A refined cell-of-origin classifier with targeted NGS and artificial intelligence shows robust predictive value in DLBCL.
Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous entity of B-cell lymphoma. Cell-of-origin (COO) classification of DLBCL is required in routine practice by the World Health Organization classification for biological and therapeutic insights. Genetic subtypes uncovered recently are based on distinct genetic alterations in DLBCL, which are different from the COO subtypes defined by gene expression signatures of normal B cells retained in DLBCL. We hypothesize that classifiers incorporating both genome-wide gene-expression and pathogenetic variables can improve the therapeutic significance of DLBCL classification. To develop such refined classifiers, we performed targeted RNA sequencing (RNA-Seq) with a commercially available next-generation sequencing (NGS) platform in a large cohort of 418 DLBCLs. Genetic and transcriptional data obtained by RNA-Seq in a single run were explored by state-of-the-art artificial intelligence (AI) to develop a NGS-COO classifier for COO assignment and NGS survival models for clinical outcome prediction. The NGS-COO model built through applying AI in the training set was robust, showing high concordance with COO classification by either Affymetrix GeneChip microarray or the NanoString Lymph2Cx assay in 2 validation sets. Although the NGS-COO model was not trained for clinical outcome, the activated B-cell-like compared with the germinal-center B-cell-like subtype had significantly poorer survival. The NGS survival models stratified 30% high-risk patients in the validation set with poor survival as in the training set. These results demonstrate that targeted RNA-Seq coupled with AI deep learning techniques provides reproducible, efficient, and affordable assays for clinical application. The clinical grade assays and NGS models integrating both genetic and transcriptional factors developed in this study may eventually support precision medicine in DLBCL.
Xu-Monette, ZY; Zhang, H; Zhu, F; Tzankov, A; Bhagat, G; Visco, C; Dybkaer, K; Chiu, A; Tam, W; Zu, Y; Hsi, ED; You, H; Huh, J; Ponzoni, M; Ferreri, AJM; Møller, MB; Parsons, BM; van Krieken, JH; Piris, MA; Winter, JN; Hagemeister, FB; Shahbaba, B; De Dios, I; Zhang, H; Li, Y; Xu, B; Albitar, M; Young, KH
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