This administration's FDA might be exceptional in its approach, and I would be more cautious in extrapolating from its decisions. The more conventional one likely to succeed it will almost certainly revert to its usual function of consigning untold millions to the invisible graveyard. For "safety," of course.
Fascinating essay. This really resonates with me and what we are trying to tackle at BeLiver.
I really like the idea of learning the joint distribution of these binary biomarkers. Though I guess removing the threshold could unlock more signal, even if it means more noise.
You've cited OncotypeDX, and I believe this is the pragmatic approach that has the potential to improve the standard of care, adding real clinical value for patients today.
Even simplistic ML models fed with the right kind of data can have strong predictive power and still unlock biological understanding and interpretability. I have doubts about images being the right kind of data for those cases.
And moving forward, I really hope foundational models will be able to add this missing inductive prior knowledge that can have an even stronger impact in areas such as precision oncology.
Ha you did the thing the original essay warns against of forgetting that fields besides your own have a surprising amount of detail! For “antibiotics for bacterial pneumonia” consider that viral and bacterial pneumonia themselves present similarly and it was a huge deal to understand the difference. Then notice that gram positive vs gram negative vs mycobacterial infections require different antibiotics, and even different bacterial species or subspecies have different resistance spectra, plus invisible-to-all-staining differences in beta lactamase expression change everything again. For chronic lung infections, you can do susceptibility testing with cultured clinical isolates but this predicts response surprisingly badly, maybe because of bacterial genetic variation within a lung, maybe because biofilm phenotypes alter metabolism and antibiotic permeability, but you can maybe resensitize to antibiotics via treatment with EDTA or succinic acid… I could go on for days (phage, vaccines, anti biofilm antibodies, …) and that’s only lung infections specifically! Then for like catheter infections you can start thinking about how like shark skin inspired coatings can prevent biofilm formation on catheters and a zillion other things
I found this deeply interesting as a recently diagnosed HCC( primary hepatocellular carcinoma)patient. I have resisted doing much research as the emotional maelstroms,daily life, explosion of urgent paperwork and aftermath of a tumor rupture (which derailed planned resection of liver with its jellyfish tumor attached and shunted me to chemo--Tecentriq+Vegzelma--)have pretty much taken all my available headspace. Could you point me to some (lay-person accessible) sources that may be useful to my particular cancer? Many thanks for that, and for the article.
No mention of "The Cancer Code by Jason Fung"?
*poring over :)
This administration's FDA might be exceptional in its approach, and I would be more cautious in extrapolating from its decisions. The more conventional one likely to succeed it will almost certainly revert to its usual function of consigning untold millions to the invisible graveyard. For "safety," of course.
Abhishaike - Nice essay. Everything is algorithmic.
Her -> great weekend fun, HER-2 -> not so much.
Anyways once again you wrote a wonderful essay that deals with a lot of what has been going on through my head during the week.
I'm 100% certain that the current LLM / GPT related SOTA methods will do so much more for biology that it ever did for text.
Other thought is that we seem to know as much about cancer as we know about consciousness or intelligence.
Fascinating essay. This really resonates with me and what we are trying to tackle at BeLiver.
I really like the idea of learning the joint distribution of these binary biomarkers. Though I guess removing the threshold could unlock more signal, even if it means more noise.
You've cited OncotypeDX, and I believe this is the pragmatic approach that has the potential to improve the standard of care, adding real clinical value for patients today.
Even simplistic ML models fed with the right kind of data can have strong predictive power and still unlock biological understanding and interpretability. I have doubts about images being the right kind of data for those cases.
And moving forward, I really hope foundational models will be able to add this missing inductive prior knowledge that can have an even stronger impact in areas such as precision oncology.
Can't resist a bit of self-promotion here. If you're interested, we've been working on this exact problem at BeLiver: https://beliver.fr/science and our foundational scientific paper (https://www.biorxiv.org/content/10.1101/2025.01.03.631224v1).
Ha you did the thing the original essay warns against of forgetting that fields besides your own have a surprising amount of detail! For “antibiotics for bacterial pneumonia” consider that viral and bacterial pneumonia themselves present similarly and it was a huge deal to understand the difference. Then notice that gram positive vs gram negative vs mycobacterial infections require different antibiotics, and even different bacterial species or subspecies have different resistance spectra, plus invisible-to-all-staining differences in beta lactamase expression change everything again. For chronic lung infections, you can do susceptibility testing with cultured clinical isolates but this predicts response surprisingly badly, maybe because of bacterial genetic variation within a lung, maybe because biofilm phenotypes alter metabolism and antibiotic permeability, but you can maybe resensitize to antibiotics via treatment with EDTA or succinic acid… I could go on for days (phage, vaccines, anti biofilm antibodies, …) and that’s only lung infections specifically! Then for like catheter infections you can start thinking about how like shark skin inspired coatings can prevent biofilm formation on catheters and a zillion other things
I found this deeply interesting as a recently diagnosed HCC( primary hepatocellular carcinoma)patient. I have resisted doing much research as the emotional maelstroms,daily life, explosion of urgent paperwork and aftermath of a tumor rupture (which derailed planned resection of liver with its jellyfish tumor attached and shunted me to chemo--Tecentriq+Vegzelma--)have pretty much taken all my available headspace. Could you point me to some (lay-person accessible) sources that may be useful to my particular cancer? Many thanks for that, and for the article.
Perfectly understandable! Unfortunately, I am not an oncologist, and whatever I do know about cancer lies outside of that particular subtype, so I'm not sure how much help I could be in linking to useful places. I have found r/cancer posts interesting to go through, and I do see that there are some threads about that particular subtype: https://www.reddit.com/r/cancer/search/?q=primary+hepatocellular+carcinoma&cId=65a1a81f-e107-493f-a8fc-97bb877e8146&iId=b7eef302-a3af-4e9d-a28f-cfaf80ce06b2
Hope everything turns out well :) hopefully some people smarter about cancer management can chime in here
Many thanks for your response, and the link.