Can Transcriptomic Profiling Enhance Precision in Predicting Hormone Responsiveness for Breast Cancer Therapy?

Accurate prediction of hormone responsiveness in breast cancer is critical for effective therapy, yet conventional histopathological assessments often suffer from inter-observer variability and limited precision, particularly in heterogeneous or borderline cases, posing a significant challenge to optimizing patient outcomes. So, in this article, the authors aim to investigate whether transcriptomic profiling via RNA sequencing can enhance the precision of predicting hormone responsiveness for breast cancer therapy. This review utilized the NCBI Gene Expression Omnibus (GEO) database, focusing on human breast cancer subjects, and incorporated two pivotal GEO series (GSE۸۱۵۳۸ and GSE۹۶۰۵۸) comprising ۳,۶۷۸ samples to compare RNA sequencing-based classifiers with traditional histopathological methods. Results from prior studies suggest that transcriptomic classifiers achieve concordance levels akin to inter-pathologist agreement, with notable findings indicating that discordant cases—where histopathology predicts hormone responsiveness but RNA sequencing does not—correlate with poorer survival outcomes. Addressing this diagnostic inconsistency, transcriptomic profiling emerges as a valuable tool to refine hormone therapy decisions. As a medical researcher, I interpret these findings to indicate that molecular insights can reveal functional tumor characteristics beyond morphological assessment, offering a more nuanced risk stratification. Encouragingly, these results foster hope for collaborative efforts among oncologists, pathologists, and bioinformaticians to integrate such technologies, potentially leading to improved clinical outcomes through personalized treatment plans

کلیدواژه ها:

Breast Neoplasms ، Gene Expression Profiling ، Hormone Receptor ، Precision Medicine ، RNA Sequencing