Cell-Free DNA Genomic and Fragmentomic Features for Early Outcome Prediction in Diffuse Large B-Cell Lymphoma

• Published in: Blood 2024-11, Vol.144 (Supplement 1), p.1588-1588
• Main Authors: Wang, Steven, Mapar, Parisa, van der Pol, Ymke, Moldovan, Norbert, Tantyo, Normastuti Adhini, van Werkhoven, Erik D., Snieder, Bart, Do Brito Valente, André, De Jonge, A., Drees, Esther, Roemer, Margaretha G.M., Ylstra, Bauke, Klerk, Clara, Strobbe, Leonie, Sandberg, Yorick, Boersma, Rinske, Koene, Harry R., Pruijt, Hans, de Heer, Koen, van Rijn, Rozemarijn, Bilgin, Yavuz M., De Jongh, Eva, Nijland, Marcel, van der Poel, Marjolein W.M., Koster, Ad, Nieuwenhuizen, Laurens, Fijnheer, Rob, Beeker, Aart, Mous, Rogier, Vergote, Vibeke KJ, Vermaat, Joost S.P., Pegtel, Dirk Michiel, Chamuleau, Martine E.D., Mouliere, Florent
• Format: Article
• Language: English
• ISSN: 0006-4971; 1528-0020
• DOI: 10.1182/blood-2024-206636

Purpose: Diffuse large B-cell lymphoma (DLBCL) patients need an accurate and early risk stratification strategy, as prompt therapy escalation may improve outcomes. Cell-free DNA (cfDNA) is an emerging liquid biopsy biomarker that has demonstrated clinical utility in guiding cancer treatment. Patients and Methods: We evaluated cfDNA genomic and fragmentomic features in 190 patients with large B-cell lymphoma from two multicenter prospective trials using Whole Genome Sequencing (WGS). Patients had a DLBCL or high-grade B-cell lymphoma (HGBL) diagnosis and were treated with R-CHOP or dose-adjusted EPOCH-R (followed by nivolumab consolidaiton for HGBL after achieving remission). Responders were defined as those in radiographic remission at the end-of-treatment (EOT) or those with a negative biopsy following a positive scan. Non-responders were defined as those with EOT PET-CT Deauville 4-5, progression, or death due to lymphoma during treatment. We extracted four cfDNA features: enhanced tumor fraction, proportion of short cfDNA fragments ranging from 20-150bp, the fragment end integrated analysis (FrEIA) score, and the Gini Diversity Index. We trained a Random Forest model with the training cohort (n = 120), performing hyperparameter optimization using 4-fold cross-validation to maximize prediction accuracy. We then evaluated the model's performance in the validation cohort (n = 61), averaging the ACT scores from the five models for classification predictions. We defined a new metric called the ACT score (Aberrations, Contribution of short fragments, Terminal motif analyses), where a threshold of 0.5 differentiates non-responders (ACT score ≥ 0.5) from responders. We evaluated the prognostic value of the ACT score for progression-free survival (PFS) and overall survival (OS), and in the context of IPI and interim PET-CT. Results: Individual cfDNA features were altered between responders and non-responders after one cycle of treatment, and the ACT score could predict EOT response (AUC 0.70). Patients with a positive ACT score had inferior outcomes compared to ACT score-negative patients [PFS: HR 3.2 (95% CI 1.4-7.2); p < 0.01; OS: HR 4.4 (95% CI 1.7-11.6); p < 0.01]. The 2-year PFS in ACT score-positive and -negative patients was 40% vs. 80%; and OS was 55% vs. 90%, respectively. In the multivariate analysis, the prognostic value of the ACT score was independent of the International Prognostic Index and interim PET-CT. Conclusions: The ACT score, computed from