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Predicting cancer prognosis and drug response from the tumor microbiome
Leandro Hermida
Friday, October 8, 2021, 9:30 am-12:30 pm Calendar
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Tumor gene expression is predictive of patient prognosis in some cancers. However, RNA-seq and whole genome sequencing data contain not only reads from host tumor and normal tissue, but also reads from the tumor microbiome, which can be used to infer the microbial abundances in each tumor. Here, we show that tumor microbial abundances, alone or in combination with tumor gene expression data, can predict cancer prognosis and drug response to some extent – microbial abundances are significantly less predictive of prognosis than gene expression, although remarkably, similarly as predictive of drug response, but in mostly different cancer-drug combinations. Thus, it appears possible to leverage existing sequencing technology, or develop new protocols, to obtain more non-redundant information about prognosis and drug response from RNA-seq and whole genome sequencing experiments than could be obtained from gene expression or mutation data alone.

Examining Committee:
Dr. Rob Patro
Dr. Eytan Ruppin
Dr. Mihai Pop
Dr. Furong Huang

Leandro Hermida is a third year Master’s student supervised by Dr. Eytan Ruppin and a member of the Cancer Data Science Laboratory (CDSL) at the National Cancer Institute (NCI). Before coming to UMD, he worked as a bioinformatics scientist for many years at the NCI and other academic institutions. He received his undergraduate degree from Vanderbilt University in chemical engineering. He is interested in the application of computer science techniques in the study of cancer biology, including machine learning and other computational methods.

This talk is organized by Tom Hurst