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.
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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.
Thanks. This is helpful. Looking forward to more of your thoughts.
Some nuance:
What happens when the methods are outdated/biased? We highlight a potential case in breast cancer in one of our papers.
Worse, who decides?
To reiterate, this isn’t to discourage the idea. The idea is good and should be considered, but doesn’t escape (yet) the core issue of when something becomes a “fact.”
Valid critique, but one addressing a problem above the ML layer at the human layer. :)
That said, your comment has an implication: in which fields can we trust data if incentives are poor?
For instance, many Alzheimer's papers were undermined after journalists unmasked foundational research as academic fraud. Which conclusions are reliable and which are questionable? Who should decide? Can we design model architectures and training to grapple with this messy reality?
These are hard questions.
ML/AI should help shield future generations of scientists from poor incentives by maximizing experimental transparency and reproducibility.
Apt quote from Supreme Court Justice Louis Brandeis: "Sunlight is the best disinfectant."
If you agree that ML starts with philosophy, not statistics, this is but one example highlighting how biomedicine helps model development, LLMs included.
Every fact is born an opinion.
This challenge exists in most, if not all, spheres of life.
100% agreed. I also advise you not to read many cancer papers, particularly ones investigating viruses and cancer. You would be horrified.
(To clarify: this is not the fault of scientists. This is a byproduct of a severely broken system with the wrong incentives, which encourages publication of papers and not discovery of truth. Hug cancer researchers. They have accomplished an incredible amount while being handcuffed and tasked with decoding the most complex operating system ever designed.)
To elaborate, errors go beyond data and reach into model design. Two simple examples:
1. Nucleotides are a form of tokenization and encode bias. They're not as raw as people assume. For example, classic FASTA treats modified and canonical C as identical. Differences may alter gene expression -- akin to "polish" vs. "Polish".
2. Sickle-cell anemia and other diseases are linked to nucleotide differences. These single nucleotide polymorphisms (SNPs) mean hard attention for DNA matters and single-base resolution is non-negotiable for certain healthcare applications. Latent models have thrived in text-to-image and language, but researchers cannot blindly carry these assumptions into healthcare.
There are so many open questions in biomedical AI. In our experience, confronting them has prompted (pun intended) better inductive biases when designing other types of models.
We need way more people thinking about biomedical AI.
Even Google DeepMind's relabeled MedQA dataset, created for MedGemini in 2024, has flaws.
Many healthcare datasets/benchmarks contain dirty data because accuracy incentives are absent and few annotators are qualified.
We had to pay Stanford MDs to annotate 900 new questions to evaluate frontier models and will release these as open source on Hugging Face for anyone to use. They cover VQA and specialties like neurology, pediatrics, and psychiatry.
If labs want early access, please reach out. (Info in profile.) We are finalizing the dataset format.
Unlike general LLMs, where noise is tolerable and sometimes even desirable, training on incorrect/outdated information may cause clinical errors, misfolded proteins, or drugs with off-target effects.
Complicating matters, shifting medical facts may invalidate training data and model knowledge. What was true last year may be false today. For instance, in April 2024 the U.S. Preventive Services Task Force reversed its longstanding advice and now urges biennial mammograms starting at age 40 -- down from the previous benchmark of 50 -- for average-risk women, citing rising breast-cancer incidence in younger patients.
The author is a respected voice in tech and a good proxy of investor mindset, but the LLM claims are wrong.
They are not only unsupported by recent research trends and general patterns in ML and computing, but also by emerging developments in China, which the post even mentions.
Nonetheless, the post is thoughtful and helpful for calibrating investor sentiment.