Introduction: BsAbs and CAR T-cell therapy have revolutionized the treatment landscape of patients with relapsed/refractory (R/R) LBCL. Regrettably, less than 40% of patients will attain durable responses, and determinants of response remain unclear. Our aim was to identify tumoral characteristics that can predict resistance to CAR T-cell therapy and BsAbs in LBCL patients. Methods: We retrospectively collected clinical data and obtained pre-treatment formalin-fixed and paraffin-embedded specimens prior CAR T-cell and BsAb treatment from 56 LBCL patients from 4 centers. Targeted NGS was performed to identify variants and copy number variations (CNVs) across a custom gene panel of 200 genes. These include lymphoma related genes, and genes involved in tumor antigen presentation, B and T lymphocyte interaction, and cell death pathway. Variables assessing response where Overall Response Rate (ORR), Complete Response Rate (CRR), Progression-Free Survival (PFS), and Overall Survival (OS). To ascertain each gene's influence on these endpoints, a logistic model was used for ORR and CRR comparisons, while Kaplan-Meier and Cox methods were employed to analyze PFS and OS. Results: A total of 56 patients were included in the study, 36 patients treated with CAR T-cell and 36 with BsAbs (16 patients received both). Samples were obtained for 31 patients prior to CAR T-cell therapy (CAR T-cell cohort) and 34 cases prior to BsAbs (BsAbs cohort). Baseline characteristics of the patient population are shown in Table 1. In summary, median age was 62 years, 57% were male, 42% had a previous indolent lymphoma and median previous lines were 2 (range 1-4). Best response included CRR in 59%, with a median PFS and OS of 13.6 (CI95% 5.98 - Not reached [NR]) and 29.5 (CI95% 15.7 - NR) months, respectively. Median follow-up for the full cohort was 18.7 months (CI95% 15.1 - 24). As per the CAR T-cell cohort, ORR and CRR were 81% and 68%, respectively. Median PFS and OS for CAR-T-cell cohort was 29.4 months (CI95% 5.98 - NR) and NR, respectively. Regarding the BsAb cohort, ORR and CRR were 66% and 50%, respectively. Median PFS and OS was 10 (CI95% 5.16 - NR) and 22.5 (CI95% 14.4 - NR) months, respectively. For the full cohort, the most frequently altered genes, considering both mutations and CNVs, were KMT2D (66%), TP53 (60%), CREBBP (58%), IGLL5 (51%), REL (42%), EZH2 (38%), BCL2 (38%), TNFRSF14 (37%), SGK1 (32%) and KLHL6 (32%) (Figure 1). The most frequent CNVs were gains of 2p16.1 (34%), 3q28 (15%), 6p(14%), 11q (12%), 21q (12%), 3q (10%); and losses of 17p (32%), 6q (26%), 1p36.32 (25%), 13q14.2 (18%), 19p13.3 (18%), 4q35.1(16%), (10q23.31 (12%), 9p21.3 (10%). Focusing on the CAR T-cell cohort (n=31), mutations and/or deletions in EP300 (7%, n=2)( p=0.01) and KLHL6 (n=2, 7%) ( p=0.04) were associated with an inferior PFS. In addition, mutations and/or deletions in KLHL6 ( p=0.02) and EP300 (n=3, 10%) ( p=0.05), and amplifications in MYC (n=3,10%) ( p=0.031) were associated with shorter OS. No differences in PFS and OS were oberved in patients with mutations and/or deletions in TP53 (n=19, 63%). Regarding RHOA patients with mutations or deletions (n=2, 7%) did not present an inferior PFS ( p=0.88) or OS ( p=0.52). The analyzed alterations did not show an impact on response rate. In terms of patients who received BsAbs (n=34), we observed that mutations and/or deletions in TP53 (n=19, 58%) ( p=0.04) , RHOA (n=5, 15%) ( p=0.05) and GNAI2 (n=5,15%) ( p=0.02)were associated with aninferior PFS. Mutations and/or deletions in TP53 (p=0.04) , RHOA (p=0.009) , GNAI2 ( p=0.01) and CD274(PDL1)-PDCD1LG2(PDL2) (12%, n=3) ( p=0.01) were associated with an inferior OS. Regarding MYC, 8 cases were mutated (n=2, 6%), amplified (n=5, 15%) or both (n=1, 3%), but these alterations were not associated with an inferior PFS (p=0.46) or OS ( p=0.27). Again, no impact on response rate was observed for patients carrying alterations. Conclusions: In our study, a pattern of genetic alterations predicting worse OS was observed in patients treated with CAR T-cells ( KLHL6 EP300 and MYC) and BsAbs (TP53, RHOA, PDL1, PDL2, GNAI2). This important information will aid in the development of targeted strategies to overcome resistance and improve treatment outcomes.
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