Will Harris, Yi Cao, Franck Morschhauser, Gilles Salles, Yanwen Jiang, Alessia Bottos, Georg Lenz, Christopher R Bolen
{"title":"gneSeqCOO: a novel method for classifying diffuse large B-cell lymphoma cell of origin based on bulk tumor RNA sequencing profiles.","authors":"Will Harris, Yi Cao, Franck Morschhauser, Gilles Salles, Yanwen Jiang, Alessia Bottos, Georg Lenz, Christopher R Bolen","doi":"10.1080/10428194.2024.2446613","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":18047,"journal":{"name":"Leukemia & Lymphoma","volume":" ","pages":"1-8"},"PeriodicalIF":2.2000,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Leukemia & Lymphoma","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1080/10428194.2024.2446613","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"HEMATOLOGY","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
Leukemia & Lymphoma in its fourth decade continues to provide an international forum for publication of high quality clinical, translational, and basic science research, and original observations relating to all aspects of hematological malignancies. The scope ranges from clinical and clinico-pathological investigations to fundamental research in disease biology, mechanisms of action of novel agents, development of combination chemotherapy, pharmacology and pharmacogenomics as well as ethics and epidemiology. Submissions of unique clinical observations or confirmatory studies are considered and published as Letters to the Editor