Determining clinical course of diffuse large B-cell lymphoma using targeted transcriptome and machine learning algorithms

Multiple studies have demonstrated that diffuse large B-cell lymphoma (DLBCL) can be divided into subgroups based on their biology; however, these biological subgroups overlap clinically. Using machine learning, we developed an approach to stratify patients with DLBCL into four subgroups based on survival characteristics. This approach uses data from the targeted transcriptome to predict these survival subgroups. Using the expression levels of 180 genes, our model reliably predicted the four survival subgroups and was validated using independent groups of patients. Multivariate analysis showed that this patient stratification strategy encompasses various biological characteristics of DLBCL, and only TP53 mutations remained an independent prognostic biomarker. This novel approach for stratifying patients with DLBCL, based on the clinical outcome of rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone therapy, can be used to identify patients who may not respond well to these types of therapy, but would otherwise benefit from alternative therapy and clinical trials.

Automated summary

Multiple studies have shown that DLBCL can be divided into distinct subgroups based on biology, but these subgroups often overlap clinically. In this study, a machine learning approach was used to stratify DLBCL patients into four survival subgroups based on gene expression data. The model accurately predicted the subgroups and their prognosis, and it was validated using independent patient groups. This stratification strategy can help identify patients who may not respond well to standard therapy and could benefit from alternative treatments or clinical trials.