This week Harry speaks with molecular geneticist Elli Papaemmanuil about how newly available genomic data could lead to major improvements in the standard of care for cancer patients, leading to an age of true precision medicine.

Papaemmanuil is an assistant professor of computational oncology at Memorial Sloan Kettering Cancer Center in New York. Her lab's research is built around the idea that the genetic sequences of tumor cells reveal distinctive acquired mutations that can allow doctors to predict the course of the disease in specific patients and help them to design individualized treatments. That idea isn't new—but it isn't yet standard practice in oncology, a situation Emmanuil is working to change, in part by using AI and data-driven approaches to analyze the vast number of genetic variations in diseases like leukemia and reduce them to a manageable number of classes amenable to customized treatment approaches.

Papaemmanuil says she decided to become a cancer geneticist from the moment she learned about the Human Genome Project as a young person growing up in Greece. She obtained her PhD at the University of London, and now she's working to understand "how we can use genomic technology and genomic data to inform patient care." She was an early adopter of microarrays to conduct genome-wide linkage studies and identify common genetic variations that predispose people to colorectal cancer, leukemia, and other cancers. More recently she's used rapid genome sequencing technology to help complete the first catalog of genes that are commonly mutated in cancer.  She says this kind of information could help identify which patients are at risk for cancer; carry out screening to find patients with early-stage cancer, when treatment outcomes are much better; and most fundamentally, to create data-driven treatment models that account for a patient's age, gender, lifestyle, radiographic data, and genomic parameters.

"At the moment our standard of care represents brute force," Papaemmanuil says. "Now we understand that there's a lot of complexity [in cancer], and that if we study large enough patient cohorts, and we have genetic information with very good clinical annotation and outcomes, we can bring the AI component into the process and use classification and prediction tools" to, in effect, put a powerful computational advisor in every oncology exam room.

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