Single-Cell RNA Sequencing for Biomarker Discovery in Triple-Negative and HER2+ Breast Cancer

Abstract

Even with the current advances in breast cancer research, many challenges remain regarding early detection and effective treatment selection for subtypes such as triple-negative breast cancer (TNBC) and HER2-positive (HER2+). Specifically, TNC is one of the most aggressive cancer subtypes and is characterized by having complex heterogeneity and high recurrence rates that remain hard to understand. Many of these complexities are thought to be better understood by the advent of more powerful technologies such as single-cell RNA sequencing that enables insights into the heterogeneity of cells and the distinct tumor cell populations that drive the biological mechanisms of tumor progression. Though the number of experiments is still limited, the ones available offer an unprecedented opportunity to improve the current computational analysis tools to better understand these complex subtypes. Therefore, this study aims to investigate the genomic landscape of TNBC and HER2 subtypes across multiple patients from multiple studies using single-cell RNA-sequencing and an in-depth computational framework to identify potential biomarkers and networks that can explain tumor heterogeneity and its role in these diseases. A total of 22 relevant datasets from the NIH GEO Database specific to Homo sapiens were initially identified. Preliminary analysis using R demonstrates the feasibility of extracting transcriptional patterns in a comparative approach, which supports future comparative analyses across datasets. This approach can potentially reveal molecular differences between TNBC and HER2+ subtypes, establishing a computational framework for biomarker discovery and contributing to advancements in targeted therapy for these challenging breast cancer subtypes.