In a landmark stride for cancer therapy, a crew of scientists at the University of California San Diego School of Medicine has trained an artificial intelligence algorithm to predict how certain tumors react to chemotherapy. Per findings published today in the esteemed journal Cancer Discovery, the AI system can forecast treatment resistance in cancer, particularly zeroing in on cervical cancer and the widely used chemotherapy drug cisplatin.
The study reveals that the genetic makeup of tumors—a tangle of mutations—can seriously sway their response to medication. Previously, doctors knew only a smattering of individual mutations that suggest treatment resistance, which often proved unreliable in clinical application. As stated by Cancer Discovery, Trey Ideker, Ph.D., a professor in the Department of Medicine at UC San Diego, remarked, "Artificial intelligence bridges that gap in our understanding, enabling us to analyze a complex array of thousands of mutations at once."
This innovative algorithm stands out for its ability to examine how diverse genetic alterations collectively impact a tumor's reaction to drugs that interfere with DNA replication. By training on a standard set of 718 genes and using publicly accessible drug response data, the model highlighted 41 molecular assemblies—clusters of interacting proteins—and their role in drug efficacy.
During the study, the model was put through its paces with cervical cancers, an area where about 35% of tumors persist despite treatment. In these trials, the AI system excelled in identifying tumors likely to submit to therapy and those prone to resist. Ideker told Cancer Discovery, "Our model's transparency is one of its strengths, first because it builds trust in the model, and second because each of these molecular assemblies we’ve identified becomes a potential new target for chemotherapy." The research indicates a promising avenue for enhancing current treatments and crafting novel therapeutic strategies.
The development of such models could mark a significant leap forward in personalized cancer treatment, potentially improving patient outcomes through more accurate predictions and targeted therapies. The collaborative efforts of UC San Diego researchers including Xiaoyu Zhao, Akshat Singhal, Sungjoon Park, JungHo Kong, and Robin E. Bachelder made this study possible, thanks to support from the National Institutes of Health and other sponsors.
