Tag Archives: chemotherapy

The Case for Low Dose FOLFIRINOX

The 2011 approval of FOLFIRINOX for pancreatic cancer1 was a breakthrough for pancreatic cancer. The one-year survival rates doubled compared to the current best available treatment, gemcitabine. However, concern for toxicity and adverse side effects quickly restricted patients to only the healthiest. In this post, we examine peer-reviewed, published evidence for low dose FOLFIRINOX maintaining effectiveness and reducing patient side effects.

How Treatment Doses are Set

I’ve written previously here how researchers set doses with phase 1 clinical trials. Briefly, in a 3×3 trial design, groups of 3 patients receive a specified dose level and are then assessed for toxic effects. If nothing “bad” happens, the next group of 3 patients receive a slightly increased dose. This cycle repeats until patients experience too many toxic effects. An additional group of patients receives a lower dose to assure the limited toxicity.

The adverse reactions of a handful of patients determines the dosages for all other patients. Note that the treatment effectiveness is not a consideration in setting was is aptly called the Maximum Tolerated Dose (MTD). To be sure, later clinical trials may see additional toxicities and modify the doses or schedule a little, but usually not much. The key idea driving this is that more chemotherapy is better.

Minimum Effective Dose

Ideally, we’d like to discover the Minimum Effective Dose (MED) for any treatment. That could entail large clinical trials with several cohorts taking different doses. To detect small differences in treatment outcomes, we must enroll large groups of patients. Tying up the few patients willing to participate in clinical trials in MED studies would delay development of new treatments.

Is More Always Better?

Smaller doses may be just as effective in treating tumors, and almost certainly result in fewer adverse side effects. Fewer side effects allow patients to stay with treatments longer. There have been a few studies with low dose FOLFIRINOX, so let’s review what they found.

The Evidence for Less FOLFIRINOX

I summarize four studies that report on efficacy and side effects of low dose FOLFIRINOX in metastatic pancreatic cancer. The following table summarizes these studies and the phase III PRODIGE 4/ACCORD 11 approval trial.

Overview of Low-Dose FOLFIRINOX studies.

Overview of Low-Dose FOLFIRINOX Studies

I’ve reported the median dose levels, when available, as compared to the phase 3 FOLFIRINOX clinical trial. Many patients did not start at a full dose, and most had dosage reductions sometime during each study.

About the Studies

The Gunturu KS, et al2 retrospective review included Yale University previously-treated patients from June 2010 to July 2011, documenting their doses, toxicities, and survival results. Reduced doses were not by design, but rather a result of physician discretion. All patients received preventative G-CSF (i.e. neupogen) to prevent neutropenia. This study had a high percentage of healthiest patients.

The Peddi PF, et al3 retrospectively reviewed the FOLFIRINOX experiences of Washington University, the Mayo Clinic, and the University of Wisconsin to compare US “real-world” experiences to the phase 3 clinical trial held in France. Physicians reduced doses at their discretion, not by study design.

Mahaseth H, et al4 retrospectively reported on Emory University’s modified FOLFIRINOX regimen that omitted bolus 5-FU and administered G-CSF to all patients.

Yale University later conducted the Stein SM5, et al prospective study after the promising results in the Gunturu KS study. Starting Irinotecan and bolus 5-FU doses were reduced by 25%, and further at physician discretion. This phase 2 clinical trial (NCT01523457) may provide the most rigorous results.

Patient Responses

I’ve summarized these study’s adverse patient reactions and tumor response statistics in the table below. Relative Risks of greater and less than 1.0 indicate that the study recorded more or less of a particular event, respectively, compared to the phase 3 clinical trial. As an example, a relative risk of 0.3 means that particular event happened 30% as often as in the phase 3 clinical trial. Bold numbers indicate statistically significant findings (p-value < 0.05). Because of the small study sizes, many promising findings did not reach statistical significance.

Patient responses in Low-Dose FOLFIRINOX studies.

Patient Responses in Low-Dose FOLFIRINOX Studies

Progression-Free (PFS) and overall survival (OS) were similar in all studies (the accurate statement is that they were “not found to be different”). Toxicity levels were almost uniformly lower in all low dose FOLFIRINOX studies.

Colored bars represent tumor responses to treatment, with width corresponding to patient counts. Depending upon an individual’s treatment goals, you may want to know different results. For instance, a patient needing to shrink a tumor for surgery may want to look at the PR+CR result. A patient desiring long-term stability may want to minimize the PD result.

Blood-Related Adverse Events

The table below contains more detail and 95% confidence intervals on blood-related adverse events. Studies with fewer participants have less certain results and wider confidence intervals. For adverse event analysis, I included both locally advanced and metastatic patients from each study.

Blood related adverse events of Low-Dose FOLFIRINOX Studies.

Blood Related Adverse Events in Low-Dose FOLFIRINOX Studies

Most adverse events for lower dose FOLFIRINOX were less than the baseline phase 3 clinical trial. However, except for neutropenia, sample sizes or adverse effects were too small to show statistical significance.

Note that the phase 3 clinical trial’s neutropenia rates were especially high. Three studies (Gunturu, Mahaseth, and Stein) used C-GSF for all participants which likely reduced the rate of neutropenia. The Peddi study used C-GSF at higher rates than the phase 3 trial, also reducing the neutropenia rates.

Non-Blood Related Adverse Events

The table below contains more detail and 95% confidence intervals on non-blood-related adverse events. For adverse event analysis, I included both locally advanced and metastatic patients from each study.

Non-blood related adverse events of Low-Dose FOLFIRINOX studies

Non-Blood Related Adverse Events in Low-Dose FOLFIRINOX Studies

Here again, most adverse events for lower dose FOLFIRINOX were less than the baseline phase 3 clinical trial, with sample sizes usually too small to show a statistically significant effect. The Peddi study had significantly less fatigue, and the Stein SM study with a 25% reduction in the initial dose of irinotecan and bolus 5-FU had significantly less vomiting.

How Low Can We Go?

Let’s take a look back at the phase 1 dose escalation trial for FOLFIRINOX6. The study establisged a MTD for oxaliplatin and irinotecan at 85mg/m2 delivered over 120 minutes and 220mg/m2 delivered over 90 minutes respectively. The reduced the recommended level to 85/180 mg/m2 when they realized the cumulative effect would not allow patients to maintain a 2 week schedule.

The dose escalation started with lower oxaliplatin/irinotecan doses as shown in the following table. Important to this discussion, almost every dose level showed anti-tumor activity – including complete responses at the two lowest levels. Participants experienced no dose-limiting toxicities at the three lowest dose levels.

Patient responses in phase 1 FOLFIRINOX dose escalation study

Patient Responses in Phase 1 FOLFIRINOX Dose Escalation Study

The primary goal to determine the dose-limiting toxicity, not effectiveness, opened enrollment to 41 patients with 8 different types of cancers. Of the 6 pancreatic cancer patients, 1 enjoyed a complete and another a partial response (dose levels not specified).


These peer-reviewed, published studies proved evidence of low dose FOLFIRINOX efficacy similar to the phase 3 trial results. Positive or negative differences in progression-free or overall survival rates cannot be seen with the small studies thus far.

Even with these small study sizes, we have statistically significant evidence of a reduction in toxicity with the low dose FOLFIRINOX regimens.

In the FOLFIRINOX dose escalation trial, patients at the two lowest oxaliplatin/irinotecan levels recorded complete responses with no limiting side effects.

Low dose FOLFIRINOX is better tolerated with significant anti-tumor activity. Oncologists should consider this regimen as an option for patients desiring efficacy but unwilling to endure severe toxic events. Patient experiences with low dose FOLFIRINOX will produce more retrospective studies that will help pinpoint a most effective dose.

I propose that oncologists start patients with low dose FOLFIRINOX and in the absence of severe side effects, increase the dose. The first rounds are often the most difficult as a patient must quickly learn how to deal with chemotherapy-induced nausea and fatigue in addition to their new cancer diagnosis.


[1]Conroy T, et al. (2011 May 12) “FOLFIRINOX versus gemcitabine for metastatic pancreatic cancer”. N Engl J Med 364(19):1817-25 PMID: 21561347.

[2]Gunturu KS, et al. (2013 Mar) “FOLFIRINOX for locally advanced and metastatic pancreatic cancer: single institution retrospective review of efficacy and toxicity”. Med Oncol 30(1):361 PMID: 23271209.

[3]Peddi PF, et al. (2012 Sep 10) “Multi-institutional experience with FOLFIRINOX in pancreatic adenocarcinoma”. JOP 13(5):497-501 PMID: 22964956.

[4]Mahaseth H, et al. (2013 Nov) “Modified FOLFIRINOX regimen with improved safety and maintained efficacy in pancreatic adenocarcinoma”. Pancreas 42(8):1311-5 PMID: 24152956.

[5]Stein SM, et al. (2016 Mar 29) “Final analysis of a phase II study of modified FOLFIRINOX in locally advanced and metastatic pancreatic cancer”. Br J Cancer 114(7):737-43 PMID: 27022826.

[6]Ychou, et al. (2003 Mar) “An open phase I study assessing the feasibility of the triple combination: oxaliplatin plus irinotecan plus leucovorin/ 5-fluorouracil every 2 weeks in patients with advanced solid tumors”. Ann Oncol 14(3):481-9 PMID: 12598357.

How Chemotherapy Works (or Doesn’t)

Why did my chemotherapy stop working? Your oncologist doesn’t have the time to give you an answer – but I do. I’m going to give you one look at how chemotherapy works. Chemotherapy changes the tumor. Understanding this allows you to prepare for the next question: what do I do next?

When I first learned this information, frankly it was somewhat depressing. I’m reminded of Simon Wardley’s graph of knowing versus expertise. A little knowledge is dangerous. A little more can be empowering.

For example, I’m disappointed when people glance at the dismal survival statistics and say that they’re not a statistic. Guess what? I’m a long term survivor and I’m in those statistics. There’s knowing the statistics and then there’s understanding the statistics. I admit that at first look the statistics is quite depressing. But a deeper look at the statistics reveals the story of the survivors. Understanding that story uncovers the path to be a survivor.

So let’s try to walk you through the area of a little knowledge to the area of understanding where there is hope and guidance. On to the land of survivors!

How a Tumor Cell is Made

In the clonal evolutionary cancer model, a normal cell gains a set of mutations over time. These mutations could happen during cell division (mitosis), when a cell divides into two cells. A random mistake in the replication process means that one of the two “daughter” cells receives a mistake in its copy of DNA. The starred arrows denote these mutation events. Here, one daughter cell gains a mutation while the other does not.

Pancreatic Cancer Progression of Mutational Changes

Pancreatic Cancer Mutation Progression

DNA mutations can block the building of proteins critical to proper cell function. For instance, a mutation halting the production of proteins necessary for cell repair makes the cell susceptible to even more mutations. You may have heard that the KRAS gene is mutated in about 95% of all pancreatic cancers. This would be how one of these mutations might happen.

As this mutated cell splits further, these mistakes are copied to both “daughter” cells. When these cells split, another mutation may happen during replication that brings these cells another step closer to becoming a tumor cell. Each time we move to the right, one of the cells has gained a new mutation.

In pancreatic cancer, it takes around 12 years1 and at least a dozen of these mutations to disable the built-in protection mechanisms in the human body and create a tumor cell. Fortunately, it is an extremely rare set of circumstances. But not rare enough.

The takeaway:

Tumor cells have multiple DNA mutations that make them cancer cells

How a Tumor Grows

By the time the cell is cancerous, it has accumulated many mutations. One mutation allows the tumor cell to grow uncontrollably and a tumor cell can divide many more times than a normal cell. Another mutation breaks the verification checks on DNA replication, resulting in more and more mutations as it divides. Still another mutation prevents the immune system from recognizing the cell as unwelcome.

Many mutations are incompatible with survival and these cells die off quickly. But a few mutations provide a new advantage over the parent cells. This is an evolutionary process played out very rapidly. Many new mutations happen throughout the tumor to try their luck at survival. The tumor cells best able to reproduce begin to dominate by numbers. The result is a heterogeneous tumor. The tumor cells are not identical copies of the first tumor cell. Each succeeding generation of cells add more DNA mutations and possibly even changes some of the original mutations.

Tumor Growth

Tumor Growth

Over a period of around 7 years1, the tumor grows large enough to cause symptoms and be detected on scans. This cancer has been growing quite slowly and for a long time. It has been forming over the last two decades. This is one reason why pancreatic cancers occur relatively late in life (average age: 65 years old).

This may be the most important takeaway:

the tumor is heterogeneous, composed of tumor cells that are not genetically identical

Why is that important? Because each tumor cell may respond to treatment differently.

Treatment Selection

After the tumor has been detected and confirmed as cancer, we often select a treatment based on chemotherapy. In pancreatic cancers, we have almost no clue which treatment will be best. One major exception is a tumor with a BRCA1 or BRCA2 mutation.

Treatments may only be effective on tumor cells with specific mutations. When chemotherapy is active in the body, cell division slows, but when divisions happen, new mutations continue to be generated. Some new mutations may find a way to get around the treatment and continue growing despite the treatment. These cells out-reproduce the other tumor cells. It’s an evolutionary survival of the fittest of tumor cells.

Let’s review ineffective, stable, and effective treatments to understand why the tumor responds the way it does. In these figures, the colors represent tumor cells with different sets of mutations.

Ineffective Treatment

Ineffective chemotherapy works only on a small set of tumor cells (red and light blue). The dominant blue and rarer pink tumor cells resistant to treatment and continue to divide. The blue and pink cells generate new mutations, creating the white and green cells resistant to the treatment.

As the few susceptible cells die off, the unaffected cells continue reproducing. The tumor consists of mainly the same types of cells as when treatment started, plus some with new mutations. On scans the tumor is seen to be growing and it’s time to select a different treatment.


Ineffective Tumor Treatment

Ineffective Tumor Treatment

Stable Treatment

This chemotherapy is effective on the dominant blue tumor cells – which is good news! However, most others (red, dark-blue and pink) are resistant to treatment. Resistant cells replace dying cells and the tumor stays about the same size. More treatment-resistant cells emerge as mutated versions of previous tumor cells.


Stable Tumor Chemotherapy

Stable Tumor Treatment

While the tumor is about the same size, the cell mutation composition is completely different than when treatment started. It is now dominated by cells that are resistant to the treatment and will begin growing again. We don’t have sufficient information to know when the treatment may fail. The tumor’s overall size may remain stable for months or even years. Many oncologists will continue the same treatment on this tumor until it is bigger or the patient can no longer handle the side effects.

Effective Treatment

Effective chemotherapy works on most, but usually not all tumor cells. Treatment-resistant tumor cells slowly emerge and multiply. Over a period of months, the tumor shrinks quickly and may even become too small to detect on scans. NED! Time to celebrate! Done with chemo! Not so fast.

Unfortunately, chemotherapy is curative in very few of cancers, like testicular cancer2,3 and Hodgkin lymphoma3. Likely, tumor cells remain and will recur with a possibly different composition of tumor mutations. What’s a patient to do?

Effective Tumor Treatment

Effective Tumor Treatment

An effective treatment does leave you with more options.

  • You have a treatment that is known to be effective against the tumor’s original composition. If there is a recurrence, you can hope that the composition is similar and try the same treatment.
  • You may become eligible for surgical resection. This is the only reliable curative option for pancreatic cancers.
  • An effective treatment may last for years. You have bought some time for new treatments to come online.

Implications for Treatment

I think that this is the key takeaway of this post:

cancer tumors are composed of a heterogeneous mass of genetically altered cells that don’t all respond identically to treatment.

What treatment outcomes support this model of how tumors react to chemotherapy?

The most effective treatments target mutations present throughout the entire tumor, such as one of the original set as the normal cell mutated into the first cancer cell. If you’ve had an exceptional response, perhaps your oncologist can contribute your treatment information to the NCI’s Exceptional Responders Initiative and help improve outcomes for others?

Successful treatments target mutations found in both tumor and germline DNA with the goal of eliminating the entire tumor before a resistant mutation develops. Cases like this have been demonstrated in BRCA2 pancreatic cancer patients4,5,6. A cancer panel test could identify germline mutations in many cancer-related genes.

Heterogeneity helps these tumors elude chemotherapy’s effects. In pancreatic cancers, this means that tumors treated with chemotherapy alone return and that surgical removal is the most reliable cure.

Mutational differences of tumor cells within the same tumor limits treatment effectiveness. Perhaps PanCan’s most successful chemotherapy is the four drug combination FOLFIRINOX because it targets multiple types of mutations simultaneously?

PanCan.org‘s Know Your Tumor program tests the tumor for DNA mutations. A report informs patients about treatments suspected to be effective against the identified mutations. Mostly, these treatments are only available through clinical trials, except for the important BRCA2/platinum and BRCA2/PARP inhibitor mutation/treatments combinations. Some hospitals and independent testing companies also provide this service.

The Future

I propose that every pancreatic adenocarcinoma patient have their germline (inherited) DNA tested for cancer-related mutations with pancreatic cancer panel tests like Ambry Genetics PancNext, GeneDx Pancreatic Cancer Panel, or Invitae‘s Pancreatic Cancer Panel. GetColor offers a direct-to-consumer hereditary cancer test. Some major cancer centers implement these tests for all their PanCan patients. Finding a mutations such as BRCA1 or BRCA2 greatly increasing your chances of effective treatments. More details on that in another post.

We need researchers to match tumor mutations and the effective treatments. Finding these matches is what PanCan.org’s Know Your Tumor program is all about.

When we apply chemotherapy to the tumor, evolutionary mutation changes favor cells unaffected by the treatment. Some cancer treatments try to counteract this effect by alternating between two treatments. Each treatment beats down one type of cell while allowing the others to exist, like the game of whack-a-mole. It is a delaying tactic that treats the cancer as a chronic condition always needing treatment. Pancreatic cancer does not yet have this type of treatment.

Several clinical trials are attempting to match specific mutation to treatments now. You can join in these or await the results.

Driving treatment decisions on 1 Aug 2016


[1]Fokas E, O’Neill E, et al. (2015). “Pancreatic ductal adenocarcinoma: From genetics to biology to radiobiology to oncoimmunology and all the way back to the clinic”. Biochim Biophys Acta 1855(1):61-82. PMID: 25489989.

[2] American Cancer Society web page: Treatment options for testicular cancer, by type and stage: (accessed 31 Jul 2016) http://www.cancer.org/cancer/testicularcancer/detailedguide/testicular-cancer-treating-by-stage

[3] Cancer Research UK web page: How chemotherapy works: (accessed 31 Jul 2016) http://www.cancerresearchuk.org/about-cancer/cancers-in-general/treatment/chemotherapy/about/how-chemotherapy-works

[4] Sonnenblick A, Kadouri L, et al. (2011). “Complete remission, in BRCA2 mutation carrier with metastatic pancreatic adenocarcinoma, treated with cisplatin based therapy”. Cancer Biol Ther 12(3):165-8. PMID: 21613821.

[5] Lohse I, Borgida A, et al. (2015). “BRCA1 and BRCA2 mutations sensitize to chemotherapy in patient-derived pancreatic cancer xenografts”. Br J Cancer 113(3):425-32. PMID: 26180923.

[6] Golan T, Kanji ZS, et al. (2014). “Overall survival and clinical characteristics of pancreatic cancer in BRCA mutation carriers”. Br J Cancer 111(6):1132-8. PMID: 25072261.

Cancer Stem Cell Model

Driving treatment decisions on 10 Nov 2015

Scientists use cancer models understand cancer. These models describe the rules cancer follows – how it starts, grows, metastasizes, and ultimately how it can be killed. Reviews of models generate new treatment ideas that models indicate should be successful.

Models of how cancer works drive the direction of cancer funding, research, prevention and treatment decisions. Faulty models lead to research producing ineffective treatments. Some researchers say that is happening now.

This post introduces the cancer stem cell model. The cancer stem cell model is not widely accepted but is gaining traction and a share of research money. It was developed as an alternative to the clonal evolution model to explain treatment failures.

Cancer Stem Cell Model

The cancer stem cell (CSC) model is an alternate model to explain the origin and diversity of cancer – and why past treatments have failed2. This model says that some (perhaps all) cancers are driven by a small number of treatment-resistant cancer cells with stem cell-like properties3,5. Stem cells have a slower life cycle and thus are largely unaffected by traditional chemotherapies that disrupt rapidly-dividing cells.6

Cancer Stem Cells Accumulate Mutations

Cancer Stem Cells Accumulate Mutations

[The Cancer Stem Cell model was hard to research. There are many papers and web seminars that present their research as, “this is the way it is – no disputes”. Sorting out what is commonly accepted or not takes a lot of review and I’m certain I don’t have it all correct. This is the best I’ve come up with.]

A cancer stem cell is very long-living, can accumulate genetic mutations over its lifetime, and then produce a nearly unlimited supply of cancer cells containing these mutations. Just as in the clonal evolution model, these cancer cells could continue to generate new mutations and divide uncontrollably.

[Whether CSC’s are actual adult stem cells that have become cancerous or are normal cells that have acquired “stem-like” properties is under active investigation.]

Tumor Growth under the Cancer Stem Cell Model

Tumor Growth under the Cancer Stem Cell Model

The key to the cancer stem cell model though, is the small colony of cancer stem cells (red cell above) that regenerate cancer cells but are killed differently than normal cancer cells.5 Killing off the stem cells will result in the eventual dissipation of the tumor as it can no longer regenerate.

Cancer Stem Cell Model on Chemotherapy

Cancer stem cell model response to chemotherapy treatments

Cancer stem cell model response to chemotherapy treatments

Accumulate Mutations

In the CSC model, a long-lived stem cell accumulates the cancer-causing mutations. It is believed that the property of a long lifetime allows it to accumulate all these mutations. A normal cell, with its much shorter lifespan, would be unlikely to accumulate enough mutations. The key difference is that at the core of the tumor is a CSC, sometimes called a tumor-initiating cell.

[I’m couching the discussion with phrases above like “it is believed”, but if you read the stem cell theory papers, these expressions of doubt are not presented. Some stem cell theorists say that a normal cell gains mutations and becomes stem cell-like to drive the cancer.]

Tumor Growth

The CSC is the major producer of all the new cancer cells3. The normal cancer cells (NCS) have limited cell division capability (just like normal cells). The CSC can continue to mutate which also results in tumor heterogeneity.


As treatment begins, susceptible cells are again destroyed. However, CSC’s, which are slowly dividing, are not susceptible to chemotherapy3,5. To make matters worse, the CSC’s, acting like stem cells, see the tumor’s tissue damage and do what all stem cells do – regenerate new tissue (more CSC’s!). The result could again be a smaller tumor, but with an even larger concentration of CSC’s. In this model, what looks like good news on a CT scan (a smaller tumor) is really bad for the future.

[From a patient perspective, the CSC and clonal evolution model behave the same on scans, but the resulting tumor is very different. If the CSC model is correct, then in the long run, giving treatments that don’t kill the CSC’s is a bad thing to do.]

Treatment Holiday

After treatment, driven by many more CSC’s, the tumor growth accelerates.

[Question for Researchers: How does this square up with patients who have long-lasting remissions to chemotherapy?]

Cancer Stem Cell Model on Chemotherapy and Stem Cell Therapy

Under the CSC theory, the correct treatment protocol is to target the CSC’s themselves. Forget about the other cancer cells. Once the CSC’s are gone, the normal cancer cells cannot keep going by themselves and eventually perish. You can use a normal chemotherapy agent in addition to the CSC treatment in order to hasten the demise.

Cancer stem cell model response to chemotherapy and stem cell treatments

Cancer stem cell model response to chemotherapy and stem cell treatments


As treatment begins, susceptible cells are again destroyed. In theory, stem cell therapy eliminates the CSC’s. Careful targeting of the CSC’s must be done to make sure that normal stem cells are not affected – which would probably be devastating to the patient.

Treatment Holiday

After treatment, with no CSC’s to replenish them, the normal cancer cells eventually die off.5


The cancer stem cell model is not embraced by many cancer experts. The primary evidence is based on a set of experiments that break a tumor down and separate the tumor cells into different types. When 20,000 (or so) tumor cells of one type are transplanted into mice, a tumor does not take hold. When just 200 tumor cells of another type are transplanted, the tumor grows. This second set of tumor cells are considered CSC’s because they initiated human cancer growth in the mice. Experiments like these identify tumor cells with stem-like properties.

Identifying Cancer Stem Cells

Identifying Cancer Stem Cells

Skeptical researchers say that a demonstration of human tumor cell growth in immunodeficient mice is insufficient. Growing human cancer cells in a mouse is too dissimilar an environment to provide proof that these are cancer stem cells.5

It should also be noted that testicular cancer that is curable with chemotherapy alone. If cancer stem cells were involved, this would not be possible. So apparently CSC’s do not drive all cancers.

Another View of the Cancer Stem Cell Model

The figure below presents another way to look at tumor growth in the cancer stem call model. Each colored area represents a cell colony with a specific set of mutations. Time progresses to the right. The height of each colored area represents the quantity of cells in the colony. New mutations are represented by stars and may originate from any established colony. The figure shows that these new mutations only originate from the tumor’s cancer stem cells (dark red) and then compete for space and resources with other colonies.

Chemotherapy treatment is effective on the normal cancer cells, but has the opposite effect on cancer stem cells which multiply in response to the tissue damage. After treatment ends, a larger number of cancer stem cells are present to begin tumor regrowth.

Eventually, one of the colonies acquires the ability to metastasize and migrates to another organ.

Cancer Stem Cell Model with Effective Chemotherapy

Cancer Stem Cell Model with Effective Chemotherapy


The cancer stem cell model is an alternate explanation for tumor growth and response to treatments. It was devised to try to explain failures of treatments based on the correctness of the clonal evolution model. The correctness of the cancer stem cell model is hotly debated.

Publication counts of Cancer Stem Cell papers started in 2004 and have been on a steep rise, indicating that it is an active topic. For comparison, publication counts for the clonal evolution model are shown in orange below.

Cancer Model Publication Counts (PubMed)

Cancer Model Publication Counts (PubMed)

Assuming that the cancer stem cell model is true, here are some points to consider.

  • CSC’s are rare, “immortal” cells found in some cancers
  • CSC’s are a tiny fraction of the total tumor (<1%)
  • Radiotherapy and chemotherapy kill off normal cancer cells, but CSC’s respond by multiplying
  • When normal cancer cells are depleted, CSC’s regenerate new cancer cells3,6
  • The only cure is complete elimination of the CSC’s:
    • Surgical resection
    • Stem-cell targeting treatment (experimiental), likely combined with traditional treatments4,6

Surgical resection of solid tumors (before metastases) remains the only curative treatment common to both models.


[1] Nowell PC (October 1976). “The clonal evolution of tumor cell populations”. Nature 194(4260):23-8. PMID: 959840.

[2] Soltysova A, Altanerova V, et al. (2005). “Cancer stem cells”. Neoplasma 52(6):435-40. PMID: 16284686.

[3] University of Michigan Cancer Stem Cell Research Introduction web page (accessed 21 Sep 2015) http://www.mcancer.org/research/stem-cells/introduction

[4] University of Michigan Cancer Stem Cell Research Treatment web page (accessed 21 Sep 2015) http://www.mcancer.org/research/stem-cells/introduction/treatment-options

[5] EuroStemCell web page: Cancer: a disease of stem cells? (accessed 21 Sep 2015) http://www.eurostemcell.org/factsheet/cancer-disease-stem-cells

[6] Science in School web page: Cancer stem cells – hope for the future? (accessed 21 Sep 2015) http://www.scienceinschool.org/2011/issue21/cscs

Clonal Evolution Model

Driving treatment decisions on 5 Nov 2015

Scientist use cancer models to understand cancer. These models describe the rules cancer follows – how it starts, grows, metastasizes, and ultimately how it can be killed. Reviews of models generate new treatment ideas that models indicate should be successful.

Models of how cancer works drive the direction of cancer funding, research, prevention and treatment decisions. Poor models lead to research producing ineffective treatments.

This post looks at the dominant cancer model used by researchers today.

[I became interested in cancer models because each model has its own ideas about the most effective treatments. Treatments suggested by this model are sometimes the absolute wrong treatment under another model. So I began to look into the differences in their recommendations. I wanted to find overlap – treatments that would work well under any model and thereby improving my odds of successful treatment. The importance of surgical resection as a cure stands out very clearly.]

Clonal Evolution Model

You are probably familiar with the dominant cancer model, the clonal evolution model, first proposed in 19761 where a single cell eventually gains enough mutations to become a full-fledged cancer tumor cell. This cancer cell continues to divide uncontrollably to form a tumor mass.

Clonal Evolution Model

Mutation Accumulation Under the Clonal Evolution Model

As the tumor grows, defects in the replication process cause “daughter” cells to add more mutations. Following evolutionary rules, cells that compete better for blood and nutrients become more plentiful. The result is a tumor mass that contains cells with various colonies of mutations dominated by colonies that proliferate best.

The resulting tumor is considered by researchers to be a heterogeneous collection of tumor cells2. This means that all tumor cells are not identical – they contain unique sets of mutations. It is thought that some of the earliest mutations will be present throughout the tumor. Mutations occurring in sections of DNA not used by these cells are called passenger mutations and don’t appear to be harmful3. But mutations that directly result in cells becoming cancerous are called driver mutations. Finding and targeting a driver mutation should lead to effective treatments. Ideally we would like to target a driver mutation that is present throughout the entire tumor.

“Precision medicine” treatments rely on genetic testing from biopsies of tumors. Unfortunately, these biopsies only grab a portion of the tumor and this sample may not be representative of the entire tumor. If a targeted therapy is identified, it may only be effective on the sampled section of the tumor. Tumor cells without the target may be unaffected and continue growing. Effectiveness depends upon “luck of the draw” in finding the right driver mutation.

[It is here where I think the importance of germline genetic testing comes in. This is look for inherited cancer-causing mutations from a sample of saliva or blood. If one of these mutations is found, I think it has a good likelihood of being a driver mutation present throughout the entire tumor. My BRCA2 mutation was found this way and directly led to my platinum-based chemotherapy treatment that reduced the tumor size by 80% (diameter) and enabled surgery.]

Tumor Growth under the Clonal Evolution Model

Tumor Growth under the Clonal Evolution Model

As the tumor continues to grow, eventually mutations will develop that allow tumor cells to metastasize and spread to distant organs.

For over 30 years we’ve been developing new treatments based on this model. Treatments are reducing the size of tumors, but complete elimination without relapse is uncommon. The lack of success has some researchers looking at different cancer models.

Clonal Evolution Model on Chemotherapy

Experience has shown that chemotherapy is usually only effective on a portion of the tumor. Its application can shrink the tumor and change the types of mutations prevalent.

The figure below describes how the tumor grows and responds to chemotherapy. When the tumor size is small (below the dashed line) it may cause adverse side effects, but it is undetectable by scans. This phase can last years and patients typically don’t know they have cancer while the tumor slowly grows in size.

Clonal evolution model response to chemotherapy treatments

Clonal evolution model response to chemotherapy treatments

Accumulate Mutations

In the clonal evolution model a normal cell accumulates mutations as it divides. After enough mutations have accumulated, the cell becomes cancerous. My post The Hallmarks of Cancer describe the cell characteristics these mutations provide for the formation of cancer.

Tumor Growth

The original tumor cell divides uncontrollably and its “offspring” create additional mutations, as indicated by the different colored cells. Some offspring grow and divide better than others. This is evolution on a very rapid time frame. In cancers cells, mutations happen much more quickly than in normal cells because of the loss of DNA repair mechanisms. Most mutations don’t survive, but some do well – even better than previous tumor cells. This is survival of the fittest within the tumor.

The tumor is said to be heterogeneous, meaning it is composed of cancer cells with many different kinds of mutations. In the figure above, tumor cells with different sets of mutations are shown as cells with different color. Tumor heterogeneity has important negative implications for genetically targeted therapies (i.e. precision medicine).


After the tumor is large enough to be detected, treatment begins. Cell mutations continue to happen and new treatment-resistant cells begin to proliferate while the others are destroyed. In this phase, the tumor may grow or shrink depending upon how well the tumor cells resist and adapt to the treatment.

In the figure above, the blue and pink colonies are susceptible and killed off by the chemotherapy. A new colony of mutated cells (light blue) are unaffected by treatment and grow rapidly. But overall, the tumor shrinks below the size of detection where the patient is then considered to be in remission and treatment is halted. Patients may assume that the tumor is completely gone, but most likely it is present but too small to detect (remission, not cured).

Colonies of cells susceptible to treatment make up smaller and smaller portions of the tumor. Treatment effectiveness comes to a halt when the susceptible cells have been killed, leaving behind the treatment-resistant colonies.

[Treatment targeting a driver mutation may be much more effective – even resulting in a long-term remission. Unfortunately, because of the tumor’s ability to mutate to readily, a mutation may come that works around even this driver mutation. Evolution is a very powerful process and is working in the tumor’s favor. Surgical resection before it gets the chance to mutate again may still be the surest cure.]

Treatment Holiday

After treatment is halted, tumor cell growth of remaining cells continues unrestrained. By this time, the tumor may be composed of completely different types of tumor cells, ones that are largely unaffected by previous treatments.

What remains is a treatment-resistant tumor. Another treatment must be selected. A new biopsy might identify another treatment that will also likely treat a portion of the tumor.

Another View of the Clonal Evolution Model

The figure below presents another way to look at tumor growth in the clonal evolution model4. Each colored area represents a cell colony with a specific set of mutations. Time progresses to the right. The height of each colored area represents the quantity of cells in the colony. New mutations are represented by stars and may originate from any established colony. The figure shows that these new mutations can originate in any part of the tumor and then competes for space and resources with the others.

Eventually, one of the colonies acquires the ability to metastasize and migrates to another organ.

Clonal Evolution Model

Clonal Evolution Model


The clonal evolution model of cancer is the widely accepted explanation for tumor growth and response to treatments. Publications on the topic have been ongoing for quite some time.

Clonal Evolution Model Publication Count

Clonal Evolution Model Publication Count

The clonal evolution model describes how cancer develops from a single cell to become a tumor, becomes heterogeneous, responds to treatment, and becomes resistant to treatments.

The model points to a few effective treatment options for patients.

  • Surgical resection before metastases
  • Treatments targeting driver mutations

Much current research and development is being done to target mutations found in tumors. The next model I’ll discuss says that we’re targeting the wrong mutations and wrong tumor cells.


[1] Nowell PC (October 1976). “The clonal evolution of tumor cell populations”. Nature 194(4260):23-8. PMID: 959840.

[2] Heppner GH. (Jun 1984). “Tumor heterogeneity”. Cancer Res. 44(6):2259-65. PMID: 6372991.

[3] Greenman, Chris, et al. (Aug 2006). “Statistical Analysis of Pathogenicity of Somatic Mutations in Cancer”. Genetics 173(4):2187-2198. PMID: 1569711.

[4] Yates LR, Campbell PJ. (Nov 2012). “Evolution of the cancer genome”. Nat Rev Genet. 13(11):795-806. PMID: 3666082.

Cancer Models

Driving treatment decisions on 25 Sep 2015

Cancer models are used by scientists to define and better understand cancer. They describe the rules cancer follows – how it starts, grows, metastasizes, and ultimately how it can be killed. Reviews of their cancer models generate new treatment proposals that the models indicate should be successful.

Cancer models are driving the direction of cancer funding, research, prevention and treatment. Faulty cancer models lead to research producing ineffective treatments. Some researchers say that is happening now.

Cancer Models Example

Let’s look at one example of a simple cancer model. It’s just a picture that describes how a normal cell might progress to a cancer cell and then a tumor. In this model, each time the cell divides, a mistake in the replication process results in a mutation (indicated by the star symbol) in one of the two daughter cells. After enough divisions with accumulated mutations, a cancer tumor cell results (highest cell in the red area). All subsequent daughter cells are cancer cells and additional mutations keep happening. The tumor ends up containing cells with varying kinds of mutations. However, the mutations present in the first tumor cell remain present in all daughter cells. These are called driver mutations1, because they drove the initial cancer and will be present throughout the entire tumor.

Model of Mutation Accumulation in the Clonal Evolution Model

Let’s just take this simple model, and ask ourselves some questions. What are this model’s weaknesses? What treatment decisions might this model recommend?

Testing the Model

First, let’s look at what our model implies about cancer growth. Does it fit with observations from real-world patients?

Mutations Only During Replication?

What does our model tell us about cancer growth? For instance, is it reasonable that mutations only happen at during replication? Well, we seem to know that cells need about 20-30 mutations before they can become cancerous. We also know that normal cells can only replicate about 40 to 60 times before their telomere length is too short to allow any more replications. Given this, and DNA’s ability to repair itself, we might have to adjust our model to allow more than one mutation per replication, or allow mutations to happen without replication, or grow longer telomeres. We might design experiments to determine which of these is more correct and then update the model to reflect the new findings.

Are There Nearby Pre-Cancerous Cells?

Our model also seems to indicate that there will be many cells nearby with some mutations, but not enough to yet be cancerous, or pre-cancerous. Is this something that a pathologist can determine? Can we separate out nearby normal-looking cells and genetically sequence them to see if they have some mutations but not all?

Timeline Of Cancer Development?

We might observe that our model seems to require quite a few generations of replications before the first cancerous cell develops. Where to all these cells go? What does this tell us about how quickly cancer could develop? What ages cancers might develop? Does this fit in with the childhood leukemia? Does it fit in with pancreatic cancer? Do we need different models for these cancers? Which cancers does it fit?

Cancer models raise many questions like these that drive directions of research that demand funding. Answering these questions allow us to tweak the model to be more accurate. The more accurate the model, the better its predictive power for new treatments.

Cancer Model Development Flowchart

Cancer Model Development Flowchart

Refining the Model

After we’ve tested the model and experimentally determined new facts, we can update the cancer model. The updated model would be tested again, continually refining the model.

Using the Model

Once we’re happy that the cancer model is mostly reflecting reality and is able to answer basic questions about cancer’s behavior, we are ready to put this model into practical use.

Treatment Decisions

What does our model tell us about treatment decisions? For one, this model seems to indicate that there are certain mutations that will be present in all daughter tumor cells. Examine those nearby pre-cancerous cells. Do they hold the key to uncovering these cancer driver mutations1? If we can find these driver mutations and target them with treatments, we might be able to kill off the entire tumor. This is the idea behind the NIH’s Precision Medicine Initiative.

Tumor Heterogeneity

Our model also says that many mutations will only be present in a part of the tumor – not the entire tumor. See those white and blue cancer cells in the model? They represent tumor cells with different sets of mutations. If we treat only for the white cells’ mutations, the blue cells might continue to grow. This observation fits current patient outcomes.

Early Detection

What does our model say about early detection? Our model indicates that precancerous cells will be around for a while before the cancerous cells develop. It there some way we might be able to detect these pre-cancerous cells? Perhaps they affect the environment around them or put out markers that might be picked up in the bloodstream? This model might drive the development of another model that looks at these issues in the cellular environment.

(Re)-Refining the Model

The cancer model is always being updated to reflect the best knowledge of how cancer works. This simple model really just deals with cancer at a very high level, but even so, it reveals directions for research, early detection, and treatments. Continual refinement of even this simple model brings forth new ideas to be tested, further feeding the models.


Don’t think that there is some “master” cancer model somewhere that all researchers work from. Each group has its own models developed from their own experience, lab tests, ideas from other groups, etc. These cancer models describe some aspect of cancer as it is understood by the local research team.

In the next posts, I’ll describe two current cancer models. The clonal evolution model2 that has been driving research and funding decisions for several decades. And the cancer stem cell model3 that is not widely accepted but is gaining traction. Which cancer model is (more) correct has a profound effect on patient treatment choices.

Our lack of progress could mean we need to take another look at our basic assumptions, the cancer model, and develop a new model that describes not only how cancer starts, grows, and metastasizes, but also accounts for why we’ve been failing in the “war on cancer”. Each model will undergo continuous revision and tweaking based on new tests to improve their accuracy.


[1] Stratton MR (9 April 2009). “The cancer genome”. Nature 485(7239):719-24. PMID: 19360079.

[2] Nowell PC (October 1976). “The clonal evolution of tumor cell populations”. Nature 194(4260):23-8. PMID: 959840.

[3] Soltysova A, Altanerova V, et al. (2005). “Cancer stem cells”. Neoplasma 52(6):435-40. PMID: 16284686.

Phase 3 Metastatic Pancreatic Adenocarcinoma Clinical Trial Results


Metastatic pancreatic adenocarcinoma patients (PDAC) don’t have a lot of good options. In this post, I’ve summarize the completed chemotherapy-based phase III trials for metastatic patients, providing links to publications, outlining the survival statistics, and identifying key subgroups that may have benefited from the treatment.

From a patient’s perspective, not much progress has been made on new treatments. It’s not for a lack of trying. Over 90% of phase 3 clinical trials result in no added benefit.

However, I believe that these treatments are effective on a minority of patients. The data used to measure effectiveness (like median OS) ignores outstanding responses to less than half the patients. I propose that looking at longer term survival rates gives clues to treatments effective on a minority of patients. Further digging into the results by researchers can uncover reasons behind the effectiveness and help us match treatments to patients.

Key Points for Patients

  • Several treatments available. We need to better match the right treatment to the right patients. Some studies have published information about subgroups that benefited substantially from treatment in a trial. This can help you choose whether that treatment is right – or wrong – for you.
  • A patient’s treatment goals should determine which trial results are important.
  • Directly comparing results between trials is misleading due to the fact that patient selection is key to the results and each trial has its own selection criteria.
  • Results in italics are values I’ve read from published graphs, not specifically published by the study paper.
  • Results in bold are for treatments often used for pancreatic cancer.


In reviewing the information to put together this information, I’ve formed some interesting opinions that I’ll share.

First, I should state that the results provided here are for the actual patients that were under clinical trial. Your results will almost certainly not be “typical”. There are uncertainties in the outcomes that are hopefully minimized because these are phase 3 clinical trials with large numbers of patients. One of my goals here is to identify the best performing treatments that can maximize your outcomes. If we can identify some treatments that work best for patients in your particular situation, this posting has done its job.

This summary lists very little about side effects of the treatments. For some, this can also be a big factor in a treatment decision. If you are in poorer health, try looking for the best performing trials that included patients with poor health (ECOG 2/3 or KPS≤70).

In the data listed, numbers provided directly from the clinical trial results are listed in normal fonts. Rates in Italics have been interpolated from graphs provided by the clinical trials publications.

Click on the following picture to show a PDF file with the results.

Phase 3 PDAC Trials (small)

Pancreatic Adenocarcinoma Phase III Clinical Trials

Subgroups Benefited

I think this is the most useful information available from the data I’ve collected.

Some clinical trial reports are superior to others. Take for instance, the reports for FOLFIRINOX and Gemcitabine/Abraxane. Both of these reports include what are called forest plots to identify subgroups of patients that did well on one treatment versus the other.

The FOLFIRINOX forest plot is here (I provide the link because I’m unsure whether I can legally embed the figure, which might be clearer). For each subgroup listed, a hazard ratio has been computed (square box), with 95% confidence intervals shown (horizontal line). Subgroups listed that have the entire confidence interval (horizontal line) to the left of 1.0 (which is just about everything) performed better under FOLFIRINOX than Gemcitabine. This kind of information can be vital to patients who belong to one of these subgroups when they’re trying to decide on a particular treatment.

In the column Subgroups Benefited, I try to list groups indicated within these reports that did benefit from one treatment arm. Sometimes, a follow-on report has the information and I have provided hyperlink in the column for these cases.


This is the percentage of patients that had at least a 20% reduction in the size of their tumor. FOLFIRINOX has the best ORR at 32%. Understand that 2/3d’s of treated patients did not have their tumors shrink using the best treatment available.

The key is finding out why these patients had their tumors shrink. Are you more likely to belong in this group? Again, this is why the subgroup analysis is important.


If your treatment goal is to remove all metastases to enable a chance at surgery, CR (Complete Response) is your result of interest. These are pitifully small numbers. But what this tells me is that some small subgroup of patients do respond remarkably well to some treatments. One example might be BRCA2 mutation patients that respond extraordinarily well to platinum-based treatments [reference needed].


If your treatment goal something like living as long as possible without worsening symptoms., this might be your series of results to look at. Often, only the median is reported, but I favor looking at the 12- or 18-month PFS percentages. This tells me how many patients had a sustained response to the treatment. Their tumor shrunk and they’re tolerating the treatment.


This is usually a clinical trial’s primary end statistic. Median numbers are what researchers compare but I find them largely irrelevant to patients. I want to see the 18- and 24-month OS percentages. These are the long-term survivors. Compare the PFS and OS percentages and know what fraction of survivors are still benefiting from the treatment.

Clinical Trial Progress

Often we patients are frustrated by the lack of progress towards more effective treatments. Here I’ve documented over 38 different chemotherapy phase III clinical trials since 2002 (I’m certain there are more). Only four of these trials led to approval, a success rate of about 10%.

In general, these are drugs that had successful phase II results and then failed during phase III. First, it helps to demonstrate that there is still significant effort being put into new treatments. But second, how many very promising treatments fail to show patient benefit.

Interesting Results

When a trial fails to show benefit, often that is the end of the road. However, sometimes the researchers see interesting things buried in their data. Here are a few I’ve uncovered.


Here’s a stellar performer that I think should be approved pronto! For Asians. And therein lies one of our problems with the clinical trial system. It is stacked in favor of Caucasians. This combination works well and is well tolerated by Asians only. Caucasians – not so much.

Gemcitabine/Cisplatin & Gemcitabine/Oxaliplatin

The trial results for these platinum-based chemotherapies did not identify any subgroups that performed well, but it has since been recognized that many patients with a wild-type BRCA2 mutation (about 5-10% of all pancreatic cancers) have responded quite well (2009 Dramatic Response; 2011 Complete Response; 2012 Complete Response; 2014 Meta-analysis). I credit my complete response to Gemcitabine/Cisplatin because of my BRCA2 mutation.


According to the traditional measurement criteria (median PFS and/or OS), this trial failed miserably. A closer reading of the results (kudos researchers!) identifies that PDAC patients with overexpression of ACOX1 in their blood did much better than others (Figure). What’s less clear is whether this finding also says that patients with an overexpression of ACOX1 in their blood should avoid Gemcitabine monotherapy.


Another research report (behind a paywall, so not fully reviewed by me) of a failed clinical trial. But a later closer analysis of the results in another paper delivers news of biomarkers (IGF-1+; IGF-2+; IGFBP-3+; IGFBP-2-) that identified a subgroup of patients that survived 3X longer than the others.


Erlotinib (aka Tarceva) was expected to target tumors that overexpress EGFR, like pancreatic cancer. While the EGFR status of patients did not seem to affect the outcomes, development of rashes did. The more severe the rash, the better the outcomes (Figure). In fact, some oncologists are increasing the doses of erlotinib until a rash develops.


There have been a surprising number of phase III clinical trials for metastatic pancreatic adenocarcinoma. Most do not pan out. And researcher responsibilities should not stop there. It appears unlikely we will soon find a treatment that works for most pancreatic cancer tumors. More effort is being made to identify subgroups that respond well, but that information is not widely disseminated to patients. Hopefully this blog entry will bridge some of that gap.