Preprint
Multiomic Analysis Identifies a High-Risk Metabolic and TME Depleted Signature that Predicts Early Clinical Failure in DLBCL
medRxiv : the preprint server for health sciences
06/10/2023
DOI: 10.1101/2023.06.07.23290748
PMCID: PMC10274962
PMID: 37333387
Abstract
PURPOSE60-70% of newly diagnosed diffuse large B-cell lymphoma (DLBCL) patients avoid events within 24 months of diagnosis (EFS24) and the remainder have poor outcomes. Recent genetic and molecular classification of DLBCL has advanced our knowledge of disease biology, yet were not designed to predict early events and guide anticipatory selection of novel therapies. To address this unmet need, we used an integrative multiomic approach to identify a signature at diagnosis that will identify DLBCL at high risk of early clinical failure. PATIENTS AND METHODSTumor biopsies from 444 newly diagnosed DLBCL were analyzed by WES and RNAseq. A combination of weighted gene correlation network analysis and differential gene expression analysis followed by integration with clinical and genomic data was used to identify a multiomic signature associated with high risk of early clinical failure. RESULTSCurrent DLBCL classifiers are unable to discriminate cases who fail EFS24. We identified a high risk RNA signature that had a hazard ratio (HR, 18.46 [95% CI 6.51-52.31] P < .001) in a univariate model, which did not attenuate after adjustment for age, IPI and COO (HR, 20.8 [95% CI, 7.14-61.09] P < .001). Further analysis revealed the signature was associated with metabolic reprogramming and a depleted immune microenvironment. Finally, WES data was integrated into the signature and we found that inclusion of ARID1A mutations resulted in identification of 45% of cases with an early clinical failure which was validated in external DLBCL cohorts. CONCLUSIONThis novel and integrative approach is the first to identify a signature at diagnosis that will identify DLBCL at high risk for early clinical failure and may have significant implications for design of therapeutic options.
Details
- Title: Subtitle
- Multiomic Analysis Identifies a High-Risk Metabolic and TME Depleted Signature that Predicts Early Clinical Failure in DLBCL
- Creators
- Kerstin Wenzl - Mayo ClinicMatt Stokes - Bristol-Myers SquibbJoseph P Novak - Mayo ClinicAllison M Bock - Mayo ClinicSana Khan - Mayo ClinicMelissa A Hopper - Mayo ClinicJordan E Krull - Mayo ClinicAbigail R Dropik - Mayo ClinicJanek S Walker - Mayo ClinicVivekananda Sarangi - Mayo ClinicRaphael Mwangi - Mayo ClinicMaria OrtizNicholas Stong - Bristol-Myers SquibbC Chris HuangMatthew J Maurer - Mayo ClinicLisa Rimsza - Mayo ClinicBrian K Link - University of IowaSusan L Slager - Mayo ClinicYan Asmann - Mayo Clinic in FloridaPatrizia Mondello - Mayo ClinicRyan Morin - BC Cancer AgencyStephen M Ansell - Mayo ClinicThomas M Habermann - Mayo ClinicAndrew L Feldman - Mayo ClinicRebecca L King - Mayo ClinicGrzegorz Nowakowski - Mayo ClinicJames R Cerhan - Mayo ClinicAnita K Gandhi - Bristol-Myers SquibbAnne J Novak - Mayo Clinic
- Resource Type
- Preprint
- Publication Details
- medRxiv : the preprint server for health sciences
- DOI
- 10.1101/2023.06.07.23290748
- PMID
- 37333387
- PMCID
- PMC10274962
- Language
- English
- Date posted
- 06/10/2023
- Academic Unit
- Hematology, Oncology, and Blood & Marrow Transplantation; Internal Medicine
- Record Identifier
- 9984438856102771
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