3 ms·

that's definitely a good approach in theory, but messier in reality. if we knew enough about which variables impacted disease, and could measure them well enou

that's definitely a good approach in theory, but messier in reality. if we knew enough about which variables impacted disease, and could measure them well enough, and had comprehensive quantitative models of each variable interacts with a biological system, we could do this, but we dont. the reason we have to triangulate is because the types of modeling you do in physics just doesn't translate to fields like life science / medicine there are too many unknowns in life sci and error bars are too high compared to physics to enable this kind of model to be too accurate. collecting all of that data (ie mutation rate, number of divisions, accumuated mutations etc) across whole tumors in enough people to matter would be incredibly costly and time consuming, and the result probably wouldnt be as useful as we'd hope.

nonbel 9y ago
>"there are too many unknowns in life sci and error bars are too high compared to physics to enable this kind of model to be too accurate. collecting all of that data (ie mutation rate, number of divisions, accumuated mutations etc) across whole tumors in enough people to matter would be incredibly costly and time consuming, and the result probably wouldnt be as useful as we'd hope." Researchers will never cure cancer if they don't collect basic information like mutation rates, number of divisions since zygote as a function of age, cells in each tissue as a function of age, etc. It is like trying to develop astronomy without recording the positions of the planets... Also, I have seen that an only-slightly-more-complicated model than that extremely simple one (assuming a decaying curve for division rate as a function of age) is able to fit the age-specific incidence of many cancers (data from SEER). Have you checked that such approaches are too difficult or are you assuming so? Can you point me to info on any failed attempts? It really sounds like giving up to me. If that is the attitude of cancer researchers, I suspect eventually people with a less defeatist attitude will take it on (possibly coming from outside the healthcare industry).
- aaavl2821 9y ago
  I think that new approaches to doing drug R&D are always welcome and if you know of any particular interesting and useful approaches id love to learn more. The "bottoms up" approach you mention seems like it could be really powerful and definitely complementary to whats being done now. I have however seen a lot of approaches "sort of" like that not work that well. I also know of a lot of less quantitative approaches taht actually work really well, and thats how a lot of R&D is done today I disagree w the statement that we'll never cure cancer w/o info on mutation rates. Many of the most impactful medicines we have were discovered without this kind of low level, quantitative info. Many through serendipity. Penicillin is the classic example. We have actually made huge progress in treating many cancers in the last five years. We can turn many melanomas from death sentences into relatively manageable chronic diseases, and a lot of blood cancers are being completely eradicated in some patients (at least for a time). We have a long way to go, but most of these insights come from triangulating various pieces of molecular biological and chemical information to create testable insights There has been a move lately to more "genomic" medicine where you identify a specific genentic mutation, and target that to treat disease. Most diseases aren't that simple though, and these are rarely curative There was also a move in the early 2000s to measure expression levels of hundreds of thousands of cancer genes to try to understand which ones would metastasize, how to treat them, etc. In some cases algorithms got pretty good at predicting rates of metastasis, but most didnt, and even fewer yielded clinically actionable insights. And I dont know of any drugs that were developed based on this work The idea of the moment is the "liquid biopsy", measuring pieces of DNA or proteins in the blood, and using machine learning to correlate these "biomarkers" with risk of getting cancer or other endpoints. The jury is still out on whether this works to my knowledge, though frankly i havent followed the science too closely No one is giving up, not in the least -- in fact more money is being invested into drug research than ever before. Its just really hard, and "triangulation" and insight are more powerful right now than purely quantitative methods. I hope that changes. But for things to change, we probably need better tools for collecting data before algorithms could be really powerful
  - nonbel 9y ago
    >"The "bottoms up" approach you mention seems like it could be really powerful and definitely complementary to whats being done now. I have however seen a lot of approaches "sort of" like that not work that well." Such as? I mean that is just the (correct) discrete version of Armitage-Doll, their continuous approximation only works for very low mutation rates and/or short time frames. What did you see that didn't work?

nonbel 9y ago

>"there are too many unknowns in life sci and error bars are too high compared to physics to enable this kind of model to be too accurate. collecting all of that data (ie mutation rate, number of divisions, accumuated mutations etc) across whole tumors in enough people to matter would be incredibly costly and time consuming, and the result probably wouldnt be as useful as we'd hope." Researchers will never cure cancer if they don't collect basic information like mutation rates, number of divisions since zygote as a function of age, cells in each tissue as a function of age, etc. It is like trying to develop astronomy without recording the positions of the planets... Also, I have seen that an only-slightly-more-complicated model than that extremely simple one (assuming a decaying curve for division rate as a function of age) is able to fit the age-specific incidence of many cancers (data from SEER). Have you checked that such approaches are too difficult or are you assuming so? Can you point me to info on any failed attempts? It really sounds like giving up to me. If that is the attitude of cancer researchers, I suspect eventually people with a less defeatist attitude will take it on (possibly coming from outside the healthcare industry).

aaavl2821 9y ago
I think that new approaches to doing drug R&D are always welcome and if you know of any particular interesting and useful approaches id love to learn more. The "bottoms up" approach you mention seems like it could be really powerful and definitely complementary to whats being done now. I have however seen a lot of approaches "sort of" like that not work that well. I also know of a lot of less quantitative approaches taht actually work really well, and thats how a lot of R&D is done today I disagree w the statement that we'll never cure cancer w/o info on mutation rates. Many of the most impactful medicines we have were discovered without this kind of low level, quantitative info. Many through serendipity. Penicillin is the classic example. We have actually made huge progress in treating many cancers in the last five years. We can turn many melanomas from death sentences into relatively manageable chronic diseases, and a lot of blood cancers are being completely eradicated in some patients (at least for a time). We have a long way to go, but most of these insights come from triangulating various pieces of molecular biological and chemical information to create testable insights There has been a move lately to more "genomic" medicine where you identify a specific genentic mutation, and target that to treat disease. Most diseases aren't that simple though, and these are rarely curative There was also a move in the early 2000s to measure expression levels of hundreds of thousands of cancer genes to try to understand which ones would metastasize, how to treat them, etc. In some cases algorithms got pretty good at predicting rates of metastasis, but most didnt, and even fewer yielded clinically actionable insights. And I dont know of any drugs that were developed based on this work The idea of the moment is the "liquid biopsy", measuring pieces of DNA or proteins in the blood, and using machine learning to correlate these "biomarkers" with risk of getting cancer or other endpoints. The jury is still out on whether this works to my knowledge, though frankly i havent followed the science too closely No one is giving up, not in the least -- in fact more money is being invested into drug research than ever before. Its just really hard, and "triangulation" and insight are more powerful right now than purely quantitative methods. I hope that changes. But for things to change, we probably need better tools for collecting data before algorithms could be really powerful
- nonbel 9y ago
  >"The "bottoms up" approach you mention seems like it could be really powerful and definitely complementary to whats being done now. I have however seen a lot of approaches "sort of" like that not work that well." Such as? I mean that is just the (correct) discrete version of Armitage-Doll, their continuous approximation only works for very low mutation rates and/or short time frames. What did you see that didn't work?