Multiomic analysis identifies a high-risk signature that predicts early clinical failure in DLBCL

Kerstin Wenzl ¹, Matthew E Stokes ², Joseph P Novak ¹

¹Division of Hematology, Mayo Clinic, Rochester, MN, USA.
²Informatics and Predictive Sciences, , Bristol Myers Squibb, Summit, NJ, USA.
³Department of Quantitative Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
⁴Informatics and Predictive Sciences, Celgene Institute for Translational Research Europe (CITRE), Seville, Spain.
⁵Translational Medicine Hematology, Bristol Myers Squibb, Summit, NJ, USA.
⁶Division of Hematopathology, Mayo Clinic, Scottsdale, AZ, USA.
⁷Division of Hematology, University of Iowa, Iowa, USA.
⁸Department of Quantitative Health Sciences Research, Mayo Clinic, Jacksonville, FL, USA.
⁹Genome Sciences Center, British Columbia Cancer Agency, Vancouver, BC, Canada.
¹⁰Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.
¹¹Division of Hematology, Mayo Clinic, Rochester, MN, USA. Novak.Anne@mayo.edu.

⁰Division of Hematology, Mayo Clinic, Rochester, MN, USA.

Blood Cancer Journal +

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June 20, 2024

View abstract on PubMed

Summary

A new diagnostic signature identifies Diffuse Large B-cell Lymphoma (DLBCL) patients at high risk for early treatment failure. This multiomic approach aids in selecting optimal therapies for aggressive DLBCL cases.

Area Of Science

Oncology
Genomics
Molecular Biology

Background

Diffuse Large B-cell Lymphoma (DLBCL) classification has advanced, but lacks predictive power for early treatment failure.
Current methods do not effectively guide selection of novel therapies for high-risk DLBCL patients.

Purpose Of The Study

To develop a diagnostic signature for identifying DLBCL patients at high risk of early clinical failure.
To integrate multiomic data for improved prediction of treatment outcomes in DLBCL.

Main Methods

Whole Exome Sequencing (WES) and RNA sequencing of 444 newly diagnosed DLBCL tumor biopsies.
Weighted gene correlation network analysis and differential gene expression analysis to identify predictive signatures.
Integration of mutation data, including ARID1A, into the diagnostic signature.

Main Results

Identified a novel signature associated with high risk of early clinical failure, independent of IPI and COO.
The signature is linked to metabolic reprogramming and a depleted immune microenvironment in DLBCL.
Incorporating ARID1A mutations into the signature identified 45% of patients with early clinical failure, validated in external cohorts.

Conclusions

A novel, integrative multiomic signature at diagnosis predicts early clinical failure in DLBCL.
This signature identifies patients who may benefit from alternative or intensified therapeutic strategies.
Findings have significant implications for personalized medicine and clinical trial design in DLBCL.