Damon Runyon has announced its newest cohort of Quantitative Biology Fellows, three exceptional early-career scientists who are applying the tools of computational science to generate and interpret cancer research data at extraordinary scale and resolution. Whether measuring cell-to-cell genetic variability within a tumor or developing algorithms that can predict if therapy will be effective, their projects extend the boundaries of what is possible in cancer research, allowing them to tackle fundamental biological and clinical questions.

Each postdoctoral scientist selected for this unique three-year award will receive independent funding ($240,000 total) to train under the joint mentorship of an established computational scientist and a cancer biologist. The grant was created to encourage quantitative scientists (from fields such as mathematics, physics, computer science, and engineering) to pursue careers in cancer research. By investing in the intersection of “wet” and “dry” lab, Damon Runyon aims to highlight the importance of these specially trained scientists in the quest for new cancer treatments. The awardees were selected by a distinguished committee of experts in the field.

“We’re entering a golden era for cancer research, and a huge component of the big breakthroughs are coming at this intersection of cancer biology, medicine, and computational science. If you believe that the role of computational science is going to be integral to the future of cancer discoveries, then we need to worry about whether we have enough leaders in this field. We should be investing in a new generation of leaders, and that’s the intent of this award,” said Todd R. Golub, MD, Damon Runyon Board Member and Chair of Damon Runyon Quantitative Biology Fellowship Award Selection Committee.

2022 Quantitative Biology Fellows

Cong Ma, PhD, with mentors Benjamin Raphael, PhD, and Li Ding, PhD (Washington University), at Princeton University, Princeton

Patients with the same cancer diagnosis may experience very distinct disease progressions and treatment responses. These differences between patients have been associated with their degree of intra-tumor heterogeneity—the genetic, epigenetic, spatial, and environmental differences between the tumor cells. Characterizing the genetic and epigenetic states of different tumor cells is key to understanding how intra-tumor heterogeneity influences tumor progression, expansion, metastasis, and treatment response. Recent advances in single-cell RNA sequencing and spatial transcriptomics (which shows the spatial distribution of RNA molecules within a tissue sample) provide new opportunities to study intra-tumor heterogeneity in higher resolution. Dr. Ma’s research aims to characterize intra-tumor heterogeneity in terms of specific genetic and epigenetic measures, and eventually develop 3D tumor models that capture this heterogeneity across multiple cancer types. Dr. Ma received her BS from Zhejiang University and her PhD in computational biology from Carnegie Mellon University.

Sukrit Singh, PhD, with mentors John D. Chodera, PhD, and Markus A. Seeliger, PhD (Stony Brook University), at Memorial Sloan Kettering Cancer Center, New York

Kinase proteins, which regulate the activity of other proteins, are a major class of cancer therapy targets, with over 65 FDA-approved drugs targeted against them. However, tumors can evolve resistance to kinase-targeting therapies, and it remains difficult to predict whether a specific tumor will resist a particular kinase-targeting drug. Dr. Singh will use protein structural models and biophysical predictions to analyze how kinase mutations cause cancers to resist therapy. As these computationally intensive calculations could require decades on a single desktop computer, he will use a computing platform called Folding@home, which harnesses idle computer time donated by citizen scientists around the world to run the calculations. By developing new algorithms to predict whether a known mutation will resist a kinase-targeting drug, Dr. Singh hopes to advance precision oncology to allow clinicians to predict a treatment’s chance of success given a patient’s tumor profile. While his work primarily focuses on resistance to the drug crizotinib, used to treat non-small-cell lung carcinomas, his approaches can be extrapolated to other tumors and cancer targets. Dr. Singh received his BA and his PhD in computational and molecular biophysics from Washington University in St. Louis. 

Yapeng Su, PhD, with mentors Philip D. Greenberg, MD, and Raphael Gottardo, PhD, at Fred Hutchinson Cancer Research Center, Seattle

One in 64 people in the U.S. develops pancreatic cancer in their lifetime and only 9% will survive 5 years. This rate has barely changed in the last 40 years; better innovative treatments are urgently needed. Among the most promising immunotherapies is adoptive T cell therapy (ACT), which involves infusion of the patients’ own immune T cells that have been engineered outside of their body to make them selectively kill cancer cells. ACT has been effective against certain blood cancers but has had limited success against solid tumors, including pancreatic cancers. Dr. Su will quantitatively assess the mechanisms that contribute to the decreased effectiveness of ACT against pancreatic cancer. He will use specimens obtained from mouse models and pancreatic cancer patients receiving ACT to develop computational frameworks that can be applied to single-cell sequencing data and other large datasets. His findings should inform the design of next-generation ACT against pancreatic cancer and potentially other solid tumors. Dr. Su received his BS from Tianjin University and his PhD in engineering/systems biology from the California Institute of Technology.