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.
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.
Recognize that sensitive cancer cells, when therapy is withdrawn, can actively outcompete and even destroy resistant cancer cells due to their fitness advantage, rather than merely controlling them.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Explore sequencing chemotherapy before immunotherapy, as evidence suggests that cancer cells surviving chemotherapy may become more vulnerable to immune-based treatments.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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