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Comment by travisoneill1 | Hacker News Reader

> Systemic approaches are important, but we need to consider that the system of cancer goes far beyond the biochemistry of cancer cells. Cancer relates to our fundamental constitution as multicellular organisms, our limited lifespan, the epidemiology of the aging population, socioeconomics and the future of society. Those who believe that the problem of cancer can be solved by killing or reprogramming cancer cells need to take a step back from the molecular technicalities and take a look at the bigger picture

He knows that this approach won't work before it has been attempted? I'll listen to criticism when it is followed by a suggestion for a better approach. And vacuous statements like "step back and look a the bigger picture" don't count.

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To clarify, that quote is not something the journalist wrote. It's the opinion of Jarle Brivik [1]. It is also a statement that was made in the context of a few current moonshots aimed at solving cancer, and is referring specifically to concrete issues Brivik has with the paradigms underpinning those concrete attempts.

In particular, the quote is not meant as some sort of claim about the metaphysics of cancer or anything ridiculous like that. Rather, it's a (perhaps not so eloquently stated) way of pointing out that the mathematical and biological models underlying the current (and again, concrete) moonshot attempts fail to account for certain important issues and that this strongly suggests that those models are not going to result in cures.

The rest of the article summarizes some of Brivik's warrants, which are well thought-out. The claim is that the models currently used are fundamentally incapable of explaining how certain environmental factors effect cancer and the rest of the body, and that these environmental factors are important for understanding how to "solve" cancer because cancer evolves over time in often unexpected ways. The article points out, for example, that there are modeling assumptions baked into Microsoft's approach that are empirically falsifiable.

Finally, I'll just re-iterate that the argument made in the article is made in the context of current proposed/ongoing moonshots, so various comments about nanobots at the like are totally off-topic.

The article is not about science fiction. The topic is not whether maybe one day in the far future we might have enough knowledge about cancer cells to take a purely mechanistic approach toward curing cancer. Whether, the article is about the allocation problem in real science, today, and makes an argument against the efficacy of the approach taken by some current, concrete, well-funded moonshots.

[1] https://www.med.uio.no/imb/english/people/aca/jbreivik/

The ability to actually reprogram cancer cells relates to...

>our fundamental constitution as multicellular organisms, our limited lifespan, the epidemiology of the aging population, socioeconomics and the future of society.

Like, hey man, you do realize that the killing or reprogramming of cancer cells goes far beyond the molecular technicalities of actually doing it?

Yup. It’s a pretty vacuous critism.

Medical technology is lacking. With sequencing, we have a very hard-to-use, slow and readonly hexedit. With crispr, we have a very buggy and feature incomplete sed -- more like ed actually.

It cool to have some sed, but we may not even need that. We just need the equivalent of basic modern unix tools, like find, grep and rm, that work reliably, with good specificity and sensitivity.

Yet basic unix tools in medicine are decades away. Maybe it will be nanotechnology, maybe it will be more hacking around current solution.

Still, the problem is just:

find /body -type cancer_12BA |grep -v cancer_12BA_market_but_usefull_stemcell |xargs rm

The solution is technological, not philosophical.

Ya making a transform to go between the physical abstractions in biology/chemistry and the data abstractions in computer science should be top priority.

Frankly, I don't see how biochemists today could possibly keep up with the advancements in AI and big data that have happened in just the last few years (and vice versa).

I'd like to see everyone take a step back and look at treating cancer as solving a large matrix where no single cause leads to an outcome. We need genetic algorithms to quickly find local maximums and minimums, we need machine learning to see correlations between seemingly unrelated findings, we need better standards for getting longterm studies into formats that are easily grokked by AI.

These are all straightforward, but I sense that there may be some resistance from the medical community in opening up the research similar to something like Folding@home. And in fairness, medical researchers are doing the heavy lifting. The first step probably has to come from hackers in the data science community, working with existing public research, and they're going to need real funding (which may not come until after the first of many cures is spotted).

Your crispr = sed analogy is apt because DNA is basically code.

Cells, however, are not files, so I would be careful extending the analogy. There is no "find" command for the same reason you can't run "find" on your physical bookshelf--objects in 3d space are not indexable files on a hard drive.

Perhaps we'll eventually have microscopic robots that can search out specific cells, in the same way Amazon uses robots to retrieve items in their warehouses. But that's a very different problem space, where the focus is largely mechanical rather than programmatic.

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