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

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.