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Development and Validation of a Gene Expression Signature to Predict Early Events in Patients with Follicular Lymphoma
Journal article   Open access   Peer reviewed

Development and Validation of a Gene Expression Signature to Predict Early Events in Patients with Follicular Lymphoma

Colleen A Ramsower, George Wright, Hongli Li, James R Cerhan, Matthew J Maurer, Raphael Mwangi, Allison C Rosenthal, Anne J Novak, Brian K Link, Thomas E Witzig, …
Blood advances, Vol.9(24), pp.6443-6454
12/23/2025
DOI: 10.1182/bloodadvances.2025016827
PMCID: PMC12755980
PMID: 40966421
url
https://doi.org/10.1182/bloodadvances.2025016827View
Published (Version of record) Open Access

Abstract

Although follicular lymphoma (FL) typically follows an indolent course, patients with FL who experience early events such as transformation or progression have increased risk of death related to lymphoma. The FL24Cx is an algorithm based on a 45-target gene expression profiling (GEP) assay, which was developed and trained using 265 formalin-fixed, paraffin-embedded tissue samples on a reliable platform to predict, at the time of diagnosis, if a patient will experience an event within 24 months. The modeling also confirmed and relied upon previously reported synergy between immune response (IR) gene expression signatures IR1 and IR2. Once locked, the 5-factor logistic regression FL24Cx model was independently validated in a retrospectively assessed cohort of 232 patients from two immunochemotherapy-treated arms of SWOG Cancer Research Network S0016 phase III clinical trial, where it assigned 169 to the low-risk group with 29 events before 24 months (17.2%) and 63 to the high-risk group with 24 events before 24 months (38.1%). The relative risk of an event within 24 months after registration among patients who were classified into the high risk group relative to patients who were classified into the low risk group was 2.2 (95% CI: 1.41 to 3.51). An upfront GEP biomarker such as the FL24Cx, rigorously validated in a clinical laboratory, with a clinically-relevant turn-around time, could identify and steer enrollment of patients at high risk for early events in clinical trials, thus enabling timely interpretation of such trials and increasing the pace of innovation.

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