The Cheating Problem
Cancer as the Oldest Betrayal in Biology
An elephant has many times more cells than a human and lives long enough for those cells to accumulate decades of mutations. Elephants should be riddled with cancer. They are not. Their cancer mortality rate is about 5 percent. Ours is 11 to 25 percent (Abegglen, Schiffman, et al., JAMA, 2015). I sit on the other side of that comparison. My myeloma clone carries a chromosomal translocation that hijacks cell survival. When I read the research on Peto's Paradox, I am not reading theory. I am reading one evolutionary clue to the system that broke inside me.
The observation that large, long-lived animals should drown in cancer but do not is called Peto's Paradox, named for the Oxford statistician and epidemiologist Richard Peto, who first formulated the problem in the 1970s. The paradox begins to resolve when you shift attention from mutations to the systems that manage them. Elephants do not get less cancer because they mutate less. They get less cancer because they evolved stacked constraints that absorb mutations before they matter.
Cancer is not fundamentally about mutations. It is about the layered systems that detect, constrain, and eliminate would-be defectors. Illness happens when enough of those layers erode.
The View from Inside
In the hospital, I started reading my own medical record with the help of AI tools. The cytogenetics section of my bone marrow biopsy listed a translocation I had never heard of: t(11;14). A piece of chromosome 11 had swapped places with a piece of chromosome 14, placing a growth-promoting gene called cyclin D1 under a powerful genetic switch that normally drives antibody production. That switch was never meant to control cyclin D1, but now it was running it at full volume. The downstream effect was that my cancer cells became disproportionately dependent on a survival protein called BCL-2, the molecule that tells a cell "do not die."
The translocation was a checkpoint failure. But it also created something new: a regulatory context that never should have existed, a growth gene lashed to an antibody accelerator. The dependency it created was the vulnerability my treatment would eventually target.
You do not feel these systems eroding. There is no pain, no warning sensation. Just a quiet shift in numbers on a page while your body reports nothing wrong. Weeks before the emergency room, a routine lab panel had come back with a flag: hypogammaglobulinemia, abnormally low immunoglobulin levels. The lab note explicitly recommended a plasma cell workup. A plasma cell workup would have meant a simple blood test, possibly a bone marrow biopsy, and almost certainly an earlier diagnosis. That note sat in the system. Nobody ordered the workup. Nobody called. When I later asked why, no one had a satisfying answer. The flag had been generated. The recommendation had been printed. The workflow that should have connected the flag to a human decision simply did not exist, or did not fire. By the time I reached the ER, the clone had been depositing toxic proteins into my heart for months. The signal that something was wrong had been there. The system that should have acted on it did not.
That experience is what drove me into the primary literature. I wanted to understand why the system failed and what, if anything, could be done about the failure rather than just the tumor it produced. What I found there was Peto's Paradox, and a way of understanding cancer that starts not with the disease but with the defenses that are supposed to prevent it.
How the Body Keeps Order
Mutations are constant. Inigo Martincorena and colleagues at the Sanger Institute showed in 2015 that in aged, sun-exposed skin (eyelid epidermis), normal tissue can carry many driver-like mutations, sometimes reaching burdens seen in some tumors, without forming cancer. Your body is managing mutations right now. The question is how.
The body's defenses operate in three layers, each catching what the previous one missed. This is a teaching model, not a formal theory. Many mechanisms span layers. The point is not to compress cancer into three boxes, but to restore the idea that containment is multi-system and redundant, and that knowing which layer failed changes what you do about it.
Layer 1 is internal checkpoints. Every cell carries its own damage inspector: p53, a transcription factor that integrates stress signals and triggers repair, arrest, or death (encoded by the gene TP53). When a cell accumulates DNA damage, p53 evaluates the severity. Minor damage gets repaired. Severe damage triggers apoptosis, programmed cell death, the cell dismantling itself before it can become a problem. When p53 loses function, damaged cells no longer halt division. They keep copying, errors and all.
Layer 2 is tissue constraints. Even a cell that has lost its internal brakes faces physical barriers. Contact inhibition stops neighboring cells from overcrowding. Terminal differentiation locks cells into specialized roles that preclude further division. The physical architecture of tissues, basement membranes, extracellular matrix, organ compartments, keeps cells in their designated locations. A rogue cell in the liver cannot easily invade the lung without first breaking through structural boundaries that exist independent of any genetic checkpoint.
Layer 3 is immune surveillance. Natural killer cells and cytotoxic T cells continuously scan for aberrant surface proteins. When they find a cell displaying the wrong molecular markers, they kill it. A natural killer cell physically docks with a suspect cell, checks its molecular ID, and injects lethal enzymes if the identification fails. This is the most adaptive layer: it can respond to threats never previously encountered, as long as the threat is visible.
The layers are interdependent. Immune surveillance depends partly on neoantigens generated when Layer 1 fails, because mutated proteins can become new immune targets. Tissue architecture is maintained partly by p53-dependent apoptosis. But different species reinforce different layers, and different cancers escape different ones. That is what makes the distinction useful.
Cancer emerges not when one layer fails but when a lineage escapes enough layers to begin adapting for its own survival rather than the organism's. Species with lower cancer rates tend to show reinforced redundancy at one or more layers, and tumors that progress tend to show measurable signatures of escape. Both patterns hold up across comparative oncology, as the examples that follow will show.
Cellular Cooperation and Defection
I saw the connection I had been circling when I read Athena Aktipis. In The Cheating Cell (2020), Aktipis, a researcher at Arizona State University who studies cancer through an evolutionary lens, frames the problem as cellular cheating: every multicellular organism is a society of cells that have agreed to cooperate, and cancer is what happens when some cells stop holding up their end of the deal. Her insight that the hallmarks of cancer, as defined by Douglas Hanahan and Robert Weinberg, map onto forms of cooperative defection reinforces the three-layer model from a different angle.
The policing problem is visible even in the simplest multicellular organisms. Dictyostelium discoideum, the social amoeba, aggregates into a multicellular slug when food runs scarce. Roughly twenty percent of the cells sacrifice themselves to form a rigid stalk while the rest become spores. Cheater lineages arise that dodge the stalk. Joan Strassmann and David Queller showed that kin recognition through surface proteins allows cells to preferentially aggregate with relatives, policing defection before it spreads. Cooperation requires enforcement, enforcement is costly, and any enforcement system can be defeated by a defector that finds the gap.
Ancient Evidence
Cancer is not a modern disease. Rothschild and colleagues found malignancies concentrated in hadrosaurs, large-bodied herbivores, when they X-rayed over ten thousand dinosaur vertebrae from museum collections. Canine transmissible venereal tumor, CTVT, originated in a single dog, as Elizabeth Murchison at the University of Cambridge traced through genomic analysis in 2014, initially estimating the lineage at roughly eleven thousand years old. Baez-Ortega and colleagues revised that estimate in 2019 to four thousand to eight thousand five hundred years. It has been transmitted between dogs continuously for millennia, accumulating roughly two million mutations. A cancer genome that persists for thousands of years is not broken. It is adapted, having escaped enough host defenses to sustain itself indefinitely.
The framework fits most adult solid tumors and hematologic malignancies best. Childhood cancers often arise from developmental errors rather than cooperative defection; virus-driven cancers represent external sabotage. These exceptions mark the framework's boundaries, not its refutation.
The Devil's Immune System Fights Back
Devil facial tumor disease in Tasmanian devils demonstrates Layer 3 escape directly. The cancer turns down the molecular ID tags (MHC class I) that immune cells use to distinguish self from threat, silencing the genes without deleting them, as Hannah Siddle and colleagues demonstrated in PNAS in 2013. Over time, immune pressure sculpts the tumor population: the cells that survive are precisely the ones that have learned to hide. The result is a cancer that spreads between animals as if the immune system were not there. This is why checkpoint blockade immunotherapy can be so effective in some human cancers: it does not teach the immune system something new, it removes the blinders the tumor evolved to impose.
But the story does not end with escape. In a small proportion of wild devils, the immune system is fighting back. Ruth Pye, David Pemberton, and colleagues documented antibody responses against the tumor and immune-mediated tumor regression in wild populations in 2016. Some infected devils developed visible tumors that then shrank and disappeared without treatment. The researchers detected serum antibodies targeting the cancer cells, suggesting that certain devil immune systems had found a way to see through the tumor's disguise. In 2017, Tovar, Pye, and colleagues went further, showing that immunized devils could mount humoral responses and that some experienced tumor regression after vaccination. Natural selection is rebuilding the surveillance system the cancer learned to defeat, and researchers are now trying to accelerate that process.
How Evolution Solved Peto's Paradox
The elephant story illustrates Layer 1 reinforcement. Joshua Schiffman, a pediatric oncologist at the Huntsman Cancer Institute, had spent years treating children with Li-Fraumeni syndrome, a condition caused by inherited TP53 mutations that produces cancer after cancer across a lifetime. He knew what happened when a single copy of the body's primary damage sensor was missing. Collaborating with Carlo Maley at Arizona State University, his team found that the elephant genome contains at least twenty TP53 copies, including one canonical gene and nineteen retrogene copies, some with evidence of transcription. Humans carry one gene with two copies, one from each parent.
Abegglen placed elephant blood cells and human blood cells side by side in culture dishes and hit them with identical doses of ionizing radiation. Flow cytometry and annexin V staining revealed the contrast starkly: elephant lymphocytes were roughly twice as likely to execute apoptosis as human lymphocytes at equivalent radiation doses. The human cells were attempting repair. Same radiation. Same dose. Same exposure time. Two opposite responses.
Picture what those numbers mean at the cellular level. The human lymphocytes struggling through repair cycles, their genomes patched but imperfect, surviving with errors they will pass to daughter cells. The elephant lymphocytes condensing, membranes blebbing, chromatin compacting, the orderly dismantling of cells that have committed to die rather than risk survival with damaged DNA. The threshold for "kill rather than fix" was set far lower in the elephant cells. That is what twenty copies of a damage sensor buys: a population of cells that would rather die clean than live uncertain.
More copies of the damage sensor. A lower tolerance for risk. Cells that die rather than gamble on repair. The evolutionary logic is specific: repair is inherently uncertain. A cell that has been repaired may carry a patch that weakened one constraint while fixing another. Over time, repaired cells represent a growing population of units that have been tested by damage and survived, exactly the population most likely to contain variants capable of exploiting future control failures. Elephant biology treats damaged cells as unacceptable risks rather than salvageable assets.
But why did human evolution favor repair over elimination? Elephants can afford to burn cells. With vastly more cells to draw from and a reproductive strategy that invests heavily in a few offspring over decades, the cost of destroying a damaged cell and replacing it from reserves is low relative to the risk of keeping it. Humans are smaller. Our cell reserves are proportionally thinner. And across most of human evolutionary history, the cancers that kill in middle and old age exerted weaker selective pressure than the infections and injuries that killed in youth. The selective pressures on human TP53 copy number are not fully understood. The repair bias may not have been selected for. It may simply have never been selected against hard enough.
Most human tumors carry mutations in TP53 or its pathway. In an elephant, those same damaged cells would have been eliminated long before they accumulated enough advantage to form a colony. Human cells, with only one gene and two copies standing guard, are far more likely to attempt repair and continue dividing, carrying forward whatever errors survived the patch. That is not a flaw. It is a trade-off that worked for most of evolutionary history and breaks down in longer lifespans.
Naked mole rats illustrate Layer 2 reinforcement. They live over thirty years, roughly five times the expected lifespan for a rodent of their body size. Rochelle Buffenstein and colleagues have documented extremely low rates of spontaneous neoplasms across decades of study. Their primary defense: high-molecular-mass hyaluronan, a substance in the tissue surrounding cells, enforces contact inhibition so stringent that cells cannot crowd together even if they try. Vera Gorbunova and Andrei Seluanov showed in Nature (2013) that removing hyaluronan made naked mole rat cells susceptible to malignant transformation. The tissue itself enforces a boundary that individual cells cannot override.
Bowhead whales, which can live over two hundred years, suggest a third strategy. When Michael Keane, Joao Pedro de Magalhaes, and colleagues sequenced the bowhead genome in 2015, they found duplications in genes associated with DNA repair and cell-cycle regulation. The implication: rather than killing damaged cells (elephants) or preventing overcrowding (mole rats), whales may reduce errors at the source, making fewer mistakes to begin with across an extraordinarily long lifespan. Functional studies have not yet confirmed this, but the genomic evidence points in a consistent direction.
Different mechanisms, same logic: constrain early, constrain continuously, do not wait for a problem to become visible before responding.
What Breaks in Humans
When I look at my own treatment timeline through this lens, the failure points become specific.
By the time a blood cancer becomes detectable through standard labs, or a solid tumor becomes visible on imaging, Layer 1 checkpoints have already failed, the clone has already adapted, and the evolutionary landscape is already complex. Technologies in liquid biopsy are moving toward earlier detection. Cell-free DNA methylation analysis, one of several emerging approaches, looks for cancer-associated chemical modifications on DNA fragments circulating in the bloodstream. None of these are validated clinical biomarkers yet. But the direction is clear: moving the point of detection upstream, from the tumor to the conditions that produce it. In my case, the upstream signal was already there. The hypogammaglobulinemia flag was not a tumor marker. It was a sign that something had gone wrong in my immune cell populations, the kind of signal that a layer-aware detection system would have caught and acted on. Instead, it was filed and forgotten.
Earlier detection alone is not enough. As H. Gilbert Welch and others have documented, finding cancer sooner can introduce its own harms. Lead-time bias means a diagnosis made earlier extends "survival from diagnosis" without necessarily changing the time of death. Overdiagnosis means detecting lesions that would never have harmed the patient. In some contexts, a molecular signal can precede localizable disease, creating uncertainty about next steps. Earlier detection must be paired with the clinical wisdom to know when and how to act.
A therapy that shrinks a tumor by 90 percent has real value. It can buy years of life and reduce symptoms. But if it selects for a resistant clone that dominates the remaining 10 percent, it has not solved the problem. It has changed the problem into something harder. Restoring immune surveillance (Layer 3), through checkpoint blockade, cellular therapy, or approaches not yet developed, rather than relying solely on cytotoxic killing, follows the logic that selection has favored across deep time: early, redundant constraints. This is not just a metaphor drawn from elephants. Robert Gatenby and colleagues at the Moffitt Cancer Center have tested this principle directly in human trials. In a study of metastatic castration-resistant prostate cancer, Jingsong Zhang and colleagues used adaptive therapy: instead of administering the maximum tolerated dose of abiraterone continuously until the cancer progressed, they adjusted doses based on PSA response, backing off when the tumor shrank and resuming when it rebounded. The logic was evolutionary. Continuous maximum dosing kills sensitive cells and clears the field for resistant ones. Adaptive dosing maintains a population of drug-sensitive cells that compete with resistant ones for resources, using evolution against the cancer rather than ignoring it. Under standard continuous dosing, resistance to abiraterone typically emerges at a median of roughly 16.5 months. In the adaptive therapy pilot, patients cycled on and off treatment based on PSA response, their tumor populations oscillating in a controlled rhythm rather than evolving unchecked toward resistance. Follow-up data showed significantly extended time to progression, with several patients maintaining response far longer than the standard median. The approach treated the cancer not as an enemy to be eradicated but as a population to be managed, its evolutionary dynamics steered rather than ignored.
The three-layer model does not generate predictions that standard oncology cannot. What it does is organize existing knowledge around the failure points that matter: which layer broke, what escaped, and where the remaining leverage is. That reorganization has clinical consequences. It points toward treatment strategies that account for selection, not just cytoreduction.
Coming Back to the Paradox
An elephant cell and a human cell receive the same dose of radiation. Under the microscope, the elephant cell executes apoptosis. The human cell enters repair. That difference, replicated across thousands of cells in a dish, across millions of years of evolutionary time, is the difference between a system that absorbs damage and one that eventually breaks under it.
Elephants and bowhead whales avoid cancer despite enormous size and long lifespans. We understand how: stacked constraints, redundant layers, evolutionary solutions to the same containment problem we face. We can learn from them.
I cannot add nineteen copies of TP53 to my genome. But I can understand which layer failed in my case. The most useful thing I did after diagnosis was ask for the cytogenetics and molecular profile of my specific disease. Not the stage. Not the grade. The molecular identity of what went wrong. That shifted the questions I could ask. Which checkpoint failed (Layer 1)? What does the microenvironment mean for resistance (Layer 2)? What immune-escape features matter, and is immune-mediated therapy an option (Layer 3)?
Understanding which layer failed, what vulnerability it created, and how treatment exploits it did not come from my oncologist's initial protocol. It came from asking for the cytogenetics, learning about my own disease, and becoming my own advocate. It no longer depends on having all the answers. It depends on asking the right questions.
Disclosure
Steve Brown is the CEO and co-founder of CureWise, an AI company helping patients become informed advocates by understanding their lab results, genomic profiles, and treatment options. This post reflects his personal experience and is not medical advice.
References
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