Tumors develop resistance to numerous drug therapies, and this remains a major obstacle in treating many types of non-surgical cancers. Melanoma provides a good model system for studying drug resistance in cancer due to its high propensity to incur resistance after a significant initial response to a drug. Genes that are highly expressed in melanoma cancer cells have been studied, but in order to further understand the collective function of these highly expressed genes we must analyze gene sets, or pathways. A single gene’s function is rarely independent of other genes, and pathway analysis takes this into account.

Our objective is to simplify single-cell RNA sequence data to model pathways and pinpoint which unique pathways are up-regulated and down-regulated in drug resistant and nonresistant melanoma cell phenotypes. Identifying these important pathways provides a more accurate depiction of melanoma heterogeneity and informs us of the pathways that are likely to be effective targets for new drug therapies, bringing us closer to overcoming drug-induced resistance.


Bioinformatics | Chemicals and Drugs | Medical Sciences | Skin and Connective Tissue Diseases | Systems Biology


Yapeng Su

Lab site

Institute for Systems Biology (ISB)

Funding Acknowledgement

The 2019 STEM Teacher and Researcher Program and this project have been made possible through support from Chevron (www.chevron.com), the National Science Foundation through the Robert Noyce Program under Grant #1836335 and 1340110, the California State University Office of the Chancellor, and California Polytechnic State University in partnership with the Institute for Systems Biology and the NanoSystems Biology Cancer Center. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funders.



URL: https://digitalcommons.calpoly.edu/star/584


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