ArticleLeukemia & lymphoma2025
gneSeqCOO: a novel method for classifying diffuse large B-cell lymphoma cell of origin based on bulk tumor RNA sequencing profiles.
Article in Leukemia & lymphoma, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Low lymphoma macrophage infiltration predicts poor outcomes for R-CHOP- but not Pola-R-CHP-treated patients with DLBCL.Blood neoplasia · 2026Article
- Malignant B-cell States Orchestrate Spatially Organized Immune Ecosystems in B-cell Lymphomas.Research square · 2026Article
- Targeting SET to counteract Lyn activation demonstrates anti-cancer potential in diffuse large B-cell lymphoma.Clinical and experimental medicine · 2025Article
- Integrated genomics with refined cell-of-origin subtyping distinguishes subtype-specific mechanisms of treatment resistance and relapse in diffuse large B-cell lymphoma.Blood cancer journal · 2025Article
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Authors and funding
8 authors.
Funding
Abstract
The cell of origin (COO) classification is an expression-based tumor algorithm identifying molecular subtypes of diffuse large B-cell lymphoma (DLBCL) with distinct prognostic characteristics. Traditional immunohistochemical methods for classifying COO subtypes have poor concordance and limited prognostic value in frontline DLBCL. In contrast, RNA-based metrics like the NanoString Lymphoma Subtyping Test (LST) define more robust subtypes with validated prognostic associations. This study introduces gneSeqCOO, an algorithm using bulk RNA Sequencing (RNASeq) profiles of individual tumor biopsies for COO classification based on a fixed reference. This method produced consistent per-sample results and was robust to variation in RNA quality and sequencing bias. Validation in >1000 DLBCL samples showed high concordance with the NanoString LST assay, even in cohorts containing only one COO subtype. gneSeqCOO presents a robust and versatile alternative to existing assays, potentially reducing the need for additional samples where RNASeq was already generated. The package is available at https://github.com/Genentech/gneSeqCOO.
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