SC20 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

Deciphering Hallmarks of Resistance in Breast Cancer

Workshop:CAFCW20: Sixth Computational Approaches for Cancer Workshop

Authors: Amrita Basu (University of California, San Francisco)

Abstract: Background: Approximately seventy percent of high-risk breast cancer patients with extensive residual cancer burden (RCB-III) after neoadjuvant therapy die within 4 years of treatment. Such ‘super non-responders’ need to be recognized and predicted early and routed to more effective treatment. Currently such predictors do not exist, and there is very little understanding of the biology driving extreme resistance. Here, we aimed to identify gene-expression signatures predicting extreme resistance within and across breast cancer receptor subtypes (HR/HER2) in the neoadjuvant I-SPY 2 TRIAL for high-risk early stage breast cancer.

Methods: 990 I-SPY 2 patients with RCB and pre-treatment gene expression data from the first 10 arms of the trial were considered in this analysis.

Results: Application of RWEN resulted in the identification of extreme-resistance signatures and prediction of RCB-III with 95% accuracy and a 3% false positive rate in 7/10 arms of the trial. Overall, many of the genes that we identified have been shown to be enriched in other resistant cancers and could explain poor outcomes in our patients.

Conclusion: Our ability to predict extreme resistance in patients receiving neoadjuvant therapy may help guide rescue treatments and improve outcomes. We have a unique opportunity to test our predictions rapidly within the clinical trial framework by learning which new drugs are most effectively matched to molecular phenotypes in breast cancer.


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