A recent preprint in medRxiv has demonstrated a novel integrative computation-based classification approach capable of effectively identifying multiomic signatures of early clinical failure in patients with diffuse large B-cell lymphoma (DLBCL).
The study found that the proposed DLBCL classifier signature was associated with event-free survival within 24 months of diagnosis (EFS24), metabolic reprogramming, and a depleted immune microenvironment.
The researchers used clinical data from the University of Iowa, Iowa City, and the Mayo Clinic. The study utilized WES and RNAseq techniques to measure the gene expression changes in tumor biopsies of 444 newly diagnosed cases of DLBCL.
A combination of weighted gene correlation network analysis and differential gene expression analysis followed by integration with genomic and clinical data was used to detect the multiomic signature associated with a high risk of early clinical failure.
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Study results revealed that no current DLBCL classifier could discriminate the cases that failed EFS24. Moreover, a high-risk RNA signature with a hazard ratio (HR) of 18.46 (P <.001) was identified in a univariate model, which did not decrease after adjustment for age, international prognostic index, or cell-of-origin (HR, 20.8, P <.001).
Additional analysis found that the observed signature was strongly associated with EFS, overall survival, metabolic reprogramming, and a depleted immune microenvironment. Furthermore, it was observed that the inclusion of ARID1A mutations after integrating WES data into the signature resulted in the identification of 45% of the cases with early clinical failure earlier validated in external DLBCL cohorts.
Additionally, a significant enrichment of TME-negative cases was observed in the high-risk group, implying the importance of overall cellular composition and TME biology in such tumors. Besides, high-risk cases were spread across the molecular classifiers, suggesting the enrichment of mutations in genes related to Notch signaling, the cell cycle, splicing, and metabolism pathways, compared to low-risk.
“Our signature and proposed classification approach may have important clinical implications,” the authors highlighted.
“While not a primary focus of this manuscript, our risk classifier identified cases with a low risk of having an early event. This may be a subgroup of patients that will benefit from standard of care treatment with R-CHOP and may be spared from use of more toxic or expensive therapies.”
DLBCL is the most common and aggressive fast-growing type of non-Hodgkin’s lymphoma. While it is understood that the majority of patients with DLBCL are potentially cured after standard therapy, there remains a subset who do not respond to front-line treatment. Studies have suggested that 60%-70% of newly diagnosed patients with DLBCL achieve EFS24, while the remaining 30% have a very poor outcome.
Wenzl K, Stokes M, Novak JP, et al. Multiomic analysis identifies a high-risk metabolic and TME depleted signature that predicts early clinical failure in DLBCL. medRxiv Published online June 10, 2023. doi:10.1101/2023.06.07.23290748