Project Description

Synthetic lethal (SL) interactions, the induced essentiality of a gene for cell survival by defects in oncogenes or tumor suppressors, are a promising approach for cancer precision medicine. Tumor cells harbor many unique combinations of mutations, each with potentially unique SL profiles that interact to determine the efficacy of SL targets. Therapeutics based on SL interactions inferred from homogeneous cell subpopulations are unlikely to be effective without a better understanding of how multi-gene SL effects are moderated or enhanced by other cell populations across a tumor.
This project will aim to develop experimental and computational systems biology frameworks to understand how tumor heterogeneity modulates SL-targeted therapies. The initial focus will be poly(ADP-ribose) polymerarse (PARP) inhibitors that target a SL interaction in the DNA damage response pathway in breast and ovarian cancer, the project eventually expanding to other cancers and SL targets. Approaches will include integration of high-dimensional cell line, mouse model, and patient datasets including proteomics, single cell sequencing, and bulk genomics. Overall, this project aims to develop effective tools to predict SL therapeutic efficacy while accounting for intra-tumor heterogeneity, an advance that would employ tumor heterogeneity as a useful predictive feature in SL approaches to cancer precision medicine.

Start Date

Spring/Summer 2021

Postdoc Qualifications

The successful candidate will possess previous experience in cancer biology as well as at least one of the following skills: machine learning, statistical modeling, tissue engineering, cell culture, and proficiency in at least one programming language. 

Co-Advisors

Luis Solorio, Assistant Professor of Biomedical Engineering, Weldon School of Biomedical Engineering, lsolorio@purdue.edu

Douglas Brubaker, Assistant Professor of Healthcare Engineering, Regenstrief Center for Healthcare Engineering, dkbrubak@purdue.edu

References

Brubaker, D.K., and Paulo, J.A.*, et al. “Proteogenomic Network Analysis of Context-Specific KRAS Signaling in Mouse-to-Human Cross-Species Translation.” Cell Systems (2019).

Marusyk, A. et al. “Intra-tumor Heterogeneity: The Rosetta Stone of Therapeutic Resistance.” Cancer Cell (2020).

Tsherniak, A. et al. “Defining a Cancer Dependency Map.” Cell (2017).