cholecystokinin which has shown to indirectly increase STAT3 concentration in low-dose leptin environments.107 Finally there is the lingering option of addressing SOCS-3 concentration changes through inhibition. Therefore, while addressing obesity appears to be a difficult mountain to climb, by better understanding the biochemical realities and changes between obese individuals and non-obese individuals both new drug therapies and food consumption strategies can be developed to combat obesity in a safe, cost-effective and appropriate manner.

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Citations:

1. Zamboni, M, et, Al. “Health consequences of obesity in the elderly: a review of four unresolved questions.” Int. J. Obes. (Lond.) 2005. 29: 1011–1029.
2. Enriori, P, et, Al. “Leptin Resistance and Obesity.” Obesity. 2006. 14: 254S-258S.
3. Whitaker, R. “Predicting Preschooler Obesity at Birth: The Role of Maternal Obesity in Early Pregnancy.” Pediatrics. 2004. 114:29-36.
4. Seeley, R.J, and Woods, S. “Monitoring of stored and available fuel by the CNS: implications for obesity.” Nat. Rev. Neurosci. 2003. 4:
901–909.
5. Zhang, Y, et, Al. “Positional cloning of the mouse obese gene and its human homologue.” Nature. 1994. 372:425–32.
6. Maffei, M, et, Al. “Leptin levels in human and rodent: measurement of plasma leptin and ob RNA in obese and weight-reduced subjects.” Nat Med. 1995. 1:1155–61.
7. Considine, R, et, Al. “Serum immunoreactive-leptin concentrations in normal-weight and obese humans.” N Engl J. Med. 1996. 334:292–5.
8. de Luca, C, et, Al. “Complete rescue of obesity, diabetes, and infertility in db/db mice by neuronspecific LEPR-B transgenes.” J. Clin. Invest. 2005. 115: 3484–3493.
9. Spiegelman, B and Flier, J. “Obesity and the regulation of energy balance.” Cell. 2001. 104:531– 43.
10. Myers, M, Cowley, M, Munzberg, H. “Mechanisms of Leptin Action and Leptin Resistance.” Annu. Rev. Physiol. 2008. 70:537-556.
11. Ge, H, et, Al. “Generation of soluble leptin receptor by ectodomain shedding of membrane-spanning receptors in vitro and in vivo.” J. Biol. Chem. 2002. 277:45898–903.
12. Uotani, S, et, Al. “Functional properties of leptin receptor isoforms: internalization and degradation of leptin and ligand-induced receptor downregulation.” Diabetes. 1999. 48:279–86.
13. Elmquist, J, Elias, C, Saper, C. “From lesions to leptin: hypothalamic control of food intake and body weight.” Neuron. 1999. 22:221–32.
14. Inui, A. “Feeding and body-weight regulation by hypothalamic neuropeptides—mediation of the actions of leptin.” Trends Neurosci. 1999. 22:62–67.
15. Baskin, D, et, Al. “Leptin receptor long-form splice-variant protein expression in neuron cell bodies of the brain and colocalization with neuropeptide Y mRNA in the arcuate nucleus.” J. Histochem. Cytochem. 1999. 47:353–62.
16. Schwartz, M, et, Al. “Central nervous system control of food intake.” Nature. 2000. 404:661–71.
17. Liu, L, et, Al. “Intracerebroventricular leptin regulates hepatic but not peripheral glucose fluxes.” J. Biol. Chem. 1998. 273:31160–67.
18. Burcelin, R, et, Al. “Acute intravenuous leptin infusion increases glucose turnover but not skeletal muscle glucose uptake in ob/ob mice.” Diabetes. 1999. 48:1264–69.
19. Kieffer, T, et, Al. “Leptin suppression of insulin secretion by the activation of ATP-sensitive K+ channels in pancreatic beta-cells.” Diabetes. 1997. 46:1087–93.
20. Covey, S, et, Al. “The pancreatic beta cell is a key site for mediating the effects of leptin on glucose homeostasis.” Cell Metab. 2006. 4:291–302.
21. Huszar, D, et, Al. “Targeted disruption of the melanocortin-4 receptor results in obesity in mice.” Cell. 1997. 88:131–41.
22. Butler, A, and Cone, R. “The melanocortin receptors: lessons from knockout models.” Neuropeptides. 2002. 36:77–84.
23. Ste, M, et, Al. “A metabolic defect promotes obesity in mice lacking melanocortin-4 receptors.” PNAS. 2000. 97:12339–44.
24. Chen, A, et, Al. “Inactivation of the mouse melanocortin-3 receptor results in increased fat mass and reduced lean body mass.” Nat. Genet. 2000. 26:97–102.
25. Erickson, J, Hollopeter, G, Palmiter, R. “Attenuation of the obesity syndrome of ob/ob mice by the loss of neuropeptide Y.” Science. 1996. 274:1704–1707.
26. Clark, J, Kalra, P, Kalra, S. “Neuropeptide Y stimulates feeding but inhibits sexual behavior in rats.” Endocrinology. 1985. 117:2435–42.
27. Smith, M, and Grove, K. “Integration of the regulation of reproductive function and energy balance: lactation as a model.” Front. Neuroendocrinol. 2002. 23:225–56.
28. Ollmann, M, et, Al. “Antagonism of central melanocortin receptors in vitro and in vivo by agouti-related protein.” Science. 1997. 278:135–38.
29. Enriori, P, et, Al. “Diet-induced obesity causes severe but reversible leptin resistance in arcuate melanocortin neurons.” Cell Metab. 2007. 5:181–94.
30. Li, J, et, Al. “Agouti-related protein-like immunoreactivity: characterization of release from hypothalamic tissue and presence in serum.” Endocrinology. 2000. 141: 1942–1950.
31. Farooqi, I, and O’Rahilly, S. “Monogenic obesity in humans.” Annu. Rev. Med. 2005. 56:443–458.
32. Maffei, M, et, Al. “Absence of mutations in the human Ob gene in obese/diabetic subjects.” Diabetes. 1996. 45:679–82.
33. El-Haschimi, et, Al. “Two defects contribute to hypothalamic leptin resistance in mice with diet-induced obesity.” J Clin Invest. 2000. 105:1827–32.
34. Lin, S, et, Al. “Development of high fat diet-induced obesity and leptin resistance in C57Bl/6J mice.” Int J Obes Relat Metab Disord. 2000. 24:639–46.
35. Prpic, V, et, Al. “Differential mechanisms and development of leptin resistance in A/J versus C57BL/6J mice during dietinduced obesity.” Endocrinology. 2003. 144:1155– 63.
36. Takahashi, N, et, Al. “Divergent effects of leptin in mice susceptible or resistant to obesity.” Horm. Metab. Res. 2002. 34: 691–697.
37. Sahu, A, Nguyen, L, O’Doherty, R. “Nutritional regulation of hypothalamic leptin receptor gene expression is defective in diet-induced obesity.” J. Neuroendocrinol. 2002. 14: 887–893.
38. Banks, W, and Farrell, C. “Impaired transport of leptin across the blood-brain barrier in obesity is acquired and reversible.” Am J Physiol Endocrinol Metab. 2003. 285:E10 –5.
39. Munzberg, H, and Myers, M. Jr. “Molecular and anatomical determinants of central leptin resistance.” Nat Neurosci. 2005. 8:566–70.
40. Banks, W. “The many lives of leptin.” Peptides. 2004. 25:331–38.
41. Bouret, S, and Simerly, R. “Developmental programming of hypothalamic feeding circuits.” Clin. Genet. 2006. 70:295–301.
42. Bjørbæk, C, et, Al. “SOCS3 mediates feedback inhibition of the leptin receptor via Tyr985.” J. Biol. Chem. 2000. 275:40649–57.
43. Bjørbæk C, et, Al. “Identification of SOCS-3 as a potential mediator of central leptin resistance.” Mol. Cell. 1998. 1:619–25.
44. Dunn SL, Bjornholm M, Bates SH, Chen Z, Seifert M, Myers MG Jr. 2005. Feedback
inhibition of leptin receptor/Jak2 signaling via Tyr1138 of the leptin receptor and
suppressor of cytokine signaling 3. Mol. Endocrinol. 19:925–38
45. Mori, H, et, Al. “Socs3 deficiency in the brain elevates leptin sensitivity and confers resistance to diet-induced obesity.” Nat. Med. 2004. 10:739–43.
46. Howard, J, et, Al. “Enhanced leptin sensitivity and attenuation of diet-induced obesity in mice with haploinsufficiency of Socs3.” Nat. Med. 2004. 10:734–38.
47. Zhang, E, et, Al. “Neuronal Shp2 tyrosine phosphatase controls energy balance and metabolism. PNAS. 2004. 101:16064–69.
48. Munzberg, H, Flier, J, and Bjorbaek, C. “Region-specific leptin resistance within the hypothalamus of diet-induced obese mice.” Endocrinology. 2004. 145:4880–4889.
49. Tups, A, et, Al. “Photoperiodic regulation of leptin sensitivity in the Siberian hamster, Phodopus sungorus, is reflected in arcuate nucleus SOCS-3 (suppressor of cytokine signaling) gene expression.” Endocrinology. 2004. 145:1185–93.
50. Krol, E, et, Al. “Altered expression of SOCS3 in the hypothalamic arcuate nucleus during seasonal body mass changes in the field vole, Microtus agrestis.” J. Neuroendocrinol. 2007. 19:83–94.
51. Faouzi, M, et, Al. “Differential accessibility of circulating leptin to individual hypothalamic sites.” Endocrinology. 2007. 148:5414–23.
52. Phillips, C, et, Al. “Dietary Saturated Fat Modulates the Association between STAT3 Polymorphisms and Abdominal Obesity in Adults.” J. Nutr. 2009. 139: 2011–2017.
53. Frangioudakis, G, Ye, J, Cooney, G. “Both saturated and n-6 polyunsaturated fat diets reduce phosphorylation of insulin receptor substrate-1 and protein kinase B in muscle during the initial stages of in vivo insulin stimulation.” Endocrinology. 2005. 146:5596–603.
54. Ruddock, M, et, Al. “Saturated fatty acids inhibit hepatic insulin action by modulating insulin receptor expression and post-receptor signalling.” J. Biochem. 2008. 144:599–607.
55. Brunner, E, Wunsch, H, Marmot, M. “What is an optimal diet? Relationship of macronutrient intake to obesity, glucose tolerance, lipoprotein cholesterol levels and the metabolic syndrome in the Whitehall II study.” Int J Obes Relat Metab Disord. 2001. 25:45–53.
56. Doucet, E, et, Al. “Dietary fat composition and human adiposity.” Eur J Clin Nutr. 1998. 52:2–6.
57. Hu, X, et, Al. “Crosstalk among Jak-STAT, Tolllike receptor, and ITAM-dependent pathways in macrophage activation.” J. Leukoc Biol. 2007. 82:237–43.
58. Wang, H, Storlien, L, and Huang, X. “Effects of dietary fat types on body fatness, leptin, and ARC leptin receptor, NPY, and AgRP mRNA expression.” Am J Physiol Endocrinol Metab. 2002. 282:E1352-E1359.
59. Figlewicz, D, et, Al. “Intraventricular insulin and leptin reverse place preference conditioned with high-fat diet in rats.” Behav. Neurosci. 2004. 118:479–87.
60. Figlewicz, D, and Woods, S. “Adiposity signals and brain reward mechanisms.” Trends Pharmacol. Sci. 2000. 21:235–36.
61. Carr, K. “Chronic food restriction: enhancing effects on drug reward and striatal cell signaling.” Physiol. Behav. 2007. 91:459–72.
62. Shizgal, P, Fulton, S, Woodside, B. “Brain reward circuitry and the regulation of energy balance.” Int. J. Obes. Relat. Metab. Disord. 2001 25(Suppl. 5):S17–21
63. Kelley, A, and Berridge, K. “The neuroscience of natural rewards: relevance to addictive drugs.” J. Neurosci. 2002. 22:3306–11.
64. Ogawa, A, et, Al. “Amylin secretion from the rat pancreas and its selective loss after streptozotocin treatment.” J Clin Invest. 1990. 85:973–976.
65. Lutz, T. “Amylinergic control of food intake.” Physiol Behav. 2006. 89:465–471.
66. Roth, J, et, Al. “Leptin responsiveness restored by amylin agonism in diet-induced obesity: Evidence from nonclinical and clinical studies.” PNAS. 2008. 105(20): 7257-7262.
67. Turek, V, et, Al. “Mechanisms of Amylin/Leptin Synergy in Rodent Models.” Endocrinology. 2010. 151:143–152.
68. Roth, J, et, Al. “Antiobesity effects of the β-cell hormone amylin in diet-induced obese rats: Effects on food intake, body weight, composition, energy expenditure, and gene expression.” Endocrinology. 2006. 147:5855–5864.
69. Mack, C, et, Al. “Pharmacological actions of the peptide hormone amylin in the long-term regulation of food intake, food preference and body weight.” Am J Physiol. 2007. 293:R1855–R1863.
70. Balthasar N, et, Al. “Leptin receptor signaling in POMC neurons is required for normal body weight homeostasis.” Neuron. 2004. 4:983–991.
71. Levin, B, Dunn-Meynell, A, Banks, WA. “Obesity-prone rats have normal blood–brain barrier transport but defective central leptin signaling before obesity onset.” Am J Physiol. 2004. 286:R143–R150.
72. Patterson, C, et, Al. “Three weeks of postweaning exercise in DIO rats produces prolonged increases in central leptin sensitivity and signaling.” AmJ Physiol Regul Integr Comp Physiol. 2009. 296:R537–R548.
73. Dhillon, H, et, Al. “Leptin directly activates SF1 neurons in the VMH, and this action by leptin is required for normal body-weight homeostasis.” Neuron. 2006. 49:191–203.
74. Jacob, R, et, Al. “The effect of leptin is enhanced by microinjection into the ventromedial hypothalamus.” Diabetes. 1997. 46:150–152.
75. Elmquist, J, et, Al. “Leptin activates distinct projections from the dorsomedial and ventromedial hypothalamic nuclei.” PNAS. 1998. 95:741–746.
76. Canabal, D, et, Al. “Glucose, insulin and leptin signaling pathways modulate nitric oxide (NO) synthesis in glucose-inhibited (GI) neurons in the ventromedial hypothalamus (VMH).” Am J Physiol. 2007. 292:R1418–R1428.
77. Majdic, G, et, Al. “Knockout mice lacking steroidogenic factor 1 are a novel genetic model of hypothalamic obesity.” Endocrinology. 2002. 143:607–614.
78. Elmquist, J, et, Al. “Distributions of leptin receptorm RNA isoforms in the rat brain.” J. Comp Neurol. 1998. 395:535–547.
79. Riediger, T, et, Al. “The anorectic hormone amylin contributes to feeding-related changes of neuronal activity in key structures of the gut-brain axis.” Am J Physiol Regul Integr Comp Physiol. 2004. 286:R114–R122.
80. Rowland, N, Crews, E, Gentry, R. “Comparison of Fos induced in rat brain by GLP-1 and amylin.” Regul Pept. 1997. 71:171–174.
81. Christopoulos, G, et, Al. “Multiple amylin receptors arise from receptor activity-modifying protein interaction with the calcitonin receptor gene product.” Mol Pharmacol. 1999. 56:235–242.
82. Riediger, T, et, Al. “Amylin potently activatesAPneurons possibly via formation of the excitatory second messenger cGMP.” Am J Physiol Regul Integr Comp Physiol. 2001. 281:R1833–R1843.
83. Lutz, T. “Roles of Amylin in Satiation, Adiposity and Brain Development.” Forum Nutr. Basel, Karger. 2010. 63:64-74.
84. Becskei, C, et, Al. “Lesion of the lateral parabrachial nucleus attenuates the anorectic effect of peripheral amylin and CCK.” Brain Res. 2007. 1162:76–84.
85. Rowland, N, and Richmond, R. “Area postrema and the anorectic actions of dexfenfluramine and amylin.” Brain Res. 1999. 820:86–91.
86. Fahrbach, S, Morrell, J, Pfaff, D. “Studies of ventromedial hypothalamic afferents in the rat using three methods of HRP application.” Exp Brain Res. 1989. 77:221–233.
87. Ono, T, et, Al. “Topographic organization of projections from the amygdala to the hypothalamus of the rat.” Neurosci Res. 1985. 2:221–238.
88. Eiden, S, et, Al. “Salmon calcitonin: a potent inhibitor of food intake in states of impaired leptin signalling in laboratory rodents.” J. Physiol. 2002. 541:1041–1048.
89. Osaka, T, et, Al. “Central and peripheral administration of amylin induces energy expenditure in anesthetized rats.” Peptides. 2008. 29:1028–1035.
90. Isaksson, B, et, Al. “Chronically administered islet amyloid polypeptide in rats serves as an adiposity inhibitor and regulates energy homeostasis.” Pancretology. 2005. 5:29–36.
91. Kojima, M, et, Al. “Ghrelin is a growth-hormone-releasing acylated peptide from stomach.” Nature. 1999. 402(6762):656-60.
92. Takaya, K, et, Al. “Ghrelin strongly stimulates growth hormone release in humans.” J. Clin. Endocrinol. Metab. 2000. 85(12):4908-11.
93. Peino, R, et, Al. “Ghrelin-induced growth hormone secretion in humans.” Eur. J. Endocrinol. 2000. 143(6):R11-4.
94. Yildiz, B, et, Al. “Alterations in the dynamics of circulating ghrelin, adiponectin, and leptin in human obesity.” PNAS. 2004. 101(28):10434-10439.
95. Wren, A, et, Al. “Ghrelin enhances appetite and increases food intake in humans.” J. Clin. Endocrinol. Metab. 2001. 86(12):5992.
96. Cummings, D, et, Al. “A preprandial rise in plasma ghrelin levels suggests a role in meal initiation in humans.” Diabetes. 2001. 50(8):1714-9.
97. Tschop, M, et, Al. “Post-prandial decrease of circulating human ghrelin levels.” J. Endocrinol Invest. 2001. 24(6):RC19-21.
98. Anderwald, C, et, Al. “Insulin-dependent modulation of plasma ghrelin and leptin concentrations is less pronounced in type 2 diabetic patients.” Diabetes. 2003. 52(7):1792-8.
99. Barazzoni, R, et, Al. “Hyperleptinemia prevents increased plasma ghrelin concentration during short-term moderate caloric restriction in rats.” Gastroenterology. 2003. 124(5):1188-92.
100. Banks, W, Burney, B, Robinson, S. “Effects of Triglycerides, Obesity, and Starvation on Ghrelin Transport Across the Blood-Brain Barrier.” Peptides. 2008. 29(11): 2061–2065.
101. Banks, W, Farr, S, Morley, J. “The effects of high fat diets on blood-brain barrier transport of leptin: Failure or Adaptation?” Physiol Behav. 2006. 88:244–248.
102. Zigman, J, and Elmquist, J. “Minireview: From anorexia to obesity--the yin and yang of body weight control.” Endocrinology. 2003. 144(9):3749-56.
103. Shiiya, T, et, Al. “Plasma ghrelin levels in lean and obese humans and the effect of glucose on ghrelin secretion.” J. Clin. Endocrinol. Metab. 2002. 87(1):240-4.
104. Adam, C, Findlay, P. “Decreased blood–brain leptin transfer in an ovine model of obesity and weight loss: resolving the cause of leptin resistance.” International Journal of Obesity. 2010. 34:980–988.
105. Rahmouni, K, et, Al. “Leptin resistance contributes to obesity and hypertension in mouse models of Bardet-Biedl syndrome.” J Clin Invest. 2008.118(4):1458–1467.
106. Burguera, B, et, Al. “Obesity Is Associated With a Decreased Leptin Transport Across the Blood-Brain Barrier in Rats” Diabetes. 2000. 1219-1223.
107. Merino, B, et, Al. “Leptin mediated hypothalamic pathway of cholecystokinin (CCK-8) to regulate body weight in free-feeding rats.” Endocrinology. 2008. 149:1994 –2000.”

Looking at the Nature of Cancer and How to Treat it

In the 1971 State of the Union address President Richard Nixon ‘declared’ war on cancer.1 Sadly despite a painstaking effort by many intelligent researchers, the war continues to drag on with no clear end in sight. Throughout the last forty years new strategies and new technologies have broadened the ability to attack cancer, but despite these advances, from a standpoint of long-term survival rates positive change has fallen to the old adage ‘walk a mile just to move an inch’. However, the advent of genetic engineering and nanotechnology has once again stimulated excitement in the cancer community driving the belief that genetic engineering eliminates the anonymity of a particular cancer and nanotechnology allows for more efficient combat. This excitement must be tempered with rationality and caution though for while new cancer-fighting strategies have flooded the research environment the methodologies which make up these strategies have yet to be fully assessed.

Scientific involvement in cancer can be divided among three different avenues: prevention, detection/diagnosis and treatment. For most of modern medicine the focus has be on that third avenue, treatment of cancer. Unfortunately most of the time in the past cancer treatment began at a much later time than desired because of limited diagnostic methodologies. As technology has advanced, there is hope for the development of better diagnostic strategies which will not only detect cancer earlier in its development, but also identify the type of cancer, information that can be used to better predict cancer prognosis and its development. With this additional information better treatments can then be applied significantly increasing survival rates.

However, the first prong of cancer, prevention, still remains slow developing because of a lack of genuine understanding regarding the origin(s) of cancer. While some claim eating food x or taking drug y should potentially reduce the probability of cancer occurrence, there is little to no scientific statistical evidence that have validated any of these claims. Some believe that certain anti-cancer drugs can be taken in a preventative manner, but these drugs also carry significantly detrimental side effects which reduces their attractiveness largely due to the low absolute percentage drops in cancer development in their application (chance of developing cancer type x goes from 1.3% to 0.9%). Based on this uncertainty this blog post will largely explore the strategies that are currently being used to combat cancer both from a standpoint of treatment and diagnosis and those that some hope to utilize in the future.

There are a number of individuals who would argue that diagnosis is the most important element in cancer treatment because very few individuals die from single organ/primary cancer, a notable exception being pancreatic cancer. Instead mortality much more frequently comes after metastasis. Another way to look at the diagnosis philosophy is that even an inefficient cancer treatment can manage cancer localized to a small region of the body, but even the most effective existing cancer treatments have a difficult time eliminating metastasized cancer. As previously mentioned this reasoning underscores one of the principle reasons cancer survival rates have increased at such a low rate over the last decades, earlier diagnosis of cancer has been methodologically difficult.

One major reason for this difficulty is due to the exhausting, side effect riddled and potentially toxic nature of cancer treatment. Due to the expense and strain required when treating a cancer case, physicians favor definitive diagnosis to confirm cancer which involves histologic examination of a specimen derived from a potential developing tumor. This entire process is commonly referred to as a biopsy. Due to the invasive nature of biopsies, physicians need some form of probable cause before considering one, probable cause which is typically acquired through some form of symptomatic, radiographic or visual abnormality. Unfortunately recognition of any of these abnormalities is rare in the very early stages of cancer development and by the time they emerge the probability for metastasis has significantly increased. Early diagnosis would be limited in importance if a very specific (no side-effects) and inexpensive cancer treatment were developed because then probable cancer cases that turned out to be false would not be detrimentally served by treatment, but such a treatment is not realistically viable at this point in time.

Currently biopsy is the only genuinely accurate means to determine whether or not an individual has cancer. The usefulness of some diagnostic techniques like mammograms have lingering questions over their statistical accuracy and whether or not they are useful to administer in the first place whereas other like Pap smears have demonstrated more reliability.2,3,4 However, biopsies have also come into question, not for their accuracy, but for whether or not they can inadvertently aid in metastasis.

The process of a biopsy involves inserting a needle into a cluster of cells thought to be cancerous and drawing off some of those cells. Through this process some have raised concerns that the penetration of the needle into the cluster of cells may dislodge some of these cells allowing them to be caught up in the bloodstream and carried to other parts of the body creating secondary cancers, in short accelerating metastasis. Some empirical evidence5 does exist to support this hypothesis, but fortunately new sterilization instruments have been developed within the injecting needle to minimize concern; unfortunately these new instrument have yet to be put into widespread use.

While biopsies are accurate one of the big goals in the cancer community is to develop a technique that rivals in accuracy, but is also much less invasive. Among the new diagnostic strategies being developed to avoid the invasiveness of a biopsy, cancer identifying biomarker are clearly gaining the most attention. Note that in biology the term ‘biomarker’ is used to describe two different types of elements.

First, biomarkers can be chemical molecules that are attached to other components in the body to identify them. For example various forms of fluorescent molecules, like luciferase, are commonly used to track the travel and/or presence of molecules in the bloodstream or observe some other biological function. Second, biomarkers can be DNA, RNA or protein that belies an abnormality within the body typically indicating some form of disease or deleterious condition.6 Basically biomarkers are biological characteristics, typically associated with DNA, RNA or protein expression, which are used to develop probability profiles regarding disease acquisition or probability of successful treatment with a given therapy.

The second definition for biomarker can be broken down further into diagnostic biomarkers and prognostic biomarkers where prognostic describes a biomarker that when present either increases or decreases the probability of a given condition over a given baseline average probability. A diagnostic biomarker confirms the existence of a given condition. In cancer treatment while significant publicity and research has gone into developing prognostic biomarkers, having gene x over gene y means greater chance for lung cancer, etc. the ‘black box’ nature of cancer negatively impacts the overall importance of those prognostic biomarkers.

The general idea with prognostic biomarkers in cancer should be to create two tiers of individuals: those without higher probabilities for cancer who continue to undertake standard screenings and those with certain prognostic biomarkers who undertake more specific and focused cancer screening in order to detect any higher probability cancers at an earlier stage in their development.

Diagnostic biomarkers have made significant gains in importance and viability over the last decade under the guidance of an improved understanding of genetics and the dream of personalized medical treatments. In order to replace biopsies, diagnostic biomarkers must be less invasive, preferably non-invasive, and accurate. The ideal for a diagnostic biomarker is an easily identifiable element which changes dramatically in concentration during the development of cancer vs. non-cancer and can be extracted through the examination of a blood, urine or saliva sample. In addition uniformity in the analysis is also necessary; it should not matter if the analysis is performed at laboratory A or laboratory B. Although speed of analysis is also viewed as important by some it is significantly less important than accuracy and invasiveness because the probabilistic mortality rate for cancer does not typically change significantly on a day-by-day basis so it should make little difference if the analysis takes 1 minute, 1 hour or 1 day to complete once an accurate biomarker has been established.

Biomarker developmental intent can take one of two paths: general or genetically specific. General uses viruses which only invade cells overexpressing a certain antigen or receptor, usually attributable to some form of cancer. The major advantage for the general method is an easier means to identify cancer, while the major disadvantage is it is unable to determine the location or the type of cancer. Not surprisingly these strengths/weaknesses are basically reversed for more genetically specific methods where one can better identify the type and possibly even the specific mutation of cancer. However, due to a wide variety of cancers among the same general diagnostic category (for example there are 15-16 different types of breast cancer, which are typically simply referred to as breast cancer in lay speak)1 there is a chance to miss cancers with small mutational differences. Due to the importance of biomarkers in the future of cancer treatment a wide variety of techniques are being explored with some promising ones highlighted below.

One of the challenges of creating personalized cancer treatments is that genetically specific biomarkers first have to be individually synthesized from each patient because of a lack of personal genetic information from that patient. Fortunately almost all cancer cells experience both DNA repair mechanisms breakdown and a widespread rearrangement of chromosomes due to chromosomal instability.7,8 In addition this near certainty of genetic disruption appears to occur near the earliest stages of tumor formation (tumorigenesis) and is thought to persist throughout tumor development.7 Therefore, cancer cells develop a form of uniqueness apart from the original cells they mutated from, yet maintain a form of homogeneity in their development between different patients. Through this pathway different cancers would develop different rearrangements which could be used to not only differentiate between cancerous cells and normal cells, but between different types of cancer.

In effort to take advantage of this form of cancer development some researchers have turned to high-throughput screening to identify these genetic changes and create biomarkers which could identify those changes.7 One technique to create these biomarkers is to use recurrent somatic structural (usually single-base) alterations, especially for hematopoietic (blood) malignancies9-12, but these structural alterations do not persists in most solid tumors limiting the techniques usefulness for total detection. Also analysis of somatic single-base alterations have polymerase error rates derived from the specific bases of interest, which can produce false positives.7

The use of Personalized Analysis of Rearranged Ends (PARE) has demonstrated that specific identified breakpoints can detect tumor DNA at very small sensitivities (small amount of tumor DNA among large quantities of non-tumor DNA) within the blood or plasma.7 One of the reasons PARE works is that like all cells tumors secrete parts of their DNA into the bloodstream, a sample of blood can then be taken and these DNA pieces amplified through polymerase chain reaction (PCR) and later matched to previously developed biomarkers. Using PARE is thought to result in almost zero chance for false positives because the diversity of the combinations that are amplified through PCR should not appear in non-tumor cells.7 This process is more accurate than examining somatic single-base alterations because of the aforementioned false positives drawn from some instances when cancer cells have similar somatic single-base alterations which mimic normal non-cancerous cells.

Unfortunately PARE analysis is still in early stages of development and significant questions still remain. The biggest question for PARE as well as all somatic single-base alteration techniques is whether or not these somatic DNA alterations persist throughout tumor development. There are two possible design methods to address this problem: first, identify a large number of PARE biomarkers for a particular type of tumor and test for all of them using the reasoning that not all of those biomarkers would be lost over progressive tumor development. Second, identify the various PARE biomarkers that exist throughout the lifecycle of a tumor tracking when certain biomarkers appear and disappear in relation to the stage of development. One useful feature with regards to the second method is that if researchers are lucky unique PARE biomarkers will exist for specific time designations in tumor development so not only could PARE biomarkers be used to identify whether or not a patient has cancer, but also its stage of development, which could demand a specific treatment regimen.

Other problems revolve around the costs associated with developing these PARE biomarker libraries. The initial sample studies which were used to generate a proof-of-concept for PARE biomarker viability utilized 12 tumor variations (6 colorectal and 6 breast) where each required an average of 194.7 million reads per patient creating an assay cost of approximately $5,000, which is very high for clinical repetitive use.7 Fortunately because the initial proof-of-concept study utilized a new technique that has not been fully brought to scale proponents believe that significant opportunities exist for cost reduction both in general sequencing demands as well as PARE specifics like less stringent mapping criteria and lower physical coverage.

The final major problem that exists in the development and use of PARE biomarker analysis is although PARE should not produce false positives a non-zero probability exists for the generation of false negatives.7 At this point PARE requires at least one rearrangement template molecule to be in the analyzed plasma sample to develop a positive result. In the very early stages of cancer development these required templates may not be available for a given sample. While not a crippling concern in the methodology, if correct it does limit the overall diagnostic potential of PARE, especially because the gap is on the front end not the back end of the cancer lifecycle.

The majority of new cancer detection techniques focus on general detection through blood sampling, but others have been developing techniques for regional detection of specific cancers. A possible advantage to these more regional strategies, despite their cancer type detection limitations, is the potential to demonstrate broad biomarker association with certain cancers creating another base point for potential treatments. One of the more promising detection methodologies involves examining saliva for specific biomarkers.

Not surprisingly the focus when testing saliva for cancer is to detect oral squamous cell carcinoma (OSCC) and other similar type cancers. While biopsies generate worthwhile results, a more specific method appears to be on the horizon. Tapping into the same type broad strategy that is used in PARE biomarker development, saliva contains a multitude of mRNA where non-cancerous mRNA and cancerous mRNA have different sequence structures.13 The origins of these mRNA samples tend to be the three major saliva glands, gingival crevice fluid and desquamated oral epithelial cells.14

In addition to mRNA, saliva also contains microRNA (miRNA) which more accurately cluster in various forms of solid tumors than mRNA creating a better opportunity for detection, thus increasing testing accuracy over mRNA in detecting cancer.15 miRNA are created through transcription by RNA polymerase II or III because they initially represent a portion of an intron of mRNA and typically act as post-transcriptional regulators.14,16 However, for miRNA to be a useful biomarker for cancer detection a significant difference in the concentration of at least one miRNA between cancer and non-cancerous states must be identified where it is attributable to the cancer itself.

A possible marker to reflect the difference between miRNA in those with OSCC and those without OSCC has been isolated to two specific forms of miRNA. In both whole and supernatant saliva two salivary miRNAs, miR-200a and miR-125a, had different concentrations relative to whether the patient suffered from OSCC or not.13 Interestingly the concentrations of these two miRNAs were lower in the OSCC patients than the non-OSCC patients.13 In most situations biomarkers are selected based on their increased concentrations for deleterious conditions not decreased due to easier detection parameters (larger concentrations over smaller concentrations). There is also evidence to support miR-200a playing a role in other head and neck cancers, so this method could be expanded from OSCC patients to other cancers.17-19 While at the moment the use of miRNA diagnostics have focused on head, neck and oral cancers, it stands to reason that expansion of this technique may provide a means to general cancer diagnosis for other types of cancers as well if more miRNA associations can be identified. Signs do look promising in this avenue of detection.

The two previous techniques, PACE and miRNA concentration changes, focused on using inherent biological elements associated with the tumor and its lifecycle as biomarkers. The advantage to this system is specificity and a general level of certainty. However, there are no guarantees regarding the successful creation of a PARE library, which even if successful is still years away. Also there is no further information regarding the probability that other cancers demonstrate miRNA concentration shifts and if they do determining what type of shift will be difficult. Therefore, other researchers have sought a more general method of detecting cancer by foregoing identifying a specific type in lieu of simply identifying any type.

The application of exogenously distributed biomarkers requires a delivery mechanism that has acute specificity towards tumors over non-tumors. There are typically four practiced techniques used to achieve this goal: ligand-guided active targeting20-22, passive Enhanced Permeability and Retention (EPR)23,24, tumor microenvironment-dependent targeting25, 26 and viral injection.27 Regarding the fourth element, viruses are sometimes referred to as ‘nature’s nanoparticles’ and due to their behavior and target functionality, that are useful for delivering genetic information to target cells. Not only are viruses effective targeting devices, but their inherent replication abilities upon reaching their targets also add a form of positive autonomy with regards to molecular expression.

Taking advantage of these elements one of the chief and more versatile tools in battling cancer is the oncolytic virus. Oncolytic viruses are viruses which are typically engineered to have dramatically higher infection rates for cancer cells over non-cancer cells. In fact the fewer non-cancer cells a particular oncolytic virus infects the better. This infection disparity is developed one of two ways: first transductional targeting which involves changing the docking antigens on the viral coat to respond to a protein that is expressed at a much higher rate in cancer cells vs. non-cancer cells.28 Second, non-transductional targeting which involves genetically engineering the virus so that it only replicates in response to cues that are limited in non-cancerous cells, but are of a higher frequency in cancer cells.28 Specific tumor-only promoters are usually the cue utilized in these types of oncolytic virus. Attenuation is also used where specific genetic deletions are applied to the viral DNA, which then progressively neutralize cancer-required proteins.

There are natural oncolytic viruses28, but engineering viruses is viewed as a more effective way to address the wide demand for oncolytic viral use due to the diversity of cancer cells. Among the types of viruses that are converted into oncolytic viruses adenoviruses and herpes simplex virus (HSV) are the most effective due to their double-stranded DNA providing greater storage stability (reducing mutation probability), consistence tolerance of high production titres, transduce both dividing and quiescent cells efficiently, express transgene products and are scalable.27

One of the chief uses of oncolytic viruses is in the associated production of an artificial biomarker for general detection of cancer. The general method of oncolytic viruses involves an association with a detection element, normally some form of fluorescent marker. The general idea is that because the vast percentage of cells infected by the oncolytic virus will be cancerous, after cell lysis due to infection or induced secretion the concentration of fluorescent marker in the blood stream should increase significantly generating a high probability that the individual has some form of cancer. Also there is the potential that the overall differences in concentration could provide a basic means to track the progression of cancer development.

One of the more promising general detection techniques involves the use of HSV with a controlled mutation of the ICP6/UL39 and late viral protein ICP34.5 to limit viral replication and amplify dependence on late gene expression.27 Upon entering a cancer cell the modified HSV virus induces the cancer cell to secrete a specific biomarker, which would then be measured from the bloodstream to determine a basic pattern of cancer progression. One group of researchers who tested this concept selected gaussia luciferase (GLuc) as a high quality biomarker for use in this particular experiment because of ease of secretion, extent of lumination (1000 times brighter than other luciferases), detectable in both blood and urine in vivo and greater sensitivity than other popular biomarkers like alkaline phosphatase.29,30

The use of GLuc as a biomarker was explored using various tumor locations such as: intra-peritoneal, subcutaneous, intra-muscular and orthotropic intra-renal tumors.27 Regardless of tumor location, GLuc results were consistent with more than 90% of the tumor mice presenting higher concentrations of GLuc than non-tumor mice.27 One concern from these experiments was whether or not smaller tumors would produce the necessary vascular surface area required for HSV to infect the tumor. A lack of sufficient HSV infection would reduce the sensitivity of the overall test. While a relevant concern, it is not surprising as the most common concern with cancer diagnostic techniques is the question of what is the minimum detection limit for the given technique.

A more important concern involves the immune response limiting the overall effectiveness of the biomarker because since approximately 80% of the population has been exposed to HSV at one point in their lifetimes there will be a significant immune response.27 The problem from the immune response is that the baseline for no cancer is no significant concentrations of biomarker in the bloodstream because HSV is only infecting a small amount of cells due to the lack of cancer cells to drive infection. An immune response can reduce the number of HSV in the bloodstream after injection, which will in turn reduce the total ceiling for biomarker secretion making it more difficult to determine whether or not the patient has cancer with appropriate confidence. This problem underscores another important requirement for such a technique, the establishment of a ratio or scale to judge tumor progression relative to biomarker concentration. Clearly such a ratio would develop small differences between patients, but a general range scale could be developed as a universal measure for broad cancer progression designation.

Another concern regarding potential immune response is elevated levels of macrophages having a detrimental effect on tumor prognosis due to recruitment of these new macrophages into the tumor microenvironment, which benefits the tumor. One possible strategy to reduce problems with the immune system would be treating the patient with immunosuppressant drugs during the treatment protocol before infection with HSV and for a short time afterwards. Some may argue that the use of immunosuppressant drugs create a problem as studies have shown that extended use of immunosuppressant drugs increase the probability of developing cancer.

Such a concern does not appear applicable in such a scenario for two reasons. First, the application of any immunosuppressant drugs would only be over a very short time frame almost eliminating any legitimate increase in probability for new cancer development. Second, the methodology of cancer development may moot this point. Not surprisingly the immune system appears to be best at neutralizing tumors in their beginning stages and significantly lose effectiveness more than likely due to the formation of the tumor microenvironment as the tumor develops. Therefore, unless the oncolytic virus detection procedure is undertaken during the very initial stages of cancer development any risk associated with the application of immunosuppressant drugs should be minimal.

In a similar vein to the idea of using oncolytic viruses to release a fluorescent molecule into the bloodstream to identify cancer, some researchers want to use oncolytic viruses to deliver fluorescent molecules that instead of amplifying in cancer cells bind to exclusive cancer cell receptors to identify specific locations of tumors. The preferred protein comes from the crystal jelly and the viruses utilized operate similar to other oncolytic viruses except these viruses do not actually lyses the cancer cells.31 Instead their amplification leads to the further amplification of these crystal jelly attached proteins which then fluoresce the cancer cells creating a visual differentiational characteristic from non-cancer cells. Developing this technique is thought to not only better determine whether or not an individual has cancer, but in the future may even assist in a more accurate surgical removal of smaller tumors that could escape detection through sight alone.

Unfortunately there are two significant problems in developing this technique. The first problem is that very few colors can penetrate tissue due to frequency mismatch. Early testing identified that low frequency red is the most successful lumination color, but is still somewhat weak.31 The second problem is overall cost. Even with successful fluorescence, accurate assessment of the fluorescence currently requires a special camera which scans the body slice by slice and typically cost $750,000 dollars.31 The concern with this problem is that there is little potential due to physical tissue barriers of developing a stronger fluorescence which will not require this type of expensive camera. Thus, the chief hope for proponents is that direct costs associated with the camera will drop during the development of the technique. While this technique may not pan out to be successful it represents an important characteristic in cancer diagnostics and treatment: focusing on a wide variety of detection methodologies using logical theory in order to determine what works and what doesn’t to develop the best approaches.

In addition to new biomarkers, an older piece of technology has been augmented to aid in the diagnosis of cancer, especially with regards to metastasis. Normally when a patient is diagnosed with cancer he/she undergoes a sentinel lymph node biopsy to determine if the cancer has metastasized. In the body a clear fluid called lymph, which is part of the interstitial fluid, moves through lymph vessels eventually leading to a lymph node.32 From a technical standpoint the interstitial fluid becomes lymph after entering the lymph capillary and mixes back into the blood after exiting through the left or right subclavian vein.32 Lymph nodes are small round organs which filter out harmful substances from the lymph like bacteria. The most common lymph nodes are found in the neck, underarms, chest, abdomen and groin. The sentinel lymph node is viewed as: the first lymph node which become cancerous after taking in cancer cells which originated from the principle tumor (the reason for this definition is because in some cases by the time the biopsy is carried out multiple lymph nodes could be cancerous).33

The biopsy involves removing the lymph node closest to the identified principle tumor using the logical reasoning that most metastasized cancer will spread in an orderly and organized manner as the blood moves away from the tumor infecting nodes closer to the principle tumor rather than randomizing infections. Traditionally the lymph node is identified by injecting a specialized radioactive dye (usually filtered sulfur colloid tagged with Technetium-99m) near the principle tumor.33 After allowing for sufficient time for migration, the dye is tracked through a scanner to determine if any lymph nodes are stained with the dye. If significant amounts of dye are in a lymph node and respond to a certain gamma probe range then that node is excised and examined further under a microscope looking for cancer appropriate histological features. If the cancer has metastasized then a number of patients (some estimate up to 35%) have to undergo additional biopsies to confirm and to further evaluate how far the cancer has metastasized.34

Depending on the number of lymph nodes removed side effects for the procedure will vary.33 For instance if only a small number of nodes are removed then the most common side effects are associated simply with those of normal minor surgery (pain and/or bruising at the site of incision) and slight difficulty moving the affected body area. When more nodes are removed then the side effects increase to the potential for swelling due to excess fluid build-up (lymphedema), numbness, infection and a burning sensation. Most physicians believe that these side effects are justified for the advantage of having a better idea to how aggressive a treatment will be required for the patient based on the prevalence of the cancer spread. However, there is argument over whether the early removal of the SLN improves survival rates.33 SLN biopsies are most commonly used for breast cancer and malignant melanoma patients.

A new technique which appears to be more effective than SLN in both reducing side effects and accuracy in determining the SNL incorporates microbubbles into an ultrasound to aid detection.34,35 Initial experiments utilizing microbubbles as an additional enhancement agent in an ultrasound has accurately identified the sentinel lymph node in 89% of eighty breast cancer patients studied.34 For this process the microbubbles are small (1-4 micrometer diameter), constructed of a lipid or albumin shell, filled with a biologically inert gas (usually perfluoropropane) and typically behave like red blood cells from a hemodynamical perspective, thus they can be modeled and accounted for during the ultrasound in a predictable fashion.35

Microbubble augmented ultrasound has also been explored as a means to monitor changes in tumor vascular which could aid study of how anti-angiogenesis agents fail by tracking the changes in tumor vascular development.35,36 Some initial studies have in fact used this technique for such a purpose. One of the major advantages to this technique is that microbubbles can be selectively applied to specific regions through conjugation with certain ligands.35-37 The application of the angiogenesis mapping technique involves targeting microbubbles to specific vascular markers such as VEGFR1 or VEGFR2. Observing changes in the expression of these receptors is a viable means of tracking vascular changes due to expression patterns largely correlating with molecular demand and availability.

Unfortunately the promise of biomarkers have lead some to place more emphasis on their importance and accuracy vs. what they genuinely deserve. The perceived usefulness and power of the biomarker has spawned a form of bias where some researchers continue to believe that a given biomarker possesses a high association with a given condition even after large meta-analyses have demonstrated much less significance to that given biomarker.38,39 Basically researchers will still cite an initial analysis of a biomarker which has a stronger positive effect than a later more thorough analysis. For example researchers would cite a paper that calculated a 25 times greater association over a later paper calculating a 4 times greater association from a meta-analysis.38

This bias is not surprising, other studies have demonstrated a positive correlating bias towards other aspects of important/popular research, however, it can be quite detrimental to determining viable treatments. The continuing lack of definitive answers regarding the origins of cancer and its progression means that refusal to abandon once promising biomarkers could create more confusion and inaccuracy in the pursuit of both cancer diagnosis and treatments. The difficultly in giving up on what once appeared to be a significant step forward in understanding and/or treating cancer is understandable, but unless biomarkers are looked upon accurately, addressing cancer appropriately will be all the more difficult.

Diagnosis is only part of the battle against cancer. Streamlining treatment efficiency through exploration of different strategies is also an essential part to the future development of a cure. While lower costs and efficiency are useful features to treatment, the primary goal needs to focus on certainty of tumor death in a way that can be properly confirmed. Presently there are three mainstream treatments of cancer: chemotherapy, surgery and radiotherapy.

Among these three treatments surgery is still the preferred choice because of its overall success rate due to the direct elimination of cancer with associated visual conformation. Unfortunately surgery is not a universal treatment. The highest probability of success for surgery logically occurs in the median lifespan of a tumor when the tumor is large enough to identify and isolate from other healthy cells, but before the tumor metastasizes. After the tumor metastasizes surgery is no longer an attractive option because of its invasiveness and the general trauma it inflicts upon the body. If the tumor is too small surgery is more risky because it is difficult to confirm whether or not the entire tumor has been removed due to lack of tumor size. Fortunately chemotherapy is widely paired with surgery as a combination treatment strategy: eliminate a significant amount of the tumor via surgery and destroy the remaining weakened cancer cells via chemotherapy.

The term ‘chemotherapy’ now describes a methodology of cancer treatment that involves the use of anti-neoplastic drugs most commonly delivered intravenously. Development of the chemotherapy strategy evolved in the very early stages of the biochemical revolution before any specific biochemical markers and genetic information was uncovered about various cancers. Without the knowledge of nanotechnology or even very specific differentiating biochemical signals or proteins, cancer treatment demanded a much broader strategy. Anti-neoplastic drugs operate by interfering with mitosis; therefore they are most effective against cells which have a fast rate of mitosis, a common feature for most cancer cells. Using this anti-mitosis strategy chemotherapy techniques could attack cancer without having specific information on the location of the cancer (necessary for surgical strategies) or the specific type of cancer (necessary for individualized treatments).

However, there are other cells in the body that have a fast rate of mitosis as well, most notably associated with bone marrow (red and white blood cells), digestive tract (stomach) and hair follicles. Note that other healthy cells could be damaged by chemotherapy as well, but with a lower probability due to their slower rates of mitosis. Due to potential collateral cell damage the most common side effects from chemotherapy include myelosuppression leading to anemia and immunosuppressant, mucositis increasing ulcer probability and alopecia almost guaranteeing the most telltale sign of chemotherapy treatment, hair loss.

Despite decades of experimenting with chemotherapeutic applications resulting in an effective treatment methodology, chemotherapy still has problems. First, due to the collateral damage nature of chemotherapy once the cancer metastasizes the effectiveness of chemotherapy significantly drops not in its ability to kill tumors, but in its ability to kill tumors without killing the patient in the process because so much more of the drug is needed.

Second, tumor identification still functions principally on a visual scale which makes it difficult to know when to stop a chemotherapy regiment. Basically the tumor shrinks to a size where it is difficult to know whether or not the patient still has cancerous tissue, thus whether or not further chemotherapy treatments should be administered under threat of causing unnecessary healthy cell loss.

Third, biology does not typically remain static, thus cancers can, and have, evolve mechanisms of resistance against various chemotherapy drugs; therefore, chemotherapy regiments that seek to significantly increase patient lifespan apply numerous drugs to reduce the probability of tumor survival and associated development of resistance. However, the application of numerous drugs per treatment can significantly increase cost of treatment. Overall chemotherapy has actually been a very effective cancer treatment when treatment could actually make a difference, but it real effectiveness has been marred by the lack of quality logistics and diagnosis.

While first and second-generation chemotherapy treatments have proven somewhat effective at managing cancer because biology does not remain static and new chemotherapy strategies are being developed. One may ask the question of why new chemotherapy treatments are needed when nanotechnology and better understanding of genetics are available to develop more target-specific treatments? Chemotherapy treatments have been the most successful non-invasive therapies and although the promise of more target-specific treatments is worthwhile, over the last few decades these treatments have not amounted to any new breakthrough treatment which can rival the effectiveness of chemotherapy at the human level. Therefore, it is important to continue to focus on developing new chemotherapy treatments until new target-specific treatments are ready.

New application techniques have been applied in chemotherapy treatment such as isolated infusion or perfusion which modify rate of delivery and total amount of chemo agent delivered. Identification of appropriate dosage through either an infusion or perfusion methodology can limit the amount of site-nonspecific collateral damage as well as potentially penetrate more solid mature tumors at the site of application.

The third commonly used method of treatment is radiation therapy which involves the application of ionizing radiation in effort to damage the DNA of cancerous cells to the point where either the cell dies straightaway or triggers apoptosis. The DNA damage is caused by either photons or charged particles applied directly or indirectly. Direct action targets the cancer cells themselves whereas indirect action occurs through ionization of water leading to the formation of free radicals which precede to damage cells within the localized radius. Photons typically produce an indirect effect where charged particles are used directly.40 One chief advantage to using charged particles is that it frequently results in double-strand breaks over single-strand breaks.40 DNA repair mechanisms are designed to address single-stranded DNA damage, thus cannot effectively manage double-stranded breaks. However, because DNA repair mechanisms are typically compromised in cancer cells, single strand breaks are also significantly dangerous to cancer cells.

Most radiation therapy requires transition from an external source to the internal tumor, thus there is a high probability that the radiation will pass through healthy tissue. A common strategy used to reduce the damage potential to this ‘obstructing’ tissue is applying beams originating from multiple angles all with an endpoint focused on the tumor. Basically multiple beams are used at an individual radiation intensity which heavily limits permanent damage potential to the contacted healthy tissue, but with all of these beams meeting at a single point (the tumor) the collective radiation intensity can hopefully destroy the tumor. The lack of lateral side scatter from the charged particles also helps limit the healthy tissue collateral damage.40

While photons comprised the first major radiation therapy, they could be falling out of favor (proton proponents have high hopes for their revival) due to two chief problems. First, oxygen is a potent radiosensitizer, thus the higher the oxygen concentrations in the localized region the higher the generated free radical concentrations and the associated cancer damage probability. Unfortunately for photon radiation therapy most solid tumors create an inflammatory microenvironment that is generally hypoxic in nature. The reduction in available oxygen reduces potential free radical concentration reducing treatment effectiveness. Second, hypoxic states may be detrimental to normal cells and tissues, but cancer cells and tumors appear to be more resilient in such an environment, possibly even more dangerous. Thus as photon radiation therapy consumes oxygen to make free radicals it not only becomes less effective, but also may make the remaining cancer cells more deadly.

Unlike photons which have a set energy value, charged particles can more precisely target a tumor incorporating the Bragg peak effect as well as be charged to different peak amounts for optimal decay upon reach the tumor.40 For example as photons move through the tumor environment they pass through healthy tissue when exiting the body. The multiple to single to multiple beam design attempt to reduce damage, but does not entirely eliminate the probability, thus any additional damage when exiting is entirely collateral because the tumor has already been targeted. In the case of charged particles the particle charge can be designed so that the bulk of the charge is expended at the tumor and the exiting particle has no damage potential.

A newer radiation therapy, brachytherapy, involves using sealed radioactive sources that are implanted into the body at precise locations. Brachytherapy can provide a larger level of radiation over a much more controlled and localized area near the tumor eliminating any collateral damage away from the tumor site.41 However, one of the chief drawbacks to brachytherapy is the surgical requirement. Adding an additional invasive step to a cancer treatment is always troublesome. Some argue that surgery as well as brachytherapy can be combined into a single surgical event, but whether or not surgeries are combined is determined on a case-by-case basis and is typically not as successful as most hope.

Overall the usefulness of radiation therapy is defined by the radiosensitivity for a given cancer. Leukemias, lymphomas and germ cell tumors are the more sensitive to radiation followed by epithelial cancers whereas melanoma and most forms of renal cell cancer are radio-resistant.40 However, while leukemia is sensitive to radiation, their widespread dissemination through the body makes treatment more complicated. Therefore, radiation therapy is mostly useful on smaller tumors that have yet to metastasis.

The above three therapies have been useful in the past and continue to be useful in the present, but a better understanding of genetics and the advent of nanotechnology have opened the door for the development of new cancer treatments which are less broad-based than chemotherapy and more site and target-specific offering the hope of greater treatment probability with reduced side effects. The two major new categories of treatment evolving from these new understandings and technologies are agents that specifically target a particular protein and delivery mechanisms which specifically target cancer cells themselves.

In the first half of the 2000s, one of the most promising new ideas for cancer treatment was derived from the chemotherapy distinctive question of ‘what is a cancer cell doing at a greater rate than normal cells other than dividing?’ One clear answer was forming new blood vessels (angiogenesis) to catalyze and maintain rapid cell division and tumor development. When cancer cells are underdeveloped and simply beginning to form solid tumors those cells are supplied with nutrients through diffusion from adjacent blood vessels. However, the rapid uncontrolled cellular replication of cancer cells demands new blood vessels to ensure proper oxygenation. Angiogenesis is commonly viewed as a fundamental step between the evolution of cancer cells from a benign state to a malignant one.

The process of tumor associated angiogenesis proceeds in a series of controlled steps, similar to sprouting angiogenesis; first endothelial cells from pre-existing blood vessels are transported and accumulate nearby in order to form the base matrix for the formation of the new blood vessel. This step requires angiogenesis factors, most notably fibroblast growth factor (FGF-1) and vascular endothelial growth factor (VEGF), to stimulate the existing endothelial cells to produce proteases to break them from their pre-existing matrices allowing for migration towards the cancer cells.42-44 The next step involves the proliferation of the endothelial cells and the extension of the solid sprouts to connect the new vessels to the existing neighboring vessels. After the sprouts extend towards the source of the angiogenic stimulus (the cancer cells) the endothelial cells migrate using integrins.42,43 Finally the sprouts begin to loop becoming fully operational vessels.

Based on the above understanding of angiogenesis and the fact that tumor size typically ceilings at 1-2 mm with little chance for metastasis when only having access to diffused oxygen and nutrients (no new blood vessels), most anti-angiogenesis drugs were designed to repress vascular endothelial growth factor (VEGF) thereby reducing the number of blood vessels that would be developed to support tumor growth.42

The hope surrounding anti-angiogenesis was because angiogenesis and tumor growth were not directly cooperatively regulated elements (one did not directly influence the other) tumor growth would remain unchanged relative to the number of blood vessels until the tumor grew to a size that could not be supported due to a lack of nutrients, a direct results of fewer blood vessels, eventually leading to the death of a vast majority, if not all, of the cancer cells. There was and still is significant aplomb regarding anti-angiogenesis drugs because of the perceived universal nature in their ability to treat cancer regardless of type (in the initial stages of development all tumors need a number of blood vessels) and with limited collateral damage.

Unfortunately some studies have demonstrated that while the theory behind eliminating blood vessels leading to tumor death makes sense, the empirical results do not strongly support the theory. While knocking out VEGF does result in fewer blood vessels those vessels that form are better organized, for when VEGF is not disrupted, blood vessels are numerous, but ‘leaky and chaotic’.42,45-46 It has been hypothesized that this better organization could provide even more nutrients to a tumor, despite the fewer number of vessels, because typically VEGF knockouts resulted in the formation of larger tumors than non-VEGF knockouts.45

In the short-term this result seems to eliminate the usefulness of VEGF blocking anti-angiogenesis drugs, but there may be a silver lining in that while fewer numbers of blood vessels may allow for better nutrient transport, they would also allow for better chemotherapy agent transport as well increasing the probability of drug delivery and tumor death.42 Unfortunately this result has not been supported by empirical evidence provided by patient use of Avastin.45,46 One explanation for this lack of coordination may simply be resistance to the chemotherapeutic agent. In fact Avastin, the most noteworthy anti-angiogenesis drug, which received FDA approval in 2004 just recently had its short-term approval as a preventative treatment for breast cancer expire with no further extension by the FDA.

One question is why do VEGF knockouts create stronger (less leaky) more organized blood vessels. Two explanations come to mind. The more obvious explanation is just like with almost everything else cancer cells heavily overexpresses VEGF creating a signal pull on the endothelial cells in a large number of different and contrasting directions instead of having one or two uniform signals directing strong migration and amalgamation of endothelial cells which would create a strong more extensive blood vessel. Also VEGF plays a role in vessel permeability, thus an excessive concentration of VEGF could create a ‘leaky’ type vessel behavior. The second explanation is that the cancer cells express VEGF and maybe another unidentified signaling molecule and while this unidentified molecule is better able to induce vessel formation, VEGF is produced at greater concentration which masks the effect of this unidentified molecule; a masking that no longer exists when VEGF is blocked by knockout or certain drugs.

Another study has demonstrated that the use of anti-angiogenesis drugs also appears to increase the invasiveness of cancer cells and the associated probability of metastasis.47 This increased probability seems to occur due to an increase in hypoxia due to the fewer blood vessels relative to tumor size which results in the cancer cells synthesizing a greater amount of Met and hepatocyte growth factor (HGF).47 HGF could act as a molecular trigger which induces cell migration. Therefore, for any form of anti-angiogenesis strategy to work it appears that Met and/or HGF need to be inhibited as well.

Another strategy has been to target the endothelial cells which create the new blood vessels themselves. One reason this method is supported is because the endothelial cells that are recruited to form these new blood vessels are thought to be much more genetically stable than the associated cancer cells. However, other studies48 support a loss of genetic stability in endothelial cells which are recruited by cancer cells. Some argue that this lack of stability could allow cancer-derived vessels to develop a higher probability for drug resistance similar to cancer cells; however, the lack of stability may be more controlled than that of the original cancer cells directed by the cancer cells themselves (1 or 2 mutations vs. a cascade), thus eliminating any serious detrimental resistance potential. In fact if this assertion is accurate, the endothelial mutation may actually provide a new means to attack cancer which will not affect non-cancerous systems.

Other elements aside from VEGF have also been identified as potential therapeutic targets for cancer treatment. Similar to the aforementioned biomarkers explored for diagnostic purposes, manipulating the changes in concentration of various elements can be used as a means to control or even eliminate cancer expansion. One of the principle advantages of targeting VEGF for cancer treatment was that the role of VEGF was known and increases significantly with the progressive advancement of cancer. Unfortunately these two principles are lacking in a lot of the other promising targets for either there is a lack of certainty in function or a lack of identifiable exclusive regional change.

The lack of certainty in function is a problem because although concentrations of a given element may change significantly between cancer and non-cancer patients, but whether or not the element acts in some measure of positive/negative feedback or is simply a byproduct of tumor growth is unclear until specific information pertaining to the function of the element is determined. For example from above one useful biomarker for head, neck and throat is miRNA 200a. However, further research is needed before miRNA 200a can be thought of as a therapeutic target or just a byproduct from a form of transcription mismatch caused by a somatic mutation transforming a normal cell into the type of cancerous cell which typifies these types of cancers.

Lack of exclusive regional change is also a problem because if the element is determined to play a feedback role of sorts in the progression of cancer it would initially look like an appealing therapeutic target. However, if that element also plays a critical role in the normal cell growth and/or function then targeting that element will more than likely make cancer treatment difficult at best with lots of side effects and collateral healthy cell damage and pointless at worse as it ends up killing the patient due to too much healthy cell damage. A good empirical example for these two principles is the relationship between tumor development and tumor necrosis factor alpha (TNFa).

New York physician William Coley first deduced the existence of TNFa in 1890 after discovering a former cancer sufferer, Fred Stein, had his cancer ‘cured’ after a post-operative bacterial infection. After talking with the patient and much research Coley developed his ‘Coley’s mixed toxins’ therapy which seemed to have some success treating tumors.49 However, recreation of Coley’s successes were few and far between. In 1962 O’Malley used endotoxin-derived serum to initiate tumor necroses in mice drawing the conclusion that the serum contained some form of ‘tumor necrotizing factor’.50 The term ‘tumor necrosis factor’ was officially coined by Carswell later in 1975 by determining that the necrosis was caused by a factor made by the host cells in response to the endotoxin, not directly through the endotoxin itself.51 Even later it was determined that TNFa and a molecule previously known as lymphotoxin bound themselves to the same receptor and were part of the same genetic family. Lymphotoxin is now commonly known as TNF-beta.52

In the immediate aftermath after confirming the existence of TNFa it appeared that TNFa would be a powerful candidate in the treatment of cancer. Early on this belief was supported by evidence demonstrating high doses of human recombinant TNF inducing necrosis in both syngeneic and xenografted tumors.53,54 At lower doses in combination with melphalan or doxorubicin chemotherapy treatments, TNFa appeared to increase tumor blood vessel permeability and aided in the delivery of the chemotherapy drug in addition to destroying tumor vasculature.55,56

Unfortunately the belief in TNFa as a cancer treatment agent was short-lived. In 1984 Wallach determined that TNF is only weakly cytotoxic to malignant cells when used alone.65 Apoptosis activated with high probability only when inactivating certain downstream TNF signaling using a combination of metabolic inhibitors.52 Later investigators discovered TNF mRNA and TNF itself in malignant and stromal cells during biopsies along with a somewhat proportional relationship between higher concentrations of TNF and lower survival rates.52 The TNF-cancer relationship finished flipping in 1996 when Kollias developed the first TNF-knockout mouse and using this model in 1998 demonstrated that TNF-knockout mice developed fewer skin tumors when treated with a skin carcinogen relative to non-TNF-knockout mice.66,67 Basically the less TNF the animal had the fewer number of tumors it developed, TNF acted as a tumor promoter.

Now instead of trying to treat cancer by increasing TNF concentration the strategy shifted to treating cancer by decreasing TNF concentration. Unfortunately TNF plays an important role in the inflammation response. Changing the inflammatory response at a general level in the body has demonstrated mixed results as TNF inhibitors have generated positive results in treatment of rheumatoid arthritis, but in other inflammatory disorders TNF inhibitors have been linked to tuberculosis along with other infections and even lymphoma in some cases.59

One interesting result with regards to expression of TNF is that its signaling pathway components appear to have widely variable concentrations between individuals.52 Such a result may demonstrate that the overall role played by TNF in cancer development is heavily variable and dependent on the type of somatic mutation that induces cancer initiation in the first place. This result may explain why in recent Phase I and Phase II cancer trials TNF antagonists have resulted in disease stabilization in only 20% of patients.60-62 Anti-TNF therapies may not be a universal treatment for all patients or even a majority of patients, but instead a controlled specific personalized treatment after identifying average TNF concentrations in the blood stream.

While TNF treatments may not be the ‘silver bullet’ some hope exists for cancer treatment, research into TNF did identify another promising target for cancer treatment, nuclear factor kappa B (NF-kB). NF-kB lies at the end of one of the multiple TNF pathways that is triggered when TNF binds to either of its receptors (TNFr-1 or TNFr-2) and is partly responsible for triggering the inflammation pathway.

Beyond the pathway connection between NF-kB and TNF there appears to be a large amount of evidence that suggests sustained activation of NF-kB is rampant in various solid tumors and cancer types. For example major components of the NF-kB pathway are activated in human lymphomas,63 pancreatic cancer,64 cervical cancer,65 carcinomas,66 prostate cancer,67 colon cancer,68 head and neck cancer,69 esophageal cancer70 and lung cancer71. While the pathway is active in all of these cancers, the methodology of this activation and its specific components differ.

Five different molecules make up the nuclear factor kappa beta (NF-kB) transcription factor family: NF-kB1 (p105/p50), NF-kB2 (p100/p52), Rel A (p65), c-Rel, and Rel B.72 These different molecules commonly associate with one another to form various heterodimeric and homodimeric elements. The formation of these hetero and homodimeric elements is normally required to induce receptor activation as the NF-kb molecules typically remaining inactive in the cytoplasm before compound formation sometimes through additional interaction of ankyrin-containing inhibitor-kBs (I-kB).72 The I-kB inhibition complex for NF-kB members is typically made up of three different inhibitor elements (IKKa, IKKb, and IKKg).72 Also note that only the Rel members of the family (Rel A, Rel B and c-Rel) have transactivation domains which can activate transcription.72,73

The most common compound element among the NF-kB family is NF-kB1/REL A or (p50/p65), which is commonly regarded as the ‘classic pathway’ and is activated by TNFa.73,74 This classic pathway has also been identified in having a role in the promotion and pathogenesis of cancer where activation is largely induced by cytotoxic agents and maintained by oncogenic activation of various tyrosine kinases. An alternative to the classic pathway is induced by binding of other TNF family members and processes p100/RelB to p52/RelB.73,74 The alternative pathway components, which includes IKKa/IKKa homodimers, largely regulate survival of premature B lymphocytes and development of peripheral lymphoid tissues.72 Both pathways are complex with various cofactors such as CK2 or Akt influencing whether NF-kB compounds will have a gene inducing or gene suppressing effect including multiple overlaps.72 This complexity makes a straight application of ‘agent that inhibits element x in the NF-kB pathway’ as a treatment difficult because in some cancers it will promote survival and in other cancers the same element will promote death.

Of the five members of the NF-kB family, fully processed NF-kB1 (p50) seems to be the most important. Not only can it form a heterodimer with p65 which can then activate the classic pathway it appears that its formation of a homodimer (p50/p50) can actually inhibit classic pathway activation.76 While the formation of homodimer (p50/p50) can still bind at the kB binding sites, recall that only those Rel family members have the necessary transactivation domains to begin gene transcription. Therefore, homodimer (p50/p50) binding instead of heterodimer (p65/p50) prevents gene transcription and the activation of the classic pathway.76,77

While cancer cells themselves initiate the initial stages of forming a microenvironment, full expansion and functionality of the microenvironment seems to require the recruitment of macrophages; a recruitment tied to NF-kB. Macrophages are multifunctional and highly versatile cells which can engulf microbes or cell debris from appropriate, usually injured, sites secrete a variety of cytokines, present T cell antigens or aid in the activation of lymphocytes.76 Not surprisingly this behavior is governed through a phenotype alteration brought on by environmental molecular cues.

These behaviors are typically categorized into two subdivisions of macrophage activation: M1 or M2. M1 activation is classified as classically activated where the macrophage exhibits a pro-inflammatory phenotype largely resulting in secretion of bactericidal factors and T-helper-1 (TH1) promotion and typically occurs through activation by lipopolysaccharides (LPS) and interferon gamma (IFNg).76,78 M2 activation is classified as alternatively activated where the macrophage exhibits an immunosuppressive phenotype largely resulting in secretion of cytokines and the promotion of the T-helper-2 response (TH2) and typically occurs through activation by IL-4, IL-10 or IL-13.76,79,80 In either case macrophages that possess a phenotype are also sometimes referred to as tissue macrophages.

Note that it appears the initial recruitment of macrophages is due to the presence of cancer, not due to molecules released by cancer. This initial recruitment of macrophages can actually lead to cancer destruction because they enter the local tumor environment with a M1 phenotype. In some situations it is rational to conclude that these macrophages actually do successfully eradicate the cancer, otherwise average cancer rates would probably be much higher than in actuality.43,81 However, in some situations the cancer cells are able to survive the initial onslaught. The means of survival probably involve one of two methodologies. First, the attrition methodology in that cancer replication outpaces the ability of the macrophages to kill cancer cells. Second, mutations in some of the cancer cell population allow these specific cells to resist the bactericidal factors released by the M1 macrophages and/or evade engulfment.

Macrophages that stay within or around the localized tumor environment for a certain, unknown, period of time interact with cancer-associated molecules which change their M1 phenotype to an M2-type phenotype.43,82,83 The converted phenotype seems to occur through a different pathway than genuine M2 phenotype activation, but behaves very similar to the M2 phenotype, thus it is called an M2-type phenotype.43 In fact this M2-type phenotype has been subdivided into 3 different classes (M2a, M2b and M2c).43 M2a is induced through IL-4 or IL-13 activation, M2b is induced through combined exposure to immune complexes and Toll-like receptor or IL-1R agonists and M3c is induced by IL-10 activation. Both M2a and M2b exert immunoregulatory functions, whereas M2c tend to suppress immune responses and induce tissue repair.43

These converted macrophages are commonly referred to as tumor-associated macrophages (TAM). Therefore, what could occur in the tumor microenvironment is that after this first ‘wave’ of macrophages fails to destroy the tumor, tumors alter that first wave into TAMs and then begin to recruit monocytes through various chemoattractants like CCL2 and VEGF.84-86 These monocytes typically extravasate across the tumor vasculature then later differentiate into TAMs.

The recruitment of TAMs starts the next stage of tumor microenvironment formation. TAMs are thought to be responsible for a large percentage of the VEGF released in the newly formed microenvironment.87 As previously discussed VEGF is one of the key components to initiating angiogenesis. In addition to VEGF, TAM also appear to up-regulate another pro-angiogenic protein, matrix metalloproteinase (MMP7) most notably in hypoxic regions of the tumor.43 MMP7 cleaves an active form of RANKL from the cell surface88 which increases RANKL concentration in the tumor microenvironment. TAMs also can provide a form of positive feedback by secreting additional amounts of IL-10 converting more macrophages into M2c types.76

RANKL is normally expressed by T-helper cells as a means to accelerate dendritic cell maturation as well as aids survival. The increase of RANKL concentration brought on by MMP7 in a tumor microenvironment could also serve the same role accelerating dendritic cell maturation which would accelerate the expansion of TAM concentration in the microenvironment. Interestingly enough there is some evidence to suggest that deletion of RANKL reduces the probability of developing breast cancer induced by medroxyprogesterone acetate (MPA).89 RANKL also appears to play some role in regulation of the T cell-dependent response. This element is interesting because a potential high concentration of RANKL in the tumor microenvironment may interfere with any coordinated immune response driven by T-helper cells by masking the tumor using certain antigen presenting dendritic cells.

Recall that one of the hypothesized reasons for the failure of Avastin was that the remaining blood vessels were able to better deliver nutrients to the tumors stimulating more effective growth than when VEGF is not blocked. There may be another explanation for the increased aggressiveness of tumors which survive Avastin treatment. Reducing the number of blood vessels could also create hypoxic conditions, which under most circumstances would be welcome because it would increase the probability of tumor death. However, recruited TAMs appear to significantly bolster the survival ability of tumor cells in hypoxic conditions.43

First, TAMs have a tendency to migrate towards hypoxic regions in the tumor microenvironment, at least in breast, endometrium, ovary, bladder, colon and oral cancers.90-93 In these hypoxic regions TAMs also up-regulate NF-kB and HIF1.94 The positive influence on tumors of HIF1 has been previously discussed and ATF4 and EGR1 have perceived positive unknown effects, thus higher expression rates should stimulate tumor growth and metastasis. Also basal NF-kB is necessary to drive significant HIF1a protein synthesis.78,90 Now it can be said that even without treatment of Avastin or another anti-angiogenesis drug that tumor microenvironments will develop hypoxic regions, but these regions appear to be more widespread after treatment with Avastin over no treatment because of the reduced number of blood vessels. Basically the tumors that are connected to blood vessels get stronger due to the greater level of nutrients provided by those vessels and those that lack vessels either die or survive in a hypoxic region due to the release of these various factors. Note that creating hypoxic regions is still thought to have more positives than negatives when treating cancer.

TAM behavior is largely controlled through the NF-kB pathway. Inhibition of IKKbeta results in a phenotype shift from M2-type to M1.78,95 In addition recall that the formation of homodimer (p50/p50) can actually inhibit classic pathway activation. It could be argued that these (p50/p50) homodimer interactions chiefly interfere with the activation of the NF-kB pathway in LPS.96 This ‘inhibition’ of LPS-derived NF-kB action reduces the probability of LPS influencing M1 phenotype shifts in macrophages thus increasing the probability for M2 phenotype shifts. While still not understood the relationship of (p50/p50) in the NF-kB pathway appears to be important. So one reason why neutralizing NF-kB behavior may demonstrate positive results in cancer treatment is the reduction of TAM in the tumor microenvironment reducing the ability of tumors to survive hypoxic conditions.

TAMs are not the only monocyte that is recruited by tumors to aid in their development. TIE2-expressing monocytes (TEMs) are monocytes which interact with angiopoietins 1-4.43 Interestingly TEMs do not express chemokine receptor 2 (CCR2) thus they are unable to bind CCL2 which means they require another recruitment factor than the CCL2 which recruits monocytes that will eventually become TAMs.43 One study suggests that ANGPT2 is the molecule chiefly responsible for recruitment of TEMs into tumors.97 In addition ANGPT2 also could regulate the rate at which TEMs release angiogenesis promoting cytokines through the suppression of IL12.97

The specific role of TEMs in tumor facilitation is unknown, but in experiments when TEM concentrations were eliminated in tumors a statistical reduction in angiogenesis and tumor growth was observed.98 However, while TEM was removed, the loss of TEMs did not influence TAM or neutrophil concentrations further supporting the notion that TEMs and TAMs are independent elements in tumor promotion. Also even though the TAM concentration was not influenced by the loss of TEM, angiogenesis loss was significant which seems to suggest that TEMs have more influence on angiogenesis than TAMs.

Tumors also recruit mast cells largely using members of the stem cell factor (SCF) family.99,100 Like TAMs and TEMs mast cells appear to have the principle purpose of aiding tumor angiogenesis for inhibition of SCF expression in rat tumors reduced tumor vascularity and angiogenic responses.101,102 However, the angiogenesis influence of mast cells may be weighted towards the initial stages of the angiogenesis over the later stages meaning blocking mast cells may be more important than blocking TAMs.103,104 The reason for this behavior could come from mast cell interaction with endothelial cells lining the vascular lumina of multiple myeloma.105 Mast cells also appear to de-granulate and release angiogenesis factors in hypoxic conditions largely directed through HIF interaction.106,107

Angiogenesis is an important element of tumor progression, but tumors recruit additional molecules which provide other tumor promoting effects. One of these molecules are Myeloid-derived suppressor cells (MDSCs). MDSCs is a broader term for a heterogeneous population of cells primarily consisting of immature myeloid progenitors for neutrophils, monocytes and dentritic cells.108 While mature neutrophils typically act as immuno-promoting, MDSCs seem to act as immuno-suppressive partially due to their competitive nature with neutrophils and corresponding low levels of major histocompatiblity complex (MHC) class II and CD80 expression.109-111 The other method MDSCs use to suppress T and Natural Killer cell activity against tumors is the release of arginase 1 and iNOS (a.k.a. NOS2A).112-114 Somewhat ironically the most accurate way to identify a group of MDSCs is to look for their immunosuppressive ability. Tumors appear to recruit MDSCs from the bone marrow into their microenvironment through cytokine BV8 (a.k.a. PROK2).115,116

While immunosuppression is the principle tumor promotion action taken by recruited MDSCs, in murine models they appear to aid in angiogenesis.108,115 However, there does not appear to be any definitive evidence that MDSCs release pro-angiogenesis factors. Their support methodology may be through their immunosuppressive behavior which allows the tumor greater opportunity to release pro-angiogenesis factors from other cells. The latter explanation may be the more accurate one because MDSCs can secrete IL-10 which increases the probability for higher concentrations of TAMs which aid in angiogenesis through the release of VEGF.117 Another explanation is that BV8 helps recruit other elements which aid angiogenesis apart from MDSCs. There is some evidence to suggest that one of these ‘other elements’ are neutrophils.118,119

Based on the duel role of BV8 as a pro-angiogenesis agent as well as an indirect immunosuppressive agent, looking for a way to neutralize the BV8 pathway could be a useful strategy in cancer treatment and even prevention. The focus on prevention could be even more appropriate because the more prominent effects of BV8 seem to occur in the earlier stages of cancer progression. One advantage in creating BV8 based treatments may be that most of the identified functions for BV8 seem to occur in development and childhood. One disadvantage, beyond the general lingering unknown, is a possible connection between BV8 and circadian rhythms.

Cell surface markers in human MDSCs are somewhat inconsistent, but support CD11b and CD33 expression, but no CD14 or Lin.120,121 One of the reasons cell surface markers are not valid identifiers for MDSCs is that there is some question to whether or not certain unknown characteristics can lead MDSCs to differentiate into mature macrophages, dentritic cells or granulocytes110,122 thus there is a wide variances of cell surface markers in MDSC clusters due to various cell types that man inhabit these clusters.

Earlier the mixed results of Avastin were discussed as a representation of addressing angiogenesis by neutralizing the signaling factors. However, others believe the best way to treat tumors is to prevent angiogenesis which would prevent tumor growth and dramatically reduce metastasis probability, but not through neutralization of angiogenesis factors, but neutralization of the cells which provide these factors. As discussed above there are four major targets to preventing angiogenesis from an origin perspective: TAMs, TEMs, mast cells and (not discussed neutrophils). Some researchers have already used specific DNA vaccines to enable cytotoxic T cells to attack TAMs resulting in significant reduction in population in pre-clinical murine models with associated reductions in tumor angiogenesis, growth and metastasis.123

Despite the success in targeting TAMs with this strategy, little information exists regarding whether or not such a strategy increases lifespan or will in the long term have results similar to Avastin administration. One of the bigger questions is that does the removal of TAMs even matter that much when TEMs appear to have a more pronounced influence on angiogenesis on an overall level and mast cells apparently driving a lot of the initial angiogenesis action. Either way more study regarding this anti-TAMs strategy needs to be done.

One mechanistic problem with limiting macrophages may be neutrophils picking up the slack. When macrophages were stripped of CCR2 (receptors for CCL2 a major recruitment molecule), neutrophils were recruited in much larger numbers than normal and augment angiogenesis maintenance.124 Such a contingency action by tumors seems likely for other long-term anti-macrophage treatment strategies as well (antibodies); therefore, somehow addressing neutrophil recruitment may also be necessary.

Another issue with attacking angiogenesis signaling molecules is that the two most prominent elements driving angiogenesis are VEGF and hypoxic conditions with both appearing to be non-starters at this moment. The disappointment in Avastin has raised questions regarding the usefulness of blocking VEGF alone. Also the hypoxic element could cause problems because a non-decisive reduction in angiogenesis through one pathway could result in the expansion of angiogenesis via hypoxic-induced agents. However, an important element to recruitment that could be fortunate is that pro-angiogenic factors do not appear to be released by recruited cells until they are actually in the tumor microenvironment. Thus tumor specific targeting elements that are properly designed could address a vast majority of the angiogenesis potential if applied early enough or in pre-emptive preparation for metastasis.

Unlike monocytes, B cells do not infiltrate into the tumor environment, thus inflammatory-based functions from innate immune cells were thought to operate through a more remote signal pathway.78,125 In the K14-HPV16 murine model B cells were able to activate Fcg receptors on myeloid cell.126 Also B1 cells could suppress LPS-induced genes (those which would drive M1 phenotype expression), possibly through the expression of IL-10, which then increase the probability that macrophages in undertake a M2-type phenotype.127 Inhibition of IL-10 switched infiltrated macrophages from M2 to M1 and stimulated a more aggressive innate immune response against the tumor.128 This result implies that IL-10 may be required for the maintenance of a tumor-induced M2 phenotype for recruited macrophages. Therefore, long-term inhibition of IL-10 may be a therapeutic strategy worth exploring.

Dendritic cells regulate adaptive immune responses and are the principle antigen-presenting cells (APC), which can induce primary and secondary T and B-cell responses.43,130 Dendritic cells are divided into two types of classes: myeloid (MDC) and plasmacytoid (PDC).130 Both are commonly identified through their unique cell surface markers (myeloid: CD11c, CD33 and Lin and plasmacytoid: CD45RA, CD4 and ILT3).131 Under normal circumstances MDCs originate in the bone marrow as immature cells which mature after acquiring any foreign antigen. After maturation the mature DC migrates to lymphoid tissue where they initiate activation of antigen-specific T cells.43 With respects to tumors, MDCs are less likely to initiate maturity reducing the number of mature DCs within cancerous tissue relative to healthy tissue.132 Instead immature MDCs are recruited into the tumor microenvironment by members of the CXCL family (notably 8 and 12), VEGF and HGF.133-135

In the tumor microenvironment immature MDCs function in two major roles. Immature MDCs aid in angiogenesis not only by releasing pro-angiogenesis cytokines, but also through their ability to be used as endothelial progenitors.134,136 There is also some question to whether or not immature MDCs can secrete members of the CXCL family to further recruit other cells into the microenvironment.137 Also some evidence exists to support dendritic cells initiating an anti-immune response via a TLR4-MyD88 pathway in the presence of dying tumor cells.138 Finally hypoxic conditions appear to reduce the probability of dendritic cell maturation perpetuating their immature, pro-tumor state.135,139 Despite all of the pro-tumor roles that dendritic cells activate, their overall role in tumor promotion is currently regarded as minimal; however that minimal effect may be amplified by the sheer number of dendritic cells in the microenvironment.

The prominent role that both TNFa and NF-kB have in tumor development was a major factor in the development of the theory that inflammation is a centerpiece in tumorigenesis. A number of individuals agree that inflammatory factors do play a role in the development of a clump of cancer cells into a full-blown tumor as the tumor microenvironment is aided in development by secretion of inflammatory promoters.140 However, there is some argument regarding whether or not inflammation actually can drive the necessary cellular changes to induce non-cancerous cells to become cancerous in the first place. Proponents of inflammation as cancer triggers have two points of argument: chronic inflammation eventually induces cancerous cells and viral induction. Viral induction, where tumorigenic pathogens such as Human Pamplona Virus (HPV) evade the immune system and establish persistent infections associated with low grade, but chronic inflammation, seems on surer footing than non-viral induced chronic inflammation.

One important side note is that when references are made to pathogen x, like HPV, causes cancer y the pathway of cancer inducement commonly comes from the NF-kB pathway.141 In large part the reason some of these pathogenic agents can enhance the development of cancer is that they typically have transactional domains which mimic NF-kB family members or NF-kB activation receptor sites.141 in the local environment where the virus persists. This interaction allows these specific pathogens the ability to enhance the developmental probability of cancer. For example individuals can still get cervical cancer even if vaccinated against HPV; however, the vaccination is thought to significantly reduce the probability of acquiring cervical cancer because it is thought that the most common pathway to developing cervical cancer seems to stem from NF-kB mimicry by HPV.

It is more difficult to link up non-viral chronic inflammation as the cause of the cancer. One method of action could be that over time the inflammation leads to the creation of a significant quantity of free radicals. These free radicals are then able to induce enough genetic damage to nearby cells localized within the region of inflammation that they eventually mutate and become cancerous. The major empirical problem with this theory is that individuals who suffer from conditions, which have origins from chronic inflammation such as rheumatoid arthritis or psoriasis, do not seem to develop cancer at any higher rates than those that do not suffer from these conditions.

Currently no quality explanation exists for these results relative to the above theory. Some proposes that other dietary or environmental carcinogens are not able to interact with the inflammation catalyzing the cancer development, but if such catalysts are required to produce cancer from chronic inflammation then it is difficult to argue that chronic inflammation gives rise to a significant number of cancer cases. In fact recent evidence demonstrates that mediators and most signaling pathways for inflammation are downstream of oncogenic mutations supporting a conclusion that cancer causes inflammation not inflammation causes cancer.140

Perhaps the best theory behind why chronic inflammatory conditions like psoriasis do not develop into cancer, as most inflammatory – cancer proponents would theorize, is that there is some evidence to suggest most of the macrophages in psoriasis express the M1 phenotype. This excessive expression of M1 phenotypes more than likely is derived from psoriasis being a T helper 1 mediated disease causing large accumulation of neutrophils and monocytes in the skin. The large-scale expression of M1 phenotype should increase the probability that those macrophages would attack cells expressing initial stages of tumor development. This increased probability of cancer termination could then mask the cancer inducing effects of chronic inflammation in chronic inflammatory conditions like psoriasis.

Recalling for a moment that one of the original ideas behind the importance of studying angiogenesis was to develop a means to design treatments which would force higher levels of oxidative stress on a tumor or pre-tumor due to a lack of nutrients and oxygen from the lack of blood vessels. Unfortunately not only has anti-angiogenesis treatments proven less effective than previously thought, the base idea of inducing high oxidative stress environments may have been in error as well. To understand whether or not this is the case it is important to investigate how tumors behave in these oxidative environments as opposed to normal cells.

Environments of high oxidative stress are created when the large increases in concentrations of reactive oxidative species (ROS) occur. Among those ROS elements the most dangerous are free radicals (O-) and super oxides which are commonly formed when electrons from the electron transport chain operate in error and interact with oxygen. In localized regions which cover tumor cells there is a higher probability for the formation of a high concentration ROS environment due to oncogenic transformation and alterations in metabolic activity (including angiogenesis processing vs. cellular growth rates).142,143 The result should not be surprising simply based on the premise that with less restraint on growth cells will undergo greater metabolic activity in order to expand their growth which will result in a greater concentration of ROS due to a lack of corresponding increase in antioxidants.

Normally an increase in ROS within the localized environment should be viewed as a positive in the context of addressing cancer because a higher ROS concentration leads to a higher mortality rate. However, in the case of cancer, there are questions about how ROS may change the nature of translation for various signaling molecules, some which stimulate cell proliferation and promoting cell motility.144,145 Initially an increase in concentration for molecules governing both proliferation and motility seem to make some sense on a logical level. A cell under high levels of oxidative stress three potential responses could be expected. First, cells would take an attrition response where they start multiplying at a much higher than normal rate with the ‘intent’ to out-propagate the threat (in this case the ROS agents), an attempt to sacrifice enough cells to either neutralize or mitigate the significance of the ROS agents in the oxidative environment. Second, cells would take a ‘bunker down’ response, similar to that of a bacterial spore, where all resources are devoted to survival of the given cell, no communication, no growth, just survival. Third, cells would simply try to move away from that high stress environment by increasing their motility. For cancer cells the first and third reaction appear predominant.

The most important consideration for addressing tumors is that their functionality commonly behaves like a double-edged sword in that they frequently develop alternative methods for accomplishing a given function. While these alternative methods can be frustrating through the complication or reduction of more common pathways, these new methods also provide possible new attack strategies if they can be successfully identified. One particular pathway which could involve a useful therapy is how tumor-based mitochondria behave in high ROS environments.

The chief safeguard to the mitochondria is that a double membrane with selective protein transport complexes protects it. However, if a detrimental agent, normally superoxides, compromises those membranes the raw mitochondrial DNA (mtDNA) is more susceptible to damage because of a lack of histones.145 Combine the lack of histones with the limited DNA repair capacity in the mitochondria and excessive ROS concentrations and mtDNA tends to be damaged more easily in cancer cells than other types of DNA. When this DNA is damaged it can compromise proper functioning of the mitochondria and result in the greater synthesis of ROS.

The release of excess ROS for the maintenance and stabilization of high ROS concentration environments is also important with respects to metastasis. Recall that above it was briefly discussed that one possible response to a high ROS environment was increased probability for motility and cellular migration. This response pathway seems to persist in cancer cells as well as normal cells. Some evidence already exists which suggests the mitochondria dysfunction and the corresponding increase in ROS production can enhance metastatic probability.145,146

The reason for this increase in metastatic probability appears to come from various ROS activating activator protein 1 (AP-1).145 AP-1 plays a major role in a number of critical biological processes including cell proliferation, apoptosis and differentiation. Among these processes AP-1 appears to also be able to bind CXCL14 (a.k.a. BRAK or MIP-2g), a chemokine of unconfirmed functionality.145 Unfortunately despite its ability to interact with AP-1 the function(s) of CXCL14 remains controversial due to conflicting empirical data.146,147 One possible function appears to be increasing the release of calcium ions in to the cytosol. It is hypothesized that this excess calcium is released from isolated ER microsomes when CXCL14 binds to IP3R.145

If this hypothesis is correct then there may be an interesting connection between CXCL14 and cytochrome C. Cytochrome C is one of the more common methods for the initiation of apoptosis. When cytochrome C is released into the cytoplasm, usually due to mitochondrial response to apoptotic precursors, it binds apoptotic protease activating factor149 and later binds to IP3R on the ER facilitating calcium release, similar to a theory regarding how CXCL14 is thought to behave. This calcium release acts as a positive feedback mechanism, creating cytotoxic levels of calcium, which results in the further release of more cytochrome C both into the cytoplasm and into the mitochondria. The release into the mitochondria leads to the activation of caspase 9 which begins the caspase cascade initiating apoptosis. So does CXCL14 create some form of cytochrome C mimic response or does it actually trigger the release of cytochrome C which is the agent which creates the cytoplasmic calcium concentration increase?

In addition this release of calcium into the cytosol may also support the notion of higher glycolysis activation in cancer cells. Most enzymes which function in glycolysis bind to the cytoskeleton and cytosolic calcium promotes assembly of actin filaments along with cytoskeleton re-organization which could in turn promote greater than normal activation of glycolysis dependent enzymes, thus increasing glycolytic activity. Intuitively such a thought process makes sense because under conditions of stressed and dysfunctional mitochondria there exists a real probability that these dysfunctional mitochondria will be unable to produce sufficient amounts of ATP through the Krebs Cycle and electron transport chain. Therefore, under periods of dysfunction the mitochondria could release molecules which indicate a dysfunction stimulating non-mitochondrial based ATP generating pathways, the most prominent one being glycolysis.

However, such a system would probably only be able to stem the ATP loss from the mitochondria for a short period of time largely because of a lack of required ATP concentration as well as eventual mitochondrial driven apoptosis. It is important note that clearly some cancer cells probably overexpress some type of anti-capsasin molecules which block mitochondrial driven apoptosis. Also there may only be a small window for metastasis through this mechanism as there have been observations that cancer cells with high ROS generations eventually reduce this excess generation falling back into a rebalanced dynamic equilibrium of ROS generation and ROS elimination, which aids overall sustained proliferation.145

One aspect of treating cancer that has not been addressed to this point is the scenario of relapse. There are two principle rationalities to explain the reemergence of cancer once a patient is thought to be in remission beyond an incredibly unlucky second separate and independent occurrence. First, that the patient was never fully in remission, some of the cancer become resistant to the applied treatment, incomplete surgical removal or an inability to target all cancer cells with chemo or radiotherapy. This explanation is understandable because there is no existing definite cancer detection technique, thus smaller cancer cells (typical post-treatment cells) are more difficult to detect. Second, during the development of the tumor cancer stem cells (CSC or cancer-initiating cells (CICs)) are produced, so even after the main tumor is eliminated these CSCs reinitiate cancer development.

Two different theories emerged to explain the functional heterogeneity (different proliferation ability and differentiated states) in cancer: small population of self-renewable cells (cancerous stem cells) that created all other cancer cells in the development of the tumor or clonal evolution where all cells have similar tumorigenic capacity.150-153 Early on there were serious questions regarding whether or not CSCs actually existed.154,155 However, while an expansion of empirical evidence has limited the concern regarding the existence of CSCs, it has not created a single predominant theory regarding their role in the development and progression of tumors. Also an increase in the supporting evidence for CSCs does not eliminate clonal evolution as a valid theory for cancer evolution as serial transplantations typically result in more aggressive tumors,156 CSC theory is just the primary cancer causing agent not the only one.

CSCs are loosely defined as stem cells that induce a cancerous state. There is no general acceptance to the origins of CSCs, do they arise from cancer cells or are do they mutate from normal stem cells. Devising these origins is difficult because CSCs have similar properties to normal stem cells like self-sustained replication, pluripotency and drug resistance genes like ABCG2.157 However, most cancer cells share most of these properties and with the genetic variances in cancer cells it cannot be dismissed that certain mutations could restore pluripotency to some cancer cells making them CSCs. It is difficult to conclude that normal tissue could become both cancerous and pluripotent in a small replicative time period, thus it makes sense to conclude that CSCs must be derived from a group of cells that already has one of those two traits, either tumors or normal stem cells. Basically the chief question of CSC origin come down to: do CSCs develop first which lead to tumor development or do tumors develop first with some of the cancerous cells mutating into CSCs?

Some evidence that supports CSCs originating from tumors is the enhanced chemotherapy resistance attributed to most CSCs seems to come more from previously exposed cancer cells over normal stem cells. Also haematopoietic malignancies moZ-TIF2, mil-AF9 and mll-enl16 all appear to grant stem-like properties on committed progenitor cells putting them on the pathway towards becoming CSCs.158-160

However, other evidence points to more CSCs coming from normal stem cell corruption. For example carcinomas seem to target epithelial stem cells and CSCs derived from carcinomas express similar molecules like Oct4 and Bmi1, have similar size, greater adhesiveness and similar patterns of gene expression.158,160,161 While one could argue that these features are common among stem cells in general, so CSCs derived from differentiated tumors would have them as well not just those derived from normal stem cells, the lack of a ‘missing link’ cell makes it difficult to decisively support that conclusion. Overall it may be just a situation where CSCs can develop from both potential pathways.

Despite the need for more information to determine a better treatment strategy, another reason behind determining the origins of CSCs is that some theorize that CSCs arising from transformation of a normal stem cell are more aggressive than those derived from a more differentiated progenitor cell.158 One association with this theory has been melanoma and its connection to epithelial stem cells. The theory tends to make more sense because it stands to reason intuitively that conversion of normal stem cells would result in faster tumor progression through greater cell division than conversion of a previously committal progenitor cell.

Some of the first pieces of evidence which supported the existence of CSCs was through fragmentation of human breast cancers into single cell suspension and isolation of subpopulations.162,163 This experiment found that tumor-initiating ability was almost exclusive to a small fraction of the cancer cells all which expressed CD44.163 Quick reminder that the term CD describes a particular cell-surface glycoprotein which involves cell-cell interaction. Other studies confirmed this CD44 expression as well as adding CD133 expression.164-166 After these studies it was concluded that CSCs either expressed CD44 and/or CD133.158 Unfortunately at the moment the expression patterns of type specific cancer seem to be either capricious or unknown as CD44 expression is found in head and neck cancers, pancreatic and breast cancer where as CD133 expression is found in brain cancer, liver and colon cancer.165-170 Also there does not appear to be a universal marker which identifies CSCs, but various groups of markers that hint at CSC characteristics. In fact some believe that CD133 is not a CSC maker, but a stem cell maker instead because it is also expressed in normal stem cells, but comparison of expression levels varies.163

The conservation of CD44 and CD133 expression through a wide variety of cancers has logically lead researchers to question whether these two elements confirm stem-cell characteristics. Work has been done to investigate the role of CD44 on CSCs. Application of a universal (works against all isoforms) CD44 antibody was thought to eliminate transplanted leukemic stem cells while a humanized monoclonal antibody for CD44 demonstrated tumor reduction in advanced oral cancer.171 Therefore it appears that CD44 does play some important role in tumor growth and/or maintenance. However, there are some concerns as administration of the CD44 ligand, hyaluronan,172 seemed to reduce breast cancer propagation, which is contradictory to what is expected. Also the biggest concern at the moment with using a CD44 antibody strategy is its major skin toxicity due to the presence of CD44 on normal skin stem cells.171 There is still hope that CD44 can be used as a therapeutic target in the right dosage because CSCs seem to express more CD44 than normal haematopoietic stem cells.171,173

Another question with regards to CSCs is their highly sporadic variable frequency relative to non-CSCs. Instead of most tumors consisting of relatively conserved flat ratios of CSCs the percentage of CSCs vary wildly relative to tumor type. For example CSCs in colorectal carcinomas have ranged between 1.8 to 24.5% and CSCs in melanoma have ranged between 1.6 and 20%.162,169 One reason behind this variance may be a non-uniform sampling or distribution of CSCs leading to inaccurate results (impurity in sample collection). However, the number of studies that report this wild fluctuation in CSC ratio seem to rule that conclusion out.

Some argue that the hypoxic, pH and nutrient variant microenvironment with necrotic elements create a form of stochastic aspect to the number of CSC within a given tumor formation.174 Therefore, it is difficult to assign a rationality to why one tumor formation may have x% of CSCs versus another tumor of same type that only has y% of CSCs. Another explanation could be that CSCs increase depending on a given event such as metastasis in order to replenish any losses from the principle tumor. Therefore, elements like CXCL14, which could promote metastasis, could also initiate the production of more CSCs.

Is there any evidence to support a suggestion that CSC ratio changes in tumors in preparation for metastasis? To address that question one must ask what role, if any, CSCs may play in metastasis. Most believe that the process of metastasis is preceded by an epithelial mesenchymal transition (EMT) at the invading edge of the tumor which leads to cellular detachment increasing the probability that the cell in question is caught up in the blood stream and can then move to a different place in the body.159,175 There is evidence to suggest that canonical pathways initiate EMT. Some have concluded that because canonical pathways can be activated during neoplasia that the reversal of differentiation that may convert progenitor cells into CSCs may also detach those cells from the tumor matrix.176 Basically this ‘accidental’ activation of the canonical pathway kills two birds with one stone with regards to cancer aggressiveness, more CSCs and increased probability of metastasis.

A more specific example of metastasis behavior was demonstrated when researchers found a distinct group of CD133+ CXCR4+ cells which exhibited stronger migratory activity in vitro over CD133+ CXCR4- cells.177 Both cell types exhibited similar tumor progression rates. In addition the CD133+ CXCR4- cells did not demonstrate metastatic activity in the liver where as CD133+ CXCR4+ cells did.177 Furthermore inhibition of the CXCR4 receptor significantly reduced metastatic potential in pancreatic tumors with no significant change to localized tumor progression.159 One outcome connecting metastasis and CSCs is that there could be two distinct types of CSCs. One that is developed for the purpose of metastasis (the more resistant nature of the CSC would be better suited for successful metastasis) and one that is developed for localized tumor progression which ‘hides’ inside a solid tumor.

On a side note the nature of metastasis also draws a question of whether or not macrophage fusion is required. Some believe that when cancer cells reach high grades of malignancy (solid tumor formation) the high plasticity of the cells along with the high inflammatory environment some of the cancer cells themselves progressively develop endothelial and immunity phenotypes. These new phenotypes allow these cancer cells to more freely transport within the blood stream increasing probability for metastasis. In contrast John Pawelek and his supporters believe that fusion between macrophages (most likely these TAMs that congregate near the tumor formation) and cancer cells fuse forming a macrophage/cancer cell hybrid cell that is capable of traveling through the blood stream.178

Both explanations have their problems. While the cancer plasticity theory makes sense, especially when considering the role of cancer stem cells in cancer progression, very little supportive evidence has been demonstrated for the ability of the inflammatory environment to induce immunity characteristics. The same story exists for the hybrid macrophage-cancer cell as while numerous studies have been done that supposedly produced hybrids, there have been no solid confirmations that those new hybrids actually were hybrids.179 The two reasons for the difficulty in detecting hybrids are that both the cancer cells and macrophages are almost virtually identical genetically and very narrow differences between genuine hybrids and two cells simply adhering to each other.

There is evidence to suggest that EMT induction is reversible which could revert converted epithelial-based CSCs back into epithelial cells.174,180 Such a strategy could be an anti-metastasis treatment option. The ability to reverse CSCs into non-CSC epithelial cells may also provide an explanation for the great variance in CSC ratio in various tumors. The undifferentiated and differentiated states of cancer cells may not be as permanent as previously thought and depending on given conditions cells may move between CSC and non-CSC states. Most of the existing CSC models do not incorporate a dynamic differentiated to undifferentiated state because of limited empirical evidence.

While little direct empirical evidence exists for this reversible differentiation ability, strong anecdotal evidence does exist. Take this scenario for example… a group of 100 cells exist where 7 are stem cells and 93 are non-stem cells. If one of the non-stem cells undergoes a genetic mutation which would induce malignancy under a non-reversible differentiation model such a mutation would be irrelevant because that mutated cell does not self-renewable capabilities, thus cannot develop into a full-growth tumor unless it also developed a type of telomerase (self-renewal) mutation; as previously noted such dual mutations seems unlikely. Therefore, such a cancerous mutation would need to occur in one of the stem cells. However, such a restriction does not appear viable to correlate known mutation probability rates vs. general cancer incidence in the general population (mutation rates would need to be much higher).

With regards to CSC chemo-resistance the more illuminating studies seems to involve brain cancer and CD133+ CSCs24. One theory behind why CD133+ cells survive over CD133- cells when exposed to a chemotherapeutic agent is that CD133+ overactivate the DNA damage checkpoint response system.159 The checkpoint system is designed to limit the ability of cells to enter mitosis with damaged DNA. Based on the overactivity of the checkpoint, CSCs may overexpress wee1 to ensure entrance into the checkpoint to repair single stand break damage. Also while CSCs are through able to survive chemotherapeutic agents than non-CSCs through drug pumps like ABCG2 another advantage CSCs have is the ability to enter the quiescent state. There is reason to believe that CSCs in the G0 phase gain a greater resistance to chemotherapy agents.181 So perhaps CSCs can linger longer in both G0 and G2 phases increasing survivability.

CSC location is also problematic when addressing treatment possibilities. CSCs are typically localized in the center of a solid tumor mass. The CSCs act as a keystone to the tumor continuing to expand the tumor from the inside in an outward fashion. Therefore, to neutralize these CSCs the treatment regiment must ‘bore a hole’ of sorts through the solid tumor to reach the center and the CSCs. However, boring that hole is difficult because as the treatment is destroying the more differentiated cancer cells that make up the outer portion of the tumor, the CSCs are continuing to synthesize new cancer cells which substitute for the dead cells in the tumor itself.

The lack of understanding surrounding CSC origin makes developing a treatment strategy complicated. Some argue that due to the general resistances against chemotherapy oncolytic viruses are the best option because of their cytotoxic and non-targeting nature. The problem with this strategy is that, more than likely due to their lack of definite differentiation, CSCs are could be resistant to infection with certain oncolytic viruses, like HSV-2 mutant, that can normally infect differentiated cancer cells.182 This problem can be overcome by adding certain specific elements like histone deacetylase inhibitor, trichostatin A, as it did for one type of breast CSC.182 However, because of incomplete information and possible heterogeneity surrounding CSCs it is difficult to confirm whether or not such a strategy can be universally applied or if it was simply valid for that specific experiment. For example Jordan identified parthenolide as a molecule that can selectively target human leukemia stem cells over normal stem or progenitor cells, but other cancerous non-leukaemia targets are unconfirmed.183 One element which demands further study is the question of whether or not intact IFN response is a defining characteristic of CSCs.

Another treatment strategy in its early stages is inducing CSC differentiation through exposure to bone morphogenetic proteins (BMPs).184 This result makes more sense when correlating the evidence that a reduction in BMP concentrations increases the probability for colon cancer.185 Forcing CSCs to differentiate could be the better treatment strategy than trying to kill them because there appear to be fewer, if any, CSC safeguards to preventing induced differentiation vs. toxic agents. Once differentiated the CSCs lose their ability to replenish cancer cells reducing the rate of tumor growth. This strategy seems to be supported by experiments which down regulated BMP resulting in more tumor aggression via alleged greater CSC proliferation.186 However, because BMPs could also cause normal stem cells to differentiate, the application of this type of therapy would need specific cancer targeting to ensure limited BMP exposure to non-CSCs. Despite these difficulties there is hope that a novel treatment can be developed to attack CSCs and undermine the continued progression and maintenance of the tumor collapsing it from the inside out.

While oncolytic viruses have been explored as a means for specific targeting it is not the only strategy. A new technological strategy attaches nanoparticles made from diamonds to chemotherapeutic agents to increase their effectiveness.187,188 The advantages of this new nanodiamond-chemo compound is that the nanodiamond is made from carbon, is non-toxic and does not drive an immune response.187 Also the addition of the nanodiamond increases the size of the chemo agent is thought to dramatically reduce the ability of tumors cells to use anti-drug pumps to eliminate the chemo agent out of the cytoplasm before it does any mortal damage to the cancer cells.187 Formation of the compound also seems to reduce chemotoxicity which should reduce detrimental side effects associated with such treatment.

Finally the nanodiamond can also act as a linker molecule bridging the chemo agent and another molecule, perhaps an antibiotic, which can increase tumor targeting efficiency. Nanodiamonds and similar elements like gold may also be an effective delivery mechanism for other agents like BMPs as well. At the moment there could be two drawbacks to adding nanodiamonds to the treatment. First, the additional cost associated with their manufacture and formation and second while initial tests have demonstrated reasonable biocompatibility there have been no real long-term studies to determine long-term side effects.

One of the trickiest issues in treating cancer is identifying sustainable treatments. As seen with Avastin above with respects to failed attempts to control tumor angiogenesis, researchers frequently discover drugs which perform well against cancer in the short-term, but when the treatment scope is expanded the effectiveness of these drugs falter making them almost no more valuable than existing treatments. One example of the ability of cancer to thwart these new drugs is in the new chemotherapy drug, vemurafenib, used to treat metastatic melanoma. In Phase III trials vemurafenib shrank tumors by almost 10 times more than dacarbazine (the standard treatment for metastatic melanoma at the time) [48% vs. 5%] and also improve six-month survival rate from 64% to 84%.189 Unfortunately the successes of vemurafenib were only short-lived as tumors switched from the BRAF triggered pathway to an alternative pathway, essentially becoming immune to the effects of vemurafenib.189 After factoring out the six-month survival rate improvement, the long-term survival rate did not change significantly. Ipilimumab, another melanoma drug, performs similar to vemurafenib in that it marginally increases short-term survival rate, but does not significantly change long-term survival rate.190

Cancer prevention has always been a questionable issue because, as demonstrated in this blog post, it is difficult to narrow down an effective measure of prevention due to the intricate nature in which cancer develops. However, for some specific cancers certain prevention therapies exist, most of them involving breast cancer because of the association between breast cancer development and oestrogen/estrogen. Early preventative treatments were Tamoxifen and Raloxifene which acted as competitive inhibitors with oestrogen. However, these two drugs did carry small risks of increasing the probability of womb lining cancer and blood clots which could increase the risk of heart attacks. The severity of these side effects were so great that most worried about using either as a preventative agent.

Some researchers believe that a new drug, Exemestane, provides a much better option due to its lack of side effects in initial clinical studies. Exemestane is an irreversible, steroidal aromatase inactivator which when bound to aromatase is converted to an intermediate that irreversibly binds to the active site on aromatase eliminating both elements.191 This type of inhibition is commonly known as ‘suicide inhibition’. Eliminating the available concentration of aromatase reduces the concentration of estrogen as aromatase is chiefly responsible for converting testosterone to estradiol. The reason for this lower estrogen concentration is that in post-menopausal women the ovaries stop producing estrogen, so estrogen production is largely reliant on the conversion of androgens. Of course exemestane is only effective against estrogen receptor positive breast cancer.191

Unfortunately there may be a drawback in using exemestane that has not yet been seen. Previous studies have demonstrated that animals exposed to aromatase inhibitors exhibit substantial gains in overall adiposity due to increased adipocyte hypertrophy and hyperplasia.192 Therefore, if individuals are placed on exemestane as a preventative treatment, especially post-menopausal women, there may be a significant increase in weight gain. At the moment there is no definitive information regarding whether or not this inhibition is developmental or time sensitive. Therefore, whether or not this weight gain side effect will affect those older women taking it for prevention is unknown.

Overall cancer research and therapy has come a long way in the last few decades, especially with regards to its functionality. Unfortunately while this additional knowledge has opened the door to new treatment options those options have yet to materialize into a cure. Fortunately promising attack avenues are still available and newer strategies are being explored each day. For example while VEGF neutralization strategies for angiogenesis may not be successful, focusing on the more overlooked TEMs than the TAMs could provide a better basis for attacking angiogenesis. Attacking BV8 may also prove to be useful not only as an anti-angiogenesis treatment, but also for anti-metastasis strategies.

Furthermore application of antibodies and/or nanomaterials to better target cancer cells could be aided by exploring RANKL inhibitory compounds which could reduce the potential of tumors to ‘hide’ from immune cells. CSCs could be attacked not directly through cytotoxic elements, but indirectly by BMP and its associated compounds to induce differentiation reducing tumor malignancy. Finally advances in diagnostic elements from PARE, miRNA screenings and microbubble ultrasounds to expansion of genetic information should improve diagnosis speed and associated survival rates. Overall the most important element to addressing cancer in the future is to continue to take positive steps forward and ensure that evidence and logic guide the way.
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Citations:

1. Moisse, Katie. “Biomarker Studies Could Realize Goal of More Effective and Personalized Cancer Medicine.” Scientific American. April 26, 2010. http://www.scientificamerican.com/article.cfm?id=biomarkers-personalized-cancer-medicine
2. Kalager, M, et, Al. “Effect of Screening Mammography on Breast-Cancer Mortality in Norway.” N. Engl. J. Med. 2010. 363(13):1203-1210.
3. Gøtzsche, P, and Nielsen, M. “Screening for breast cancer with mammography.” Cochrane Database Syst Rev. 2006. 4: CD001877.
4. Tiffen, J, and Mahon, S. “Cervical cancer: What should we tell women about screening?” Clinical Journal of Oncology Nursing. 2006.10(4):527-531.
5. Wiksell, H, et Al. “Prevention of tumour cell dissemination in diagnostic needle procedures” British Journal of Cancer. 2010. 103: 1706 – 1709.
6. Biomarkers (medical). 2011. Wikipedia Entry.
7. Leary, R, et, Al. “Development of Personalized Tumor Biomarkers Using Massively Parallel Sequencing.” Sci Transl Med. 2010. Feb 24: 2(20): 20ra14. doi:10.1126/scitranslmed.3000702.
8. Lengauer C, Kinzler, K, and Vogelstein, B. “Genetic instabilities in human cancers.” Nature. 1998. 396:643–649.
9. Grimwade, D, et, Al. “Prospective minimal residual disease monitoring to predict relapse of acute promyelocytic leukemia and to direct pre-emptive arsenic trioxide therapy.” J. Clin. Oncol. 2009. 27:3650–3658.
10. Bregni, M, et, Al. “Minimal residual disease in acute lymphoblastic leukemia detected by immune selection and gene rearrangement analysis.” J. Clin. Oncol. 1989. 7:338–343.
11. Van der Velden, V, et, Al. “Optimization of PCR-based minimal residual disease diagnostics for childhood acute lymphoblastic leukemia in a multi-center setting.” Leukemia. 2007. 21:706–713.
12. Hughes, T, et, Al. “Monitoring CML patients responding to treatment with tyrosine kinase inhibitors: Review and recommendations for harmonizing current methodology for detecting BCR-ABL transcripts and kinase domain mutations and for expressing results.” Blood. 2006. 108:28–37.
13. Park, N, et, Al. “Salivary microRNA: Discovery, Characterization, and Clinical Utility for Oral Cancer Detection.” Clin Cancer Res. 2009. 15(17): 5473–5477.
14. Borchert, G, Lanier, W, and Davidson, B. “RNA polymerase III transcribes human microRNAs.” Nat Struct Mol Biol. 2006. 13:1097–1101.
15. Lu, J, et, Al. “MicroRNA expression profiles classify human cancers.” Nature. 2005. 435:834–838.
16. Zeng, Y. “Principles of micro-RNA production and maturation.” Oncogene. 2006. 25:6156–6162.
17. Jiang, J, et, Al. “Real-time expression profiling of microRNA precursors in human cancer cell lines.” Nucleic Acids Res. 2005. 33:5394–5403.
18. Iorio, M, et, Al. “MicroRNA signatures in human ovarian cancer.” Cancer Res. 2007. 67:8699–8707.
19. Tran, N, et, Al. “MicroRNA expression profiles in head and neck cancer cell lines.” Biochem Biophys Res Commun. 2007. 358:12–17.

20. Kirpotin, D, et, Al. “Antibody Targeting of Long-Circulating Lipidic Nanoparticles Does Not Increase Tumor Localization but Does Increase Internalization in Animal Models.” Cancer Res. 2006. 66: 6732–6740.
21. Brannon-Peppas, L, and Blanchette, J. “Nanoparticle and targeted systems for cancer therapy.” Advanced Drug Delivery Reviews. 56: 1649–1659.
22. Kocbek, P, et, Al. “Targeting cancer cells using PLGA nanoparticles surface modified with monoclonal antibody.” Journal of Controlled Release. 2007. 120: 18–26.
23. Maeda, H, et, Al. “Vascular permeability enhancement in solid tumor: various factors, mechanisms involved and its implications.” International Immunopharmacology. 2003. 3: 319–328.
24. Maeda, H. “The enhanced permeability and retention (EPR) effect in tumor vasculature: the key role of tumor-selective macromolecular drug targeting.” Advan Enzyme Regul. 2001. 41: 189–207.
25. Sugahara, K, et, Al. “Coadministration of a tumor-penetrating peptide enhances the efficacy of cancer drugs.” Science. 2010. 328: 1031–1035.
26. Ko, J, et, Al. “Tumoral acidic extracellular pH targeting of pH-responsive MPEG-poly([beta]-amino ester) block copolymer micelles for cancer therapy.” Journal of Controlled Release. 2007. 123: 109–115.
27. Browne, A, et, Al. “Cancer Screening by Systemic Administration of a Gene Delivery Vector Encoding Tumor-Selective Secretable Biomarker Expression.” PLoS ONE. May 2011. 6(5): e19530.
28. Oncolytic Viruses. 2011. Wikipedia Entry.
29. Tannous, B, et, Al. “Codon-Optimized Gaussia Luciferase cDNA for Mammalian Gene Expression in Culture and in Vivo.” Mol Ther. 2005. 11: 435–443.
30. Wurdinger, T, et, Al. “A secreted luciferase for ex vivo monitoring of in vivo processes.” Nat Meth. 2008. 5: 171–173.
31. “Making cancer glow away: How to track down tiny tumours.” The Economist. Jan 4th, 2011. http://www.economist.com/node/17843620
32. Lymph. 2011. Wikipedia Entry.
33. Sentinel Lymph Node. 2011. Wikipedia Entry.
34. Sever, A, et, Al. “Preoperative localization of sentinel lymph nodes using intradermal microbubbles and contrast-enhanced ultrasonography in patients with breast cancer.” British Journal of Surgery. 2009. 96(11):1295-1299.
35. Korpanty, G, et, Al. “Monitoring Response to Anticancer Therapy by Targeting Microbubbles to Tumor Vasculature.” Clin Cancer Res. 2007. 13(1): 323-330.
36. Bloch, S, Dayton, P, and Ferrara, K. “Targeted imaging using ultrasound contrast agents. Progress and opportunities for clinical and research applications.” IEEE EngMed Biol Mag. 2004. 23:18-29.
37. Klibanov, A. “Targeted delivery of gas-filled microspheres, contrast agents for ultrasound imaging.” Adv Drug Deliv Rev. 1999. 37:139-157.
38. Andre, F, et, Al. “Biomarker studies: a call for a comprehensive biomarker study registry.” Nat. Rev. Clin. Oncol. 2011. 8:171–176.
39. Song, F. et Al. “Dissemination and publication of research findings: an updated review of related biases.” Health Technol. Assess. 2010. 14: 1–193.
40. Radiotherapy. 2011. Wikipedia.
41. Brachytherapy. 2011. Wikipedia.
42. Hayden, E. "Cutting off cancer's supply lines." Nature. 2009. 458(7239): 686–687.
43. Murdoch, C, et, Al. “The role of myeloid cells in the promotion of tumour angiogenesis.” Nature. 2008. 618(8): 618-631.
44. Valkovic, T, et, al. “Correlation between vascular endothelial growth factor, angiogenesis, and tumorassociated macrophages in invasive ductal breast carcinoma.” Virchows Arch. 2002. 440: 583–588.
45. Paez-Ribes, M, et, Al. “Antiangiogenic therapy elicits malignant progression of tumors to increased local invasion and distant metastasis.” Cancer Cell. 2009. 15:220-231.
46. Ebos, J, et, Al. “Accelerated Metastasis after Short-Term Treatment with a Potent Inhibitor of Tumor Angiogenesis.” Cancer Cell. 2009. 15(3):232-239.
47. Pennacchietti, S, et, Al. “Hypoxia promotes invasive growth by transcriptional activation of the met protooncogene.” Cancer Cell. 2003. 3(4):347-361.
48. Folkman, J, and Klagsbrun, M. “Angiogenic Factors.” Science. 1987. 235(4787):442-447.
49. Coley, W. “The treatment of malignant tumors by repeated inoculations of erysipelas: with a report of ten original cases. Am. J. Med. Sci. 1893. 105:487-511.
50. O’Malley, W, Achinstein, B, and Shear, M. “Action of bacterial polysaccharide on tumours. II. Damage of sarcoma 37 by serum of mice treated with serratia marcescens polysaccharide, and induced tolerance.” J. Natl. Cancer Inst. 1962. 29:1169-1175.
51. Carswell, E, et, Al. “An endotoxin-induced serum factor that causes necrosis of tumours. PNAS. 1975. 72:3666-3670.
52. Balkwill, F, “Tumour necrosis factor and cancer.” Nature Reviews: Cancer. 2009. 9:361-371.
53. Brouckaert, P, et, Al. “In vivo anti-tumour activity of recombinant human and murine TNF, alone and in combination with murine IFN-gamma on a syngeneic murine melanoma.” Int. J. Cancer. 1986. 38:763-769.
54. Balkwill, F, et, Al. “Human tumour xenografts treated with recombinant human tumor necrosis factor alone or in combination with interferons. Cancer Res. 1986. 46:3990-3993.
55. Lienard, D, et, Al. “High-dose recombinant tumor necrosis factor alpha in combination with interferon gamma and melphalan in isolation perfusion of the limbs for melanoma and sarcoma.” J. Clin. Oncol. 1992. 10:52-60.
56. Nooijen, P, et, Al. “Synergistic effects of TNF-alpha and melphalan in an isolated limb perfusion model of rat sarcoma: a histopathological, immunohistochemical and electron microscopical study.” Br. J. Cancer. 1996. 74:1908-1915.
57. Pasparakis, M, et, Al. “Immune and inflammatory responses in TNF-alpha deficient mice: a critical requirement for TNF-alpha in the formation of primary B cell follicles, follicular dendritic cell networks and germinal centers, and in the maturation of the humoral immune response.” J. Exp. Med. 1996. 184:1397-1411.
58. Moore, R, et, Al. “Tumour necrosis factor-alpha deficient mice are resistant to skin carcinogenesis.” Nature Med. 1999. 5:828-831.
59. Stix, G. “Is Chronic Inflammation the Key to Unlocking the Mysteries of Cancer?” Scientific American. 2008.
60. Madhusudan, S, et, Al. “A phase II study of Etanercept (Enbrel), a tumour necrosis factor-alpha inhibitor in patients with metastatic breast cancer. Clin. Cancer Res. 2004. 10:6528-6534.
61. Harrison, M, et, Al. “Tumor necrosis factor alpha as a new target for renal cell carcinoma: two sequential phase II trials of infliximab at standard and high dose.” J. Clin. Oncol. 2007. 25:4542-4549.
62. Brown, E, et, Al. “A clinical study assessing the tolerability and biological effects of infliximab, a TNF-alpha inhibitor, in patients with advanced cancer.” Ann. Oncol. 2008. 19:1340-1346.
63. Bargou, R, et, Al. “Constitutive nuclear factor-kappaB-RelA activation is required for proliferation and survival of Hodgkin’s disease tumor cells.” J. Clin Invest 1997. 100:2961- 2969.
64. Wang, W, et, Al. “The nuclear factor-kappa B RelA transcription factor is constitutively activated in human pancreatic adenocarcinoma cells.” Clin Cancer Res. 1999. 5:119-127.
65. Nair, A, et, Al. “NF-kappaB is constitutively activated in high-grade squamous intraepithelial lesions and squamous cell carcinomas of the human uterine cervix.” Oncogene. 2003. 22:50-58.
66. Sovak M, et, Al. “Aberrant nuclear factor-kappaB/Rel expression and the pathogenesis of breast cancer.” J. Clin Invest 1997. 100:2952-2960.
67. Suh, J, et, Al. “Mechanisms of constitutive NF-kappaB activation in human prostate cancer cells.” Prostate. 2002. 52:183-200.
68. Kojima, M, et, Al. “Increased nuclear factor-nB activation in human colorectal carcinoma and its correlation with tumor progression.” Anticancer Res. 2004. 24:675-681.
69. Ondrey, F, et, Al. “Constitutive activation of transcription factors NF-(kappa)B,A P-1, and NF-IL6 in human head and neck squamous cell carcinoma cell lines that express pro-inflammatory and pro-angiogenic cytokines.” Mol Carcinog. 1999. 26:119-129.
70. Abdel-Latif, M, et, Al. “NF-kappaBactivationin esophageal adenocarcinoma: relationship to Barrett’s metaplasia,su rvival, and response to neoadjuvant chemoradiotherapy.” Ann Surg. 2004. 239:491-500.
71. Mukhopadhyay, T, Roth, J, and Maxwell, S. “Altered expression of the p50 subunit of the NF-kappa B transcription factor complex in non-small cell lung carcinoma.” Oncogene. 1995. 11:999-1003.
72. Van Waes, Carter. “Nuclear Factor-kB in Development, Prevention, and Therapy.” Clin Cancer Res. 2007. 13:1076-1082.
73. Baldwin, A. “Control of oncogenesis and cancer therapy resistance by the transcription factor NF-kappaB.” J. Clin Invest. 2001. 107:241-246.
74. Hayden, M, and Ghosh, S. “Signaling to NF-kappaB.” Genes Dev. 2004. 18:2195-2224.
75. Luo, J, Kamata, H, and Karin, M. “IKK/NF-kappaB signaling: balancing life and death-a new approach to cancer therapy.” J. Clin Invest. 2005. 115:2625-2632.
76. Hagemann, Thorsten, et, Al. “Regulation of macrophage function in tumors: the multifaceted role of NF-kB.” Blood. 2009. 113(14): 3139-3146.
77. Bohuslav, J, et, Al. “Regulation of an essential innate immune response by the p50 subunit of NF-kappaB.” J. Clin. Invest. 1998. 102:1645-1652.
78. Sica, A, et, Al. “Role of tumour-associated macrophages in cancer-related inflammation.” Exp Oncol. 2010. 32(3):153-158.
79. Taylor, P, and Gordon, S. “Monocyte heterogeneity and innate immunity.” Immunity. 2003. 19:2-4.
80. Mantovani, A, et, Al. “Macrophage polarization: tumor-associated macrophages as a paradigm for polarized M2 mononuclear phagocytes.” Trends Immunol. 2002. 23:549–55.
81. Biswas, S, Sica, A, and Lewis, C. “Plasticity of macrophage function during tumor progression: regulation by distinct molecular mechanisms.” J. Immunol. 2008. 180:2011–2017.
82. Biswas, S, et, Al. “A distinct and unique transcriptional program expressed by tumor-associated macrophages (defective NF-κB and enhanced IRF-3/STAT1 activation).” Blood. 2006. 107:2112–2122.
83. Saccani, A, et, Al. “p50 nuclear factor-κB overexpression in tumor-associated macrophages inhibits M1 inflammatory responses and antitumor resistance.” Cancer Res. 2006. 66:11432–11440.
84. Bottazzi, B, et, Al. “Monocyte chemotactic cytokine gene transfer modulates macrophage infiltration, growth, and susceptibility to IL-2 therapy of a murine melanoma.” J. Immunol.1992. 148: 1280–1285.
85. Loberg, R, et, Al. “CCL2 as an important mediator of prostate cancer growth in vivo through the regulation of macrophage infiltration.” Neoplasia. 2007. 9:556–562.
86. Gazzaniga, S, et, Al. “Targeting tumor-associated macrophages and inhibition of MCP-1 reduce angiogenesis and tumor growth in a human melanoma xenograft.” J. Invest. Dermatol. 2007. 127: 2031–2041.
87. Lin, E, et, Al. “Macrophages regulate the angiogenic switch in a mouse model of breast cancer.” Cancer Res. 2006. 66:11238–11246.
88. Lynch, C, et, Al. “MMP-7 promotes prostate cancerinduced osteolysis via the solubilization of RANKL.” Cancer Cell. 2005. 7:485–496.
89. RANKL. 2011. Wikipedia Entry.
90. Onita, T, et, Al. “Hypoxia-induced, perinecrotic expression of endothelial Per-ARNT-Sim domain protein-1/hypoxia-inducible factor-2a correlates with tumor progression, vascularization, and focal macrophage infiltration in bladder cancer.” Clin. Cancer Res. 2002. 8:471–480.
91. Li, C, et, Al. “Infiltration of tumor-associated macrophages in human oral squamous cell carcinoma.” Oncol. Rep. 2002. 9:1219–1223.
92. Ohno, S, et, Al. “Correlation of histological localization of tumor-associated macrophages with clinicopathological features in endometrial cancer.” Anticancer Res. 2004. 24:3335–3342.
93. Negus, R, et, Al. “Quantitative assessment of the leukocyte infiltrate in ovarian cancer and its relationship to the expression of C-C chemokines.” Am. J. Pathol. 1997. 150: 1723–1734.
94. Zampetaki, A, et, Al. “Hypoxia induces macrophage inflammatory protein-2 (MIP-2) gene expression in murine macrophages via NF-kB: the prominent role of p42/p44 and PI3 kinase pathways.” FASEB J. 2004. 18: U808–U827.
95. Hiratsuka, S, et, Al. “Tumour-mediated upregulation of chemoattractants and recruitment of myeloid cells predetermines lung metastasis.” Nature Cell Biol. 2006. 8:1369–1375.
96. Saccani, A, et, Al. “p50 nuclear factor-kappaB overexpression in tumor-associated macrophages inhibits M1 inflammatory responses and antitumor resistance.” Cancer Res. 2006. 66:11432-11440.
97. Murdoch, C, et, Al. “Expression of Tie-2 by human monocytes and their responses to angiopoietin-2.” J. Immunol. 2007. 178:7405–7411.
98. De Palma, M, et, Al. “Tie-2 identifies a hematopoietic lineage of proangiogenic monocytes required for tumor vessel formation and a mesenchymal population of pericyte progenitors.” Cancer Cell. 2005. 8:211–226.
99. Meininger, C, et, Al. “The c-kit receptor ligand functions as a mast cell chemoattractant.” Blood. 1992. 79:958–963.
100. Wershil, B, et, Al. “The rat c-kit ligand, stem cell factor, induces c-kit receptor-dependent mouse mast cell activation in vivo. Evidence that signaling through the c-kit receptor can induce expression of cellular function. J. Exp. Med. 1992. 175:245–255.
101. Zhang, W, et, Al. “Modulation of tumor angiogenesis by stem cell factor.” Cancer Res. 2000. 60:6757–6762.
102. Starkey, J, Crowle, P, and Taubenberger, S. “Mastcell- deficient W/Wv mice exhibit a decreased rate of tumor angiogenesis.” Int. J. Cancer. 1988. 42:48–52.
103. Coussens, L, et, Al. “Inflammatory mast cells upregulate angiogenesis during squamous epithelial carcinogenesis.” Genes Dev. 1999. 13:1382–1397.
104. Ho, K. “Ultrastructure of cerebellar capillary hemangioblastoma. II. Mast cells and angiogenesis.” Acta Neuropathol. 1984. 64:308–318.
105. Nico, B, et, Al. “Mast cells contribute to vasculogenic mimicry in multiple myeloma.” Stem Cells Dev. 2008. 17:19–22.
106. Fujita, Y, et, Al. “Involvement of adrenomedullin induced by hypoxia in angiogenesis in human renal cell carcinoma.” Int. J. Urol. 2002. 9:285–295.
107. Garayoa, M, et, Al. “Hypoxia-inducible factor-1 (HIF-1) up-regulates adrenomedullin expression in human tumor cell lines during oxygen deprivation: a possible promotion mechanism of carcinogenesis.” Mol.Endocrinol. 2000. 14:848–862.
108. Yang, L, et, Al. “Expansion of myeloid immune suppressor Gr+CD11b+ cells in tumor-bearing host directly promotes tumor angiogenesis.” Cancer Cell. 2004. 6:409–421.
109. Movahedi, K, et, Al. “Identification of discrete tumorinduced myeloid-derived suppressor cell subpopulations with distinct T cell-suppressive activity.” Blood. 2008. 111:4233–4244.
110. Bronte, V, et, Al. “Identification of a CD11b+/Gr-1+/ CD31+ myeloid progenitor capable of activating or suppressing CD8+ T cells.” Blood. 2000. 96:3838–3846.
111. Kusmartsev, S, and Gabrilovich, D. “Immature myeloid cells and cancer-associated immune suppression.” Cancer Immunol. Immunother. 2002. 51:293–298.
112. Dolcetti, L, et, Al. “Myeloid-derived suppressor cell role in tumor-related inflammation.” Cancer Letter. 2008. doi:10.1016/j.canlet.2008.03.012.
113. Sinha, P, et, Al. “Tumor immunity: a balancing act between T cell activation, macrophage activation and tumor-induced immune suppression.” Cancer Immunol. Immunother. 2005. 54: 1137–1142.
114. Nagaraj, S, and Gabrilovich, D. “Myeloid-derived suppressor cells.” Adv. Exp. Med. Biol. 2007. 601:213–223.
115. Shojaei, F, et, Al. “Bv8 regulates myeloid-cell-dependent tumour angiogenesis.” Nature. 2007. 450:825–831.
116. LeCouter, J, et, Al. “Bv8 and endocrine gland-derived vascular endothelial growth factor stimulate hematopoiesis and hematopoietic cell mobilization.” PNAS. 2004. 101:16813–16818.
117. Sinha, P, et, Al. “Cross-talk between myeloid- derived suppressor cells and macrophages subverts tumor immunity toward a type 2 response.” J. Immunol. 2007. 179:977-983.
118. Nozawa, H, et, Al. “Infiltrating neutrophils mediate the initial angiogenic switch in a mouse model of multistage carcinogenesis.” PNAS. 2006. 103:12493–12498.
119. Shojaei, F. “Role of Bv8 in neutrophil-dependent angiogenesis in a transgenic model of cancer progression.” PNAS. 2008. 105(7): 2640-2645.
120. Almand, B, et, Al. “Increased production of immature myeloid cells in cancer patients: a mechanism of immunosuppression in cancer.” J. Immunol. 2001. 166:678–689.
121. Diaz-Montero, C, et, Al. “Increased circulating myeloid-derived suppressor cells correlate with clinical cancer stage, metastatic tumor burden, and doxorubicin–cyclophosphamide chemotherapy.” Cancer Immunol. Immunother. doi: 10.1007/s00262- 008-0523-4.
122. Kusmartsev, S, and Gabrilovich, D. “Role of immature myeloid cells in mechanisms of immune evasion in cancer.” Cancer Immunol. Immunother. 2006. 55:237–245.
123. Luo, Y, et, Al. “Targeting tumor-associated macrophages as a novel strategy against breast cancer.” J. Clin. Invest. 2006. 116:2132–2141.
124. Pahler, J, et, Al. “Plasticity in tumor-promoting inflammation: impairment of macrophage recruitment evokes a compensatory neutrophil response.” Neoplasia. 2008. 10:329–339.
125. Mantovani, A. “Cancer: inflammation by remote control.” Nature. 2005. 435:752–3.
126. Andreu, P, et, Al. “FcRgamma activation regulates inflammation-associated squamous carcinogenesis.” Cancer Cell. 2010. 17:121–34.
127. Wong, S, et, Al. “Macrophage polarization to a unique phenotype driven by B cells.” Eur. J. Immunol. 2009. 182:6494–507.
128. Sica, A, et, Al. “Macrophage polarization in tumour progression.” Semin. Cancer Biol. 2008. 18:349–55.
129. Banchereau, J, and Steinman, R. “Dendritic cells and the control of immunity.” Nature. 1998. 392:245–252.
130. O’Neill, H, et, Al. “Dendritic cell development in longterm spleen stromal cultures.” Stem Cells. 2004. 22:475–486.
131. Colonna, M, Trinchieri, G, and Liu, Y. “Plasmacytoid dendritic cells in immunity.” Nature Immunol. 2004. 5:1219–1226.
132. Troy, A, et, Al. “Minimal recruitment and activation of dendritic cells within renal cell carcinoma.” Clin. Cancer Res. 1998. 4:585–593.
133. Gabrilovich, D, et, Al. “Production of vascular endothelial growth factor by human tumors inhibits the functional maturation of dendritic cells.” Nature Med. 1996. 2:1096–1103.
134. Curiel, T, et, Al. “Dendritic cell subsets differentially regulate angiogenesis in human ovarian cancer.” Cancer Res. 2004. 64:5535–5538.
135. Feijoo, E, et, Al. “Dendritic cells delivered inside human carcinomas are sequestered by interleukin-8.” Int. J. Cancer. 2005. 116:275–281.
136. Gottfried, E, et, Al. “Differentiation of human tumourassociated dendritic cells into endothelial-like cells: an alternative pathway of tumour angiogenesis.” Scand. J. Immunol. 2007. 65:329–335.
137. Scimone, M, et, Al. “Migration of polymorphonuclear leucocytes is influenced by dendritic cells.” Immunology. 2005. 114:375–385.
138. Apetoh, L, et, Al. “Toll-like receptor 4-dependent contribution of the immune system to anticancer chemotherapy and radiotherapy.” Nat. Med. 2007. 13:1050–9.
139. Dikov, M, et, Al. “Vascular endothelial growth factor effects on nuclear factor-kB activation in hematopoietic progenitor cells.” Cancer Res. 2001. 61:2015–2021.
140. Mantovani, A, et, Al. “Cancer-related inflammation.” Nature. 2008. 454:436-444.
141. Hiscott, J, Kwon, H, and Genin, P. “Hostile takeovers: viral appropriation of the NF-kappaB pathway.” J. Clin. Invest. 2001. 107:143-51.
142. Pelicano, H, Carney, D, and Huang, P. “ROS stress in cancer cells and therapeutic implications.” Drug Resist Update. 2004. 7:97–110.
143. Moreno-Sanchez, R, et, Al. “Energy metabolism in tumor cells.” FEBS J. 2007. 274:1393–418.
144. Storz, P. “Reactive oxygen species in tumor progression.” Front. Biosci. 2005. 10:1881–96.
145. Pelicano, H, et, Al. “Mitochondrial Dysfunction and Reactive Oxygen Species Imbalance Promote Breast Cancer Cell Motility through a CXCL14-Mediated Mechanism.” Cancer Res. 2009. 69:2375-2383.
146. Ishikawa, K, et, Al. “ROS generating mitochondrial DNA mutations can regulate tumor cell metastasis.” Science. 2008. 320:661–4.
147. Shellenberger, T, et, Al. “BRAK/ CXCL14 is a potent inhibitor of angiogenesis and is a chemotactic factor for immature dendritic cells.” Cancer Res. 2004. 64:8262–70.
148. Shurin, G, et, Al. “Loss of new chemokine CXCL14 in tumor tissue is associated with low infiltration by dendritic cells (DC), while restoration of human CXCL14 expression in tumor cells causes attraction of DC both in vitro and in vivo.” J. Immunol. 2005. 174:5490–8.
149. Cytochrome c. 2011. Wikipedia Entry
150. Bonnet, D, and Dick, J. “Human acute myeloid leukemia is organized as a hierarchy that originates from a primitive hematopoietic cell.” Nature Med. 1997. 3:730–737.
151. Reya, T, et, Al. “Stem cells, cancer, and cancer stem cells.” Nature. 2001. 414:105–111.
152. Nowell, P. “The clonal evolution of tumor cell populations.” Science. 1976. 194:23–28.
153. Campbell, L, and Polyak, K. “Breast tumor heterogeneity: cancer stem cells or clonal evolution?” Cell Cycle. 2007. 6:2332–2338.
154. Denekamp, J. “Tumour stem cells: facts, interpretation and consequences.” Radiother. Oncol. 1994. 30:6–10.
155. Hill, R. “Identifying cancer stem cells in solid tumors: case not proven.” Cancer Res. 2006. 66:1891–1895.
156. Clark, E, et, Al. “Genomic analysis of metastasis reveals an essential role for RhoC.” Nature. 2000. 406:532–535.
157. Patrawala, L, et, Al. “Side population is enriched in tumorigenic, stem-like cancer cells, whereas ABCG2+ and ABCG2– cancer cells are similarly tumorigenic.” Cancer Res. 2005. 65:6207–6219.
158. Visvader, J, and Lindeman, G. “Cancer stem cells in solid tumours: accumulating evidence and unresolved questions.” Nature Reviews: Cancer. 2008. 8:755-768.
159. Huntly, B, et, Al. “MOZ–TIF2, but not BCR–ABL, confers properties of leukemic stem cells to committed murine hematopoietic progenitors.” Cancer Cell. 2004. 6:587–596.
160. Krivtsov, A, et, Al. “Transformation from committed progenitor to leukaemia stem cell initiated by MLL–AF9.” Nature. 2006. 442:818–822.
161. Wright, M, et, Al. “Brca1 breast tumors contain distinct CD44+/CD24– and CD133– cells with cancer stem cell characteristics.” Breast Cancer Res. 2008. 10:R10.
162. Schatton, T, et, Al. “Identification of cells initiating human melanomas.” Nature. 2008. 451:345–349.
163. Mackenzie, I. “Cancer stem cells.” Annals of Oncology. 2008. 19:(Supplement 5): v40–v43.
164. Al Hajj, M, et, Al. “Prospective identification of tumorigenic breast cancer cells.” PNAS. 2003. 100:3983–3988.
165. Patrawala, L, et, Al. “Highly purified CD44+ prostate cancer cells from xenograft human tumors are enriched in tumorigenic and metastatic progenitor cells.” Oncogene. 2006. 25:1696–1708.
166. Singh, S, et, Al. “Identification of a cancer stem cell in human brain tumors.” Cancer Res. 2003. 63:5821–5828.
167. Li, C, et, Al. “Identification of pancreatic cancer stem cells.” Cancer Res. 2007. 67:1030–1037.
168. Ricci-Vitiani, L, et, Al. “Identification and expansion of human colon-cancer-initiating cells.” Nature. 2007. 445:111–115.
169. O’Brien, C, et, Al. “A human colon cancer cell capable of initiating tumour growth in immunodeficient mice.” Nature. 2007. 445:106–110.
170. Prince, M, et, Al. “Identification of a subpopulation of cells with cancer stem cell properties in head and neck squamous cell carcinoma.” PNAS. 2007. 104:973–978.
171. Jin, L, et, Al. “Targeting of CD44 eradicates human acute myeloid leukemic stem cells.” Nat. Med. 2006. 12:1167–1174.
172. Herrera-Gayol, A, and Jothy, S. “Effect of hyaluronan on xenotransplanted breast cancer.” Exp. Mol. Pathol. 2002. 72:179–185.
173. Krause, D, et, Al. “Requirement for CD44 in homing and engraftment of BCR–ABL-expressing leukemic stem cells.” Nature Med. 2006. 12:1175–1180.
174. Gupta, P, Chaffer, C, and Weinberg, R. “Cancer stem cells: mirage or reality?” Nature: Medicine. 2009. 15(9): 1010-1012.
175. Thiery, J. “Epithelial–mesenchymal transitions in tumour progression.” Nature Rev. Cancer. 2002. 2:442–454.
176. Mani, S, et, Al. “Mesenchyme Forkhead 1 (FOXC2) plays a key role in metastasis and is associated with aggressive basal-like breast cancers.” PNAS. 2007. 104:10069–10074.
177. Hermann, P, et, Al. “Distinct populations of cancer stem cells determine tumor growth and metastatic activity in human pancreatic cancer.” Cell Stem Cell. 2007. 1:313–323.
178. Pawelek, J. “Tumour-cell fusion as a source of myeloid traits in cancer.” The Lancet: Oncology. 2005. 6(12): 988-993.
179. Vignery, A. “Macrophage fusion: Are somatic and cancer cells possible partners?” TRENDS in Cellular Biology. 2005. 15(4):188-193.
180. Hugo, H, et, Al. “Epithelial—mesenchymal and mesenchymal—epithelial transitions in carcinoma progression.” J. of Cellular Physiology. 2007. 213(2):374-383.
181. Ishikawa, F, et, Al. “Chemotherapy-resistant human AML stem cells home to and engraft within the bonemarrow endosteal region.” Nature Biotechnol. 2007. 25:1315–1321.
182. Zhang, M, et, Al. “Identification of tumor-initiating cells in a p53 null mouse model of breast cancer.” Cancer Res. 2008. 68:4674–4682.
183. Guzman, M, et, Al. “The sesquiterpene lactone parthenolide induces apoptosis of human acute myelogenous leukemia stem and progenitor cells.” Blood. 2005. 105:4163–4169.
184. Piccirillo, S, et, Al. “Bone morphogenetic proteins inhibit the tumorigenic potential of human brain tumour-initiating cells.” Nature. 2006. 444:761–765.
185. Kodach, L, et, Al. "The bone morphogenetic protein pathway is inactivated in the majority of sporadic colorectal cancers." Gastroenterology. 2008. 134(5): 1332-1341.
186. Lee, J, et, Al. “Epigenetic-mediated dysfunction of the bone morphogenetic protein pathway inhibits differentiation of glioblastoma-initiating cells.” Cancer Cell. 2008. 13:69–80.
187. Chow, E, et, Al. “Nanodiamond Therapeutic Delivery Agents Mediate Enhanced Chemoresistant Tumor Treatment.” Cancer. 2011. 3(73):1-10.
188. Xing, Y, and Dai, . “Nanodiamonds for nanomedicine.” Nanomedicine. 2009. 4(2):207-218.
189. Chapman, P, et, Al. “Improved survival with vemurafenib in melanoma with BRAF V600E mutation.” N. Engl. J. Med. 2011. 10.1056/nejmoa1103782.
190. Hodi, S, et, Al. “Improved Survival with Ipilimumab in Patients with Metastatic Melanoma.” N. Engl. J. Med. 2011. 10.1056/nejmoa1003466.
191. G, Paul, et, Al. “Exemestane for Breast-Cancer Prevention in Postmenopausal Women.” N. Engl. J. Med. 2011. 364:2381-2391.
192. Grun, F, and Blumberg, B. “Perturbed nuclear receptor signaling by environmental obesogens as emerging factors in the obesity crisis.” Rev Endocr Metab Disord. 2007. 8:161–171
193. Murata, Y, et, Al. “Effect of estrogen deficiency in the male: the ArKO mouse model.” Mol Cell Endocrinol. 2002. 193(1–2):7–12.