Dataset Information

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

ABSTRACT: 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. Tumor 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 was used to identify a signature associated with high risk of early clinical failure independent of IPI and COO. Further analysis revealed the signature was associated with metabolic reprogramming and identified cases with 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. This novel and integrative approach is the first to identify a signature at diagnosis, in a real-world cohort of DLBCL, that identifies patients at high risk for early clinical failure and may have significant implications for design of therapeutic options.

SUBMITTER: Wenzl K

PROVIDER: S-EPMC11189905 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Wenzl Kerstin K Stokes Matthew E ME Novak Joseph P JP Bock Allison M AM Khan Sana S Hopper Melissa A MA Krull Jordan E JE Dropik Abigail R AR Walker Janek S JS Sarangi Vivekananda V Mwangi Raphael R Ortiz Maria M Stong Nicholas N Huang C Chris CC Maurer Matthew J MJ Rimsza Lisa L Link Brian K BK Slager Susan L SL Asmann Yan Y Mondello Patrizia P Morin Ryan R Ansell Stephen M SM Habermann Thomas M TM Witzig Thomas E TE Feldman Andrew L AL King Rebecca L RL Nowakowski Grzegorz G Cerhan James R JR Gandhi Anita K AK Novak Anne J AJ

Blood cancer journal 20240620 1

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. Tumor biopsies from 444 newly diagnosed DLBCL were analyzed by WES and RNAseq. A combination of weighted gene correlation network ...[more]

PMID: 38902256

Similar Datasets

Project description:Diffuse large B-cell lymphoma (DLBCL) has striking clinical and molecular variability. Although a more precise identification of the multiple determinants of this variability is still under investigation, there is a consensus that high-clinical-risk DLBCL cases require a risk-adapted therapy, since intensification of chemotherapy with autologous stem-cell transplantation (ASCT) has been shown to improve the prognosis for high-risk patients in randomised clinical trials. In spite of this, the protocols used for these patients have a high morbidity, associated with ASCT and the use of multiple drugs. This makes it important to identify patients that may take benefit from risk-adapted therapies, through the recognition of biological markers that provide information about both the tumoral cells and the microenvironment. Unfortunately, many of the studies so far performed have relied on heterogeneous series of patients, staged or treated with different protocols. For instance, some of the variability in DLBCL arises from the fact that this diagnosis is applied to de novo and secondary tumours, nodal and extranodal, irrespective of clinical stage, patient age and associated infections. Additionally, DLBCL includes some specific variants, such as mediastinal DLBCL and T/HRBCL, with specific prognostic parameters. This could prevent the identification of potential predictive biomarkers, because the results of many studies show that the search for predictive biomarkers should be promoted in the context of samples of clinically homogeneous patients enrolled in clinical trials. A further source of variability is the dependence of some predictive markers on specific therapeutic approaches, as is the case for the Bcl6 expression in DLBCL, since Bcl-6+ cases have been shown not to benefit from the addition of R to CHOP. Here we have analysed a series of high-clinical-risk DLBCLs by a two-stage approach, first identifying functional signatures by expression analysis, then analysing surrogate biomarkers using tissue microarrays (TMAs). This eclectic approach could reveal new aspects of the relationship between the neoplastic cells and the microenvironment, leading to the identification of previously unknown prognostic markers. At the same time, the use of functional signatures to analyse expression-profiling data avoids the poor reproducibility of the data obtained from gene-by-gene analysis, and benefits from the existence of a growing body of data concerning the major pathways deregulated in DLBCL. To avoid the bias of semiquantitative scoring, in this study we have quantified the markers included in the multivariate analysis. Keywords: new biological variables, risk-adapted therapies

Project description:BackgroundCardiovascular diseases may originate in childhood. Biomarkers identifying individuals with increased risk for disease are needed to support early detection and to optimise prevention strategies.MethodsIn this prospective study, by applying a machine learning to high throughput NMR-based metabolomics data, we identified circulating childhood metabolic predictors of adult cardiovascular disease risk (MetS score) in a cohort of 396 females, followed from childhood (mean age 11·2 years) to early adulthood (mean age 18·1 years). The results obtained from the discovery cohort were validated in a large longitudinal birth cohort of females and males followed from puberty to adulthood (n = 2664) and in four cross-sectional data sets (n = 6341).FindingsThe identified childhood metabolic signature included three circulating biomarkers, glycoprotein acetyls (GlycA), large high-density lipoprotein phospholipids (L-HDL-PL), and the ratio of apolipoprotein B to apolipoprotein A-1 (ApoB/ApoA) that were associated with increased cardio-metabolic risk in early adulthood (AUC = 0·641‒0·802, all p<0·01). These associations were confirmed in all validation cohorts with similar effect estimates both in females (AUC = 0·667‒0·905, all p<0·01) and males (AUC = 0·734‒0·889, all p<0·01) as well as in elderly patients with and without type 2 diabetes (AUC = 0·517‒0·700, all p<0·01). We subsequently applied random intercept cross-lagged panel model analysis, which suggested bidirectional causal relationship between metabolic biomarkers and cardio-metabolic risk score from childhood to early adulthood.InterpretationThese results provide evidence for the utility of a circulating metabolomics panel to identify children and adolescents at risk for future cardiovascular disease, to whom preventive measures and follow-up could be indicated.FundingThis study was financially supported by the Academy of Finland, Ministry of Education of Finland and University of Jyv€askyl€a, the National Nature Science Foundation of China (Grant 31571219), the 111 Project (B17029), the Shanghai Jiao Tong University Zhiyuan Foundation (Grant CP2014013), China Postdoc Scholarship Council (201806230001), the Food and Health Bureau of Hong Kong SAR's Health and Medical Research Fund (HMRF grants 15162161 and 07181036) and the CUHK Direct Grants for Research (2016¢033 and 2018¢034), and a postdoctoral fellowship from K. Carole Ellison (to T.W.). The UK Medical Research Council and Wellcome (Grant ref: 217065/Z/19/Z) and the University of Bristol provide core support for ALSPAC. NFBC1966 received financial support from University of Oulu Grant no. 24000692, Oulu University Hospital Grant no. 24301140, ERDF European Regional Development Fund Grant no. 539/2010 A31592. This work was supported by European Union's Horizon 2020 research and innovation programme LongITools 874739.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets