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Platform-Independent Classification System to Predict Molecular Subtypes of High-Grade Serous Ovarian Carcinoma.

ABSTRACT: PURPOSE:Molecular cancer subtyping is an important tool in predicting prognosis and developing novel precision medicine approaches. We developed a novel platform-independent gene expression-based classification system for molecular subtyping of patients with high-grade serous ovarian carcinoma (HGSOC). METHODS:Unprocessed exon array (569 tumor and nine normal) and RNA sequencing (RNA-seq; 376 tumor) HGSOC data sets, with clinical annotations, were downloaded from the Genomic Data Commons portal. Sample clustering was performed by non-negative matrix factorization by using isoform-level expression estimates. The association between the subtypes and overall survival was evaluated by Cox proportional hazards regression model after adjusting for the covariates. A novel classification system was developed for HGSOC molecular subtyping. Robustness and generalizability of the gene signatures were validated using independent microarray and RNA-seq data sets. RESULTS:Sample clustering recaptured the four known The Cancer Genome Atlas molecular subtypes but switched the subtype for 22% of the cases, which resulted in significant (P = .006) survival differences among the refined subgroups. After adjusting for covariate effects, the mesenchymal subgroup was found to be at an increased hazard for death compared with the immunoreactive subgroup. Both gene- and isoform-level signatures achieved more than 92% prediction accuracy when tested on independent samples profiled on the exon array platform. When the classifier was applied to RNA-seq data, the subtyping calls agreed with the predictions made from exon array data for 95% of the 279 samples profiled by both platforms. CONCLUSION:Isoform-level expression analysis successfully stratifies patients with HGSOC into groups with differing prognosis and has led to the development of robust, platform-independent gene signatures for HGSOC molecular subtyping. The association of the refined The Cancer Genome Atlas HGSOC subtypes with overall survival, independent of covariates, enhances the clinical annotation of the HGSOC cohort.

SUBMITTER: Shilpi A

PROVIDER: S-EPMC6873993 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Platform-Independent Classification System to Predict Molecular Subtypes of High-Grade Serous Ovarian Carcinoma.

Shilpi Arunima A Kandpal Manoj M Ji Yanrong Y Seagle Brandon L BL Shahabi Shohreh S Davuluri Ramana V RV

JCO clinical cancer informatics 20190401

<h4>Purpose</h4>Molecular cancer subtyping is an important tool in predicting prognosis and developing novel precision medicine approaches. We developed a novel platform-independent gene expression-based classification system for molecular subtyping of patients with high-grade serous ovarian carcinoma (HGSOC).<h4>Methods</h4>Unprocessed exon array (569 tumor and nine normal) and RNA sequencing (RNA-seq; 376 tumor) HGSOC data sets, with clinical annotations, were downloaded from the Genomic Data ...[more]

PMID: 31002564

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Project description:While TP53 mutation is widely considered to be a defining feature of tubo-ovarian high-grade serous carcinoma (HGSC), rare TP53-mutation-negative cases have been reported. To gain further insight into this rare subset, a retrospective review was conducted on 25 TP53-wildtype tubo-ovarian HGSCs, constituting 2.5% of 987 HGSCs profiled by the MSK-IMPACT sequencing platform. Consistent with serous differentiation, positive staining for Pax8 and WT1 was present in virtually all TP53-wildtype HGSCs. Other characteristic features of HGSC, such as serous tubal intraepithelial carcinoma, or genetic alterations of CCNE1 and BRCA1/2 were identified in these tumors, furthering supporting their classification as bona fide HGSC, despite lacking TP53 mutations. Overall, the level of chromosomal instability of TP53-wildtype HGSCs was intermediate between low-grade serous carcinoma (LGSC) and TP53-mutated HGSC. Morphologic assessment by observers blinded to mutation status revealed a significant subset of tumors with Grade 2 nuclear atypia (which exceeds the degree of atypia allowed for LGSC, but less than typically encountered for HGSC) combined with micropapillary features (6/19, 32%, chemotherapy-naive TP53-wildtype HGSCs compared to 0/21, 0%, TP53-mutated HGSCs; p = 0.007). Some TP53-wildtype HGSCs harbored driver mutations in KRAS (n = 3), BRAF (n = 1) or NRAS (n = 2). Overall, 10 (40%) cases had "LGSC-like" morphology (i.e., Grade 2 nuclear atypia and micropapillary features) and/or RAS/RAF mutation, and most of these showed a wildtype p53 pattern of expression by immunohistochemistry (7/9, 78%). The remaining TP53-wildtype HGSCs (n = 15, 60%) exhibited severe nuclear atypia (Grade 3) and were morphologically indistinguishable from conventional TP53-mutated HGSC. Despite lacking genetic alterations of TP53, these "usual HGSC-like" tumors often showed evidence of p53 dysfunction, including downregulation of expression ('null' or equivocal p53 staining in 9/14, 64%) or MDM2 amplification (n = 2). Our results support the existence of TP53-wildtype HGSCs, which comprise a heterogeneous group of tumors which may arise via distinct pathogenic mechanisms.

Project description:ImportanceDespite similar histologic appearance among high-grade serous ovarian cancers (HGSOCs), clinical observations suggest vast differences in gross appearance. There is currently no systematic framework by which to classify HGSOCs according to their gross morphologic characteristics.ObjectiveTo develop and characterize a gross morphologic classification system for HGSOC.Design, setting, and participantsThis cohort study included patients with suspected advanced-stage ovarian cancer who presented between April 1, 2013, and August 5, 2016, to the University of Texas MD Anderson Cancer Center, a large referral center. Patients underwent laparoscopic assessment of disease burden before treatment and received a histopathologic diagnosis of HGSOC. Researchers assigning morphologic subtype and performing molecular analyses were blinded to clinical outcomes. Data analysis was performed between April 2020 and November 2021.ExposuresGross tumor morphologic characteristics.Main outcomes and measuresClinical outcomes and multiomic profiles of representative tumor samples of type I or type II morphologic subtypes were compared.ResultsOf 112 women (mean [SD] age 62.7 [9.7] years) included in the study, most patients (84% [94]) exhibited a predominant morphologic subtype and many (63% [71]) had a uniform morphologic subtype at all involved sites. Compared with those with uniform type I morphologic subtype, patients with uniform type II morphologic subtype were more likely to have a favorable Fagotti score (83% [19 of 23] vs 46% [22 of 48]; P = .004) and thus to be triaged to primary tumor reductive surgery. Similarly, patients with uniform type II morphologic subtype also had significantly higher mean (SD) estimated blood loss (639 [559; 95% CI, 391-887] mL vs 415 [527; 95% CI, 253-577] mL; P = .006) and longer mean (SD) operative time (408 [130; 95% CI, 350-466] minutes vs 333 [113; 95% CI, 298-367] minutes; P = .03) during tumor reductive surgery. Type I tumors had enrichment of epithelial-mesenchymal transition (false discovery rate [FDR] q-value, 3.10 × 10-24), hypoxia (FDR q-value, 1.52 × 10-5), and angiogenesis pathways (FDR q-value, 2.11 × 10-2), whereas type II tumors had enrichment of pathways related to MYC signaling (FDR q-value, 2.04 × 10-9) and cell cycle progression (FDR q-value, 1.10 × 10-5) by integrated proteomic and transcriptomic analysis. Abundances of metabolites and lipids also differed between the 2 morphologic subtypes.Conclusions and relevanceThis study identified 2 novel, gross morphologic subtypes of HGSOC, each with unique clinical features and molecular signatures. The findings may have implications for triaging patients to surgery or chemotherapy, identifying outcomes, and developing tailored therapeutic strategies.

Dataset Information

Platform-Independent Classification System to Predict Molecular Subtypes of High-Grade Serous Ovarian Carcinoma.

Publications

Platform-Independent Classification System to Predict Molecular Subtypes of High-Grade Serous Ovarian Carcinoma.

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