Description:

The Department of Biochemistry and Molecular Genetics welcomes Computational Biology Faculty Candidate:

Collin Tokheim, Ph.D.
Research Fellow, Department of Data Science, Dana-Farber Cancer Institute, Harvard T.H. Chan School of Public Health

Abstract: Since their clinical discovery in 2014, Targeted Protein Degradation (TPD) drugs have started to expand the druggable targets in oncology. Unlike existing drugs that block the activity of a target, TPD drugs degrade a protein-of-interest by hijacking the cell’s Ubiquitin-Proteasome System (UPS). However, there is an incomplete understanding about how protein degradation is dysregulated in cancer and how TPD drugs may counteract this effect. Leveraging multi-omics data across more than 9,000 human tumors and 33 cancer types, we found that over 19% of all cancer driver genes impact UPS function. Moreover, we developed a deep learning model (deepDegron) to identify mutations that result in loss of protein degradation signals (degrons), and experimentally validated predictions of point mutations (e.g. in GATA3) and gene fusions (e.g. BCR-ABL) that result in escape from protein degradation. In a second study, we found that altered protein degradation of Cebpd can influence immunotherapy response in a Triple Negative Breast Cancer (TNBC) model by complementing in vivo CRISPR screens with multi-omic data analysis spanning the epigenome, transcriptome and proteome. Lastly, to assess for potential therapeutic vulnerabilities, we developed a machine learning model, MAPD (Model-based Analysis of Protein Degradability), to predict which protein targets are likely degradable by TPD compounds from unbiased proteomic experiments of the kinome. In summary, new computational methods uncovered an underappreciated oncogenic role for dysregulated protein degradation with potential therapeutic implications.