LAUSR

Ovarian cancer accounts for the highest mortality among gynecologic cancers, mainly due to intrinsic or acquired chemoresistance. While mechanistic-based methods have been used to identify compounds that can overcome chemoresistance, an effective comprehensive drug screening has yet to be developed. We applied a transcriptome based drug sensitivity prediction method, to the Cancer Genome Atlas (TCGA) ovarian cancer dataset to impute patient tumor response to over 100 different drugs. By stratifying patients based on their predicted response to standard of care (SOC) chemotherapy, we identified drugs that are likely more sensitive in SOC resistant ovarian tumors. Five drugs (ABT-888, BIBW2992, gefitinib, AZD6244 and lenalidomide) exhibit higher efficacy in SOC resistant ovarian tumors when multi-platform of transcriptome profiling methods were employed. Additional in vitro and clinical sample validations were carried out and verified the effectiveness of these agents. Our candidate drugs hold great potential to improve clinical outcome of chemoresistant ovarian cancer.