Single-Cell RNA-Seq Reveals Chromosomal Instability-Associated Transcriptomic Profiles in Breast Cancer

Vollständiger Abstract

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Background/Objectives: Breast cancer (BC) is the most frequently diagnosed malignancy and a leading cause of cancer-related mortality in women worldwide. This disease is highly heterogeneous and dynamic, and chromosomal instability (CIN) plays a key role in the acquisition of these traits by generating genetic diversity that promotes tumor adaptation and influences therapeutic response and prognosis. Although several methods have been developed to quantify CIN, they are not readily applicable to human tumors and are limited in resolution, hindering a comprehensive understanding of intratumoral heterogeneity. In this study, we aimed to quantify CIN levels and clonal heterogeneity (CH) in HER2-positive (HER2+) and triple-negative (TNBC) breast cancer using single-cell RNA sequencing (scRNA-seq) data. Methods: We analyzed publicly available scRNA-seq data from HER2+ and TNBC tumors and non-malignant controls. CIN was scored at single-cell resolution using transcriptomic signatures, clonal heterogeneity was estimated from single-cell diversity metrics, and copy number alterations were inferred computationally. Differential expression and functional enrichment analyses were performed between cells with very low and extreme CIN levels, with key comparisons confirmed at the patient level. Results: Our analyses revealed pronounced intra- and intertumoral heterogeneity, with higher CIN levels in TNBC than in HER2+ and control samples. Genes differentially expressed in cells with extreme CIN values were mainly involved in cell division and related processes, and included candidate biomarkers not previously reported in this context. Our findings suggest a positive but statistically non-significant trend was observed between CIN and CH. Conclusions: Single-cell approaches such as scRNA-seq provide a powerful framework to elucidate CIN-related mechanisms and to identify potential biomarkers of BC aggressiveness and prognosis, supporting their further application in the study of intratumoral heterogeneity.