HedonicEscalator
Two exciting recent innovations on pancreatic cancer!
First, the personalized mRNA vaccine autogene cevumeran, developed by BioNTech and Genentech, just reported 6-year follow-up results from their Phase 1 clinical trial. 16 patients were treated, 8 were responders (showed signs of immune reaction to vaccine), 8 were non-responders.
7⁄8 responders (87.5%) survived 6 years after surgery, 2⁄8 nonresponders survived (25%).
AACR meeting notes https://www.aacr.org/blog/2026/04/20/live-updates-from-the-aacr-annual-meeting-2026-monday-april-20/
The most important result in this small trial is that vaccine response is strongly correlated with better outcomes. But for context, the trial was restricted to patients with operable pancreatic cancer. Patients diagnosed with stage 1 or 2 pancreatic cancer have a 5-year survival rate of 12%. Patients who get their pancreatic cancer surgically removed have a 5-year post-surgery survival rate of 20%. This makes the overall 6-year post-surgery survival rate of 56% among the 16 trial patients pretty impressive. Keep in mind that the trial patients may have been healthier than average for other reasons, and small n is small n, so we shouldn’t be too hasty until we see Phase 2 and 3 data.
Source on survival rates https://www.pancreaticcancer.org.uk/information/just-diagnosed-with-pancreatic-cancer/if-you-can-have-surgery-to-remove-the-cancer-early-pancreatic-cancer/prognosis-if-you-can-have-surgery/
Second, a small molecule drug I almost missed in the mRNA hype but arguably even cooler, the tri-complex ras inhibitor (!!!) daraxonrasib, developed by Revolution Medicines. Ras is a protein involved in many cancers, with PDAC (the most common pancreatic cancer) being especially dependent on ras, but it has historically been considered impossible to target due to its chemical properties. Daraxonrasib is, as far as I’m aware, the first drug to target generic forms of ras. It does so with an exotic “tri-complex” strategy involving gluing a different protein, cyclophilin A, to ras in order to disable it. Crazy stuff!
Phase 3 results found that daraxonrasib doubled survival time among patients with metastatic pancreatic cancer, 6.7 months to 13.2 months (p < 0.0001). Side effects are kind of nasty, but “well tolerated, with a manageable safety profile” by advanced cancer standards.
Revolution Medicines announcement https://ir.revmed.com/news-releases/news-release-details/daraxonrasib-demonstrates-unprecedented-overall-survival-benefit
Derek Lowe coverage https://www.science.org/content/blog-post/progress-against-pancreatic-cancer-part-one
Lecture by executive/scientist at Revolution Medicines https://www.youtube.com/watch?v=bU3IwuDJx24
Many of the incredible advancements we’ve made in oncology has been won slowly through another pathway we can target, another drug buying a few more months, another targeted technique to mitigate side effects. Progress is made one step at a time, and these are big steps. I’m excited for the future of biotech.
Nanopore basecalling is a great example of a technology that genuinely depends on deep learning! (It also fits my characterization of problems suitable for deep learning: predicting bases from electric signals is highly complex, and you have massive amounts of labeled training data).
When it comes down to it, “usually preferred” is hard to quantify. What I’m trying to get at is that when I read the methods section of (post-2020) papers in this field, I almost always find that they only mention the use of traditional algorithms, not deep learning ones. Even fewer discoveries are dependent on deep learning. Meanwhile, there are scores of papers floating around enthusiastically presenting DL algorithms which don’t ever see use in the lab. It’s easy to overestimate the role of “AI” by skimming through abstracts.
It looks like DeepConsensus is being used, but it’s not obvious to me how much better it is than traditional methods. Is it more of an incremental improvement useful in specific situations, or more of a paradigm shift no analyst can ignore? I’d love to hear someone working in a related field explain how often deep learning tools are used in practice.
Thanks for the response!
Can you explain to me what your counterfactual vision for “the world if non-sunburn exposure causes cancer” looks like? For example, do you expect that oncologists would track whether melanoma patients are in the subgroup of “white outdoor-working sunscreen-abstainers [without any sunburn history]”, and report those statistics? Please give some specific examples of what data you’d expect to see, and how we’d be able to gather and verify it.
Can you also explain to me what background you have on carcinogenesis? For example, are you familiar with initiators vs. promoters, the two-hit/multi-hit model, common oncogenes and tumor suppressor genes, etc.? It would be helpful to understand what your internal model of how cancer develops looks like currently before I write any more about the topic.
It’s not controversial. I don’t think anyone knowledgeable in oncology would dispute that sub-sunburn UV exposure significantly increases risk of skin cancer. My aim here was to explain why we can be confident of this belief in as straightforward of a way as I can.
I haven’t looked at your links in detail, but the first and second cover the same ground as the first and second points I made (observable DNA damage + cancer from indoor tanning). As for the third, I wanted to avoid resting any points solely on cohort studies because I didn’t want to get into too much of the controversy there. Though I ended up getting pulled into it on Reddit anyways.
Of course that cuts both ways, since if it were a pure win to be protected from the sun, we would all be dark skinned.
Not necessarily.
We know that humans need sunlight for optimal Vitamin D production, so this does happen to be true. But your logic here doesn’t hold up. Synthesizing melanin is metabolically costly. Cave creatures, which have no use for pigmentation but also incur no obvious direct disadvantages from it, often evolve albinism because of the benefits of repurposing melanin precursors for other needs.
Humans aren’t cavefish, but the principle applies. If you don’t need melanin, making it is a waste of energy, and benefits from sunlight aren’t necessary to explain the evolutionary pressure against pigmentation.
Cohort studies claiming benefits from UV exposure are not credible. It is impossible to separate UV exposure from positive traits correlated with UV exposure, such as exercising or having a social life, and no, you can’t just control for it.
It is true that your risk of dying from melanoma is low, and not worth freaking out too much about.
You should do a blinded test of your sun exposure with identical placebo sunscreen to see if the UV exposure itself is necessary for positive effects on your mood, for science.
IQ is normalized to mean 100, standard deviation 15 from a sample population of test takers, usually matched by age. The mean is set to 100, by design.
Technically, since widely used norming populations are usually drawn from developed countries and exclude people with severe disabilities, the “actual” mean IQ, if you were to test everyone in the world, is lower than 100.
The AI angle, since many people are hyping the “AI for pancreatic cancer” line:
Yes, deep learning is used in the development of the mRNA vaccine. NetMHCpan, a small neural network, is used to help select immunogenic neoantigens (choose the most promising mutant proteins to target) for autogene cevumeran.
Autogene cevumeran paper https://www.nature.com/articles/s41591-024-03334-7
Most of the computational pipeline detailed in the paper consists of traditional tools, in line with what I’ve written about AI and personalized mRNA vaccines before. Deep learning is extremely useful for some things, but it should be understood as a specialized tool, not a silver bullet, for now.
AI & mRNA blog https://hedonicescalator.substack.com/p/did-paul-conyngham-really-use-ai
I’m not sure if deep learning was used in the development of daraxonrasib. A brief glance at the paper and previous work shows plenty of references to traditional computational tools, but nothing that stands out to me as modern DL.
Daraxonrasib paper https://pubs.acs.org/doi/full/10.1021/acs.jmedchem.4c02314
Previous work https://pmc.ncbi.nlm.nih.gov/articles/PMC10474815/
The company behind daraxonrasib, Revolution Medicines, is quite enthusiastic about ML. They recently made a deal with AI drug discovery platform Iambic Therapeutics. I don’t doubt Iambic’s tools will soon prove useful, but it’s fair to say “deep learning for drug discovery” is still in the early stages of development.
Iambic announcement http://iambic.ai/post/revolution-medicines-collaboration