Page 46 - Rappaport Institute Magazine 2024

CELL BIOLOGY AND CANCER SCIENCE
Keren Yizhak, PhD Assistant Professor of Computational Cancer Genomics
PhD, 2015 – Tel Aviv University, Israel
Computationally studying the tumor microenvironment for improving patient response
The big data revolution significantly affects biomedical research and brings us closer to fulfilling the vision of personalized medicine. Our lab is found at the heart of this revolution. We focus on applying and developing computational tools for analyzing large-scale molecular data from human patients, with the goal of studying immunotherapy resistance, improving biomarkers of response, and identifying drug target combination that will enhance immunity. For example, we developed a new tool based on machine learning for identifying somatic mutations from RNA-sequencing, thus improving the FDA-approved biomarker, tumor mutational burden, for predicting patient response to immunotherapy. Furthermore, we analyzed ~1M immune cells from cancer patients treated with immunotherapy, and discovered novel cellular metabolic states that are predictive of patient response across different cancer types. Our overarching goal is to deepen our understanding of the tumor microenvironment in different conditions and across cancer types, and generate combined markers and drug targets that will improve patient outcome.
Selected Publications
ˆ Defining T cell states associated with response to checkpoint immunotherapy in melanoma. Sade-Feldman, M.*, Yizhak, K.*, Bjorgaard, S., Ray, J., De Boer, C., Jenkins, R., Lieb, D., Chen, J., Frederick, D., Barzily-Rokni, M., Freeman, S., Reuben, A., Hoover, P., Villani, A., Ivanova, E., Portell, A., Lizotte, P., Aref, A., Eliane, J., Hammond, M., Vitzthum, H., Blackmon, S., Li, B., Gopalakrishnan, V., Reddy, S., Cooper, Z., Paweletz, C., Barbie, D., Stemmer-Rachamimov, A., Flaherty, K., Wargo, J., Boland, G., Sullivan, R., Getz, G., Hacohen, N. *Equal contribution. Cell; 175 (4), 998-1013 (2018)
ˆ RNA sequence analysis reveals macroscopic somatic clonal expansion across normal tissues. Yizhak, K., Aguet, F., Kim, J., Hess, J., Kubler, K., Grimsby J., Frazer, R., Zhang, H., Haradhvala, N., Rosebrock, D., Livitz, D., Li, X., Arich-Landkof, E., Shoresf, N., Stewart, C., Segre, A., Branton, P., Polak, P., Ardlie, K., Getz, G. Science; 364 (2019) ˆ Estimating tumor mutational burden from RNA-sequencing without a matched-normal sample. Katzir R, Rudberg, N. Yizhak K. Nat. Commun. 13, 3092 (2022)
ˆ Metabolic predictors of response to immune checkpoint blockade therapy. Shorer, O, Yizhak K. iScience. 2023;26.
Grants and Awards
2019 - 2023 – Israel Science Foundation, Characterizing the tumor and immune landscape of melanoma patients treated with combined checkpoint blockade and MAPK targeted therapy (PI)
2020 - 2024 – Israel Science Foundation – Israel Personal Medicine Partership, Defining sensitivity and overcoming resistance to PARP inhibition in pancreatic ductal adenocarcinoma using combined genomics and metabolomics tools (co-PI)
2021 - 2024 – Ministry of Science and Technology, Identifying metabolic drug targets that enhance response and overcome resistance to immune checkpoint blockade therapy in melanoma (PI)
2023 - 2026 – Israel Cancer Research Fund, Identifying biomarkers of response to immunotherapy using immune single-cell RNA-seq data (PI)
2019 - 2020 – The Sherman-Saifer fellowship 2020 - 2022 – Alon Fellowship
Collaborators
Nir Hacohen – Massachusetts general Hospital Russell Jenkins - Massachusetts general Hospital
kyizhak@technion.ac.il
Keren Yizhak Lab
Analysis workflow – utilizing molecular data from human patients treated with immunotherapy and analyzing the different cells and their expression patterns in different time points to build a predictor of response, and to identify novel drug targets.