Multi-omics analysis unveils tumor heterogeneity and immunotherapy predictive model in breast cancer for precision medicine and early detection

IF 7.7 2区医学 Q1 Biochemistry, Genetics and Molecular Biology

Neoplasia Pub Date : 2026-01-01 Epub Date: 2025-12-03 DOI:10.1016/j.neo.2025.101260

Zhenxiong Zhao , Zhencang Zheng , Shenglu Jiang , Lingling Zhang , Xiufeng Tang

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引用次数: 0

Abstract

Background

Intratumoral 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.

Methods

We 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.

Results

Five 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.

Conclusions

This 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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多组学分析揭示乳腺癌肿瘤异质性和免疫治疗预测模型，用于精准医学和早期检测。

背景：肿瘤内异质性有助于乳腺癌的治疗抵抗和免疫逃避，使治疗策略更加复杂。该研究整合了单细胞RNA测序（scRNA-seq）、空间转录组学和大量RNA-seq反褶积来表征肿瘤亚群，并建立了一个强大的预后模型。方法：我们采用多组学方法，结合scRNA-seq、空间转录组学和大量RNA-seq数据反卷积来探索乳腺癌肿瘤的分子多样性。根据不同的基因表达谱确定肿瘤亚型，并进行功能通路分析以评估其与临床结果（包括治疗耐药性和免疫逃避）的关联。整合来自TCGA和GEO队列的数据以验证预后和免疫相关发现。使用CoxBoost+GBM算法建立了一个可靠的预后模型，用于预测患者的生存和免疫治疗反应。结果：确定了五种不同的肿瘤亚型，每种亚型都具有独特的功能特征，强调了乳腺癌异质性的复杂性。发现基底样乳腺癌（BLBC）细胞在免疫逃避和免疫治疗反应差中起核心作用，高基底样细胞浸润与较差的生存结果相关。空间转录组学揭示了BLBC细胞在临床亚型（包括ER+肿瘤）中的广泛存在，表明它们参与了治疗耐药性。基于CoxBoost+GBM的预后模型显示出对患者生存和免疫治疗疗效的强大预测能力。结论：本研究提供了乳腺癌异质性的遗传和免疫决定因素的综合观点，重点关注BLBC在免疫逃逸和治疗抵抗中的作用。这些见解增强了多组学方法在精确预防、早期检测和个性化免疫治疗策略方面的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Neoplasia 医学-肿瘤学

CiteScore

9.20

自引率

2.10%

发文量

审稿时长

26 days

期刊介绍： Neoplasia publishes the results of novel investigations in all areas of oncology research. The title Neoplasia was chosen to convey the journal’s breadth, which encompasses the traditional disciplines of cancer research as well as emerging fields and interdisciplinary investigations. Neoplasia is interested in studies describing new molecular and genetic findings relating to the neoplastic phenotype and in laboratory and clinical studies demonstrating creative applications of advances in the basic sciences to risk assessment, prognostic indications, detection, diagnosis, and treatment. In addition to regular Research Reports, Neoplasia also publishes Reviews and Meeting Reports. Neoplasia is committed to ensuring a thorough, fair, and rapid review and publication schedule to further its mission of serving both the scientific and clinical communities by disseminating important data and ideas in cancer research.