Dataset Information

Integrated multiomic predictors for ovarian cancer survival.

ABSTRACT: Increasingly affordable high-throughput molecular profiling technologies have made feasible the measurement of omics-wide interindividual variations for the purposes of predicting cancer prognosis. While multiple types of genetic, epigenetic and expression changes have been implicated in ovarian cancer, existing prognostic biomarker strategies are constrained to analyzing a single class of molecular variations. The extra predictive power afforded by the integration of multiple omics types remains largely unexplored. In this study, we performed integrative analysis on tumor-based exome-, transcriptome- and methylome-wide molecular profiles from The Cancer Genome Atlas (TCGA) for variations in cancer-relevant genes to construct robust, cross-validated multiomic predictors for ovarian cancer survival. These integrated polygenic survival scores (PSSs) were able to predict 5-year overall (OS) and progression-free survival in the Caucasian subsample with high accuracy (AUROC = 0.87 and 0.81, respectively). These findings suggest that the PSSs are able to predict long-term OS in TCGA patients with accuracy beyond that of previously proposed protein-based biomarker strategies. Our findings reveal the promise of an integrated omics-based approach in enhancing existing prognostic strategies. Future investigations should be aimed toward prospective external validation, strategies for standardizing application and the integration of germline variants.

SUBMITTER: Fu A

PROVIDER: S-EPMC6031025 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Integrated multiomic predictors for ovarian cancer survival.

Fu Alan A Chang Helena R HR Zhang Zuo-Feng ZF

Carcinogenesis 20180701 7

Increasingly affordable high-throughput molecular profiling technologies have made feasible the measurement of omics-wide interindividual variations for the purposes of predicting cancer prognosis. While multiple types of genetic, epigenetic and expression changes have been implicated in ovarian cancer, existing prognostic biomarker strategies are constrained to analyzing a single class of molecular variations. The extra predictive power afforded by the integration of multiple omics types remain ...[more]

PMID: 29897425

Similar Datasets

Project description:ObjectiveAlthough ovarian cancer is a deadly disease, approximately a third of women survive ≥9 years after diagnosis. The factors associated with achieving long-term survival are not well understood. In this study, data from the Surveillance, Epidemiology, and End Results (SEER) program were used to determine predictors of survival trajectories among women with epithelial ovarian cancer and across histotype (high-grade serous carcinoma (HGSC) and non-HGSC).MethodsData on 35,868 women diagnosed with epithelial ovarian cancer in 2004-2016 were extracted from SEER. Extended Cox proportional hazards regression was used to estimate overall and histotype-specific associations between patient and tumor characteristics and all-cause mortality within each survival time (t) interval (t < 3, 3 ≤ t < 6, 6 ≤ t < 9, and 9 ≤ t < 13 years).ResultsAge at diagnosis, marital status, race/ethnicity, stage, and surgery were more strongly associated with mortality in the short-term survival period, and these associations waned with increasing survival time. Exceptions to this pattern were age >70 years at diagnosis, where a high risk of mortality was observed in both the t < 3 and t ≥ 9 year time periods, and non-Hispanic Asian/Pacific Islanders, where a more pronounced inverse association with mortality was observed in t ≥ 9 years after diagnosis. Similar associations were observed for HGSC, although the waning effect was not apparent for most characteristics. Mortality associations for non-HGSC were more pronounced for stage and race/ethnicity, primarily for non-Hispanic Asian/Pacific Islanders.ConclusionsMost patient and tumor characteristics were more strongly associated with mortality in the years following diagnosis, but have declining impact with increasing survival time. Given this waning effect, it is critical to identify factors impacting risk of mortality as ovarian cancer patients advance through the survival trajectory.

Project description:Ovarian cancer (OC), the eighth-leading cause of cancer-related death among females worldwide, is mainly represented by epithelial OC (EOC) that can be further subdivided into four subtypes: serous (75%), endometrioid (10%), clear cell (10%), and mucinous (3%). Major reasons for high mortality are the poor biological understanding of the OC mechanisms and a lack of reliable markers defining each EOC subtype. MicroRNAs (miRNAs) are small non-coding RNA molecules that regulate gene expression primarily by targeting messenger RNA (mRNA) transcripts. Their aberrant expression patterns have been associated with cancer development, including OC. However, the role of miRNAs in tumorigenesis is still to be determined, mainly due to the lack of consensus regarding optimal methodologies for identification and validation of miRNAs and their targets. Several tools for computational target prediction exist, but false interpretations remain a problem. The experimental validation of every potential miRNA-mRNA pair is not feasible, as it is laborious and expensive. In this study, we analyzed the correlation between global miRNA and mRNA expression patterns derived from microarray profiling of 197 EOC patients to identify the signatures of miRNA-mRNA interactions associated with overall survival (OS). The aim was to investigate whether these miRNA-mRNA signatures might have a prognostic value for OS in different subtypes of EOC. The content of our cohort (162 serous carcinomas, 15 endometrioid carcinomas, 11 mucinous carcinomas, and 9 clear cell carcinomas) reflects a real-world scenario of EOC. Several interaction pairs between 6 miRNAs (hsa-miR-126-3p, hsa-miR-223-3p, hsa-miR-23a-5p, hsa-miR-27a-5p, hsa-miR-486-5p, and hsa-miR-506-3p) and 8 mRNAs (ATF3, CH25H, EMP1, HBB, HBEGF, NAMPT, POSTN, and PROCR) were identified and the findings appear to be well supported by the literature. This indicates that our study has a potential to reveal miRNA-mRNA signatures relevant for EOC. Thus, the evaluation on independent cohorts will further evaluate the performance of such findings.

Project description:BackgroundImmunotherapy of acute myeloid leukemia has experienced considerable advances, however novel target antigens continue to be sought after. To this end, unbiased approaches for surface protein detection are limited and integration with other data types, such as gene expression and somatic mutational burden, are poorly utilized. The Cell Surface Capture technology provides an unbiased, discovery-driven approach to map the surface proteins on cells of interest. Yet, direct utilization of primary patient samples has been limited by the considerable number of viable cells needed.MethodsHere, we optimized the Cell Surface Capture protocol to enable direct interrogation of primary patient samples and applied our optimized protocol to a set of samples from patients with acute myeloid leukemia (AML) to generate the AML surfaceome. We then further curated this AML surfaceome to exclude antigens expressed on healthy tissues and integrated mutational burden data from hematologic cancers to further enrich for targets which are likely to be essential to leukemia biology. Finally, we validated our findings in a separate cohort of AML patient samples.ResultsOur protocol modifications allowed us to double the yield in identified proteins and increased the specificity from 54 to 80.4% compared to previous approaches. Using primary AML patient samples, we were able to identify a total of 621 surface proteins comprising the AML surfaceome. We integrated this data with gene expression and mutational burden data to curate a set of robust putative target antigens. Seventy-six proteins were selected as potential candidates for further investigation of which we validated the most promising novel candidate markers, and identified CD148, ITGA4 and Integrin beta-7 as promising targets in AML. Integrin beta-7 showed the most promising combination of expression in patient AML samples, and low or absent expression on healthy hematopoietic tissue.ConclusionTaken together, we demonstrate the feasibility of a highly optimized surfaceome detection method to interrogate the entire AML surfaceome directly from primary patient samples and integrate this data with gene expression and mutational burden data to achieve a robust, multiomic target identification platform. This approach has the potential to accelerate the unbiased target identification for immunotherapy of AML.

Dataset Information

Integrated multiomic predictors for ovarian cancer survival.

Publications

Integrated multiomic predictors for ovarian cancer survival.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets