#181 - Robert Gatenby, M.D.: Viewing cancer through an evolutionary lens and why this offers a radically different approach to treatment
Dr. Bob Gatenby, a radiologist, discusses his evolutionary approach to cancer, applying mathematical models and ecological principles to treatment. He shares insights from a pilot trial on metastatic prostate cancer using adaptive therapy, showing improved survival and reduced drug use by strategically cycling treatment to manage drug resistance.
Deep Dive Analysis
17 Topic Outline
Bob Gatenby's Unconventional Path to Medicine
Disappointment with Medical Education and Dogma
Applying Physics First Principles to Cancer
Necessity of Mathematical Models for Complex Systems
Relating Predator-Prey Models to Cancer Dynamics
Insights from Pesticide Resistance and Integrated Pest Management
Adaptive Therapy Pilot Trial for Metastatic Prostate Cancer
Using Sensitive Cancer Cells to Destroy Resistant Cells
Antibiotic Resistance and the Problem of Overtreatment
Vulnerability of Small Cancer Cell Populations and Alle Effects
Strategic Sequencing of Therapies for Cancer Extinction
Role of Immunotherapy in the Cancer Treatment Toolkit
Understanding Cancer Metastasis: Source-Sink Dynamics
Defining Eco- and Evo-Indices in Cancer Biology
Challenges and Opportunities in Early Cancer Screening
Future Directions for Adaptive Therapy Trials
Addressing Cancer Patients Who Have 'Failed Therapy'
9 Key Concepts
Non-linear Dynamics
Human beings tend to think linearly, but complex systems like cancer exhibit non-linear dynamics with feedback loops, making intuitive predictions often incorrect. Mathematical models are essential to understand and predict behavior in such systems.
Adaptive Therapy
A cancer treatment strategy that aims to manage tumor growth by intermittently administering drugs, rather than continuously, to maintain a population of drug-sensitive cancer cells. This approach prevents drug-resistant cells from dominating by allowing sensitive cells to outcompete them in the absence of the drug.
Integrated Pest Management
An agricultural strategy that uses just enough pesticide to control pest populations and prevent crop damage, but not to eradicate them. This approach minimizes selection pressure for pesticide resistance, allowing sensitive pests to remain and outcompete resistant ones.
Cost of Resistance
The metabolic or fitness burden incurred by a cell (or organism) to develop and maintain mechanisms of resistance to a drug or toxin. In the absence of the selective pressure (the drug), resistant cells are often less 'fit' than sensitive cells and can be outcompeted.
Extinction Approach (Cancer)
A therapeutic strategy that involves applying a sequence of distinct perturbations or therapies, none of which might cause extinction alone, to drive small, vulnerable cancer cell populations to extinction. This contrasts with seeking a single 'magic bullet' drug.
Alle Effect
A phenomenon where small populations may not grow as fast as large populations and can be more vulnerable to extinction. In cancer, this means small, fragmented tumor cell populations may lack the collective advantages (e.g., making blood vessels, extracellular matrix, or evading the immune system) that larger tumors possess.
Eco-Index
A concept referring to the diversity of microenvironments or 'niches' within a tumor. These varied environments apply different selection forces, leading to diverse cancer cell phenotypes and genotypes.
Evo-Index
A concept referring to the genetic diversity and evolutionary changes occurring within a tumor population over time. This diversity is often a consequence of cancer cells adapting to the varied environmental selection pressures within the tumor.
Source-Sink Habitats (Cancer)
The idea that some tumor regions (source habitats) with good blood supply produce many cells that then migrate to other regions (sink habitats) with different conditions, influencing the dynamics of metastasis. The success of these metastatic cells depends on their interaction with the local adaptive landscape.
11 Questions Answered
Human intuition is poor at predicting outcomes in complex, non-linear systems like cancer, which involve feedback loops and evolutionary dynamics; mathematics provides the necessary framework to capture these complexities.
Applying maximum pesticide doses selects for resistant pests, leading to long-term failure; similarly, maximum tolerated dose chemotherapy selects for drug-resistant cancer cells, making the treatment eventually ineffective.
Adaptive therapy aims to manage the tumor by maintaining a population of drug-sensitive cells that outcompete drug-resistant cells in the absence of treatment, thereby extending the effectiveness of the drug and patient survival.
Yes, the fitness cost of resistance means that in the absence of the drug, sensitive cells can outcompete and even drive resistant cells toward extinction, especially if the fitness ratio is high enough.
Similar to cancer, continuous high-dose antibiotic treatment can select for resistant bacterial strains, potentially making future infections harder to treat and contributing to the rise of 'superbugs'.
Small populations are more susceptible to stochastic events and Alle effects, where group advantages (like forming blood vessels or evading the immune system) are diminished, making them easier to drive to extinction.
Sequencing allows for targeting smaller, more vulnerable populations after an initial therapy, and can exploit the adaptive strategies developed by cancer cells in response to previous treatments, making them susceptible to subsequent ones.
Immunotherapy is viewed as a 'closer' therapy, most effective when the cancer cell population is small and fragmented, as Alle effects make them more vulnerable to immune attack and less able to evade the immune system.
This is not a 'planned event' by cancer cells, but rather due to factors like the number of cells shed from the primary tumor into specific circulatory pathways (e.g., pancreas to portal vein to liver) and the interaction of cancer cell phenotypes with the local adaptive landscape of the metastatic site.
The eco-index refers to the diversity of microenvironments (niches) within a tumor, while the evo-index describes the genetic diversity and evolutionary changes of cancer cells, which are often consequences of adapting to these varied environments.
Intuitively, yes, because smaller tumor populations are more vulnerable to extinction due to stochasticity and Alle effects, and have had less time to accumulate diverse resistance mutations, making them potentially easier to eradicate.
31 Actionable Insights
1. Adaptive Cancer Therapy
Instead of continuous maximum tolerated dose, apply just enough therapy to reduce the cancer population to a manageable level, then reduce or withdraw treatment to allow sensitive cells to outcompete resistant ones, preventing the selection and proliferation of highly resistant strains.
2. Post-Remission Extinction Strategy
After initial effective therapy reduces cancer to very small or undetectable levels, do not continue the same treatment. Instead, apply a sequence of different, smaller perturbations to exploit the vulnerability of the remaining small, resistant population and drive it to extinction.
3. Leverage Sensitive Cells
Recognize that sensitive cancer cells, when therapy is withdrawn, can actively outcompete and even destroy resistant cancer cells due to their fitness advantage, rather than merely controlling them.
4. Sequence Therapies Strategically
Sequence different therapies rather than administering them all at once, as this allows you to exploit the fitness cost of resistance mechanisms. As the tumor population shrinks and fragments, it becomes more vulnerable to subsequent, subtle perturbations.
5. Target Fragmented Tumors
When therapies reduce large cancers to small, fragmented, and undetectable populations (e.g., after neoadjuvant therapy), this is the optimal time to intensify treatment and aim for eradication, as these small, isolated groups are highly vulnerable due to stochasticity and Allee effects.
6. Immunotherapy as Closer
Consider immunotherapy as a ‘closer’ in cancer treatment, most effectively deployed when the tumor population is already small and fragmented (e.g., after initial debulking therapies), as Allee effects make small populations more vulnerable to immune response.
7. Optimize Existing Cancer Drugs
Instead of solely focusing on developing new cancer drugs, prioritize rethinking and optimizing the strategic use of existing, older drugs, as current approaches often fail to leverage their full potential in an evolutionarily informed manner.
8. Avoid Continuous ADT
For metastatic prostate cancer, once PSA normalizes or becomes undetectable with androgen deprivation therapy (ADT), avoid continuous ADT due to its severe metabolic side effects. Instead, apply additional, different therapies to prevent resistance and aim for extinction.
9. Prostate Cancer Adaptive Protocol
For prostate cancer, administer treatment until the tumor shrinks to 50% of its pre-treatment value, then withdraw the drug. Allow the tumor to grow back, leveraging the fitness advantage of sensitive cells in the absence of treatment to outcompete resistant cells.
10. Pediatric Oncology Multi-Drug Strategy
Emulate pediatric oncology’s successful leukemia treatment strategy: follow initial induction therapy with immediate, sequential application of different drug groups, even when no apparent tumor remains, to prevent relapse and achieve cure.
11. Optimize Antibiotic Use
Question the practice of prolonged antibiotic courses (e.g., 10 days when symptoms resolve in 3) as this may contribute to antibiotic resistance by selecting for resistant bacteria; consider adaptive approaches to keep resistant populations low.
12. Challenge Medical Dogma
Be willing to question established medical dogma and consider alternative approaches, especially in areas where current methods have shown limited success, as the medical community can be very conservative and resistant to change.
13. Recognize Non-Linearity
When dealing with complex systems, recognize that human intuition often struggles with non-linear dynamics and feedback loops. Seek to understand first principles and underlying mathematics rather than relying solely on intuition.
14. Embrace Humility
When facing complex problems, especially in fields like medicine, cultivate humility and be willing to consult mathematical models and computer simulations, as human intuition and confidence can be insufficient without them.
15. Iterative Problem Solving
For highly complex and chaotic systems like cancer, focus on short-term predictions and interventions (e.g., next 3 months), gathering new data, and recalibrating models for subsequent steps, rather than attempting to predict the entire long-term course.
16. Prioritize Early Cancer Screening
Given that smaller cancer populations are more vulnerable to extinction and have less genetic diversity, prioritize early cancer screening to detect tumors when they are most susceptible to treatment.
17. Gather Cancer Ecological Data
To better understand and treat cancer, collect fundamental eco-evolutionary data such as birth rates, death rates, and nutrient cycles (carbon, iron, nitrogen) of cancer cell populations, similar to how ecologists study ecosystems.
18. Require Cancer Drug Resistance Plans
Advocate for cancer drug development and approval processes to include mandatory resistance management plans, requiring identification of resistance mechanisms and strategies to prevent them, similar to regulations for pesticide manufacturers.
19. Target Resistance’s Weakness
Design therapy sequences to anticipate how cancer will evolve resistance, then attack the specific ‘Achilles heel’ or vulnerability that is exposed by that particular resistance strategy.
20. Chemo-Immuno Sequence
Explore sequencing chemotherapy before immunotherapy, as evidence suggests that cancer cells surviving chemotherapy may become more vulnerable to immune-based treatments.
21. Add Environmental Perturbations
Beyond drugs, consider adding environmental or metabolic perturbations (e.g., anti-angiogenics to reduce blood flow, antacids, or other metabolic interventions) in combination with drug therapies to create a more difficult environment for cancer cells.
22. Prioritize Phenotype-Environment
When studying cancer evolution, prioritize understanding the morphological matching of cell phenotype to its environment and the environmental selection forces, rather than solely focusing on genetic mutations, as genes are often consequences, not causes, of evolution.
23. Utilize Imaging for Evolution
Employ non-destructive imaging techniques (radiologic studies) to monitor intratumoral evolution over time, aiming to bridge macroscopic images with microscopic cellular and molecular changes to understand how cancer adapts during therapy.
24. Landscape Ecology for Cancer
Adapt landscape ecology techniques to cancer imaging by identifying distinct ‘habitats’ within tumors from macroscopic images, then extrapolating cellular and molecular dynamics from these defined areas to understand intratumoral evolution.
25. Interpret Liquid Biopsy Carefully
When using circulating tumor DNA or cells, carefully consider their origin and representativeness of the entire tumor population, as they may primarily reflect less fit, dying cells (losers in the evolutionary game) rather than the most aggressive, resistant ones.
26. Caution with In Vitro Data
Exercise caution when extrapolating results from in vitro cancer studies (e.g., cells grown in a dish) to in vivo patient outcomes, as the environmental selection forces and evolutionary pressures are vastly different in a lab setting.
27. Find Open-Minded Oncologists
If considering adaptive or evolution-based cancer therapies, seek out oncologists who are brave and willing to go against conventional dogma, as these approaches require swimming against the mainstream medical current.
28. Discuss Adaptive Therapy
If you or a loved one are facing limited cancer treatment options, discuss the possibility of adaptive or evolution-based therapies with your oncologist, recognizing that finding a willing physician may be challenging due to non-standard practice concerns.
29. Adaptive Therapy for Cancers
Consider applying adaptive and extinction-based therapies to cancers that respond extremely well to initial treatment, such as ovarian cancer, small cell lung cancer, and initial metastatic prostate cancer, as these are ’low-hanging fruit’ for potential eradication.
30. Novel GBM Treatment Sequences
Consider exploring unconventional treatment sequences for glioblastoma, such as radiation therapy before surgery, to study how cells evolve resistance during radiation and potentially inform more effective strategies, despite current medical reluctance.
31. Rethink Pancreatic Fibrosis
Re-evaluate the role of fibrosis in pancreatic cancer; if it’s a host response rather than a tumor adaptive strategy, promoting fibroblasts might be beneficial by increasing competition for space and potentially killing off tumor cells, contrary to current approaches.
6 Key Quotes
I think that in cancers, we see non-linearities all the time. And again, the feedback, the evolutionary dynamics of resistance, for example, is a good example of that. And we can't intuitively predict those things. We need to actually understand first principles and the underlying mathematics to capture that piece of it.
Bob Gatenby
If it worked, there wasn't resistance, but you know, from, from experience and over and over and over again, what, what you found was that you use this large dose pesticides, you can get a short term gain, but in the long term, it doesn't work. So believe your eyes.
Bob Gatenby
You can actually use the sensitive cells to destroy the resistant cells.
Bob Gatenby
In cancer, we're always looking for the magic bullet, the one that will eradicate the cancer and not the normal cells. And for a century, we've been looking for magic bullets, but maybe all we need is a series of pretty good bullets.
Bob Gatenby
In nonlinear systems, your intuition can be very misleading.
Peter Attia
Normal tissue cannot evolve because its birth and death is dependent on tissue controls. That's right. So knowing that chemotherapy, which is the earliest form of drug in the modern arsenal against cancer, says, how can we kill a cell that is not responding to cell cycle signaling?
Peter Attia
2 Protocols
Adaptive Therapy for Metastatic Prostate Cancer (Pilot Trial)
Bob Gatenby- Administer abiraterone (drug) until the tumor's PSA value responds to 50% of its pre-treatment value.
- Withdraw the drug and allow the tumor to grow back.
- Re-administer the drug when the PSA returns to the pre-treatment level.
- Repeat cycling, ideally hitting three successive cycles where the resistant cell population is driven towards extinction.
Pediatric Leukemia Treatment (General Approach)
Bob Gatenby- Administer induction therapy to reduce tumor burden.
- Immediately follow with a different group of drugs.
- Administer another group of drugs sequentially.