DETERMINANTS OF RESISTANCE TO ENGINEERED T‐CELL THERAPIES TARGETING CD19 IN LYMPHOMA

Abstract

Introduction: Anti-CD19 chimeric antigen receptor (CAR19) T-cells have activity in patients with relapsed/refractory large B-cell lymphoma (rrLBCL), but over half of patients ultimately relapse. We applied cell-free DNA (cfDNA) analysis to patients receiving Axicabtagene Ciloleucel (axi-cel) to identify determinants of resistance and characterize molecular thresholds predictive of treatment failure. Methods: We developed a novel hybrid-capture approach allowing evaluation of both circulating tumor-derived DNA (ctDNA) and CAR19-derived cfDNA (Fig 1A). We applied this to 381 plasma, tumor, and germline DNA samples from 64 rrLBCL patients, including prior to and during treatment and at relapse. We evaluated all samples for somatic alterations across 246 genes, as well as quantitative levels of ctDNA and CAR19-cfDNA. Results: The median follow-up for the cohort was 12.5 months, and 55% (35/64) progressed after axi-cel. We identified 100.5 mutations/case, which was similar to a cohort of 136 untreated DLBCL patients (P>0.8). Notable differences included more alterations in TP53 (P = 0.02), EP300 (P = 0.02), SETD1B (P = 0.04), ARID5B (P = 0.04), BTK (P = 0.04), CD79A (P = 0.03), CXCR4 (P = 0.03) and RHOA (P = 0.03) in rrDLBCL, and fewer alterations in CD79B (P = 0.04) and PIM1 (P = 0.03). When considering ctDNA quantity, both pre- and on-treatment levels were prognostic for PFS, with higher levels correlating with adverse outcome (Pre-LD [HR = 1.5, CI = 1.1-1.9], Day 0 [HR = 1.6, CI = 1.2-2.3], Day +7 [HR = 1.5, CI = 1.1 – 2.0], Day +28 [HR = 1.7, CI = 1.4-2.2]; Fig 1C). In contrast, higher CAR19-cfDNA levels at Day +7 were associated with favorable outcome (HR 0.52, CI 0.32-0.87). CAR19-cfDNA was also correlated with CAR19 T-cell levels by flow cytometry (Pearson r = 0.7, P < 0.001; Fig 1B). When we assessed the effect of specific mutations on outcomes, we observed recurrent emergence and clonal selection of variants at relapse, including mutations in CD19, PAX5 and TP53 (Fig 1D). Finally, mutations in multiple genes were identified as being prognostic for outcome, including CD58, PAX5 and IRF8 (Fig 1E). Notably these include both immune-mediated and target-mediated putative mechanisms of resistance. For example, CD58 encodes a costimulatory T-cell molecule which has been implicated in CAR19 resistance (Majzner et al., Blood 2020), and PAX5 and IRF8 encode transcriptional regulatory elements that are central to B-cell differentiation and phenotype. Conclusions: Baseline and interim ctDNA and CAR19-cfDNA measurements have prognostic significance in LBCL patients being treated with CAR19 T-cells. Additionally, genomic alterations in several genes, including CD19, CD58, PAX5 and IRF8 are associated with inferior outcomes, and thus represent candidate resistance mechanisms that warrant further study with the goal of improving future generations of CAR T-cell therapy. (A) Overview of experimental schema (B) Relationship between CAR19 T-cell qualification by cfDNA and flow cytometry (C) Results of univariate cox proportional hazards for EFS for indicated variables. (D) Clonal selection of mutations in specific genes in patients experiencing relapse shown as a volcano plo. Mutated genes under significant positive selection are shown on the right in red; size of dot proportional to number of mutations (also shown in parentheses). (E) Left: Recurrently mutated genes in patients receiving axi-cel therapy,stratified by progression vs. non- progression following CAR19 therapy. Right: effect of mutations in given gene on EFS (hazard ration from proportional hazard model); significant values (P < 0.05) shown in green. EFS, event-free survival; HR, hazard-ratio; ctDNA, circulating tumor DNA; cfDNA, cell-free DNA; CAR19, anti-CD19 chimeric antigen receptor T-cell; pre-lymphodepletion; SNV, single nucleotide variant Keywords: Genomics, Epigenomics, and Other -Omics, Cellulartherapies, Liquid biopsy Conflicts of interests pertinent to the abstract D. M Kurtz Consultant or advisory role: Roche, Genentech, Foresight Diagnostics Stock ownership: Foresight Diagnostics M. S Khodadoust Research funding: Corvus Pharmaceuticals R. Majzner Consultant or advisory role: Xyphos Inc, Lyell Immunopharma C. L Mackall Consultant or advisory role: Lyell, Nektar, PACT, Bryologyx, Vor, Roche, Adaptimmune, Glaxo-Smith-Kline, Allogene, Apricity Health, Unum Therapeutics Stock ownership: Lyell, Allogene, Apricity Health, Unum Therapeutics Research funding: Obsidian M. Diehn Consultant or advisory role: Roche, Foresight Diagnostics, Quanticell, Novartis, Astra Zeneca, BioNTech Stock ownership: Foresight Diagnostics, D. M Miklos Consultant or advisory role: Miltenyi Biotech, Allogene, Kite-Gilead, BMS, Celgene, Juno, Novartis, Adaptive Biotechnologies, Precision Bioscience Research funding: Becton Dickinson, Kite-Gilead, Novartis A. A Alizadeh Consultant or advisory role: Foresight Diagnostics, Genentech, Janssen, Pharmacyclics, Gilead, Celgene, Chugai, Roche Stock ownership: Foresight Diagnostics Research funding: Pfizer. AACR-ICML JOINT SESSION: ARTIFICIAL INTELLIGENCE IN LYMPHOMA DIAGNOSIS AND TREATMENT