Selecting optimum miRNA panel for miRNA signature-based companion diagnostic model to predict the response of R-CHOP treatment in diffuse large B-cell lymphoma

Abstract

Diffuse large B-cell lymphoma (DLBCL) is the most common type of malignant lymphoma. Although the first-line treatment, R-CHOP treatment, shows efficacy in approximately 80% of patients with DLBCL, some patients have refractory disease or relapse after the initial response to therapy, resulting in a significantly poorer prognosis. In this study, we developed a microRNA (miRNA) signature-based companion diagnostic model to predict the response of patients with DLBCL to R-CHOP treatment by integrating two clinical study datasets. To select the optimum miRNA combination as a panel, we examined three feature selection methods (p-value-based ranking, stepwise method, and Boruta), together with 11 types of classifiers systematically. Boruta selection enabled a higher area under the curve (AUC) with a lower number of miRNAs compared with other feature selection methods, leading to an AUC of 0.751 via the random forest classifier using 36 miRNAs. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis suggested that Boruta avoided multiple selection of miRNAs with similar functions, thereby preventing the decrease in diagnostic ability via collinearity. The AUC value first increased with an increasing number of miRNAs and then became almost constant at approximately 30 miRNAs, suggesting the existence of the optimum number of miRNAs as a panel for future clinical translation of multiple miRNA-based diagnostics.

Keywords: Biomarker signature; Companion diagnostics; Diffuse large B-cell lymphoma; Machine learning; miRNA.