TY - JOUR
T1 - Determining clinical course of diffuse large B-cell lymphoma using targeted transcriptome and machine learning algorithms
AU - Albitar, Maher
AU - Zhang, Hong
AU - Goy, Andre
AU - Xu-Monette, Zijun Y.
AU - Bhagat, Govind
AU - Visco, Carlo
AU - Tzankov, Alexandar
AU - Fang, Xiaosheng
AU - Zhu, Feng
AU - Dybkaer, Karen
AU - Chiu, April
AU - Tam, Wayne
AU - Zu, Youli
AU - Hsi, Eric D.
AU - Hagemeister, Fredrick B.
AU - Huh, Jooryung
AU - Ponzoni, Maurilio
AU - Ferreri, Andrés J.M.
AU - Møller, Michael B.
AU - Parsons, Benjamin M.
AU - van Krieken, J. Han
AU - Piris, Miguel A.
AU - Winter, Jane N.
AU - Li, Yong
AU - Xu, Bing
AU - Young, Ken H.
N1 - Funding Information:
MA and HZ are employees and equity holders of Genomic Testing Corporative, LCA. KHY receives research support from Roche Molecular Diagnostics, Adaptive Biotechnologies, Gilead Sciences, Seattle Genetics, Daiichi Sankyo, Incyte Corporation, and HTG Molecular Diagnostics. The remaining authors declare no competing interests.
Publisher Copyright:
© 2022, The Author(s).
PY - 2022/2/1
Y1 - 2022/2/1
N2 - 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.
AB - 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.
KW - Algorithms
KW - Antineoplastic Combined Chemotherapy Protocols/therapeutic use
KW - Cyclophosphamide/therapeutic use
KW - Doxorubicin/therapeutic use
KW - Humans
KW - Lymphoma, Large B-Cell, Diffuse/diagnosis
KW - Machine Learning
KW - Prednisone/therapeutic use
KW - Prognosis
KW - Rituximab/therapeutic use
KW - Transcriptome
KW - Vincristine/therapeutic use
UR - http://www.scopus.com/inward/record.url?scp=85124009436&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85124009436&partnerID=8YFLogxK
U2 - 10.1038/s41408-022-00617-5
DO - 10.1038/s41408-022-00617-5
M3 - Article
C2 - 35105854
AN - SCOPUS:85124009436
SN - 2044-5385
VL - 12
SP - 25
JO - Blood Cancer Journal
JF - Blood Cancer Journal
IS - 2
M1 - 25
ER -