Can AI predict how a cancer will outsmart a drug before it happens?
This episode explores the science behind the Forbeck Forum Evolution and Modeling of Cancer Genetic Networks and Cell States, drawing on research from Drs. Yogesh Goyal and Elena Kuzmin. For decades, cancer research often meant studying one piece of the machine in exquisite detail. Here, the focus shifts to the whole traffic grid of the cell, using structural variants, cell-state modeling, and therapy resistance to understand how cancer adapts.
One surprising finding is that AI models trained on functional screening data from immune cells have successfully predicted drug resistance in melanoma. The cells are unrelated, but the underlying biological rules carry across both. Another is that roughly 70% of human genes have duplicates, which tumors can use like backup generators to survive targeted therapies. By combining CRISPR and deep learning to map those duplicates, researchers can anticipate how a cancer cell may reorganize to resist treatment and work to cut off its escape routes.
At the heart of it all is collaboration. Forbeck Forums bring physicists, mathematicians, computer scientists, and clinicians to the same table, giving early-career scientists the chance to build new tools together from the start.
Complex science, made a little easier to follow. Learn more and support the Forbeck Foundation’s cancer research at forbeckfoundation.org, and please share this episode with someone who might enjoy it.
