Respectful Insolence

ResearchBlogging.orgThree years ago, I wrote about what I considered to be a fascinating and promising approach to understanding tumor biology. This method involved understanding that tumors are in general made up of a heterogeneous collection of cells. Using this knowledge, it is possible to apply evolutionary principles to cancer, treating a tumor as, in essence, an ecosystem. Indeed, that is exactly what Maley et al did three years ago. They applied evolutionary principles to the precancerous lesion in the distal esophagus known as Barrett’s esophagus by examining various measures of population diversity in the tumor specimen and calculated a measure of diversity from ecology and evolution known as the Shannon diversity index. Their conclusion was that the probability of Barrett’s esophagus progressing to full-blown esophageal cancer correlated strongly with an elevated Shannon index.

However, that study, fascinating as it was, only provided prognostic information. True, we’ve been applying evolutionary principles to cancer for a long time, but, given the relative failure of many chemotherapy approaches for various tumors, clearly there is considerable room for improvement. The question is: How do we do better?

Last week, there was a lot of hype about a study that hadn’t been released yet. Indeed, there was a story in Wired entitled To Survive Cancer, Live With It and an editorial by the study’s lead author in Nature entitled A change in strategy in the war on cancer. Not bad for a study that hadn’t been released yet. Several of you sent me these articles, but, intrepid science blogger that I am, I waited until the actual study was published in the June 1 episode of Cancer Research. It’s a clever study, but the hype is a bit overblown. For example:

For all the weapons deployed in the war on cancer, from chemicals to radiation to nanotechnology, the underlying strategy has remained the same: Detect and destroy, with no compromise given to the killer. But Robert Gatenby wants to strike a peace.

A mathematical oncologist at the Moffitt Cancer Center, Gatenby is part of a new generation of researchers who conceive of cancer as a dynamic, evolutionary system. According to his models, trying to wipe cancer out altogether actually makes it stronger by helping drug-resistant cells flourish. Rather than fighting cancer by trying to eradicate its every last cell, he suggests doctors might fare better by intentionally keeping tumors in a long-term stalemate.

Maybe I’m being a bit pissy, but what annoys me about the news reports on this study is that this is not by any means a new idea. Remember, one of my major research interests is the inhibition of tumor angiogenesis. Consequently, I know that the late, great Judah Folkman first proposed the concept of using antiangiogenic therapy to turn cancer into a chronic disease decades earlier. The only difference is the strategy that he proposed.

But lets see what Dr. Gatenby proposes. What makes it interesting is that his study actually looks at how we’ve applied evolutionary principles to cancer until recently, argues that we’ve been doing it wrong, and proposes a way to use the evolutionary dynamics of applied ecology. He may well be on to something. First, here’s the problem:

The German Nobel laureate Paul Ehrlich introduced the concept of ‘magic bullets’ more than 100 years ago: compounds that could be engineered to selectively target and kill tumour cells or disease-causing organisms without affecting the normal cells in the body. The success of antibiotics 50 years later seemed to be a strong validation of Ehrlich’s idea. Indeed, so influential and enduring was medicine’s triumph over bacteria that the ‘war on cancer’ continues to be driven by the implicit assumption that magic bullets will one day be found for the disease.

Yet lessons learned in dealing with exotic species, combined with recent mathematical models of the evolutionary dynamics of tumours, indicate that eradicating most disseminated cancers may be impossible. And, more importantly, trying to do so could make the problem worse.

Traditionally, cytotoxic chemotherapy has been given in a regimen known as “dose dense.” Basically, that means giving as much chemotherapy as the patient can tolerate up to what is known as the “maximum tolerated” dose and giving it over as short a period of time as possible. This strategy is based on what is called the Norton-Simon model. One key assumption behind this model is that chemotherapy fails because of the evolution of resistant cells after chemotherapy has begun. The idea behind this strategy is to hit the tumor cells as hard as possible as fast as possible to kill as many cells as possible and minimize the opportunity to develop resistance. Dose-dense chemotherapy has definitely resulted in improvements in survival in multiple tumors but rarely results in cure, at least in the common “solid” malignancies that kill so many, such as breast, prostate, lung, and colon cancer. However, that improvement comes at a price: Markedly increased toxicity and side effects.

Based on Judah Folkman’s work, around the turn of the century Robert Kerbel proposed a new regimen known as metronomic chemotherapy. Metronomic therapy involves giving chemotherapy either continuously or at frequent dosing but at a much lower dose, the idea being that, because blood vessels are lined by genetically stable endothelial cells, they do not evolve resistance, and chemotherapy can be antiangiogenic. The idea was to deliver the same total dose of chemotherapy but without all the toxicity, meanwhile keeping the tumor in check or shrinking it by the effect the chemotherapy has on the tumor blood vessels. The drawback is that long periods of therapy may be required and the cumulative doses may end up being actually higher than dose dense.

One aspect that is shared among both of these therapy modalities is that they both involve fixed schedules and fixed doses. What Dr. Gatenby proposes to get around this is to apply what he calls “adaptive therapy.” This therapy is based on population ecology and the observation that the development of resistance does not come free. Indeed, resistant cells need to expend energy in order to do what cells do to overcome chemotherapy; for exmple, to repair DNA faster, pump the chemotherapy out of the cell, bypass intracellular signaling pathways blocked by new targeted therapies, or crank out enough peptides that induce the ingrowth of new blood vessels in order to overcome therapies that block these factors. In other words resistant cells tend to have a lower fitness under normal conditions. It is only the selective pressure of chemotherapy that allows resistant cells to proliferate faster than normal cells, and, indeed, resistant cells tend to lose their resistance when the selective pressure is removed.

Given this concept, Gatenby likens adaptive therapy to controling invasive species:

Gatenby: How people treat invasive species can provide an analogy for thinking about cancer therapy. In treating a field for a pest, for example, you might treat three-quarters of it with a pesticide, and leave the other quarter untreated. Pesticide-sensitive pests remain there, and they spread out into the field after treatment, preventing pesticide resistance from becoming dominant.

Using pesticides on an entire field is like what we’re doing with cancer now. And we all agree that we’d rather get rid of the pests altogether, but if you can’t do it, if every time you have an infestation you treat it and get resistance, then you try a different strategy. The alternative is to try to reduce the pest population so that it doesn’t damage your crop, and accept the fact that they’re going to be there. That’s what I’m talking about with cancer.

Wired.com: What type of treatment would that involve?

Gatenby: Instead of fixing the dose of the drugs, you fix the size of the tumor. Your whole goal is to keep the tumor stable. You continuously alter the drug, the dose, the timing of the dose, with that goal in mind.

And:

Our models show that in the absence of therapy, cancer cells that haven’t evolved resistance will proliferate at the expense of the less-fit resistant ones. And, when a large number of the sensitive cells are killed, for instance by aggressive therapies, the resistant types are able to proliferate unconstrained. This means that high doses of chemotherapy might actually increase the likelihood of a tumour becoming unresponsive to further therapy.

So, just as the judicious use of pesticides can be used to successfully control invasive species, a therapeutic strategy explicitly designed to maintain a stable, tolerable tumour volume could increase a patient’s survival by allowing sensitive cells to suppress the growth of resistant ones.

It’s a fascinating concept. The idea is to keep from killing off too many of the sensitive cancer cells, so that they can grow to a certain point and keep the resistant cells in check. But can it work?

The Cancer Research paper published yesterday presents evidence that, at least in mouse models, it might be able to. I will admit that a lot of the mathematics in the paper are beyond me. There was a time when I was in college and taking all sorts of calculus and differential equations when these equations wouldn’t make my brain hurt to look at them, but if you don’t use it you lose it, and lost it I have (mostly). Suffice it to say that the model takes into account estimates of variability of fitness in tumor cells making up the population, dosing, differential uptake with tumor size, and other critical parameters. The concept of adaptive therapy requires that chemotherapy doses be adjusted to maintain constant tumor volume, increasing dosage if the tumor grows and decreasing it if the tumor shrinks. First, the mathematical model:

i-4fc82ecb260a713d180b8f0005a8235b-figure3.jpg

The graphs above represent modeling of dose dense/maximum tolerated dose (MTD) therapy, adaptive therapy, adaptive therapy (ADAP), and three varieties of metrnomic therapy, continuous infusion, high frequency, and low frequency. Four combinations of mixed cell populations were tested, including:

  1. FR with high free-field fitness and high sensitivity to therapy
  2. R with lower fitness and low sensitivity to therapy
  3. S with low fitness and high sensitivity
  4. ER with high intrinsic sensitivity and fitness but in an environment that restricts proliferation and response.

Combinations that were modeled included: (a) ”FS and R,” (b) ”S and FR,” (c) ”FS and R and ER,” and (d) ”FS and ER.” Strikingly, by day 1,500 of tumor growth (1,100 days after therapy was started), the tumor treated using the MTD strategy had grown to be the largest whereas those treated with metronomic therapy were smallest. When the simulations were run out to many thousands more days, until the tumor burden achieved the lethal threshold, all patients in the MTD and metronomic therapies eventually succumbed to their disease. In this model the tumors treated with adaptive therapy remained stable even after a period exceeding 10,000 days. In other words, tumors treated with MTD had the best initial response rate to therapy but tended to develop resistance rapidly, while tumors treated with metronomic chemotherapy remained stable and did not grow appreciably for much longer but nonetheless eventually developed resistance to the point where the tumor escaped therapy and killed the host. In contrast, tumors treated with adaptive therapy remained stable for a very long time.

Mathematical models are all well and good, but does adaptive chemotherapy work for real? To test that, Gatenby designed an adaptive therapy protocol for a mouse model of ovarian cancer. It was a tricky experiment to do, as his group had to measure the tumor burden every three days and then adjust the chemotherapy dose according to their behavior, decreasing the dose for each mouse if its tumor shrank and increasing the dose if it grew. All of this was done fore each and every mouse, meaning that there could be as many doses of chemotherapy as there were mice in the adaptive therapy group. Here’s the method:

The adaptive group received an initial dose of 50 mg/kg and thereafter the tumors were evaluated every 3 days and the dose was adjusted to maintain a stable tumor volume. The algorithm for dosing basically represented “a shot in the dark” because no prior experience was available to parameterize the models. Drug doses were established in increments of 10 mg/kg starting at the starting dose of 50 mg/kg. A treatment decision was made at the time of each measurement. If the tumor remained stable (defined as the no more than a 10% change from the prior volume using caliper measurements), no drug would be administered. If the tumor diminished in size or remained stable for two or more measurements, the next dose would be decreased by one 10 mg/kg decrement. If the tumor increased in size greater than 10%, the same dose of drug would be administered. If the tumor again increased in size, the dose would be increased to the next higher level.

As you can see, this is a pretty labor intensive regimen. No doubt Gatenby will be able to refine his method and develop a protocol that isn’t in essence a reasonable guess, but for now there isn’t a lot to guide scientists in developing such adaptive protocols.

Here’s the result:

i-7cd021c7793493f757a7ea2b55c8250f-Figure5a.jpg

In the mice, the adaptive regimen using carboplatin clearly worked better than the standard carboplatin regimen, suggesting that adaptive therapy can work. As Gatenby puts it:

Our analysis shows that, in the absence of therapy, the fitter, chemosensitive cells actually suppress the growth of the less fit but resistant population. Therapies designed to kill maximum numbers of cancer cells produce an environment in which the resistant cells both survive and are unopposed by the fitter, chemosensitive populations. This permits rapid regrowth of a therapy-resistant cancer. Alternatively, if therapy is limited to allow a significant number of chemosensitive cells to survive, they will, in turn, suppress the growth of the resistant population. We hypothesized that under these circumstances, adaptive therapy should be designed to maintain a normal cohort of surviving sensitive cells.

The power of evolutionary principles is that they apply to more than just populations of organisms. They can equally apply to populations of cells within an organism, like cancer. In other words, evolution acts at both the organism level and the cellular leve. Tumors, given their genetic instability, enormous heterogeneity, and subpopulations of cells with different fitness and sensitivity to selective pressures are a perfect system to apply the principles of evolutionary ecology to. What’s fascinating about this study is that it appears that using evolutionary principles in a savvier way than we have in the past can work. In theory and in at least one animal model, it can produce more effective chemotherapeutic regimens. Indeed, one fascinating observation is that, the longer the tumors were treated with adaptive therapy, the less chemotherapy was required and the longer the intervals between doses that were needed to maintain a constant volume. Like the concept of antiangiogenic therapy proposed by Judah Folkman, however, applying evolution to cancer may require a rethinking of how we deal with cancer.

Unfortunately, I don’t see an obvious or immediate application of adaptive chemtherapy in humans. The reason is that it would be very cumbersome, labor-intensive, and expensive. Tumor measurements far more frequent than what we routinely do now would be required, as would frequent adjustments in chemotherapy dosing. As a strictly practical matter, it would be very hard to implement. Indeed, this model was very simplistic in that it tested adaptive chemotherapy using one drug. In reality, very few chemotherapy regimens in common use involve only one drug, and any truly adaptive therapy would have to adjust multiple drugs, with a concomitant exponential increase in complexity administrating it. Also, from a strictly clinical standpoint, sumors that are large or advanced would need to be shrunk because their size causes serious symptoms. How that would be integrated into an adaptive regimen remains to be seen.

Finally, as Gatenby himself points out, these sorts of approaches will not render the search for cures unnecessary. After all, consider other chronic diseases. Diabetes, for instance, can be managed quite well on a chronic basis, but what patient with type I diabetes wouldn’t want to be cured and thus able to throw away his insulin syringes? In the case of cancer, cures remain preferable, but, like the case of diabetes, sometimes settling for chronic management is the best we can do.

REFERENCE:

Gatenby, R., Silva, A., Gillies, R., & Frieden, B. (2009). Adaptive Therapy Cancer Research, 69 (11), 4894-4903 DOI: 10.1158/0008-5472.CAN-08-3658

Comments

  1. #1 Daniel J. Andrews
    June 2, 2009

    Thanks Orac. That answered some of the questions I had, and once I read they had to measure the tumor size regularly I didn’t see how that was going to be feasible in humans given the already overburdened health care system. At the moment it sounds like a possible treatment for those who can afford a private doctor and clinic of their own. Anyway, I learned a lot there, some more food for thought, and it’ll be interesting to see what develops from this.

  2. #2 Tacroy
    June 2, 2009

    I’m imagining something like those insulin pumps they give diabetics nowadays – make a small enough blood lab on a chip, run incoming blood over and have it detect the rate at which cancer cells are dying (or some other proxy for cancer size), and have the pump do the calculations and push appropriate quantities of chemo drugs. The machine would have to be calibrated frequently based on actual tumor size because I doubt we’d find a good enough proxy, but still – it could work. You could have cancer and also have a decent night out on the town, or at the very least not be completely bedridden.

  3. #3 Nick Anthis
    June 2, 2009

    Interesting. I might be missing something, but it seems to me that the line graphs for the in vivo work (mean tumor burden vs. days after cell injection) should have error bars for each point. Still, the study looks pretty compelling, at least as a proof of principle exercise.

  4. #4 Hypocee
    June 2, 2009

    Presumably you could slowly shrink the tumor either to nothing or at least to some minimum size, or cut the thing out after it’s stabilized with much less risk of metastasis?

  5. #5 Agoraphobic Kleptomanaic
    June 2, 2009

    @2

    I’m imagining something like those insulin pumps they give diabetics nowadays

    This was my initial idea too. I don’t know very much about cancer, but it seems that a calibration tool, like Tacroy describes, along with a lot of data about the average sizes of tumor shrinkage after doses, etc. could provide for a way to keep this manageable as described in the study, even if it wasn’t examined frequently by a doctor. The amount of data needed for this to be viable though, is fairly large I would imagine, since Cancer seems to be such a large tangled group of problems.

  6. #6 Dave Ruddell
    June 2, 2009

    Can we expect adaptive homeopathy? Maybe based on adaptive quantum effects?

  7. #7 David
    June 2, 2009

    great post, thanks for the journal club.

  8. #8 Tacroy
    June 2, 2009

    Can we expect adaptive homeopathy? Maybe based on adaptive quantum effects?

    Oh hospitals already use those! They call them “IV drips”. They’re so effective they’re pretty much an integral part of the standard of care.

  9. #9 rob
    June 2, 2009

    The theory is fine I guess. My problem is that I don’t think it actually works.
    The invivo data is less than convincing… FIRST of all they are using SCID mice. These mice have essentially no immune system. Eliminating the immune system in this type of hypothesis really hurts it’s credibility. There are lots of immune competent mouse models that have tumours that the size is easily monitored. Why didn’t they use it? Probably didn’t work.
    SECOND the “adaptive treatment” arm received almost twice as much drug, over a longer time course. The control group shows a reduction in tumour burden initially while drug is being administered (4days). Then the tumours grow in the std treatment but not in the adaptive. Perhaps this is just due to the fact that the adaptive groups is still receiving drug treatments while the std is not.
    More convincing would have been the same total drug given. Or the high dose continued in the std treatment group.
    I have heard this theory for quite some time now, and I really like it. However I have yet to see some great data to back it up. I’m not saying this is bad, just not great.

  10. #10 rob
    June 2, 2009

    The theory is fine I guess. My problem is that I don’t think it actually works.
    The invivo data is less than convincing… FIRST of all they are using SCID mice. These mice have essentially no immune system. Eliminating the immune system in this type of hypothesis really hurts it’s credibility. There are lots of immune competent mouse models that have tumours that the size is easily monitored. Why didn’t they use it? Probably didn’t work.
    SECOND the “adaptive treatment” arm received almost twice as much drug, over a longer time course. The control group shows a reduction in tumour burden initially while drug is being administered (4days). Then the tumours grow in the std treatment but not in the adaptive. Perhaps this is just due to the fact that the adaptive groups is still receiving drug treatments while the std is not.
    More convincing would have been the same total drug given. Or the high dose continued in the std treatment group.
    I have heard this theory for quite some time now, and I really like it. However I have yet to see some great data to back it up. I’m not saying this is bad, just not great.

  11. #11 rob
    June 2, 2009

    The theory is fine I guess. My problem is that I don’t think it actually works.
    The invivo data is less than convincing… FIRST of all they are using SCID mice. These mice have essentially no immune system. Eliminating the immune system in this type of hypothesis really hurts it’s credibility. There are lots of immune competent mouse models that have tumours that the size is easily monitored. Why didn’t they use it? Probably didn’t work.
    SECOND the “adaptive treatment” arm received almost twice as much drug, over a longer time course. The control group shows a reduction in tumour burden initially while drug is being administered (4days). Then the tumours grow in the std treatment but not in the adaptive. Perhaps this is just due to the fact that the adaptive groups is still receiving drug treatments while the std is not.
    More convincing would have been the same total drug given. Or the high dose continued in the std treatment group.
    I have heard this theory for quite some time now, and I really like it. However I have yet to see some great data to back it up. I’m not saying this is bad, just not great.

  12. #12 Orac
    June 2, 2009

    FIRST of all they are using SCID mice. These mice have essentially no immune system. Eliminating the immune system in this type of hypothesis really hurts it’s credibility. There are lots of immune competent mouse models that have tumours that the size is easily monitored. Why didn’t they use it? Probably didn’t work.

    Why does using SCID mice hurt the credibility of this model? Personally, as a first step proof of principle, I think it’s imperative to eliminate as many variables as possible, including the immune response. Likely the investigators wanted to use human tumor cells with which they had experience to show that it works in a human cell system, rather than relying on a syngeneic mouse tumor model, like Lewis Lung carcinoma in C57BL/6 mice (my favorite, back from my days studying angiostatin and endostatin).

    SECOND the “adaptive treatment” arm received almost twice as much drug, over a longer time course. The control group shows a reduction in tumour burden initially while drug is being administered (4days). Then the tumours grow in the std treatment but not in the adaptive. Perhaps this is just due to the fact that the adaptive groups is still receiving drug treatments while the std is not.

    More convincing would have been the same total drug given. Or the high dose continued in the std treatment group.

    No, the reason the standard treatment arm only got three doses separated by four days each is because the investigators were trying to mimic common “dose dense” chemotherapy regimens, in which the entire dose of chemotherapy is given up front in as short a period of time as possible. That’s the way it’s done in the clinic (except in humans the doses are usually separated by 1-3 weeks, depending on the regimen). Remember, the idea behind dose dense therapy is to kill the tumor up front, or, as we sometimes put it, to kill the tumor before you kill the patient because the dose given goes right up to the maximum tolerated dose.

    It’s been known for some time that using metronomic chemotherapy allows a larger total dose of chemotherapy given over a longer period of time with lower toxicity. It works well in mice, but unfortunately is less stunningly effective in humans (kind of like antiangiogenic therapy, alas). The larger total dose of chemotherapy is one reason why it’s postulated that metronomic chemotherapy can be more effective than dose dense. There’s no reason to think that adaptive chemotherapy wouldn’t behave similarly and allow for a larger total dose. In other words, your criticism is not really a strong reason to doubt whether this paper presents proof of principle because the mouse model for dose dense therapy is not an unreasonable one, even though the total dose of chemotherapy was lower. The reason is that, by pushing the dose to the MTD given in a very short time, one pushes chemotherapy to the very limits of tolerable toxicity.

    Also, note that in the adaptive chemotherapy group the dose required to maintain tumor stability decreased with time from 50 mg/kg to 10 mg/kg. In the experiment I showed, the individtual doses were 50, 40, 40, 30, 30, 20, 20, 10, 10, 10, 10, 10, 10, 10, 10, 10 mg/kg.

    I’m not saying that this is a perfect model, but I have to disagree with your criticisms of it. It’s a neat proof of principle. I agree that it would have been nice to see it applied to one or two more tumor models, but c’est la vie. I’m sure we’ll be seeing more soon enough.

  13. #13 Brian
    June 2, 2009

    As for it’s not-newness, this same idea was tried some years ago in HIV patients, to good initial results (lowering the load of resistant virus), but I didn’t follow it over the long term.

  14. #14 Donna B.
    June 2, 2009

    I understand the theory behind the adaptive chemo dosing, but I want to know if the same holds true of radiation. It seems it would not as radiation kills cells of all kinds, and I’ve never head of cells being resistant to radiation.

    Am I wrong?

  15. #15 IAMB
    June 2, 2009

    Donna, I’m not sure if it’s the same in humans, but my lab managed to up the radiation tolerance of a certain microbe by ridiculous amounts through successive dosing on a LINAC (kept only the survivors after each shot). We actually have some survivors in the 23-24k Gy dose range now.

  16. #16 Ian Musgrave
    June 3, 2009

    Two words. Compensatory mutations. Bacteria with resistance mutations which produce fitness deficits don’t stay at reduced fitnessforever, eventually compensatory mutations come along which restore fitness to wild type. This has been an issue in bacterial therapy, where long-term removal of antibiotics have not resulted in disappearance of the resistant strains, due to compensatory mutations. While compensatory mutations restoring fitness to resitant strains is currently only well described for bacteria and viruses, I would be very surprised if it did not happen in cancer cells.

    Thus, I suspect the adaptive strategy will fail during chronic application of these drugs. If you target multiple, independent tumour mechanisms simultaneously you might have more hope.

    Gag mutations strongly contribute to HIV-1 resistance to protease inhibitors in highly drug-experienced patients besides compensating for fitness loss.

    Dam E, Quercia R, Glass B, Descamps D, Launay O, Duval X, Kräusslich HG, Hance AJ, Clavel F; ANRS 109 Study Group.

    PLoS Pathog. 2009 Mar;5(3):e1000345. Epub 2009 Mar 20.

    Evolution of primary and compensatory lamivudine resistance mutations in chronic hepatitis B virus-infected patients during long-term lamivudine treatment, assessed by a line probe assay.

    Libbrecht E, Doutreloigne J, Van De Velde H, Yuen MF, Lai CL, Shapiro F, Sablon E.

    J Clin Microbiol. 2007 Dec;45(12):3935-41. Epub 2007 Oct 3.

    Adaptation to the deleterious effects of antimicrobial drug resistance mutations by compensatory evolution.

    Maisnier-Patin S, Andersson DI.

    Res Microbiol. 2004 Jun;155(5):360-9. Review.

    The biological cost of mutational antibiotic resistance: any practical conclusions?

    Andersson DI.

    Curr Opin Microbiol. 2006 Oct;9(5):461-5. Epub 2006 Aug 4. Review.

  17. #17 rob
    June 3, 2009

    @Orac.
    I still don’t think that the SCID mouse thing was justified. The theory should hold for mouse and human cancer cells, no need for xenografts. And the immune system is a major player in the efficacy of cancer treatments. If you rapidly debulk the tumour in the std treatment perhaps the immune system gets rid of the rest of the tumour and the std treatment is superior, for example.
    However your points about the authors attempting to make their treatment regimins clinically relevant are good ones.
    I guess I just wonder if the real conclusion here is that the drug works and the more you use the better it works. It is an alternative explanation that unfortunately I don’t think you can rule out with the experiments they did.
    Also as Nick (above) already pointed out error bars on the experimental data would have been informative.
    sorry about posting three times previously. I hope this appears once!

  18. #18 Danielle
    November 10, 2009

    Hi, obviously this site is not for lay people. Buy my brother has been diagnosed with stage 4 eosephagus cancer now in the liver, he has been told that chemo will not work, and may give him another month or so. He has asked me to research metronomic treatment. Does anyone know of any clinical test he might take part or any resources that might be helpful.

  19. #19 Chris
    November 10, 2009

    Danielle, you might have better luck posting on a more recent posting, like the one from today.

    Best luck to your brother.

  20. #20 Anonymous
    November 11, 2009

    many thanks Chris

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