Yuexin Liu1, Lalit Patel1, Gordon B Mills1, Karen H Lu1, Anil K Sood1, Li Ding1, Raju Kucherlapati1, Elaine R Mardis1, Douglas A Levine1, Ilya Shmulevich1, Russell R Broaddus1, Wei Zhang2. 1. Department of Pathology (YL, LP, RRB, WZ) and Department of Systems Biology (GBM) and Departments of Gynecologic Oncology and Reproductive Medicine and Cancer Biology (KHL, AKS), The University of Texas MD Anderson Cancer Center, Houston, TX; The Genome Institute, Washington University, St. Louis, MO (LD, ERM); Department of Genetics, Harvard Medical School, Boston, MA (RK); Gynecology Service, Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York City, NY (DAL); The Institute for Systems Biology, Seattle, WA (IS). 2. Department of Pathology (YL, LP, RRB, WZ) and Department of Systems Biology (GBM) and Departments of Gynecologic Oncology and Reproductive Medicine and Cancer Biology (KHL, AKS), The University of Texas MD Anderson Cancer Center, Houston, TX; The Genome Institute, Washington University, St. Louis, MO (LD, ERM); Department of Genetics, Harvard Medical School, Boston, MA (RK); Gynecology Service, Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York City, NY (DAL); The Institute for Systems Biology, Seattle, WA (IS). wzhang@mdanderson.org rbroaddus@mdanderson.org.
Abstract
BACKGROUND: Endometrioid endometrial carcinoma (EEC) is the most common form of endometrial carcinoma. The heterogeneous clinical course of EEC is an obstacle to individualized patient care. METHODS: We performed an integrated analysis on the multiple-dimensional data types including whole-exome and RNA sequencing, RPPA profiling, and clinical data from 271 EEC cases in The Cancer Genome Atlas (TCGA) to identify molecular fingerprints that may account for this clinical heterogeneity. Significance analysis of microarray was used to identify marker genes of each subtype that were subject to pathway analysis. Association of molecular subtypes with clinical features and mutation data was analyzed with the Mann Whitney, Chi-square, Fisher's exact, and Kruskal-Wallis tests. Survival analysis was evaluated with log-rank test. All statistical tests were two-sided. RESULTS: Four transcriptome subtypes with distinct clinicopathologic characteristics and mutation spectra were identified from the TCGA dataset and validated in an independent sample cohort of 184 EEC cases. Cluster II consisted of younger, obese patients with low-grade EEC but diminished survival. CTNNB1 exon 3 mutations were present in 87.0% (47/54) of Cluster II (P < .001) that exhibited a low overall mutation rate; this was statistically significantly associated with Wnt/β-catenin signaling activation (P < .001). High expression levels of CTNNB1 (P = .001), MYC (P = .01), and CCND1 (P = .01) were associated with poorer overall survival in low-grade EEC tumors. CONCLUSIONS: CTNNB1 exon 3 mutations are likely a driver that characterize an aggressive subset of low-grade and low-stage EEC occurring in younger women.
BACKGROUND:Endometrioid endometrial carcinoma (EEC) is the most common form of endometrial carcinoma. The heterogeneous clinical course of EEC is an obstacle to individualized patient care. METHODS: We performed an integrated analysis on the multiple-dimensional data types including whole-exome and RNA sequencing, RPPA profiling, and clinical data from 271 EEC cases in The Cancer Genome Atlas (TCGA) to identify molecular fingerprints that may account for this clinical heterogeneity. Significance analysis of microarray was used to identify marker genes of each subtype that were subject to pathway analysis. Association of molecular subtypes with clinical features and mutation data was analyzed with the Mann Whitney, Chi-square, Fisher's exact, and Kruskal-Wallis tests. Survival analysis was evaluated with log-rank test. All statistical tests were two-sided. RESULTS: Four transcriptome subtypes with distinct clinicopathologic characteristics and mutation spectra were identified from the TCGA dataset and validated in an independent sample cohort of 184 EEC cases. Cluster II consisted of younger, obesepatients with low-grade EEC but diminished survival. CTNNB1 exon 3 mutations were present in 87.0% (47/54) of Cluster II (P < .001) that exhibited a low overall mutation rate; this was statistically significantly associated with Wnt/β-catenin signaling activation (P < .001). High expression levels of CTNNB1 (P = .001), MYC (P = .01), and CCND1 (P = .01) were associated with poorer overall survival in low-grade EEC tumors. CONCLUSIONS:CTNNB1 exon 3 mutations are likely a driver that characterize an aggressive subset of low-grade and low-stage EEC occurring in younger women.
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