MicroRNA signature for interpretable breast cancer classification with subtype clue

Abstract

MicroRNAs (miRNAs) are short non-coding RNAs engaged in cellular regulation by suppressing genes at their post-transcriptional stage. Evidence of their involvement in breast cancer and the possibility of quantifying the their concentration in the blood has sparked the hope of using them as reliable, inexpensive and non-invasive biomarkers.

While differential expression analysis succeeded in identifying groups of disregulated miRNAs among tumor and healthy samples, its intrinsic dual nature makes it inadequate for cancer subtype detection. Using artificial intelligence or machine learning to uncover complex profiles of miRNA expression associated with different breast cancer subtypes has poorly been investigated and only few recent works have explored this possibility. However, the use of the same dataset both for training and testing leaves the issue of the robustness of these results still open.

In this paper, we propose a two-stage method that leverages on two ad-hoc classifiers for tumor/healthy classification and subtype identification. We assess our results using two completely independent datasets: TGCA for training and GSE68085 for testing. Experiments show that our strategy is extraordinarily effective especially for tumor/healthy classification, where we achieved an accuracy of 0.99. Yet, by means of a feature importance mechanism, our method is able to display which miRNAs lead to every single sample classification so as to enable a personalized medicine approach to therapy as well as the algorithm explainability required by the EU GDPR regulation and other similar legislations.