Deep learning-based classifier of diffuse large B-cell lymphoma cell-of-origin with clinical outcome. (21st November 2022)

• Record Type: Journal Article
• Authors: Viswanathan, Aswathi; Kundal, Kavita; Sengupta, Avik; Kumar, Ambuj; Kumar, Keerthana Vinod; Holmes, Antony B; Kumar, Rahul
• Is Part Of: Briefings in functional genomics. Volume 22:Number 1(2023)
• Page Start: 42
• Page End: 48
• Publication Date: 2022-11-21
• Subjects: DLBCL -- cell-of-origin -- multilayer perceptron -- deep learning -- clinical outcome; Genomics -- Methodology -- Periodicals; Genomics -- Technological innovations -- Periodicals; 572.86072
• DOI: 10.1093/bfgp/elac038
• Languages: English
• ISSNs: 2041-2649

Abstract

Abstract: Diffuse large B-cell lymphoma (DLBCL) is an aggressive form of non-Hodgkin lymphoma with poor response to R-CHOP therapy due to remarkable heterogeneity. Based on gene expression, DLBCL cases were divided into two subtypes, i.e. ABC and GCB, where ABC subtype is associated with poor outcomes. Due to its association with clinical outcome, this classification, also known as cell-of-origin (COO), is an efficient way to predict the response to R-CHOP therapy. Previous COO classification methods have some shortcomings, e.g. limited number of samples in the training dataset. These shortcomings challenge the robustness of methods and make it difficult to implicate these methods at clinical level. To overcome the shortcomings of previous methods, we developed a deep learning-based classifier model on a cohort of 381 DLBCL patients using expression data of 20 genes. We implemented multilayer perceptron (MLP) to train deep learning-based classifier, named MLP-COO. MLP-COO achieved accuracy of 99.70% and 94.70% on training and testing datasets, respectively, with 10-fold cross-validation. We also assessed its performance on an independent dataset of 294 DLBCL patients. On independent dataset, we achieved an accuracy of 95.90% with MCC of 0.917. To show its broader applicability, we used this classifier to predict the clinical outcome using survival data from two large cohorts of DLBCL patients. In survival analysis, MLP-COO recapitulates the survival probabilities of DLBCL patients based on their COO in both cohorts. We anticipate that MLP-COO model developed in this study will benefit in the accurate COO prediction of DLBCL patients and their clinical outcomes.