Unraveling tumor heterogeneity: Quantitative insights from scRNA-seq analysis in breast cancer subtypes

Abstract

Tumors are complex systems characterized by genetic, transcriptomic, phenotypic, and microenvironmental variations. The complexity of this heterogeneity plays a crucial role in metastasis, tumor progression, and recurrence. In this work, we utilized publicly available single-cell transcriptomics data from human breast cancer samples (ER+, HER2+, and triple-negative) to evaluate key concepts pertinent to cancer biology. Quantitative assessments included measures based on copy number alterations (CNAs), entropy, transcriptomic heterogeneity, and different protein-protein interaction networks (PPINs).We found that entropy and PPIN activity related to the cell cycle delineate cell clusters with notably elevated mitotic activity, particularly elevated in aggressive breast cancer subtypes. Additionally, CNA distributions differentiate between ER+ and HER2+/TN subtypes. Further, we identified positive correlations among the CNA score, entropy, and the activities of PPINs associated with the cell cycle, as well as basal and mesenchymal cell lines. These scores reveal associations with tumor characteristics, reflecting the known malignancy spectrum across breast cancer subtypes.By bridging the gap between existing literature and a comprehensive quantitative approach, we present a novel framework for quantifying cancer traits from scRNA-seq data by establishing several scores. This approach highlights the potential for deeper insights into tumor biology compared to conventional marker-based approaches.