Multi-omics Profiling of Asian Breast Cancers
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ABSTRACT:
PROVIDER: EGAS00001002621 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description: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-β signaling expression signatures, suggesting that younger Asian BCs harbor more immune-active microenvironment than western BCs.
| S-EPMC5928087 | biostudies-literature
Project description:We observed higher proportions of HER2+ and Luminal B but lower proportions of ER+ and Luminal A subtypes along with lower estrogen receptor (ER) expression in SMC compared to TCGA. Germline pathogenic mutations affecting BRCA1 or BRCA2 were found in 11% of SMC but only 5% of TCGA. TP53 was also more frequently mutated in SMC (48%) than in TCGA (32%).
2018-04-17 | GSE113184 | GEO
Project description:Multi-omics Profiling of Younger Asian Breast Cancers Reveals Distinctive Molecular Signatures
| PRJNA450393 | ENA
Project description:Abstract
The triple-negative breast cancer (TNBC) accounts for approximately 15% of all Breast Cancer (BC) cases. However, the prognosis and clinical outcomes of TNBC are worse than those of other BC subtypes due to a greater tumor and few therapeutically targetable oncogenic drivers. Numerous studies have employed genomic and transcriptomic approaches to identify clinically actionable TNBC subtypes in a comprehensive and unbiased manner using. While these analyses have advanced our knowledge of the molecular changes underlying TNBC, their clinical utility remains limited thus far. Given that proteins are the principal effector molecules of cellular signaling and function, we use a proteomic approach to quantitatively compare the abundances of 6,306 proteins across 55 formalin-fixed and paraffin-embedded (FFPE) TNBC tumors to reveal actionable pathways for anti-cancer treatment. We identified four major TNBC clusters by unsupervised clustering analysis of protein abundances. In addition, analyses of clinicopathological characteristics revealed associations between proteomic results and clinical phenotypes exhibited by each subtype. We validate the findings of our proteomics analysis, by inferring immune and stromal cell type composition from genome-wide DNA methylation profiles. Finally, quantitative proteomics on TNBC cell lines was conducted to identify representative in vitro models for each subtype. Collectively, our multi-omics data provide novel subtype-specific insights such as potential biomarkers, molecular drivers, and pharmacologic vulnerabilities for further investigations.
2024-08-28 | MSV000095715 | MassIVE
Project description:SNP genotyping of human breast tumors
2019-04-17 | GSE100529 | GEO
Project description:Metastatic breast cancer is the leading cause of death amongst patients that develop breast cancer. The molecular drivers of metastatic breast cancer are largely unknown. Treatment of metastatic breast cancer is limited to systemic cytotoxic chemotherapies, which often deteriorate patient quality of life, and tumors often become refractory, resulting in incurable disease. The most frequently mutated gene associated with metastatic breast cancer is TP53. The TP53R248W hotspot missense mutation is associated with poor prognosis in breast cancer. To understand the intrinsic transcriptomic and genomic landscape of metastatic breast cancer driven by mutant p53, we generated a somatic mouse model driven by mammary epithelial specific expression of Trp53R248W. Primary tumors were highly metastatic and reflected the human molecular subtypes of luminal A, luminal B, HER2, and TNBC, with TNBC comprising the majority (79%) of these tumors. Transcriptomic profiling revealed primary tumor heterogeneity characterized by three intrinsic subtypes (1) stem-cell like; (2) well-differentiated metabolic like; and (3) immune suppressed. Stem-cell like tumors activate ribosome biosynthesis and E2F signaling. These tumors harbor amplifications in Met, Birc3, Yap1 and deletions in Nf1, Pik3r1, and Rad17. Well-differentiated metabolic like mammary tumors activate cytochrome P450 enzymes, estrogen signaling and branched chain amino acid degradation and harbor mutations in the Pten pathway. Immune suppressed tumors enrich for processes related to immune suppression. These tumors have high mutation burden, frequently mutate Traf7 and delete Cdkn2a. This is the most comprehensive transcriptomic and genomic profiling of mutant p53 driven breast tumors and sheds light on their heterogenous nature and distinct intrinsic programs that may inform targeted therapy for metastatic breast cancer.
2025-12-19 | GSE301300 | GEO
Project description:Transcriptomic profiling of human breast tumors using RNA sequencing
2019-04-17 | GSE100799 | GEO
Project description:Metastatic breast cancer is the leading cause of death amongst patients that develop breast cancer. The molecular drivers of metastatic breast cancer are largely unknown. Treatment of metastatic breast cancer is limited to systemic cytotoxic chemotherapies, which often deteriorate patient quality of life, and tumors often become refractory, resulting in incurable disease. The most frequently mutated gene associated with metastatic breast cancer is TP53. The TP53R248W hotspot missense mutation is associated with poor prognosis in breast cancer. To understand the intrinsic transcriptomic and genomic landscape of metastatic breast cancer driven by mutant p53, we generated a somatic mouse model driven by mammary epithelial specific expression of Trp53R248W. Primary tumors were highly metastatic and reflected the human molecular subtypes of luminal A, luminal B, HER2, and TNBC, with TNBC comprising the majority (79%) of these tumors. Transcriptomic profiling revealed primary tumor heterogeneity characterized by three intrinsic subtypes (1) stem-cell like; (2) well-differentiated metabolic like; and (3) immune suppressed. Stem-cell like tumors activate ribosome biosynthesis and E2F signaling. These tumors harbor amplifications in Met, Birc3, Yap1 and deletions in Nf1, Pik3r1, and Rad17. Well-differentiated metabolic like mammary tumors activate cytochrome P450 enzymes, estrogen signaling and branched chain amino acid degradation and harbor mutations in the Pten pathway. Immune suppressed tumors enrich for processes related to immune suppression. These tumors have high mutation burden, frequently mutate Traf7 and delete Cdkn2a. This is the most comprehensive transcriptomic and genomic profiling of mutant p53 driven breast tumors and sheds light on their heterogenous nature and distinct intrinsic programs that may inform targeted therapy for metastatic breast cancer.
2025-12-19 | GSE301299 | GEO
Project description:Transcriptomic profiling of human breast tumors. Genomic and expression profiling using 38K BAC array-CGH and Illumina HT-12 beadchips were performed on 53 invasive breast tumors to assess the impact of gene dosage on gene expression patterns and the effect of other mechanisms on transcriptional levels. Array-CGH results was validated by FISH using tumors showing 8p11-p12 DNA amplification and expression profiling was confirmed using qPCR for 11 transcripts. Low-level gain, high-level gain/amplification, heterozygous loss and homozygous deletion (henceforth referred to as gain, amplification, loss and deletion) were defined as log2 ratio thresholds set at +0.2, >= +0.5, -0.2 and <=-1.0, respectively.
2017-03-30 | GSE97177 | GEO
Project description:Analysis of different proteomics profile in primary tumors of metastatic and non-metastatic breast cancers.
2023-03-09 | PXD037288 | Pride