Discovering drugs to overcome chemoresistance in ovarian cancers based on the cancer genome atlas tumor transcriptome profile

Fan Wang; Jeremy T-H. Chang; Zhenyu Zhang; Gladys Morrison; Aritro Nath; Steven Bhutra; Rong Stephanie Huang

doi:10.18632/oncotarget.22870

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Research Papers:

Discovering drugs to overcome chemoresistance in ovarian cancers based on the cancer genome atlas tumor transcriptome profile

Fan Wang, Jeremy T-H. Chang, Zhenyu Zhang, Gladys Morrison, Aritro Nath, Steven Bhutra and Rong Stephanie Huang _

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Oncotarget. 2017; 8:115102-115113. https://doi.org/10.18632/oncotarget.22870

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Abstract

Fan Wang1, Jeremy T-H. Chang2, Zhenyu Zhang3, Gladys Morrison1, Aritro Nath1,4, Steven Bhutra1 and Rong Stephanie Huang1,4

1Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, IL, USA

2Biological Sciences Collegiate Division, University of Chicago, Chicago, IL, USA

3Center for Data Intensive Science, University of Chicago, Chicago, IL, USA

4Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN, USA

Correspondence to:

Rong Stephanie Huang, email: [email protected]

Keywords: ovarian cancer; chemoresistance; drug repurposing; TCGA; pharmacogenomics

Received: June 14, 2017 Accepted: August 25, 2017 Published: December 04, 2017

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.

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Research Papers:

Discovering drugs to overcome chemoresistance in ovarian cancers based on the cancer genome atlas tumor transcriptome profile

Abstract