Whole-exome sequencing combined with functional genomics reveals novel candidate driver cancer genes in endometrial cancer

Endometrial cancer is the most common gynecological malignancy, with more than 280,000 cases occurring annually worldwide. Although previous studies have identified important common somatic mutations in endometrial cancer, they have primarily focused on a small set of known cancer genes and have thus provided a limited view of the molecular basis underlying this disease. Here we have developed an integrated systems-biology approach to identifying novel cancer genes contributing to endometrial tumorigenesis. We first performed whole-exome sequencing on 13 endometrial cancers and matched normal samples, systematically identifying somatic alterations with high precision and sensitivity. We then combined bioinformatics prioritization with high-throughput screening (including both shRNA-mediated knockdown and expression of wild-type and mutant constructs) in a highly sensitive cell viability assay. Our results revealed 12 potential driver cancer genes including 10 tumor-suppressor candidates (ARID1A, INHBA, KMO, TTLL5, GRM8, IGFBP3, AKTIP, PHKA2, TRPS1, and WNT11) and two oncogene candidates (ERBB3 and RPS6KC1). The results in the ''sensor'' cell line were recapitulated by siRNA-mediated knockdown in endometrial cancer cell lines. Focusing on ARID1A, we integrated mutation profiles with functional proteomics in 222 endometrial cancer samples, demonstrating that ARID1A mutations frequently co-occur with mutations in the phosphatidylinositol 3-kinase (PI3K) pathway and are associated with PI3K pathway activation. siRNA knockdown in endometrial cancer cell lines increased AKT phosphorylation supporting ARID1A as a novel regulator of PI3K pathway activity. Our study presents the first unbiased view of somatic coding mutations in endometrial cancer and provides functional evidence for diverse driver genes and mutations in this disease. © 2012, Published by Cold Spring Harbor Laboratory Press.Link_to_subscribed_fulltex

Comprehensive molecular characterization of gastric adenocarcinoma

Gastric cancer is a leading cause of cancer deaths, but analysis of its molecular and clinical characteristics has been complicated by histological and aetiological heterogeneity. Here we describe a comprehensive molecular evaluation of 295 primary gastric adenocarcinomas as part of The Cancer Genome Atlas (TCGA) project. We propose a molecular classification dividing gastric cancer into four subtypes: tumours positive for Epstein–Barr virus, which display recurrent PIK3CA mutations, extreme DNA hypermethylation, and amplification of JAK2, CD274 (also known as PD-L1) and PDCD1LG2 (also knownasPD-L2); microsatellite unstable tumours, which show elevated mutation rates, including mutations of genes encoding targetable oncogenic signalling proteins; genomically stable tumours, which are enriched for the diffuse histological variant and mutations of RHOA or fusions involving RHO-family GTPase-activating proteins; and tumours with chromosomal instability, which show marked aneuploidy and focal amplification of receptor tyrosine kinases. Identification of these subtypes provides a roadmap for patient stratification and trials of targeted therapies

Molecular Subtypes of Glioblastoma Are Relevant to Lower Grade Glioma

<div><p>Background</p><p>Gliomas are the most common primary malignant brain tumors in adults with great heterogeneity in histopathology and clinical course. The intent was to evaluate the relevance of known glioblastoma (GBM) expression and methylation based subtypes to grade II and III gliomas (ie. lower grade gliomas).</p><p>Methods</p><p>Gene expression array, single nucleotide polymorphism (SNP) array and clinical data were obtained for 228 GBMs and 176 grade II/II gliomas (GII/III) from the publically available Rembrandt dataset. Two additional datasets with <i>IDH1</i> mutation status were utilized as validation datasets (one publicly available dataset and one newly generated dataset from MD Anderson). Unsupervised clustering was performed and compared to gene expression subtypes assigned using the Verhaak et al 840-gene classifier. The glioma-CpG Island Methylator Phenotype (G-CIMP) was assigned using prediction models by Fine et al.</p><p>Results</p><p>Unsupervised clustering by gene expression aligned with the Verhaak 840-gene subtype group assignments. GII/IIIs were preferentially assigned to the proneural subtype with <i>IDH1</i> mutation and G-CIMP. GBMs were evenly distributed among the four subtypes. Proneural, <i>IDH1</i> mutant, G-CIMP GII/III s had significantly better survival than other molecular subtypes. Only 6% of GBMs were proneural and had either IDH1 mutation or G-CIMP but these tumors had significantly better survival than other GBMs. Copy number changes in chromosomes 1p and 19q were associated with GII/IIIs, while these changes in <i>CDKN2A</i>, <i>PTEN</i> and <i>EGFR</i> were more commonly associated with GBMs.</p><p>Conclusions</p><p>GBM gene-expression and methylation based subtypes are relevant for GII/III s and associate with overall survival differences. A better understanding of the association between these subtypes and GII/IIIs could further knowledge regarding prognosis and mechanisms of glioma progression.</p></div

Survival analysis of gene expression subtype and <i>IDH1</i>/G-CIMP status by histological group and by grade of tumor adjusted for age at diagnosis.

<p>Merged dataset of JCO, Rembrandt and DASL (A: Oligo II& III (N = 46); B: Astro II & III (N = 132); C: GBM (N = 387); D: Grade II(N = 71); E: Grade III (N = 107)).</p

Somatic copy number analysis for Rembrandt dataset by histological group, gene expression subtype and IDH1/G-CIMP status (p values were accessed via fisher’s exact test).

<p>Somatic copy number analysis for Rembrandt dataset by histological group, gene expression subtype and IDH1/G-CIMP status (p values were accessed via fisher’s exact test).</p

Overall analysis approach.

<p>Overall analysis approach.</p

Clinical information and median survival by gene expression subtype and histological group for overall study combined dataset (n = 404 Rembrandt+171 JCO+141 DASL = 716 TOTAL).

<p>* Included in all three dataset.</p><p>Median survival in months (95% CI) was adjusted for age. P values were reported using Log-rank test.</p><p>&Included in Rembrandt only.</p