Researcher Spotlight: Using AI to Predict Which Cancer Mutations Matter

When Jessica White graduated from college 15 years ago, computational biomedical research looked drastically different than it does today.

“Back then, the amount of data that we had for the kinds of experiments I ran, I could process that in Excel,” she said. “We didn’t have access to the large-scale sequencing resources that we have now.”

Over the next decade, White watched the field transform while working in the biopharmaceutical industry, including at a company developing targeted cancer therapeutics. As new genomic technologies emerged and data sets grew exponentially larger, she realized that this was the type of research she wanted to do.

White returned to school to pursue a PhD in computational biology, and her industry experience gave her a clear focus for her research: helping more cancer patients benefit from precision medicines.

Today, White is a PhD candidate in the Tri-Institutional Program at Memorial Sloan Kettering Cancer Center and a recipient of a 2026 PhRMA Foundation Predoctoral Fellowship in Drug Discovery. Her research focuses on building tools to leverage large-scale biological and patient data to better understand cancer-driving mutations that often go overlooked.

The focus of White’s work is a family of proteins called kinases, which play a critical role in helping cells communicate and respond to signals. Because kinase mutations are involved in many cancers and autoimmune diseases, they are one of the most important targets in drug development, with more than 100 kinase inhibitors already approved in the U.S. “These inhibitors are kind of like the bread and butter of a lot of drug development programs,” White said.

However, many of these drugs are designed to target a small number of well-known genetic mutations. Patients with more rare mutations have no way of knowing whether an existing therapy may work for them.

White’s research aims to address this challenge by studying what scientists call the “long tail” of kinase mutations — the thousands of less common genetic changes that are not well understood. Using computational tools she has developed, White is integrating data about kinase biology and structure with genomic data from more than 100,000 patients treated at Memorial Sloan Kettering. The goal is to identify patterns that may reveal which mutations are important and which existing drugs could potentially be effective against them.

The approach is already producing promising insights. White and her colleagues have identified mutations occurring in critical regions of kinases that remain poorly characterized in existing clinical knowledge bases. With a better understanding of these variants, research may eventually be able to guide treatment decisions for patients who currently fall outside the scope of approved targeted therapies.

“There are a lot of people who get to do cool and interesting stuff in tech and data analysis, but to be able to do that on behalf of patients — and some of the most ill patients — I think is really, really rewarding,” White said.

The PhRMA Foundation award has allowed White to focus on completing and publishing her research without the stress of worrying about funding. She said not many funding organizations or governmental agencies invest in the kind of cutting-edge research she is conducting.

“A lot of people think industry will fund it, but this is really more experimental and novel, and we’re not at the level of identifying a target yet, so it’s not something that industry is going to invest in directly,” she said. “Being able to have support from an organization whose purview is specifically drug discovery and drug development is really beneficial.”

Looking ahead, White is interested in returning to industry and continuing her work using AI and genetic coding tools to expand access to current genomic therapies, so fewer patients are left behind.

“That’s really what my focus is going forward: enabling the use of novel cutting-edge therapies in a larger and larger patient population,” she said. “Hopefully my skill set that I’ve acquired will translate and I’ll be able to leverage that in industry again.”