Great question. The bar for proof in biomedicine is naturally high. I only shared facts because so much is unknown.
If you can find a lab exploring the question, maybe you can support them by helping to raise money for experiments.
As a fun intellectual exercise, dive into the topic and challenge yourself to think about what kind of experiments could shed more light on the subject.
I will aim to put together a Cancer 101 for engineers, not sure how to share. Maybe I'll post here or will post to our biomedical GitHub so it can evolve over time?
For people questioning why to involve GPT and AI assistants:
GPT and AI assistants cannot be fully trusted, but they can personalize learning.
The chief challenge for the framework/handbook will be resolving how to personalize guidance into cancer research while grounding knowledge in trustworthy sources.
For instance, the framework will anchor abstract, dry biological concepts in personally meaningful tracks. Imagine someone you care about is battling lung cancer — the framework may orient learning around the molecular drivers and signaling pathways at play, or perhaps how to explore the treatment landscape while respecting established practices. If you're fortunate enough to not know someone affected by cancer, GPT can help find a personal angle.
The sheer depth of information is staggering. People devote entire careers to niche specialities, and these experts still don't know everything in their niche because our understanding of human biology and disease is constantly evolving. Adapting depth should also depend on the individual and can only be achieved via AI. Static curriculums do not maximize learning in 2026.
On second thought, I will publish something regardless of interest.
It will be an "Cancer for Engineers" framework, delivered via free, open-source Custom GPTs and Claude Skills. (Gemini gems are less reliable in our experience.)
The goal: to ease engineers into cancer via AI personalized introductory curriculums with varying time commitments to enable deeper independent investigation or fast exits if interest wanes: 4 hours, 8 hours, 12 hours.
Basically 1-3 hours per week for a month.
The reason I think some engineers may find cancer interesting, aside from the societal impact:
The human body is like a complex operating system. Cancer is a severe runtime error. Tracing root causes -- like genetic mutations, signaling errors, or immune evasion -- has many parallels to diagnosing system failures.
BTW if anyone from Kaggle/GDM is reading this, we are having issues submitting a benchmark paper for NeurIPS based on the Kaggle Benchmark.
Google models seem to get a different scheduling priority, ironically, enough and take >20 hours to complete a benchmark task that other models like Opus 4.6 finish in <1 hour -- same code path, same task. Would love help if possible since the abstract deadline is Monday (It's last minute because we didn't originally plan to submit this, but someone suggested it.)
Here are more fascinating facts about caffeine and cancer.
Caffeine affects the immune system via at least two opposing mechanisms.
Mechanism 1: A2A receptor antagonism (immunostimulatory)
Tumors and damaged tissues release adenosine, which engages the A2A receptor on immune cells and signals them to stand down. Caffeine antagonizes (i.e., blocks) this receptor.
Mechanism 2: Raising intracellular cAMP (immunosuppressive)
Caffeine also inhibits phosphodiesterase, the enzyme that hydrolyzes (i.e., breaks down) cAMP. cAMP accumulates inside immune cells, which acts as a "calm down" signal.
Note: both mechanisms are dose-dependent. At dietary caffeine levels, A2A antagonism likely dominates, whereas PDE inhibition is weak and mainly relevant at higher concentrations. However, the net immune effect in the tumor microenvironment remains unproven.
---
If you would like to learn more, I can outline a framework for technical folks to ease in and become more informed on cancer. Gaps abound. The more people who understand cancer, the faster we get to cures. Moreover, personalized cancer treatment is the obvious future. Knowledge acquired now may pay off later (but hopefully not needed).
If you're a wealthy person lacking a neurobiology background, how do you decide which research efforts are the most promising? Which labs do you back?
Generally, you rely on experts.
Who typically became experts by adhering to the conventional wisdom set by gatekeepers.
"Science advances one funeral at a time" feels apt.
Sadly, the problem isn't confined to Alzheimer's.
Whenever only a few people decide what is "right," the same pattern of stifled innovation will generally manifest itself not by design or from malice, but because it's hard for a small group to be 100% right on what works and what doesn't -- especially on matters as inscrutable as neuroimmune diseases.
VCs are soccer stars, but founders play basketball.
It’s easy to dunk on VCs, but the herd effect is rational after considering the typical VC’s background, the intense competition for good deals, and the job requirements — to prudently deploy capital.
Who wants to pitch their boss on investing $1-10M in a product no one uses, built by a team of anons?
This is not to defend the process, but merely explain it. It’s not so different from customer marketing. To win a VC, first understand the VC.
Once hired, VCs cannot easily get fired yet they exert immense strategic control.
Nonetheless, many founders interview summer interns harder than VCs.
Heuristic: after removing capital, would you hire the VC to be your boss?
Great VCs are worth the equity and will turbocharge startups. When you find one, don't haggle. Get a fair deal, and get right back to coding.
Bad VCs will destroy companies the same way soccer stars would destroy basketball teams if made the head coach.
The association between pathogens and cancer is under-appreciated, mostly due to limitations in detection methods.
For instance, it is not uncommon for cancer studies to design assays around non-oncogenic strains, or for assays to use primer sequences with binding sites mismatched to a large number of NCBI GenBank genomes.
Another example: studies relying on The Cancer Genome Atlas (TCGA), which is a rich database for cancer investigations. However, the TCGA made a deliberate tradeoff to standardize quantification of eukaryotic coding transcripts but at the cost of excluding non-poly(A) transcripts like EBER1/2 and other viral non-coding RNAs -- thus potentially understating viral presence.
A more accurate title: "Are Cornell Students Meritocratic and Efficiency-Seeking? Evidence from 271 MBA students and 67 Undergraduate Business Students."
This topic is important and the study interesting, but the methods exhibit the same generalizability bias as the famous Dunning-Kruger study.
The referenced MBA students -- and by extension, the elites -- only reflect 271 students across two years, all from the same university.
By analyzing biased samples, we risk misguided discourse on a sensitive subject.
More like alarming anecdote. :) Google did a wonderful job relabeling MedQA, a core benchmark, but even they missed some (e.g., question 448 in the test set remains wrong according to Stanford doctors).
For ML, start with MedGemma. It's a great family. 4B is tiny and easy to experiment with. Pick an area and try finetuning.
Note the new image encoder, MedSigLIP, which leverages another cool Google model, SigLIP. It's unclear if MedSigLIP is the right approach (open question!), but it's innovative and worth studying for newcomers. Follow Lucas Beyer, SigLIP's senior author and now at Meta. He'll drop tons of computer vision knowledge (and entertaining takes).
For bio, read 10 papers in a domain of passion (e.g., lung cancer). If you (or AI) can't find one biased/outdated assumption or method, I'll gift a $20 Starbucks gift card. (Ping on Twitter.) This matters because data is downstream of study design, and of course models are downstream of data.
Thanks, but no one truly understands biomedicine, let alone biomedical ML.
Feynman's quote -- "A scientist is never certain" -- is apt for biomedical ML.
Context: imagine the human body as the most devilish operating system ever: 10b+ lines of code (more than merely genomics), tight coupling everywhere, zero comments. Oh, and one faulty line may cause death.
Are you more interested in data, ML, or biology (e.g., predicting cancerous mutations or drug toxicology)?
Biomedical data underlies everything and may be the easiest starting point because it's so bad/limited.
We had to pay Stanford doctors to annotate QA questions because existing datasets were so unreliable. (MCQ dataset partially released, full release coming).
For ML, MedGemma from Google DeepMind is open and at the frontier.
Biology mostly requires publishing, but still there are ways to help.
After sharing preferences, I can offer a more targeted path.
Agreed. There is deep potential for ML in healthcare. We need more contributors advancing research in this space. One opportunity as people look around: many priors merit reconsideration.
For instance, genomic data that may seem identical may not actually be identical. In classic biological representations (FASTA), canonical cytosine and methylated cytosine are both collapsed into the letter "C" even though differences may spur differential gene expression.
What's the optimal tokenization algorithm and architecture for genomic models? How about protein binding prediction? Unclear!
There are so many open questions in biomedical ML.
The openness-impact ratio is arguably as high in biomedicine as anywhere else: if you help answer some of these questions, you could save lives.
Hopefully, awesome frameworks like this lower barriers and attract more people.