Abstract
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.
Highlights
Ovarian cancer is the leading cause of death among gynecological cancers with an average 5-year survival rate of only 46% [1]
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
Predicting drug sensitivities in ovarian tumors based on their transcriptome profiles Using pRRophetic, we generated 1,773 predicted drug IC50s for all tumors in the Cancer Genome Atlas (TCGA) ovarian cancer datasets
Summary
Ovarian cancer is the leading cause of death among gynecological cancers with an average 5-year survival rate of only 46% [1]. The identification and development of effective drugs against chemoresistant ovarian tumors is of great importance. High throughput drug screening has been conducted in large variety of cancer cell lines [9,10,11]. Efforts to adapt the high throughput drug screening results in order to overcome ovarian chemoresistance have not been reported. To this end, our lab has previously developed pRRophetic, a transcriptome based drug sensitivity prediction tool, which relates cell line drug sensitivity screening datasets with the corresponding cell line transcriptome data to predict in vivo drug IC50s with great accuracy [12, 13]
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