A refined cell-of-origin classifier with targeted NGS and artificial intelligence shows robust predictive value in DLBCL
Authors: Carlo Visco
J Han van Krieken
April Chiu
Babak Shahbaba
Govind Bhagat
Youli Zu
Eric D Hsi
Wayne Tam
Hongwei Zhang
Zijun Y Xu-Monette
Michael B M√∏ller
Miguel A Piris
Feng Zhu
Ivan De Dios
Benjamin M Parsons
Hong Zhang
Alexandar Tzankov
Yong Li
Hua You
Andr√©s J M Ferreri
Jooryung Huh
Maher Albitar
Karen Dybkaer
Fredrick B Hagemeister
Ken H Young
Jane N Winter
Maurilio Ponzoni
Bing Xu
Journal: Blood advances (2020)
Discipline: CLINICAL MEDICINE
Keywords: Diffuse large B-cell lymphoma; Cell-of-origin classification; Genetic subtypes; RNA sequencing; Next-generation sequencing
Affiliations: Division of Hematopathology and Department of Pathology, Duke University Medical Center, Durham, NC., Department of Hematology, Shanxi Cancer Hospital, Taiyuan, China., Institute of Pathology, University Hospital Basel, Basel, Switzerland., Department of Pathology, Columbia University Medical Center and New York Presbyterian Hospital, New York, NY., Department of Hematology, University of Verona, Verona, Italy., Department of Hematology, Aalborg University Hospital, Aalborg, Denmark., Department of Pathology, Mayo Clinic, Rochester, MN., Department of Pathology, Weill Medical College of Cornell University, New York, NY., Department of Pathology, Houston Methodist Hospital, Houston, TX., Department of Pathology, Cleveland Clinic, Cleveland, OH., Department of Hematology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China., Department of Pathology, Asan Medical Center, Ulsan University College of Medicine, Seoul, Korea., Department of Hematology and Pathology, San Raffaele H. Scientific Institute, Milan, Italy., Department of Pathology, Odense University Hospital, Odense, Denmark., Department of Hematology, Gundersen Lutheran Health System, La Crosse, WI., Department of Pathology, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands., Department of Pathology, Fundaci√≥n Jim√©nez D√≠az, Centro de Investigaci√≥n Biom√©dica en Red de C√°ncer (CIBERONC), Madrid, Spain., Department of Hematology, Feinberg School of Medicine, Northwestern University, Chicago, IL., Department of Lymphoma and Myeloma, The University of Texas MD Anderson Cancer Center, Houston, TX., Department of Biostatistics, Donald Bren School of Information and Computer Sciences, University of California, Irvine, CA., Genomic Testing Cooperative, Irvine, CA., Department of Computer Science, Georgia Southern University, Savannah, GA., Department of Medicine, Baylor College of Medicine, Houston, TX., Department of Hematology, The First Affiliated Hospital of Xiamen University, Xiamen, Fujian, China; and., Hematologic Malignancy Program, Duke Cancer Institute, Durham, NC.

Abstract:  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. 

Retracted: No

PubPeer comments:
 Dear authors, it looks like in the supplement table the  NGS predicted classification was wronlgy deposited, because it does not correlate with GEP COO at all (as well as with OS).  , 
 Dear Ecdyonurus Insignis, Thank you so much for finding the mistake. I checked my files and emails, and found they are different from the published data. The supplementary data file we originally submitted to Blood and what I sent out for validation and approval on May 8th before re-submission to Blood Adv are correct. If you like to get the correct data before we notify the journal, maybe we can send you through email? Thank you very much again! Xu-Monette , 
 #2 Thanks for the fast reply! , 
 #3 We are making correction with journal editor on supplemental table now. It may take a few days. If you like, please communicate with me or Dr. Xu-monette directly. Very best, Ken H Young, MD ,