UCSF

Cancer is among the leading causes of mortality worldwide and the number of cancer-related deaths is expected to rise to 16.4 million by 2040. Given the wealth of publicly available cancer data that has been generated over the past few decades, it is now possible to investigate cancer at an unprecedented scale using computational approaches. This body of work covers three projects that leverage human tumor samples to evaluate cancer models, predict cancer therapeutics, and investigate the prognostic value of infiltrating B cell repertoires. In the first project, we compared cell lines from the Cancer Cell Line Encyclopedia to primary tumor samples from the Cancer Genome Atlas (TCGA) to evaluate how well each cell lines represents its primary tumors. We predicted subtype classifications for individual cell lines and proposed a new pan-cancer cell line panel with the most representative cell lines across 22 tumor types to facilitate pan-cancer studies. In the second project, we applied a computational drug repositioning approach to identify compounds to sensitize drug resistant breast cancers using patient samples from the I-SPY2 TRIAL and the Connectivity Map drug perturbation dataset. We identified a drug hit, fulvestrant, which we validated experimentally and found that it increased drug response in a paclitaxel-resistant breast cancer cell line. In the third project, we extracted B cell repertoires from TCGA RNA-seq samples and performed diversity and network analysis. We then evaluated the prognostic value of these repertoire features and identified significant associations with survival in a subset of tumor types. Together, this research demonstrates how computational methods can leverage publicly available datasets to extract new insights into cancer biology