Integrative bioinformatic analyses of an oncogenomic profile reveal the biology of endometrial cancer and guide drug discovery
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Henry Sung-Ching Wong1,2, Yung-Shun Juan3,4, Mei-Shin Wu2, Yan-Feng Zhang5, Yu-Wen Hsu2,6,7,8, Huang-Hui Chen8,9, Wei-Min Liu8,9, Wei-Chiao Chang1,2,10,11
1Master Program for Clinical Pharmacogenomics and Pharmacoproteomics, School of Pharmacy, Taipei Medical University, Taipei, Taiwan
2Department of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, Taipei, Taiwan
3Department of Urology, Kaohsiung Municipal Hsiao-Kang Hospital, Kaohsiung, Taiwan
4Department of Urology, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
5HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
6The Ph.D. Program for Translational Medicine, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
7Academia Sinica, Taipei, Taiwan
8Department of Obstetrics and Gynecology, School of Medicine, Taipei Medical University, Taipei, Taiwan
9Department of Obstetrics and Gynecology, Taipei Medical University Hospital, Taipei, Taiwan
10Department of Pharmacy, Taipei Medical University-Wan Fang Hospital, Taipei, Taiwan
11Center for Biomarkers and Biotech Drugs, Kaohsiung Medical University, Kaohsiung, Taiwan
Wei-Chiao Chang, e-mail: email@example.com
Keywords: endometrial cancers, expression-associated somatic mutations, cancer genomics, cancer drivers, cancer drug repurposing
Received: August 12, 2015 Accepted: November 16, 2015 Published: December 22, 2015
A major challenge in personalized cancer medicine is to establish a systematic approach to translate huge oncogenomic datasets to clinical situations and facilitate drug discovery for cancers such as endometrial carcinoma. We performed a genome-wide somatic mutation-expression association study in a total of 219 endometrial cancer patients from TCGA database, by evaluating the correlation between ~5,800 somatic mutations to ~13,500 gene expression levels (in total, ~78, 500, 000 pairs). A bioinformatics pipeline was devised to identify expression-associated single nucleotide variations (eSNVs) which are crucial for endometrial cancer progression and patient prognoses. We further prioritized 394 biologically risky mutational candidates which mapped to 275 gene loci and demonstrated that these genes collaborated with expression features were significantly enriched in targets of drugs approved for solid tumors, suggesting the plausibility of drug repurposing. Taken together, we integrated a fundamental endometrial cancer genomic profile into clinical circumstances, further shedding light for clinical implementation of genomic-based therapies and guidance for drug discovery.
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