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Integrative molecular profiling identifies a novel cluster of estrogen receptor-positive breast cancer in very young women.

ABSTRACT: Very young breast cancer patients are more common in Asian countries than Western countries and are thought to have worse prognosis than older patients. The aim of the current study was to identify molecular characteristics of young patients with estrogen receptor (ER)-positive breast cancer by analyzing mutations and copy number variants (CNV), and by applying expression profiling. The whole exome and transcriptome of 47 Korean young breast cancer (KYBR) patients (age <35) were analyzed. Genomic profiles were constructed using mutations, CNV and differential gene expression from sequencing data. Pathway analyses were also performed using gene sets to identify biological processes. Our data were compared with young ER+ breast cancer patients in The Cancer Genome Atlas (TCGA) dataset. TP53, PIK3CA and GATA3 were highly recurrent somatic mutation genes. APOBEC-associated mutation signature was more frequent in KYBR compared with young TCGA patients. Integrative profiling was used to classify our patients into 3 subgroups based on molecular characteristics. Group A showed luminal A-like subtype and IGF1R signal dysregulation. Luminal B patients were classified into groups B and C, which showed chromosomal instability and enrichment for APOBEC3A/B deletions, respectively. Group B was characterized by 11q13 (CCND1) amplification and activation of the ubiquitin-mediated proteolysis pathway. Group C showed 17q12 (ERBB2) amplification and lower ER and progesterone receptor expression. Group C was also distinguished by immune activation and lower epithelial-mesenchyme transition (EMT) degree compared with group B. This study showed that integrative genomic profiling could classify very young patients with breast cancer into molecular subgroups that are potentially linked to different clinical characteristics.

SUBMITTER: Park C

PROVIDER: S-EPMC6500962 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND: Breast cancer is rarely diagnosed in very young women (35 years old or younger), and it often presents with distinct clinical-pathological features related to a more aggressive phenotype and worse prognosis when diagnosed at this early age. A pending question is whether breast cancer in very young women arises from the deregulation of different underlying mechanisms, something that will make this disease an entity differentiated from breast cancer diagnosed in older patients. METHODS: We performed a comprehensive study of miRNA expression using miRNA Affymetrix2.0 array on paraffin-embedded tumour tissue of 42 breast cancer patients 35 years old or younger, 17 patients between 45 and 65 years old and 29 older than 65 years. Data were statistically analyzed by t-test and a hierarchical clustering via average linkage method was conducted. Results were validated by qRT-PCR. Putative targeted pathways were obtained using DIANA miRPath online software. RESULTS: The results show a differential and unique miRNA expression profile of 121 miRNAs (p-value <0.05), 96 of those with a FDR-value <0.05. Hierarchical clustering grouped the samples according to their age, but not by subtype nor by tumour characteristics. We were able to validate by qRT-PCR differences in the expression of 6 miRNAs: miR-1228*, miR-3196, miR-1275, miR-92b, miR-139 and miR-1207. Moreover, all of the miRNAs maintained the expression trend. The validated miRNAs pointed out pathways related to cell motility, invasion and proliferation. CONCLUSIONS: The study suggests that breast cancer in very young women appears as a distinct molecular signature. To our knowledge, this is the first time that a validated microRNA profile, distinctive to breast cancer in very young women, has been presented. The miRNA signature may be relevant to open an important field of research in order to elucidate the underlying mechanism in this particular disease, which in a more clinical setting, could potentially help to identify therapeutic targets in this particular set of patients.

Project description:BackgroundExisting studies have demonstrated that the integrative analysis of histopathological images and genomic data can be used to better understand the onset and progression of many diseases, as well as identify new diagnostic and prognostic biomarkers. However, since the development of pathological phenotypes are influenced by a variety of complex biological processes, complete understanding of the underlying gene regulatory mechanisms for the cell and tissue morphology is still a challenge. In this study, we explored the relationship between the chromatin accessibility changes and the epithelial tissue proportion in histopathological images of estrogen receptor (ER) positive breast cancer.MethodsAn established whole slide image processing pipeline based on deep learning was used to perform global segmentation of epithelial and stromal tissues. We then used canonical correlation analysis to detect the epithelial tissue proportion-associated regulatory regions. By integrating ATAC-seq data with matched RNA-seq data, we found the potential target genes that associated with these regulatory regions. Then we used these genes to perform the following pathway and survival analysis.ResultsUsing canonical correlation analysis, we detected 436 potential regulatory regions that exhibited significant correlation between quantitative chromatin accessibility changes and the epithelial tissue proportion in tumors from 54 patients (FDR < 0.05). We then found that these 436 regulatory regions were associated with 74 potential target genes. After functional enrichment analysis, we observed that these potential target genes were enriched in cancer-associated pathways. We further demonstrated that using the gene expression signals and the epithelial tissue proportion extracted from this integration framework could stratify patient prognoses more accurately, outperforming predictions based on only omics or image features.ConclusionThis integrative analysis is a useful strategy for identifying potential regulatory regions in the human genome that are associated with tumor tissue quantification. This study will enable efficient prioritization of genomic regulatory regions identified by ATAC-seq data for further studies to validate their causal regulatory function. Ultimately, identifying epithelial tissue proportion-associated regulatory regions will further our understanding of the underlying molecular mechanisms of disease and inform the development of potential therapeutic targets.

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Integrative molecular profiling identifies a novel cluster of estrogen receptor-positive breast cancer in very young women.

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