Analysis of chemotherapeutic response in ovarian cancers using publicly available high-throughput data.
A third of patients with epithelial ovarian cancer (OVCA) will not respond to standard treatment. The determination of a robust signature that predicts chemoresponse could lead to the identification of molecular markers for response as well as possible clinical implementation in the future to identify patients at risk of failing therapy. This pilot study was designed to identify biologic processes affecting candidate pathways associated with chemoresponse and to create a robust gene signature for follow-up studies. After identifying common pathways associated with chemoresponse in serous OVCA in three independent gene-expression experiments, we assessed the biologic processes associated with them using The Cancer Genome Atlas (TCGA) dataset for serous OVCA. We identified differential copy-number alterations (CNA), mutations, DNA methylation, and miRNA expression between patients that responded to standard treatment and those who did not or recurred prematurely. We correlated these significant parameters with gene expression to create a signature of 422 genes associated with chemoresponse. A consensus clustering of this signature identified two differentiated clusters with unique molecular patterns: cluster 1 was significant for cellular signaling and immune response (mainly cell-mediated); and cluster 2 was significant for pathways involving DNA-damage repair and replication, cell cycle, and apoptosis. Validation through consensus clustering was performed in five independent OVCA gene-expression experiments. Genes were located in the same cluster with consistent agreement in all five studies (κ coefficient ≥ 0.6 in 4). Integrating high-throughput biologic data have created a robust molecular signature that predicts chemoresponse in OVCA.
Authors' Affiliations: Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of Iowa Hospitals and Clinics, Iowa City, Iowa; jesus-gonzalezbosquet@uiowa.edu.
Jesus Gonzalez
JG
Bosquet
Department of Women's Oncology, Experimental Therapeutics Program, Department of Oncologic Sciences;
Douglas C
DC
Marchion
Department of Women's Oncology, Gynecologic Oncology, Department of Oncologic Sciences, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida; and.
HyeSook
H
Chon
Department of Women's Oncology, Gynecologic Oncology, Department of Oncologic Sciences, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida; and.
Johnathan M
JM
Lancaster
Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, Maryland.
Stephen
S
Chanock
D000328:Adult; D000368:Aged; D000077216:Carcinoma, Ovarian Epithelial; D055028:Comparative Genomic Hybridization; D019295:Computational Biology; D056915:DNA Copy Number Variations; D019175:DNA Methylation; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods; D015972:Gene Expression Regulation, Neoplastic; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D008875:Middle Aged; D060787:Neoplasm Grading; D009367:Neoplasm Staging; D009375:Neoplasms, Glandular and Epithelial / Q000188:drug therapy / Q000235:genetics; D010051:Ovarian Neoplasms / Q000188:drug therapy* / Q000235:genetics* / Q000378:metabolism / Q000473:pathology; D015398:Signal Transduction; D016896:Treatment Outcome