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Genetic and transcriptomic analyses of diffuse large B-cell lymphoma patients with poor outcomes within two years of diagnosis

Authors :
Ren, Weicheng
Wan, Hui
Abd Own, Sulaf
Berglund, Mattias
Wang, Xianhuo
Yang, Mingyu
Li, Xiaobo
Liu, Dongbing
Ye, Xiaofei
Sonnevi, Kristina
Enblad, Gunilla
Amini, Rose-Marie
Sander, Birgitta
Wu, Kui
Zhang, Huilai
Wahlin, Bjoern Engelbrekt
Smedby, Karin E.
Pan-Hammarstrom, Qiang
Ren, Weicheng
Wan, Hui
Abd Own, Sulaf
Berglund, Mattias
Wang, Xianhuo
Yang, Mingyu
Li, Xiaobo
Liu, Dongbing
Ye, Xiaofei
Sonnevi, Kristina
Enblad, Gunilla
Amini, Rose-Marie
Sander, Birgitta
Wu, Kui
Zhang, Huilai
Wahlin, Bjoern Engelbrekt
Smedby, Karin E.
Pan-Hammarstrom, Qiang
Publication Year :
2024

Abstract

Despite the improvements in clinical outcomes for DLBCL, a significant proportion of patients still face challenges with refractory/relapsed (R/R) disease after receiving first-line R-CHOP treatment. To further elucidate the underlying mechanism of R/R disease and to develop methods for identifying patients at risk of early disease progression, we integrated clinical, genetic and transcriptomic data derived from 2805 R-CHOP-treated patients from seven independent cohorts. Among these, 887 patients exhibited R/R disease within two years (poor outcome), and 1918 patients remained in remission at two years (good outcome). Our analysis identified four preferentially mutated genes (TP53, MYD88, SPEN, MYC) in the untreated (diagnostic) tumor samples from patients with poor outcomes. Furthermore, transcriptomic analysis revealed a distinct gene expression pattern linked to poor outcomes, affecting pathways involved in cell adhesion/migration, T-cell activation/regulation, PI3K, and NF-kappa B signaling. Moreover, we developed and validated a 24-gene expression score as an independent prognostic predictor for treatment outcomes. This score also demonstrated efficacy in further stratifying high-risk patients when integrated with existing genetic or cell-of-origin subtypes, including the unclassified cases in these models. Finally, based on these findings, we developed an online analysis tool (https://lymphprog.serve.scilifelab.se/app/lymphprog) that can be used for prognostic prediction for DLBCL patients.

Details

Database :
OAIster
Notes :
application/pdf, English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1457574003
Document Type :
Electronic Resource
Full Text :
https://doi.org/10.1038.s41375-023-02120-7