What fundamental flaw did the guest identify in how medical schools taught about cancer growth?

He noted that the 'scripted answer' in medical school—that cancers grow because cancer cells grow faster than normal cells—is fundamentally incorrect. The key difference lies in cancer cells' uncontrolled response to cell cycle signaling, not just faster division rates.

Why is a mathematical approach critical for understanding and treating cancer, according to the guest?

Humans think linearly, but complex biological systems like cancer behave non-linearly with feedback loops. Intuition is often misleading in these systems. Mathematics provides the necessary framework to understand these complex dynamics and predict outcomes in ways the human brain cannot intuitively grasp, leading to more effective strategies.

How do predator-prey models in ecology relate to understanding cancer biology?

Ecological predator-prey models, like those of foxes and rabbits, demonstrate population rises and falls due to competition and resource availability. This framework applies to cancer, where the immune system can act as a predator. However, there are distinctions, as the immune system kills cancer cells rather than 'eating' them for sustenance, changing the dynamics.

What key insight transferable to cancer treatment came from studying pesticide resistance in the Diamondback moth?

The Diamondback moth taught that applying maximum doses of a pesticide selects for resistance, leading to untreatable populations. This led to 'Integrated Pest Management,' which aims to keep pest populations low enough to prevent damage without eradicating them and applies to cancer by suggesting adaptive dosing to preserve sensitive cells that compete with resistant ones.

How did the adaptive therapy trial for prostate cancer defy initial mathematical model predictions?

Initial models predicted that intermittent therapy (adaptive therapy) would control tumor growth for 2 to 20 cycles before resistance would inevitably lead to failure. However, a pilot trial showed that some patients with metastatic prostate cancer were still cycling after five years. This was due to a higher-than-expected fitness differential (a ratio of 7, instead of 2 or 3) between sensitive and resistant cells, allowing three successive cycles to drive the resistant population toward extinction.

Could over-treating bacterial infections with antibiotics contribute to resistance, similar to cancer chemotherapy?

Yes, the guest believes this is certainly true. Continuing a 10-day antibiotic course even after a patient feels better and cultures are negative might select for resistant bacteria, similar to how chemotherapy can select for drug-resistant cancer cells. The World Health Organization (WHO) is already using evolution-based models to plan antibiotic delivery to prevent resistance.

What is the 'extinction vortex' concept in oncology, and how does it relate to treatment strategies?

The extinction vortex describes how small populations become increasingly vulnerable to various perturbations. In cancer, after initial therapies reduce a large tumor to small, fragmented populations, these remaining cells are highly susceptible to stochastic effects and 'Allee effects.' This suggests that sequential, multi-cause perturbations (a 'knife fight,' not a 'boxing match') can drive these small resistant populations to extinction, rather than relying on a single 'magic bullet.'

How does sequencing different cancer therapies offer an advantage over combining them all at once?

While combining drugs initially offers better control than one drug alone, it often yields diminishing returns with increased toxicity. Sequencing drugs allows each therapy to reduce the tumor population, making the remaining, smaller populations (with increased resistance) vulnerable to the next, different drug. This approach leverages the cost of resistance and the fragmented nature of smaller tumor colonies to drive them towards extinction.

Why are angiogenesis inhibitors (anti-VEGF drugs) often not as successful as chemotherapy, and where do they fit in adaptive therapy?

While logically appealing to starve tumors by inhibiting blood vessel growth (angiogenesis), anti-VEGF drugs alone haven't been blockbuster successes. However, they could play a critical role in adaptive therapy when tumors are small and fragmented, contributing to environmental changes (like reduced substrate delivery) that further disadvantage resistant cancer cells.

How does immunotherapy, particularly checkpoint inhibitors, fit into the adaptive therapy toolkit?

Immunotherapy is considered a critical component, sometimes acting as a 'magic bullet' for specific responsive tumors. For others, it's seen as a 'closer' in the baseball analogy: most effective when the tumor population is already small and fragmented. Cells that survive initial chemotherapy might become more vulnerable to immune attacks due to adaptive strategies that expose their 'Achilles heel'.

What is the 'source-sink' trade-off in cancer metastasis, and what insights does it offer?

The host defines source habitats (e.g., breast, prostate) where primary tumors grow, and sink habitats (e.g., bone, liver) where they metastasize and kill the patient. The guest suggests that metastasis is not a planned event but rather an 'inter-species introduction' where cells 'spill' from source to sink environments. The dynamics at the metastatic site are crucial, as most injected cancer cells die there, highlighting the vulnerability of small invading populations due to stochastic and Allee effects.

Why is focusing solely on the genetics of cancer, particularly in in vitro studies, potentially misleading?

Cancer biologists often assume evolution is solely driven by random mutation and selection in a stable environment. However, variations in the tumor microenvironment drive different phenotypes, which in turn lead to genetic changes. Studying cancer cells in a dish removes them from these crucial environmental selection forces, leading to evolved cells that may not accurately represent in vivo cancer, yielding confusing and less useful literature.

What types of cancer does the guest believe are most suitable for adaptive or extinction-based therapies?

Cancers that respond extremely well to initial therapy, reducing the tumor to a virtually undetectable size, are prime candidates. Examples include ovarian cancer (with a good serum marker), small cell lung cancer (initially responsive but often fatal), and pre-metastatic prostate cancer (where ADT makes PSA undetectable).

What cancer type does the guest consider the toughest to treat with current evolutionary approaches, and why?

Glioblastoma multiforme (GBM) is considered the toughest. It's difficult to locally control, inaccessible in the brain, and conventional treatments like radiation might induce further mutagenesis, accelerating its ability to out-compete its environment. There's a fundamental missing understanding of its biology.

Key Moments

181 - Viewing cancer through an evolutionary lens: a radically different approach to treatment

Peter Attia MD

People & Blogs5 min read139 min video

Oct 25, 2021|20,377 views|513|46

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TL;DR

Evolutionary theory offers a new approach to cancer treatment, moving beyond dogma to understand and exploit cancer's adaptive nature.

Key Insights

Cancer treatment has historically relied on dogma and intuition, which are often misleading in complex, non-linear biological systems.

Applying evolutionary and ecological principles, particularly predator-prey and pest management models, provides a framework for understanding cancer.

Adaptive therapy, which involves intermittent drug use and strategically cycling treatments, can potentially improve patient outcomes and reduce resistance.

The fitness difference between sensitive and resistant cancer cells is crucial; exploiting this difference can drive resistant cells towards extinction.

Cancer is not just a genetic disease but an eco-evolutionary one, where the tumor microenvironment and its heterogeneity play critical roles in its development and response to therapy.

Future cancer treatment should consider multi-cause, multi-event strategies rather than seeking a single 'magic bullet,' potentially leveraging immunotherapy and other agents strategically.

THE LIMITATIONS OF CANCER TREATMENT DOGMA

The conversation begins by highlighting the historical reliance on dogma and intuition in cancer treatment, noting that biological systems, especially cancer, are non-linear and complex. This approach often leads to ineffective strategies because human intuition is geared towards linear thinking. Dr. Gatenby shares his background transitioning from physics to medicine, expressing initial disappointment with the rote memorization and dogmatic approach in medical education. This sentiment is echoed by Dr. Attia, underscoring a common frustration with the lack of first-principles thinking in traditional medical training.

EMBRACING MATHEMATICS AND EVOLUTIONARY THEORY

Gatenby's journey led him to seek mathematical frameworks to understand cancer, drawing parallels to physics where fundamental principles explain complex phenomena. He proposed that cancer, as a living system, must obey the laws of Darwinian evolution. This realization spurred him to relearn mathematics and develop population dynamic equations to model cancer's interaction with normal cells and its own internal competition, shifting the paradigm from a purely biological to a mathematical and evolutionary perspective.

INSIGHTS FROM PEST MANAGEMENT AND ECOLOGY

A pivotal insight came from studying ecological models, particularly pesticide resistance in pests like the diamondback moth. The realization that widespread pesticide use selects for resistant populations, leading to long-term ineffectiveness, mirrors cancer drug resistance. This led to the concept of Integrated Pest Management (IPM), which focuses on managing, not eradicating, pests to preserve pesticide effectiveness. This principle of managing populations to maintain the fitness of sensitive strains over resistant ones became a cornerstone for Gatenby's adaptive therapy approach.

ADAPTIVE THERAPY AND THE FITNESS DIFFERENTIAL STRATEGY

Gatenby introduced 'adaptive therapy,' which leverages the fitness difference between sensitive and resistant cancer cells. By intermittently applying treatments, sensitive cells, which are fitter in the absence of drugs, can outcompete and suppress resistant cells. A pilot trial in prostate cancer patients demonstrated significantly longer progression-free survival with adaptive therapy compared to standard maximum tolerated dose, suggesting that strategically cycling treatments can exploit this fitness differential and potentially drive resistant populations towards extinction.

CANCER AS A COMPLEX ECO-EVOLUTIONARY SYSTEM

The discussion emphasizes that cancer is not just a collection of mutated cells but a complex eco-evolutionary system with considerable heterogeneity. Tumors harbor diverse microenvironments, each exerting different selective pressures, leading to varied phenotypes and genotypes. Understanding these 'habitats' within the tumor and the 'species' (cancer cell lineages) that inhabit them is crucial. Gatenby suggests that focusing solely on genetic mutations overlooks the critical role of the tumor's ecosystem and the dynamic interactions within it.

STRATEGIC THERAPY SEQUENCING AND THE FUTURE OF TREATMENT

The conversation explores how this evolutionary understanding informs treatment strategies. Instead of a 'magic bullet' approach, future therapies should involve carefully sequenced combinations of agents, potentially including chemotherapy, anti-VEGF drugs, and immunotherapy. This 'extinction therapy' aims to exploit vulnerabilities exposed by prior treatments, using the immune system as a 'closer.' The goal is to strategically attack cancer cells, leveraging their adaptive nature and microenvironment dynamics rather than simply overwhelming them with maximum doses.

CHALLENGES AND OPPORTUNITIES IN IMPLEMENTATION

Despite promising results, implementing these evolutionary-based approaches faces significant hurdles, including resistance to new ideas within the medical community, lack of pharmaceutical incentives for repurposed drugs, and the inherent difficulty of convincing oncologists to deviate from established protocols. However, there's hope that as more data emerges and younger oncologists embrace these concepts, adaptive and extinction therapies will become more mainstream, offering new hope for patients with limited options.

THE EVOLUTIONARY PERSPECTIVE ON RESISTANCE AND THERAPY

The discussion highlights that resistance mechanisms are often a costly evolutionary trade-off for cancer cells. When drug pressure is removed, sensitive cells—which don't bear the cost of resistance mechanisms—can proliferate more effectively. This dynamic implies that intermittent therapy, which allows sensitive cells to recover, is key. The challenge lies in precisely timing treatments to maintain this fitness differential and prevent resistant clones from gaining dominance, especially in the context of highly heterogeneous tumors.

ADDRESSING DIFFICULT CANCERS AND THERAPEUTIC SEQUENCING

While optimism exists for cancers like prostate cancer and sarcomas, particularly those that initially respond well to therapy, extremely aggressive cancers like glioblastoma remain formidable challenges due to their location and inherent resistance mechanisms. Even with aggressive initial treatments, these tumors persist. The conversation suggests that even seemingly localized disease, like early-stage breast cancer with circulating tumor cells, presents an opportunity for adaptive strategies to prevent metastasis by targeting these small, vulnerable populations.

LEARNING FROM EXTINCTION EVENTS AND BIOMARKERS

Gatenby draws parallels between cancer therapy and ecological extinction events, noting that most natural extinctions are multi-cause and multi-event, not single catastrophic occurrences. This suggests that cancer eradication may also require a sequence of diverse interventions rather than a single potent agent. The importance of biomarkers, like PSA for prostate cancer, is emphasized for guiding treatment timing and assessing response, though the integration of imaging and other data sources is seen as critical for a comprehensive understanding of tumor evolution.

Mentioned in This Episode

●Organizations

●Books

●Concepts

●People Referenced

Common Questions

Initially a physics major at Princeton, the guest felt he wasn't 'smart enough' for physics and was drawn to medicine during the Vietnam War era out of a desire to help humanity. However, he 'hated every minute of medical school,' finding it less scientific and more focused on rote memorization than he expected.

Topics

Mental Health & Psychology Science & Mathematics Cancer Treatment Evolutionary Biology Mathematical Modeling Medicine & Clinical Adaptive Therapy Drug Resistance Tumor Microenvironment Population Dynamics

Mentioned in this video

Books

The Doctor's Plague

A book mentioned alongside 'The Ghost Map' to illustrate that the medical community is very conservative and slow to adopt new ideas, even simple ones like handwashing.

Origin of Species

Darwin's seminal work, mentioned to illustrate the importance of observing phenotypes in relation to the environment, a concept applicable to understanding cancer evolution.

Cancer Research

The flagship journal of the AACR, which the guest started reading and found a lack of organizing principles across articles, leading him to seek first principles in cancer biology.

The Ghost Map

A book mentioned as an example of how slowly the medical community takes to adopt fundamental changes, such as aseptic technique, over decades.

Concepts

tuberculosis

A global problem mentioned in the context of antibiotic resistance and the WHO's use of evolution-based models for treatment planning.

Fields Medal

Described as the Nobel Prize for mathematics, awarded every four years to mathematicians under 40.

Integrated Pest Management

A strategy developed during the Nixon administration that recognizes pests cannot be eradicated and instead aims to keep populations low enough to prevent crop damage without selecting for resistance.

P-53

A tumor suppressor gene, a vaccine for which was tested in lung cancer patients, suggesting that cells surviving chemotherapy might become more vulnerable to immunotherapy.

PD-1

A checkpoint receptor, mutations in which make patients highly responsive to anti-PD-1 drugs like Keytruda.

People

Jin Seong Zhang

The brave oncologist who ran the prostate cancer adaptive therapy trial, saluted for his courage in going against conventional medical practices.

Ludwig von Bertalanffy

Often credited with the Gompertz model of tumor growth, mentioned by Peter Attia in the context of cancer growth rate literature.

Damon Reed

A brave pediatric oncologist who started an extinction therapy trial for children with metastatic sarcoma, another aggressive cancer.

Edward Witten

A former TA of the guest, who later won a Fields Medal and has been very involved in string theory.

Benjamin Franklin

An example of a scientist who observed a phenomenon (a storm moving against the wind) that contradicted intuitive linear thinking, illustrating the unreliability of intuition in complex systems.

Charles Darwin

His work 'Origin of Species' is cited as an example of pairing phenotype to environment rather than focusing solely on genetics, a parallel to the eco-evolutionary view of cancer.

Richard Feynman

Nobel Prize-winning physicist whose shadow was still present at Princeton during the guest's undergraduate years. He was department chair and a great physicist of his generation.

Judah Folkman

Pioneering researcher who pointed out the idea that cancer cells make blood vessels, leading to the development of anti-VEGF drugs.

Richard Nixon

The US President who initiated the 'War on Cancer' in the 1970s, establishing a historical context for cancer research efforts.

Organizations

National Institutes of Health

Where Peter Attia spent time during medical school, working on cancer growth rates literature.

MIT

Mentioned as where Richard Feynman did his undergraduate studies, contrasting with his PhD at Princeton.

Fox Chase Cancer Center

The institution where the guest had his first job outside of training, leading him to focus on cancer research and developing his evolutionary approach.

American Association for Cancer Research

The organization whose flagship journal, Cancer Research, the guest read. The journal's content highlighted a lack of unifying theoretical frameworks in cancer biology.

Princeton University

The institution where the guest studied as an undergraduate in the late 1960s/early 1970s, initially aspiring to be a physicist.

World Health Organization

Actively uses evolution-based mathematical models to plan how they deliver treatment for bacterial infections globally, aiming to prevent resistance.

National Cancer Institute

Where Peter Attia spent time during medical school, working on cancer growth rates literature.

Software & Apps

Wired

An article in Wired magazine about the guest's work on cancer captivated Peter Attia and led him to seek an interview.

Drugs & Medications

Keytruda

An anti-PD-1 drug, a type of immunotherapy that can be very effective and durable in patients with specific checkpoint mutations.

Interleukin-2

A 'gen 1 immunotherapy' that showed a 10-20% response rate in melanoma, hinting at the potential for immune-based therapies in adjuvant settings.

rhabdomyosarcoma

A type of metastatic sarcoma in children that responds well to initial chemotherapy but often relapses, making it a target for extinction therapy protocols.

Malaria

A disease mentioned as a global problem where the WHO uses evolution-based models to manage treatment and prevent drug resistance.

Gentamicin

An antibiotic for which Peter Attia developed a mathematical model to predict plasma concentration decay and optimize dosing during his residency.

Abiraterone

An off-patent drug used in the pilot adaptive therapy trial for prostate cancer, which showed significant improvement in time to progression compared to standard care.

Products

Gleevec

An example of a drug that targets a single gene mutation in specific cancers, achieving high success.