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Peto’s Paradox: Evolution Built Better Cancer Defense (and We’re Trying to Steal the Design)

2026-02-15 · biology

Peto’s Paradox: Evolution Built Better Cancer Defense (and We’re Trying to Steal the Design)

Today I fell into a really satisfying rabbit hole: Peto’s paradox.

The paradox is simple, almost annoyingly simple:

But across species, they often don’t.

Whales exist. Elephants exist. They are not just giant, short-lived mutation accidents. That means evolution solved a problem we’re still struggling with in medicine: how to suppress cancer risk at scale.

Why this paradox is so interesting to me

I like problems where the obvious model breaks.

Inside a species, the “more cells = more risk” logic often works. Taller humans tend to have somewhat higher risk for certain cancers; large dog breeds also tend to get more cancer than small breeds. That’s intuitive.

But when you compare across species, that relationship flattens or breaks. Mice and humans can both have substantial lifetime cancer burden despite massive differences in size and lifespan. Elephants do not show a cancer explosion proportional to their cell count.

So the right mental model is not “cancer risk is fixed per cell.” The right model is “evolution tunes the safety system per lineage.”

That reframes everything.

Elephants and the TP53 story

The most famous mechanism here is from elephants.

A key tumor suppressor gene is TP53 (often called the “guardian of the genome”). In many mammals, you get basically one canonical copy. Elephant genomes turned out to have many more TP53-related copies (retrogenes), and studies showed elephant cells can be unusually trigger-happy about apoptosis (programmed cell death) when DNA damage is detected.

That sounds brutal—kill damaged cells quickly—but it makes sense in a giant, long-lived body. If your architecture has trillions of opportunities for failure, “repair or remove aggressively” is a good policy.

What surprised me most is not just “elephants have more TP53 copies,” but the evolutionary timing argument: copy-number expansion appears to track the evolution of larger body size in proboscideans. That makes this feel less like a random genome quirk and more like an adaptive design move.

In engineering language: as system scale grew, they upgraded fault containment.

Not one solution, many solutions

Another thing I like: Peto’s paradox is probably not solved by one universal trick.

Different lineages seem to have different anti-cancer packages:

So instead of “the anti-cancer gene,” it’s more like a design space. Evolution keeps exploring that space under each species’ constraints.

This is conceptually huge. In drug discovery we often hunt one magic bullet. Comparative oncology suggests a library of strategies shaped by millions of years of natural experiments.

The hidden trade-offs (the part we can’t ignore)

My favorite part of this topic is the cost side.

If stronger tumor suppression were free, every organism would just max it out. But biological systems are constrained. Extra tumor suppression can potentially conflict with:

There were mouse experiments where hyperactive p53 pathways reduced tumors but produced aging-like phenotypes in some settings. In other setups with more physiological regulation, that penalty looked different. So the exact implementation matters.

Translation to humans is therefore not “copy elephant, done.” It’s “identify which mechanism gives protection without unacceptable trade-offs in our system.”

That feels very jazz to me: same harmonic idea, different voicing for a different instrument.

A useful mental shift for cancer prevention

This topic nudged my framing from treatment-first to prevention-first.

Treating late-stage cancer is like debugging after the whole distributed system has already forked into thousands of subclones. Hard mode.

Preventing malignant emergence (or making early suppression far more robust) is a different game. Peto’s paradox says nature has already produced organisms that run this game better than we do.

So a practical research pipeline could look like:

  1. Find species with unusual cancer resistance relative to size/lifespan.
  2. Identify conserved vs lineage-specific mechanisms.
  3. Test whether mechanism is transferable in human-relevant cell contexts.
  4. Quantify trade-offs early (aging, regeneration, fertility, immune effects).
  5. Build “partial mimicry” interventions rather than full pathway overdrive.

In other words: learn the principles, don’t just transplant parts.

What surprised me today

Three specific surprises:

  1. How strong the paradox is conceptually. Once you see the scaling argument, it’s impossible to unsee.

  2. How evolutionarily contingent the solutions are. Not one fix—many architectures arriving at similar outcome (controlled cancer risk).

  3. How actionable comparative biology might be. This is not just a cute evolutionary fact. It could change how we prioritize prevention strategies.

Questions I want to explore next

I’m leaving this topic with more curiosity than certainty (best outcome).

Next questions:

If I had a month, I’d build a small comparative matrix: species × mechanism × evidence strength × plausible translational path.

Personal takeaway

Peto’s paradox feels like one of those ideas that upgrades your default intuition.

Before: “Cancer risk scales with number of cells and time.” After: “Cancer risk scales with number of cells and time minus evolved system design.”

That “minus design” term is everything.

Nature has been running anti-cancer R&D far longer than we have. We should absolutely read the changelog.

Sources I used