Abstract

Purpose: Molecular classification of endometrial cancer identified distinct molecular subgroups. However, the largest subset of endometrial cancers remains poorly characterized and is referred to as the "nonspecific molecular profile" (NSMP) subgroup. Here, we aimed at refining the classification of this subgroup by profiling somatic copy-number aberrations (SCNAs).Experimental Design: SCNAs were analyzed in 141 endometrial cancers using whole-genome SNP arrays and pooled with 361 endometrial cancers from The Cancer Genome Atlas. Genomic Identification of Significant Targets in Cancer (GISTIC) identified statistically enriched SCNAs and penalized Cox regression assessed survival effects. The prognostic significance of relevant SCNAs was validated using multiplex ligation-dependent probe amplification in 840 endometrial cancers from the PORTEC-1/2 trials. Copy-number status of genes was correlated with gene expression to identify potential cancer drivers. One plausible oncogene was validated in vitro using antisense oligonucleotide-based strategy.Results: SCNAs affecting chromosome 1q32.1 significantly correlated with worse relapse-free survival (RFS) in the NSMP subgroup (HR, 2.12; 95% CI, 1.26-3.59; P = 0.005). This effect was replicated in NSMP endometrial cancers from PORTEC-1/2 (HR, 2.34; 95% CI, 1.17-4.70; P = 0.017). A new molecular classification including the 1q32.1 amplification improved risk prediction of recurrence. MDM4 gene expression strongly correlated with 1q32.1 amplification. Silencing MDM4 inhibited cell growth in cell lines carrying 1q32.1 amplification, but not in those without MDM4 amplification. Vice versa, increasing MDM4 expression in nonamplified cell lines stimulated cell proliferation.Conclusions: 1q32.1 amplification was identified as a prognostic marker for poorly characterized NSMP endometrial cancers, refining the molecular classification of this subgroup. We functionally validated MDM4 as a potential oncogenic driver in the 1q32.1 region. Clin Cancer Res; 23(23); 7232-41. ©2017 AACR.

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