Eytan Ruppin, M.D., Ph.D.
Eytan Ruppin, M.D., Ph.D., is a computational biologist whose research is focused on developing and harnessing data science approaches for the integration of multi-omics data to better understand the pathogenesis of cancer, its evolution and treatment. We collaborate with many experimental cancer labs, aiming to develop and utilize computational approaches to jointly gain a network-level integrative view of the systems we study. From a translational perspective, together with our collaborators, we aim to predict and test novel drug targets and biomarkers to treat cancer more effectively.
1. The discovery of genetic interactions in cancer as a basis for advancing genome-wide based cancer treatments
A main focus in cancer research is on studying a few hundred cancer driver genes, to identify ‘actionable’ mutations that can be targeted therapeutically. Complementing this approach, my lab has focused in recent years on studying the value of genetic interactions (GIs) between genes across the whole genome to advance cancer research and treatment. This approach has been motivated by recent work in our lab and others showing that (i) genetic interactions between genes are critical in tumor development and drug response, and that (ii) such interactions can be computationally identified by analyzing large-scale genomics and patient data. This work encompasses several types of genetic interactions (GI) that are relevant to cancer therapeutics, including Synthetic Lethal (SL) interactions, Synthetic Dosage Lethal (SDL) interactions and Synthetic Rescue (SR) interactions. Based on the tools we have developed for the data-driven identification of all three types of GI networks from large cohorts of tumor samples, we have shown that the cancer GIs identified can be successfully used for a variety of important challenges including: (a) Generating the first clinically-derived pan-cancer SL and SDL networks, we show that these networks can successfully predict the response of cancer patients to many widely used drug treatments, offering a complementary approach to existing mutation-based methods for precision-based cancer therapy. (b) Aiming to fight resistance to cancer therapy, we have identified genome-wide pancancer SR networks, which are predictive of patients’ drug response and resistance to the majority of current cancer drugs. This approach provides a basis for new combination-based therapeutic strategies improving the effectiveness of existing cancer therapies by identifying and targeting drug resistance pathways.
Going forward in this line of research, we intend to explore two main paths: (a) precision medicine – inferring patient specific GI-mediated drug response by analyzing the transcriptomics of their tumors, and (b) drug development – identifying new SL-based drug targets, combined with their SL-derived patient stratification profiles.
2. Cancer immunotherapy
In the last two years we have become actively engaged in collaborative studies in cancer immunotherapy, ranging from studying the role of urea cycle dysregulation in modulating the response to checkpoint inhibitors in different cancer types, studying the role of intratumor heterogeneity in shaping the immune response and its effectiveness, and very recently, building machine learning based predictors of patients’ response to checkpoint therapies in melanoma. Ongoing collaborative studies focus on genome wide identification of effective combinations involving checkpoint inhibitors (possibly with targeted therapies) and on the identification of new targets for CAR-T therapy.
Selected Key Publications
Harnessing synthetic lethality to predict the response to cancer treatment.Nat Commun. in press, 2018. [ Journal Article ]
Synthetic dosage lethality in the human metabolic network is highly predictive of tumor growth and cancer patient survival.Proc Natl Acad Sci U S A. 112(39): 12217-22, 2015. [ Journal Article ]
- Nature. 527(7578): 379-383, 2015. [ Journal Article ]
- Cell. 158(5): 1199-1209, 2014. [ Journal Article ]
- Nature. 477(7363): 225-8, 2011. [ Journal Article ]
Eytan Ruppin received his M.D. and Ph.D. (Computer Science) from Tel-Aviv University where he has served as a professor of Computer Science & Medicine since 1995, conducting computational multi-disciplinary research spanning a wide variety of topics, including neuroscience, evolutionary computation, natural language processing, machine learning and systems biology. He joined the University of Maryland in July 2014 as a Computer Science professor and director of its center for bioinformatics and computational biology (CBCB), before joining the NCI in January 2018. He received the Rothschild and Alon fellowships, the Schtacher Award and a McDonnell Foundation grant award and is a member of the editorial board of EMBO Reports and Molecular Systems Biology. He is a co-founder of startup companies involved in precision medicine and cancer drug discovery.
|Noam Auslander BSc.||Predoctoral Visiting Fellow (Graduate Student)|
|Kouyuan Cheng BSc.||Predoctoral Visiting Fellow (Graduate Student)|
|David Crawford BSc.||Predoctoral Visiting Fellow (Graduate Student)|
|Leandro Hermida BSc.||Predoctoral Visiting Fellow (Graduate Student)|
|Joo Sang Lee Ph.D.||Postdoctoral Fellow (Visiting)|
|Sanna Madan BSc.||Predoctoral Fellow (CRTA)|
|Assaf Magen BSc.||Predoctoral Visiting Fellow (Graduate Student)|
|Nishant Nair Ph.D.||Postdoctoral Fellow (Visiting)|
|Sushant Patkar BSc.||Predoctoral Visiting Fellow (Graduate Student)|
|Wells Robinson BSc.||Predoctoral Visiting Fellow (Graduate Student)|
|Sanju Sinha BSc.||Predoctoral Visiting Fellow (Graduate Student)|