About the Investigator:

Dr. Yizhak completed her undergraduate studies in the Computational Biology program at Hebrew  University. She then received her MSc and PhD in Computer Science, focusing on studying human metabolism in aging and cancer. She performed her postdoctoral work at the Broad Institute of MIT and Harvard, where she developed methods for studying tumor immunity at the single-cell level and somatic clonality in normal tissues. When she returned to Israel, she established the Computational Cancer Genomics Lab at the Technion, where she applies and develops computational tools for studying cancer genomics and immunity.

About the Research:

Finding markers to predict if a patient will respond to cancer treatment is a top priority. Scientists have been using single-cell RNA sequencing to look for these markers. They found specific genes and cellular states linked to how well a patient responds to treatment. This information can help build models that predict how effective the treatment will be for different people. However, these markers that help predict a patient’s response to cancer treatment only work for specific types of cancer and can’t be used for others.

The Yizhak lab plans to analyze about 1 million immune cells from cancer patients who received immunotherapy. Using machine learning techniques, they hope to create models that can predict a patient’s response to treatment across different types of cancer instead of just looking at one specific gene. Preliminary results from Dr. Yizhak’s lab show that this process is doable and that different cellular groups perform better than others in predicting outcomes.

This study will try to identify the best cellular groups and genes for accurate predictions. Moreover, the research team will utilize several techniques to identify potential combinations of biomarkers and study how they interact. They will then test these biomarkers in bulk samples to ensure their efficacy for clinical use. Overall, the proposed research should help to determine the most effective biomarkers for treating patients using immunotherapy