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Multi-omics profiling of younger Asian breast cancers reveals distinctive molecular signatures.

ABSTRACT: Breast cancer (BC) in the Asia Pacific regions is enriched in younger patients and rapidly rising in incidence yet its molecular bases remain poorly characterized. Here we analyze the whole exomes and transcriptomes of 187 primary tumors from a Korean BC cohort (SMC) enriched in pre-menopausal patients and perform systematic comparison with a primarily Caucasian and post-menopausal BC cohort (TCGA). SMC harbors higher proportions of HER2+ and Luminal B subtypes, lower proportion of Luminal A with decreased ESR1 expression compared to TCGA. We also observe increased mutation prevalence affecting BRCA1, BRCA2, and TP53 in SMC with an enrichment of a mutation signature linked to homologous recombination repair deficiency in TNBC. Finally, virtual microdissection and multivariate analyses reveal that Korean BC status is independently associated with increased TIL and decreased TGF-? signaling expression signatures, suggesting that younger Asian BCs harbor more immune-active microenvironment than western BCs.

SUBMITTER: Kan Z

PROVIDER: S-EPMC5928087 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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Multi-omics profiling of younger Asian breast cancers reveals distinctive molecular signatures.

Kan Zhengyan Z Ding Ying Y Kim Jinho J Jung Hae Hyun HH Chung Woosung W Lal Samir S Cho Soonweng S Fernandez-Banet Julio J Lee Se Kyung SK Kim Seok Won SW Lee Jeong Eon JE Choi Yoon-La YL Deng Shibing S Kim Ji-Yeon JY Ahn Jin Seok JS Sha Ying Y Mu Xinmeng Jasmine XJ Nam Jae-Yong JY Im Young-Hyuck YH Lee Soohyeon S Park Woong-Yang WY Nam Seok Jin SJ Park Yeon Hee YH

Nature communications 20180430 1

Breast cancer (BC) in the Asia Pacific regions is enriched in younger patients and rapidly rising in incidence yet its molecular bases remain poorly characterized. Here we analyze the whole exomes and transcriptomes of 187 primary tumors from a Korean BC cohort (SMC) enriched in pre-menopausal patients and perform systematic comparison with a primarily Caucasian and post-menopausal BC cohort (TCGA). SMC harbors higher proportions of HER2+ and Luminal B subtypes, lower proportion of Luminal A wit ...[more]

PMID: 29713003

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Project description:BackgroundComplex disorders, such as Alzheimer's disease (AD), result from the combined influence of multiple biological and environmental factors. The integration of high-throughput data from multiple omics platforms can provide system overviews, improving our understanding of complex biological processes underlying human disease. In this study, integrated data from four omics platforms were used to characterise biological signatures of AD.MethodThe study cohort consists of 455 participants (Control:148, Cases:307) from the Religious Orders Study and Memory and Aging Project (ROSMAP). Genotype (SNP), methylation (CpG), RNA and proteomics data were collected, quality-controlled and pre-processed (SNP = 130; CpG = 83; RNA = 91; Proteomics = 119). Using a diagnosis of Mild Cognitive Impairment (MCI)/AD combined as the target phenotype, we first used Partial Least Squares Regression as an unsupervised classification framework to assess the prediction capabilities for each omics dataset individually. We then used a variation of the sparse generalized canonical correlation analysis (sGCCA) to assess predictions of the combined datasets and identify multi-omics signatures characterising each group of participants.ResultsAnalysing datasets individually we found methylation data provided the best predictions with an accuracy of 0.63 (95%CI = [0.54-0.71]), followed by RNA, 0.61 (95%CI = [0.52-0.69]), SNP, 0.59 (95%CI = [0.51-0.68]) and proteomics, 0.58 (95%CI = [0.51-0.67]). After integration of the four datasets, predictions were dramatically improved with a resulting accuracy of 0.95 (95% CI = [0.89-0.98]).ConclusionThe integration of data from multiple platforms is a powerful approach to explore biological systems and better characterise the biological signatures of AD. The results suggest that integrative methods can identify biomarker panels with improved predictive performance compared to individual platforms alone. Further validation in independent cohorts is required to validate and refine the results presented in this study.

Dataset Information

Multi-omics profiling of younger Asian breast cancers reveals distinctive molecular signatures.

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Multi-omics profiling of younger Asian breast cancers reveals distinctive molecular signatures.

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