ArticleNeoplasia (New York, N.Y.)2026
Multi-omics analysis unveils tumor heterogeneity and immunotherapy predictive model in breast cancer for precision medicine and early detection.
Article in Neoplasia (New York, N.Y.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed.
- AI-driven multi-omics integration in breast cancer: clinical applications, immunotherapy prediction, and translational challenges.Frontiers in molecular biosciences · 2026Review
- Discovery and validation of programmed cell death-associated key biomarker genes in ischemic stroke via ssGSEA/WGCNA and LASSO-SVM-RFE.Frontiers in molecular biosciences · 2026Article
- Systems toxicology integration uncovers trophoblast apoptosis as a pivotal mechanism underlying PFAS-related recurrent miscarriage.Frontiers in cell and developmental biology · 2026Article
- Advances in the application of multi-omics in tumor immunotherapy.Frontiers in genetics · 2026Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundIntratumoral heterogeneity contributes to therapy resistance and immune evasion in breast cancer, making treatment strategies more complex. This study integrates single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and bulk RNA-seq deconvolution to characterize tumor subpopulations and develop a robust prognostic model.
methodsWe employed a multi-omics approach combining scRNA-seq, spatial transcriptomics, and bulk RNA-seq data deconvolution to explore the molecular diversity within breast cancer tumors. Tumor subtypes were identified based on distinct gene expression profiles, and functional pathway analysis was conducted to evaluate associations with clinical outcomes, including therapy resistance and immune evasion. Data from TCGA and GEO cohorts were integrated to validate the prognostic and immune-related findings. A CoxBoost+GBM algorithm was used to develop a robust prognostic model for patient survival and immunotherapy response prediction.
resultsFive distinct tumor subtypes were identified, each with unique functional profiles, underscoring the complexity of breast cancer heterogeneity. Basal-like breast cancer (BLBC) cells were found to play a central role in immune evasion and poor immunotherapy response, with high basal-like cell infiltration correlating with worse survival outcomes. Spatial transcriptomics revealed the widespread presence of BLBC cells across clinical subtypes, including ER+ tumors, suggesting their involvement in therapy resistance. A prognostic model based on CoxBoost+GBM demonstrated strong predictive power for patient survival and immunotherapy efficacy.
conclusionsThis study provides a comprehensive view of the genetic and immune determinants of breast cancer heterogeneity, with a focus on BLBC's role in immune escape and treatment resistance. These insights enhance the potential of multi-omics approaches in precision prevention, early detection, and personalized immunotherapy strategies.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.