Cancer kills only the confused.
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Cancer kills only the confused.

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DEEPWAVE

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Made with 💙 by me (how to).

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Status: Draft Epistemic status: Hypothesis

I was very surprised when reading Alberts Molecular Biology of the Cell that “The Changes in Tumor Cells That Lead to Metastasis [cancer spreading through the body] Are Still Largely a Mystery”. This is my attempt at an answer, plus some thoughts on what cancer even is.

Metastasis can only happen at Phase Boundaries

To understand cancer, I think you have to ask: why do young people get less of it? Why is cancer something that occurs at all times in most species, but is effectively only lethal to aged animals?

There are two standard answers to this: either cancer is caused by combining certain mutations (a lottery), and that just gets more likely with time (you draw a ticket every day, eventually you get unlucky), or because our immune system, which keeps cancer in check, gets weaker as we age. These two certainly play a role, but I want to propose a more fundamental theory: for cancer to spread (metastasize, which ultimately kills you) it needs to find areas where new cancer can form. Animals that don’t die from cancer (young people, naked mole rats, plants, etc.) don’t control the arising of cancer, but they effectively make it impossible for cancer to spread. The way they do this is by minimizing the phase boundaries between separate cellular communication collectives, where the goal state of the local environment (the “phase identity”) is less clearly defined. As we age, these areas increase, as intercellular signaling degrades, and we get lethal forms of cancer.

An Antropomorphized Intro to Cancer

Let's unpack this hypothesis. First, what IS cancer? In the simplest worldview, cancer is just "a change in the level at which evolutionary pressure dominates". (For more on this, see the interview with Athena Aktipis on why cancer is a fundamental phenomena in our universe or her book) When I'm healthy, evolution works on me, all my cells sacrifice each other (literally. There are immune cells which throw out their DNA as nets to trap invaders, the most badass seppuku imaginable), for the greater good (me). But sometimes, cells stop sharing that goal and become rogue, selfish, individuals. Cancer is rebellion.

Now this usually doesn't pose a problem, our internal military (immune system) squashes down rebellions all the time. But more importantly, our cells talk to each other constantly, indoctrinating, controlling, checking in with their neighbors, and justifying each and every action to the collective. If any cell notices they are stepping out of line, they rather commit suicide (apoptosis) than harm the great overlord (the germline, me). This explains why, in my opinion, the most important oncogenes (a mutation that makes cancer more likely) are all related to gap junctions, see Bioelectric signaling: Reprogrammable circuits underlying embryogenesis, regeneration, and cancer: Cell. Gap junctions are connections between cells; tey allow them to exchange their cell plasma, their internals, and therefore all their inputs, beliefs, decisions, and outputs. They exchange their selves. Now if you and your partner both had perfect, constant, involuntary mind-reading, would it even make sense to talk about two different you's?

This mechanism - of sharing goals between large collections of cells through gap junctions (and exosomes) - is what keeps you alive, and rebellions at bay. But sometimes, though unfortunate accidents, cells not only acquire mutations that make them dangerous (want to spread their ideas, acquire resources, don't want to die, etc. all the classical convergent instrumental goals of misaligned optimizers), they also lose their gap junctions, their safety mechanism. You have cancer.

Why cancer isn’t bad - at first

What happens if you get a collection of cells not wanting to be you anymore? They revert back to the single cell behavioral program, increasing growth rates (removing growth cycle check-points) evolvability (removing repair machinery), even switching to a faster, but less efficient metabolism (Warburg Effect). But your body is remarkably resilient to this. Unless the cancer occurs in a critical system like the central nervous system or the bone marrow, only 10 to 30% of solid tumors kill you without first metastasizing - essentially overwhelming the whole system. This makes the study of why cancer metastasize more often with age the most important question when it comes to solving cancer.

Becoming an invasive species

First, it’s important to note that cancer wants to metastasize - making backup copies of yourself is a good strategy (or rather those who don’t don’t survive).

But how could you do that, from the point of cancer? To you everything around you is a hostile environment, you’re lucky that you’ve found a survivable niche in the first place. But how do you find a new one?

This is essentially the question that all species face: how do you find new environmental niches that have the right conditions? Evolution solved came up with two solutions: taking the bus (seeds, parasites) or walking there themselves (flagella, muscles).

I don’t know if anyone has found a cancer that evolved their own motion apparatus (I could see that happen in plants or organisms without a circulatory system, which would be very fascinating). But we know a lot of cancers who just “drop their children into the wind”, aka. the blood stream.

Interestingly, even that is not enough: many cancers are dividing themselves into the bloodstream - and still there is no new cancer:

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Three hypotheses offer themselves:

  1. The cancer is not “strong” enough: Body niches are so diverse that it would require additional mutations for cancer to be able to survive in more than the original place. Older cancers have a higher chance to mutate strong enough cancers.
  2. The immune system and kidneys filter out all invaders before it’s too late: it’s easier for cells to hide from the immune system in their original place than in a foreign environment.
    1. Older organism have a worse immune system because the immune cells themself are old
    2. Older organisms have a harder time teaching the immune system what is cancer and what not. Invasion becomes easier because it’s easier to hide.
  3. Older organisms have more niches: if the number of niches increases with age, than the chances of finding one that fits increases. Invasion is easier if there are more places to hide.

Given that we have found no link between genes and ability to metastasize, we can probably rule out 1. 2a has been explored widely - and led to approaches like CAR T-cell therapy. But I think 2b and 3 are the ones that will finally allow us to make significant progress when it comes to preventing metastasis.

Both of them make the case that what’s worse in older individuals is not the cancer or the immune system changing, but your self is changing. If the opposite of cancer is … you, then a changing you could be to blame.

So how do you change with age?

What I mean with Phase Boundaries

There happens to be a really fascinating phenomena when you couple cells (or really anything) together. If you assume just one thing, namely that the strength of a connection depends on the similarity between neighbors (what wires together fires together) you automatically get “phases”, “transitions” and “boundaries”. This is a really fundamental thing to understand: Start with a collection of connected things. Invent one property they all have in common, but with different values, like “political tribe”. Start by randomly assigning each one a value. Now let them look at their neighbor to change their position, but only have them update, if they already agree with that position: you get clusters. You get tightly coupled regions, that all have the same opinion. (you can do some math to change the average size of the region, but you’ll always get regions).

This is critical to understand for biological systems, because it turns out there is at least one such property: voltage membrane potential (think of it like how easy it is for charged particles to enter or leave a cell). VmemV_{mem}, how it’s called, is influenced by gap junctions - connected cells have a similar value - but also the strength of the gap junction is determined by how similar VmemV_{mem} was in the first place. For a fascinating and highly important paper, that explores this in detail see:

But the details don’t really matter, there is probably more than bioelectricity that has this - after all, I think this is the mechanism by which multicellular organisms create compartments. If you take an undifferentiated (not yet specialized cell), it looks at its surroundings - and then specializes accordingly. But its surroundings are exactly this: a set of values that are regionally very different, but highly uniform in each and every one of them.

Now if you have phases, you also have boundaries: regions in which cellular connections are in an either-or state, undecided, confused. It requires comparatively little effort to “switch” these: if they are pushed just slightly in one direction, they fall into a new equilibrium. Think of these regions as countries with swing states: if you get only a tiny fraction of them, you get the whole.

On Cancer:

On Misalignment:

Why Would AI Want to do Bad Things? Instrumental Convergence

How can we predict that AGI with unknown goals would behave badly by default? The Orthogonality Thesis video: https://www.youtube.com/watch?v=hEUO6pjwFOo Instrumental Convergence: https://arbital.com/p/instrumental_convergence/ Omohundro 2008, Basic AI Drives: https://selfawaresystems.files.wordpress.com/2008/01/ai_drives_final.pdf With thanks to my excellent Patrons at https://www.patreon.com/robertskmiles : Jason Hise Steef Jason Strack Chad Jones Stefan Skiles Jordan Medina Manuel Weichselbaum 1RV34 Scott Worley JJ Hepboin Alex Flint James McCuen Richárd Nagyfi Ville Ahlgren Alec Johnson Simon Strandgaard Joshua Richardson Jonatan R Michael Greve The Guru Of Vision Fabrizio Pisani Alexander Hartvig Nielsen Volodymyr David Tjäder Paul Mason Ben Scanlon Julius Brash Mike Bird Tom O'Connor Gunnar Guðvarðarson Shevis Johnson Erik de Bruijn Robin Green Alexei Vasilkov Maksym Taran Laura Olds Jon Halliday Robert Werner Paul Hobbs Jeroen De Dauw Konsta William Hendley DGJono robertvanduursen Scott Stevens Michael Ore Dmitri Afanasjev Brian Sandberg Einar Ueland Marcel Ward Andrew Weir Taylor Smith Ben Archer Scott McCarthy Kabs Kabs Phil Tendayi Mawushe Gabriel Behm Anne Kohlbrenner Jake Fish Bjorn Nyblad Jussi Männistö Mr Fantastic Matanya Loewenthal Wr4thon Dave Tapley Archy de Berker Kevin Vincent Sanders Marc Pauly Andy Kobre Brian Gillespie Martin Wind Peggy Youell Poker Chen Kees Darko Sperac Paul Moffat Noel Kocheril Jelle Langen Lars Scholz

Why Would AI Want to do Bad Things? Instrumental Convergence

On Phase Boundaries:

Brain Criticality - Optimizing Neural Computations

To try everything Brilliant has to offer—free—for a full 30 days, visit http://brilliant.org/ArtemKirsanov/. The first 200 of you will get 20% off Brilliant’s annual premium subscription. My name is Artem, I'm a computational neuroscience student and researcher. In this video we talk about the concept of critical point – how the brain might optimize information processing by hovering near a phase transition. Patreon: https://www.patreon.com/artemkirsanov Twitter: https://twitter.com/ArtemKRSV OUTLINE: 00:00 Introduction 01:11 - Phase transitions in nature 05:05 - The Ising Model 09:33 - Correlation length and long-range communication 13:14 - Scale-free properties and power laws 20:20 - Neuronal avalanches 25:00 - The branching model 31:05 - Optimizing information transmission 34:06 - Brilliant.org 35:41 - Recap and outro The book: https://mitpress.mit.edu/9780262544030/the-cortex-and-the-critical-point/ REFERENCES (in no particular order): 1. Zimmern, V. Why Brain Criticality Is Clinically Relevant: A Scoping Review. Front. Neural Circuits 14, 54 (2020). 2. Beggs, J. M. The criticality hypothesis: how local cortical networks might optimize information processing. Phil. Trans. R. Soc. A. 366, 329–343 (2008). 3. Beggs, J. M. The cortex and the critical point: understanding the power of emergence. (The MIT Press, 2022). 4. Heffern, E. F. W., Huelskamp, H., Bahar, S. & Inglis, R. F. Phase transitions in biology: from bird flocks to population dynamics. Proc. R. Soc. B. 288, 20211111 (2021). 5. Beggs, J. M. & Plenz, D. Neuronal Avalanches in Neocortical Circuits. J. Neurosci. 23, 11167–11177 (2003). 6. Avramiea, A.-E., Masood, A., Mansvelder, H. D. & Linkenkaer-Hansen, K. Long-Range Amplitude Coupling Is Optimized for Brain Networks That Function at Criticality. J. Neurosci. 42, 2221–2233 (2022). 7. O’Byrne, J. & Jerbi, K. How critical is brain criticality? Trends in Neurosciences 45, 820–837 (2022). 8. Haldeman, C. & Beggs, J. M. Critical Branching Captures Activity in Living Neural Networks and Maximizes the Number of Metastable States. Phys. Rev. Lett. 94, 058101 (2005). 9. Beggs, J. M. Being critical of criticality in the brain. Frontiers in Physiology. Derivation that only power laws are scale-free: https://youtu.be/m6FQqXAHNT8 CREDITS: Icons by https://biorender.com Brain 3D models were modeled with Blender software using publicly available BrainGlobe atlases (https://brainglobe.info/atlas-api) Ising model zooming animations: https://youtu.be/MxRddFrEnPc This video was sponsored by Brilliant

Brain Criticality - Optimizing Neural Computations

Citation

In academic work, please cite this essay as:

Groß, Heye, “Metastasis can only happen at Phase Boundaries”, heye.earth (2023-11-15), available at https://heye.earth/projects/cancer-kills-only-the-confused