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Multi-omic analysis identifies metabolic biomarkers for early detection of breast cancer and prediction of therapeutic responses

ABSTRACT: Reliable blood-based tests for identifying early-stage breast cancer remain elusive. Employing single-cell transcriptomic sequencing analysis, we illustrate a close correlation between nucleotide metabolism in breast tumor cells and activation of regulatory T cells (Tregs) in the tumor microenvironment, which show distinction in subtypes of triple-negative breast cancer (TNBC) and non-TNBC patients and likely to impact on prognosis of BC through A2AR-Treg pathway. Combining machine learning with absolute quantitative plasma metabolomics, we establish an effective diagnostic model for early-stage breast cancer, utilizing a four-metabolite panel including two nucleoside metabolites, inosine and uridine. This metabolomics study, involving 1111 participants, demonstrates high accuracy across training, test, and independent validation cohorts. Surprisingly, inosine and uridine prove predictive of the response to neoadjuvant chemotherapy (NAC) in TNBC patients. This study deepens the understanding of nucleotide metabolism in development of breast cancer and introduces a promising non-invasive approach with low radiation exposure for early breast cancer detection and predicting NAC response in TNBC patients.

ORGANISM(S): Homo sapiens

PROVIDER: GSE268662 | GEO | 2024/08/03

REPOSITORIES: GEO

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Project description:Breast cancer is a heterogeneous disease for which prognosis and treatment strategies are largely governed by the receptor status (estrogen, progesterone and Her2-neu) of the tumor cells. Gene expression profiling of whole breast tumors further stratifies breast cancer into several molecular subtypes which also co-segregate with the receptor status of the tumor cells. We postulated that cancer associated fibroblasts (CAFs) within the tumor stroma may exhibit subtype specific gene expression profiles and thus contribute to the biology of the disease in a subtype specific manner. Several studies have reported gene expression profile differences between CAFs and normal breast fibroblasts but in none of these studies were the results stratified based on tumor subtypes. To address whether gene expression in breast cancer associated fibroblasts varies between breast cancer subtypes, we compared the gene expression profiles of early passage primary CAFs isolated from twenty human breast cancer samples representing three main subtypes; seven ER+, seven triple negative (TNBC) and six Her2+. We observed significant expression differences between CAFs derived from Her2+ breast cancer and CAFs from TNBC and ER+ cancers, particularly in pathways associated with cytoskeleton and integrin signaling. In the case of Her2+ breast cancer, the signaling pathways found to be selectively up regulated in CAFs may contribute to the more invasive properties and unfavorable prognosis of Her2+ breast cancer. These data demonstrate that in addition to the distinct molecular profiles that characterize the neoplastic cells, CAF gene expression is also differentially regulated in distinct subtypes of breast cancer. We isolated CAFs from twenty primary breast cancer samples representing three main subtypes (ER+ (n=7), TNBC (n=7), Her2+ (n=6)) and performed gene expression profile analyses on RNA isolated from these early passage CAFs. Those samples were done in two batches with 4 samples repeated in both batches. One TNBC sample was found to be an outlier and not used in the analysis.

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Multi-omic analysis identifies metabolic biomarkers for early detection of breast cancer and prediction of therapeutic responses

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