Eran Segal, PhD
Project Title
Personalized prediction of treatment response and disease progression in pancreatic cancer
About the Investigator
Professor Segal’s research focuses on determining which factors, whether they be from our cells, microbes living in our guts, or the environment, influence our health. He received a B.Sc. in computer science summa cum laude from Tel Aviv University, and his Ph.D. in computer science from Stanford University. After an independent research position at the Rockefeller University, he returned to Israel and joined the Weizmann Institute of Science and is now Professor of Computer Science — Department of Computer Science and Applied Mathematics, as well as head of the Crown Human Genome and the Center for Microbiome Research.
About the Research
Pancreatic cancer is a deadly cancer in which less than ten percent of patients survive five years after diagnosis. Although surgery can be highly effective in the early stages of disease, most patients are diagnosed with advanced metastatic disease. Current treatment options for metastatic disease are limited to highly toxic chemotherapy regimens with variable success. Diagnosis and personalization of treatment is a major challenge in pancreatic cancer, as it is highly variable and unpredictable, and we currently have very limited knowledge on what internal and external factors influence this variability.
The goal of Professor Segal’s research is to understand how to predict pancreatic cancer development and response to treatments using person-specific signals in our blood. He will develop non-invasive tests and data-driven tools that harness signals from the blood linked to the dietary, lifestyle, clinical and gut microbiome factors that influence them. These tests will classify patients into clinically meaningful subtypes, predict who will benefit from surgery or current treatments, and suggest new targets for therapy. As these tests, also pair signals in the blood with information on dietary, lifestyle and gut microbiome signatures; complementary advice on changes which may be preventative or enhance therapeutic responses can be provided. Using advanced but transparent AI methods, he will discover compact, clinically affordable biomarker panels and easy‑to‑use algorithms for clinicians. The results of the project are expected to lead to more precise care, improved diagnostics, and clinical management of pancreatic cancer.