Journal Article

Multi-transcriptomics predicts clinical outcome in systemically untreated breast cancer patients with extensive follow-up

Burton, Mark, Larsen, Martin J., Lænkholm, Anne-Vibeke, Do, Thi T. N., Kruse, Torben A., Jylling, Anne Marie Bak, Ejlertsen, Bent, Sørensen, Kristina P., Tan, Qihua, Block, Ines, Thomassen, Mads

2025

Overview

Background Prognostic tools for determining patients with indolent breast cancers (BCs) are far from optimal, leading to extensive overtreatment. Several studies have demonstrated mRNAs, lncRNAs and miRNAs to have prognostic potential in BC. Because mRNAs, lncRNAs, and miRNAs capture distinct transcriptomic information, we hypothesized that combining them would improve classification performance. Methods Our pair-matched design study included fresh frozen primary tumor samples from 160 lymph node negative and systemically untreated BC patients of which 80 developed recurrence while 80 remained recurrence-free (mean follow-up of 20.9 years). We integrated three classes of RNA and subsequently performed classification using seven machine learning methods followed by a voting scheme. Results Under the criteria of ≥ 90% sensitivity, individual classifications resulted in specificities ranging from 74–91% for the integrated dataset and 56–66%, 58–71% and 69–86% for mRNAs, lncRNAs and miRNAs individually. The specificity level for the multi-transcriptomic dataset was 85% after voting while it was 38%, 48% and 82% for mRNAs, lncRNAs and miRNAs, respectively. In the clinical setting, very high sensitivity may be requested. In the most stringent clinical setting with a sensitivity of 99%, the integrated dataset also outperformed the others with a specificity of 41% compared to 0%, 9% and 28% for mRNAs, lncRNAs and miRNAs, respectively. Conclusion Our results strongly suggest an improvement of prognostic power for classification using an integrated dataset compared to individual classes of RNA and thus encourage researches to opt for an integration of datasets rather than analyzing them separately.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Adult / Aged / Annotations / Biomarkers, Tumor - genetics / Biomedical and Life Sciences / Biomedicine / Biopsy / Breast cancer / Breast Neoplasms - genetics / Breast Neoplasms - mortality / Breast Neoplasms - pathology / Breast Neoplasms - therapy / Cancer / Cancer patients / Cancer Research / Cancer therapies / Chemotherapy / Classification / Datasets / Female / Follow-Up Studies / Gene expression / Gene Expression Profiling - methods / Gene Expression Regulation, Neoplastic / Humans / Lymph nodes / Machine learning / Machine learning methods / Messenger RNA / MicroRNA / MicroRNAs / MicroRNAs - genetics / Middle Aged / Multi-RNA-based classifier / Multi-transcriptomics / Neoplasm Recurrence, Local - genetics / Non-coding RNA / Oncology / Oncology, Experimental / Patient outcomes / Patients / Prognosis / Radiation therapy / RNA, Long Noncoding - genetics / RNA, Messenger - genetics / Support vector machines / Surgical Oncology / Systemically untreated patients / Transcriptome / Transcriptomics / Tumors