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Introduction Patients (pts) with relapsed/refractory (R/R) follicular lymphoma (FL) experience progressively worse outcomes with each successive line of therapy; complete response (CR) rates are 44% with third-line (3L), 27% with 4L, and 22% with 5L+ therapies (Ghione P et al. Haematologica 2023). CR duration is particularly important considering the rapid disease progression observed with 3L+ therapies in pts with FL, with especially poor outcomes in those with high-risk features (Liu J et al. Front Oncol 2023; Philips T et al. ASH 2023). In ELM-2 (NCT03888105), odronextamab (an investigational CD20×CD3 bispecific antibody) demonstrated deep and durable responses and generally manageable safety in pts with R/R FL who had received ≥2 prior lines of systemic therapy. Median progression-free survival (PFS) and overall survival (OS) in pts with CR were 28 months (95% CI 23.0-not evaluable [NE]) and not reached (95% CI 40.4-NE), respectively. Grade ≥3 treatment-emergent adverse events (TEAEs) occurred in 86% of pts (Taszner M et al. EHA 2024). For the current analysis, we present details on the efficacy and safety of 3L+ odronextamab treatment in pts with R/R FL who attained CR in ELM-2, including those with high-risk features. Methods ELM-2 is an ongoing, open-label, multicenter, Phase 2 trial of odronextamab in pts with R/R B-cell non-Hodgkin lymphoma, including a cohort with FL Grade 1-3a. Intravenous odronextamab was administered in step-up doses with steroid prophylaxis during Cycle (C) 1, followed by 80 mg on Days 1, 8, and 15 of C2-4. After C4, dosing continued at 160 mg once every 2 weeks (Q2W); pts switched to Q4W dosing if they had a durable CR for ≥9 months. The primary endpoint was objective response rate (ORR) according to Lugano classification, by independent central review (ICR). Secondary endpoints included CR rate, PFS, OS, duration of response, and safety. Results Primary results in pts evaluable for efficacy and safety (N=128) using the Oct 20, 2023, data cutoff have been reported. Median duration of efficacy follow-up was 20.1 months (95% CI 17.3-27.8). The ORR was 80.5% (103/128 pts; 95% CI 72.5-86.9), and CR rate was 73.4% (94/128; 95% CI 64.9-80.9). Median duration of CR (DOCR) was 25.1 months (95% CI 20.5-NE), and median time to CR was 2.7 months (range 2.3-7.9). Baseline characteristics of pts with CR (n=94) were similar to those of the overall population, and median duration of treatment exposure was 13.8 months (range 1.8-45.0). In total, 88 pts (93.6%) had CR at the first response assessment (~Week 12) and 6 (6.4%) reached CR later, including 3 pts who had stable disease initially. Twenty-eight pts (29.8%) were still receiving treatment at data cutoff, and 66 (70.2%) had discontinued treatment. Of those who discontinued, 42 still had CR at the last response assessment. Primary reasons for treatment discontinuation were disease progression (n=17), TEAEs (n=16 [including 6 pts with COVID-19]), physician decision (n=12), withdrawal by subject (n=11), and death (n=8). All 8 deaths resulted from TEAEs, including COVID-19 in 5 pts. Eighty-five pts (90.4%) with CR had high-risk features (POD24, 48.9%; FLIPI score 3-5, 55.3%; refractory to last treatment, 70.2%); median DOCR and median time to CR in these pts were similar to those in all pts (23.7 months [95% CI 18.2-NE] and 2.7 months [range 2.3-7.9], respectively). At the time of the current analysis, 44 pts with CR by ICR (46.8%) had switched to Q4W dosing, with a median duration of exposure of 17.7 months (range 13.1-45.0). Among these pts, 22 had discontinued treatment at data cutoff due to adverse events (n=7 [including 1 with COVID-19]), physician decision (n=6), death (n=3), withdrawal by subject (n=3), progressive disease (n=2), and an “other” reason (n=1). Conclusions Pts with R/R FL and CR to 3L+ odronextamab treatment in ELM-2 had a median DOCR of 25.1 months that was prolonged beyond the median duration of treatment exposure (13.8 months). Almost half (46.8%) of pts proceeded to monthly odronextamab administration, with a median duration of treatment exposure of 17.7 months. These data show the sustainability of CR in pts with FL treated with 3L+ odronextamab, including in those with high-risk features. The safety profile of odronextamab was generally manageable in the overall population, including in pts with CR, despite their prolonged drug exposure. These findings support the potential of odronextamab as a treatment for R/R FL.

Background: FL3B is the least common among FL subtypes, accounting for 5-10% of cases. Almost half of FL3B tumors have concurrent diffuse large B cell lymphoma (DLBCL) in the diagnostic tissue biopsy, while FL3B rarely coexists with FL1-2 or FL3A. Several lines of evidence suggest that FL3B displays distinct biological and clinical characteristics resembling aggressive DLBCL rather than the indolent FL1-3A. Accordingly, DLBCL anthracycline-containing regimens, such as R-CHOP, are the mainstay of therapy for FL3B with or without coexisting DLBCL. However, while some studies show that R-CHOP therapy in FL3B was associated with 5-year overall survival (OS) rate of 54%, others report outcomes comparable to FL3A with 79-90% survival. Additional controversy involves the clinical implication of pure FL3B (FL3Bp) vs FL3B with concurrent DLBCL (FL3Bc). Thus, it remains unclear if FL3B clinically resembles DLBCL or lower-grade FL and whether FL3Bp has a different prognosis from FL3Bc. To address these questions, we performed a pooled study of two large prospective multicenter study cohorts to compare the clinical characteristics and outcomes of newly diagnosed FL3B with FL1-2, FL3A and DLBCL patients, all of whom were uniformly treated with frontline R-CHOP. Patients and Methods: Weevaluated 469 newly diagnosed patients with FL1-2 (n=216), FL3A (n=170) and FL3B (n=83) who received R-CHOP within 6 months of diagnosis and 739 de novo DLBCL (GCB n=455 and non-GCB n=284) who were prospectively enrolled into the Molecular Epidemiology Resource (MER) and the Lymphoma Epidemiology of Outcomes (LEO) cohorts between 2002 and 2021. FL3B cases were designated as FL3Bc and FL3Bp based on the presence or absence of coexisting DLBCL, respectively. Histologic diagnosis was determined by an expert pathologist at each participating center and centrally reviewed. Event free survival (EFS) and OS were defined as time from diagnosis to progression, relapse, retreatment or death, and early progression defined as failing to achieve EFS at 24 months (EFS24). Associations between clinical features and EFS or OS were evaluated using Kaplan Meier curves and Cox proportional hazards models unadjusted and adjusted for age and FLIPI (comparisons with FL1-2 and FL3A) or IPI (comparisons with DLBCL). Results: The median age was 61 years (range, 18-94 years). Among FL3B patients, 21 (25%) had FL3Bc and 62 (75%) FL3Bp. Baseline characteristics and outcomes were broadly similar between the two FL3B subtypes. We then compared all FL3B patients to those with lower-grade FL. Patients with FL3B showed similar clinical features to those with FL1-2 and FL3A, with exception for a lower tumor burden. After R-CHOP, FL1-2 patients had an inferior EFS (HR 2.13 95%CI 1.31-3.46) than those with FL3B, whereas there was no difference compared to FL3A. OS was similar across all FL grades. Although FL1-2 patients failed to achieve EFS24 more frequently than FL3B and FL3A (34% vs 18% and 15%, p<0.001), FL3B patients who did fail EFS24 had almost three folds higher risk of subsequent mortality (HR 2.84 95%CI 1.35-6.01) than other FLs. At 5-year follow-up, FL3B patients had twice the risk of relapse with an aggressive subtype (i.e., DLBCL, high-grade B-cell lymphoma) than those with FL1-2 and FL3A, who instead often recurred with indolent disease (i.e., FL1-2, FL3A; p<0.001). We further compared FL3B patients to those with DLBCL classified based on cell-of-origin into GCB and non-GCB subtypes. At diagnosis, patients with FL3B had more favorable clinical features than those with GCB and non-GCB DLBCL. Compared to FL3B, patients with non-GCB DLBCL had an inferior EFS (HR 1.73 95%CI 1.07-2.79) and OS (HR 2.04 95%CI 1.11-3.75), while those with GCB DLBCL did not show a significant difference in EFS (HR 1.41 95%CI 0.88-2.25) or OS (HR 1.69, 95%CI 0.93-3.06) likely due to insufficient statistical power. Patients with FL3B failed EFS24 less frequently than patients with GCB or non-GCB DLBCL (17% vs 25% and 32%; p=0.01). Among patients who failed EFS24, those with FL3B had a similar subsequent OS as both GCB (HR 1.41 95%CI 0.73-2.72) and non-GCB DLBCL (HR 1.27 95%CI 0.65-2.47). Conclusions: We report that FL3Bp and FL3Bc appear clinically similar. While most FL3B patients have a prognosis comparable to FL1-2 and FL3A, 18% exhibit aggressive behavior with poor outcome reminiscent of DLBCL. Future studies with a larger population will be needed to confirm our findings.

Introduction: Uniform assessment of response to treatment is crucial to managing myeloma and developing new therapies. Measurement of the monoclonal protein (s) secreted by the myeloma cells forms the cornerstone of disease assessment in MM and has primarily been performed by electrophoretic methods in the serum and urine, as well as assessment of free light chains in the serum. According to international myeloma working group (IMWG) guidelines, serial serum-free light chain (sFLC) is included in MM response assessment to define stringent complete response and for evaluating partial response (PR) or progressive disease (PD) in non/oligo-secretory MM. This study aims to determine the added value of serial sFLC measurements in monitoring response and progression in MM with measurable disease by comparing the kinetics of FLC with M spike measured by electrophoresis in serum or urine. Methods: This retrospective observational study included newly diagnosed MM patients evaluated at Mayo Clinic in Rochester between 2011-2021. All patients had measurable sFLC (involved FLC level ≥ 10mg/dL, with abnormal sFLC ratio) along with measurable M-protein by serum or urine protein electrophoresis at diagnosis. Serial measurements of sFLC and M-protein by electrophoresis (sPEP/uPEP) were prospectively collected from the electronic medical records at different time points. FLC-response and FLC-PD were evaluated based on IMWG criteria. Results: The study cohort included 839 patients, 52% with measurable M protein by electrophoresis in serum, 27% in both serum and urine, and 21% in urine-only. A significant association was observed between sFLC and electrophoretic response during the induction and at the time of best response (p <.001). Serum FLC showed a comparable response rate of 98% (≥ PR) at the time of best electrophoretic response (p <.001). Ninety-nine percent of early FLC responders eventually achieved an overall response by sPEP/uPEP (p <.001). In the urine-only group, 99% achieved at least a PR by uPEP, and all showed at least a PR based on sFLC. We observed comparable median percentage changes in serial dFLC and urine M-protein, with parallel trends (p <0.001). The correlation coefficients between the change of sFLC and urine M-protein showed a moderate/strong correlation with coefficients (r) of 0.791, 0.597, 0598, 0.545, after cycles 1, 2, 3, 4, respectively, (p < .001). The response for all cohorts was detected earlier by dFLC (median time 1.1 months, 95% CI: 1.06-1.17), in comparison with serum M-protein (1.5 months, 95% CI: 1.40-1.62) and urine M-protein (2.8 months, 95% CI: 2.42-3.10) with p < .001. In the urine-only group, earlier detection of response by dFLC (1.1 months, 95% CI: 1.00-1.2) was observed, compared to urine M-protein (2.4 months, 95% CI: 2.00-2.7) (p <.001). After a median follow-up of 52 (IQR 27.6, 72.3) months, 56% had PD: 62% by s-FLC, 56% by serum M-protein, and 24% by urine M-protein. Among patients with PD by uPEP, all except one patient met sFLC-PD. Within patients with PD, sFLC was the only detectable tumor marker in 12% of cases at the time of the second line of treatment. After four cycles of induction, there was no difference in PFS between VGPR response determined by sFLC, sPEP, and uPEP (p =0.538). The median second-PFS for patients with onlysFLC-PD was similar to those with urine M-protein PD with or without sFLC-PD (HR 1.28, 95% CI =0.77-2.13. p 0.334). However, the median overall survival (OS) from the first relapse was significantly better for patients with onlysFLC-PD than those with urine M-protein PD (HR 1.87, 95% CI =1.07-3.27. p 0.026). Conclusions: In this study, achieving sFLC-VGPR has a similar impact on PFS as electrophoretic-VGPR after induction, indicating that serial sFLC can potentially replace the requirement for urine M-protein response (< 100mg in 24-hours urine) as part of the electrophoretic-VGPR criteria, as required by IMWG. A similar response rate between sFLC and urine paraprotein confirms the current cutoffs (90% for urine and 50% for dFLC for VGPR) used for response categories for s-FLC. We also observed that in 12% of patients with PD, sFLC was the only marker of progression, highlighting the value of serial s-FLC in monitoring relapse. In conclusion, our study supports the integration of serial sFLC measurements for monitoring response and progression in MM, even with electrophoretic measurable disease.

Introduction Bispecific antibodies (BsAbs) have become a key component of treatment in relapsed/refractory multiple myeloma (MM). However, despite promising response rates demonstrated in clinical trials, BsAbs have been shown to have a distinct adverse event profile including increased risk of infections as compared with conventional treatment regimens. The aim of this study was to explore the infectious risk of MM patients on active treatment with Teclistamab. Methods This was a retrospective study of MM patients treated with Teclistamab at Taussig Cancer Center from December 16th 2022 to March 31st 2024. Patients who completed step up dosing and received at least one dose of Teclistamab at maintenance dose were included in the study. The study period spanned from the date of the first dose of Teclistamab up to 6 months after the last dose of Teclistamab, change in therapy or death. Infections which required inpatient hospitalization occurring during the study period were included in the study. Neutropenia was defined as absolute neutropenia count <1500 cells/uL. Hypogammaglobulinemia was defined as serum IgG levels below 700 mg/dL. Data were collected on baseline patient characteristics, MM disease characteristics, and infection characteristics. The primary endpoint was rate of infections per patient-year. Results A total of 58 MM patients who were treated with Teclistamab at Taussig Cancer Center were screened for the study. 46/58 (79.3%) of those patients met inclusion criteria and were included in the study. Among the 46 patients included in the study, there were 20 infectious episodes that required inpatient hospitalization, occurring among 18 patients. The median age of the 46 patients was 66 with an interquartile range (IQR) of 62-74. Patients had broad exposure to novel agents including proteasome inhibitors (46, 100.0%), immunomodulatory drugs (44, 95.7%) and monoclonal antibodies (44, 95.7%). Other notable treatment exposures included CAR T-cell therapy (15, 32.6%), and autologous stem cell transplant (3, 6.5%). 6/9 (66.7%) of patients who underwent previous CAR T-cell therapy and 2/3 (66.7%) of patients who underwent previous autologous stem cell transplant developed an infection requiring hospitalization. No statistically significant difference was found in patients with infection and prior CAR T-cell therapy or autologous hematopoietic stem cell transplant. The median number of lines of therapy from MM diagnosis to start of Teclistamab therapy was 7 (IQR: 5.5-8). The median time from start of Teclistamab therapy till first infectious episode was 84.5 days (IQR: 27-177). Bacterial infections were most common (12/20, 60%), with the most common organism being Streptococcus spp. (2/12, 17%). The most common source of infection was respiratory (10/20, 50%). Neutropenia was present at diagnosis for 5/20 (25%) infectious episodes with 4/5 (80%) of these episodes having Grade 4 neutropenia. Hypogammaglobulinemia occurred during the study period in 43/46 (93.5%) patients with a median IgG nadir of 168 (IQR: 118-320). 22/43 (51.2%) patients received intravenous immunoglobulin (IVIG) during the study period, with 3/22 (13.6%) of these patients going on to develop an infection that required inpatient hospitalization. Of the 21 patients not administered IVIG, 13/21 (61.9%) went on to develop an infection that required inpatient hospitalization (p-value .0016). The median length of hospital stay for all infectious episodes was 7.5 days (IQR: 4-12.5). 6/20 (30%) infectious episodes required ICU stay with a median length of stay of 4.5 days (IQR: 2-18). The overall rate of infections per patient-year was 0.82. The rate of infections per patient-year for the 22 patients with hypogammaglobulinemia who did receive IVIG was .28. The rate of infections per patient-year for the 21 patients with hypogammaglobulinemia who did not receive IVIG was 1.5 (p-value = .06). The overall infection-related mortality rate was 3/20 (15%). Conclusion Multiple myeloma patients treated with Teclistamab experienced a high rate of infections requiring hospitalization and ICU care while on active treatment regardless of prior CAR T-cell and autologous hemopoietic stem cell transplant. Preventive measures, such as administration of prophylactic IVIG to patients with hypogammaglobulinemia, improved outcomes and should be taken to reduce infectious risk in this susceptible patient population.

Introduction: Despite advent of multiple tyrosine kinase inhibitors (TKIs) and newer treatment methods for both AML and ALL, the outcomes of patients (pts) with chronic myeloid leukemia in blast phase (CML-BP) remain poor. Consensus guidelines on treatment of CML-BP are scarce. A recent study from the European leukemia network (ELN) blast phase registry reported outcomes of 240 CML-BP pts. They also reported marked heterogeneity in treatment patterns and lack of an accepted standard. Hence, we are undertaking this study through the H Jean Khoury Cure CML Consortium (HJKC3) to study the treatment patterns and outcomes of pts with CML-BP in the US. Methods: This is a multi-institution, retrospective study. IRB approval was obtained at each institution separately and data sharing agreements were executed. All pts diagnosed with CML-BP based on modified MD Anderson Criteria (≥30% blasts in the blood or bone marrow or extramedullary (EM) disease) from 2010 to 2024 were included. Categorical variables were compared using the Chi-square test. Overall survival (OS) was calculated from the time of diagnosis to death from any cause. Survival probabilities were computed using the Kaplan-Meier method and compared with the log-rank test. Results: Of the 72 pts with CML-BP included in this analysis, 29 (40.3%) were female and 43 (59.7%) were males. At the time of diagnosis, 36 (50%) had CML-CP (chronic myeloid leukemia in chronic phase), 11 (15.3%) pts were diagnosed with CML-AP (accelerated phase CML) and 25 (34.7%) pts with de-novo CML-BP. For the 47 pts that were initially diagnosed with CML-CP or CML-AP, the median time to progression to CML-BP was 26 months (1-207 months). Median age at diagnosis of CML-BP was 54 years (range 21-86 years). EM disease was present in 17 (23.6%) pts (7 myeloid sarcoma, 5 central nervous system and 5 skin) with 3 pts presenting with EM disease only. Blasts were of myeloid phenotype in 41 (56.9%), lymphoid in 29 (40.3%) and mixed in 2 (2.8%) pts. The bcr-abl protein was p210 in 59 (81.9%), p190 in 5 (6.9%) and p230 in 1 (1.4%) pt. Four pts had prior allogeneic stem cell transplant (alloSCT) for CML-CP. Of the pts who had an ABL kinase domain mutation tested, 42.9% (18/42) pts were found to have an ABL kinase domain mutation. The most common TKD mutation was T315I (n=6). Next generation sequencing was performed in 32 pts. The most common mutations were ASXL1 (n=10), RUNX1 (n=4), BCOR (n=2), BCORL1 (n=2), DNMT3A (n=2) and 1 each in CSF3R, KMT2A, EZH2, NF1 and TP53. Of the 72 pts, 68 underwent CML-BP directed therapy. While only 9 (13.2%) pts were treated with TKI alone (4 dasatinib, 2 imatinib, 2 ponatinib and 1 asciminib), 54 (79.4%) pts were treated with a combination of chemotherapy with TKI (24 dasatinib, 13 ponatinib, 9 imatinib, 6 nilotinib, 1 bosutinib and 1 asciminib), and 5 (7.4%) pts were treated with chemotherapy alone. The response rate after CML-BP directed therapy was 72.1% (n=49). Best response was return to CML-CP in 17 pts, CHR with PCyR in 1 pt, CCyR in 11 pts, MMR in 5 pts, MR4 in 4 pts, MR4.5 in 5 pts and MR5 in 6 pts. Rate of response with third and second generation TKI was significantly better than first generation TKI (80% and 77.1% vs 36.3%; p=0.02287). 30 (44.1%) pts proceeded to alloSCT. Of the 49 pts that responded, 16 (32.6%) relapsed. The median time to relapse was 5 months (range 1-46 months). At a median follow up of 57 months, the median OS for all pts was 18 months. There was no difference in OS based on blast phenotype (14 months for myeloid, 21 for lymphoid and 7.5 for mixed; p=0.1185), initial diagnosis (38 months CP-CML, 13 for CML-AP and 16 for de-novo BP pts; p=0.6380), or TKI (13 months for first generation, 20 for second and not reached for third generation TKI; p=0.1729). The OS of pts that were treated with a combination of TKI and chemotherapy was significantly longer compared to pts treated with TKI or chemo alone (28 vs 13 or 4.5 months; p=0.0003). Of the 32 pts that responded, OS for pts that underwent alloSCT was significantly longer compared to pts that did not (not reached vs 14 months; p= 0.0022). Conclusion: CML-BP continues to have a dismal prognosis. Treatment is heterogenous involving various combinations of TKI and chemotherapy. In our analysis, treatment with second or third generation TKI led to better responses. In addition, treatment with chemotherapy in combination with TKI and alloSCT led to improved OS. Updated results from further multi-institution analysis will be presented at the meeting.

Background: Anaplastic large cell lymphomas (ALCLs) represent a heterogeneous group of T-cell lymphomas that currently are classified by the presence or absence of ALK tyrosine kinase (TK) fusion genes (ALK+ or ALK−) and clinical presentation (systemic, cutaneous, or breast implant-associated). Two overarching molecular types of ALCL recently were discovered, defined by the presence (Type I) or absence (Type II) of a gene expression signature highly enriched for JAK-STAT3 activation. Fusions involving non-ALK TK genes occur in some ALK− cases, but the fusion landscape of ALCL remains incompletely characterized. Methods: Expert consensus pathology review was conducted in the Lymphoma/Leukemia Molecular Profiling Project (LLMPP). RNAseq was performed and fusions were identified using FusionCatcher. Here, we focused on recurrent in-frame coding fusions. Previously unreported fusions were validated by RT-PCR. Type I/II was assigned using a previously validated gene expression-based model. Genes with adjusted (adj) P<0.05 were considered differentially expressed. Overall survival (OS) was assessed for systemic ALCL, when available. Results: We evaluated 379 ALCLs (229M/150F; mean age, 56 y). Of 199 candidate fusions (excluding reciprocal events), 28 were recurrent and passed quality metrics. At least 1 of these 28 fusions was present in 150 cases (40%). ALK fusions were present in 106 ALK+ ALCLs (all Type I; P<0.0001). Of these, NPM1::ALK was seen in 68/106 (64%). Alternate partners (X::ALK) included ATIC (N=24) and CTLC, COL1A2, MSN, MYH9, RNF213, SATB1, TFH, TPM3, and TRAF1 (1-3 cases each). X::ALK was associated with older age (mean, 52 y) than NPM1::ALK (32 y; P<0.0001) and showed relative overexpression of 49 genes, including multiple activators of small GTPases such as CGNL1 (FC, 5.6; Padj=2.4×10-6), SRGAP1, ALS2, DOCK1, and NCKAP1. ALK expression was similar in cases with NPM1::ALK and X::ALK and there was no significant difference in OS. Non-ALK TK fusions were seen in 17/273 ALK− cases (6%), including TYK2 (N=9); JAK2 (N=6); and ROS1 (N=2). All were Type I ALCLs (P<0.0001). ALK− cases with non-ALK TK fusions overexpressed 38 genes compared to ALK− cases without TK fusions, including cytokines involved in the IL17 signaling pathway such as CXCL6 (FC, 12.5; Padj=4.2×10-3), CXCL1, and CSF3. We then assessed the relationship between ALK− cases with non-ALK TK fusions and ALK+ cases by separately comparing each subset to ALK− cases without TK fusions. FC values for ALK− cases with non-ALK TK fusions were significantly correlated with FC values for ALK+ ALCLs over the set of expressed genes (R=0.54; P<0.0001). At a median follow-up of 32 months, 0/4 systemic ALCL patients with non-ALK TK fusions had died, while 30% of those with ALK− ALCL without TK fusions had died, but this was not statistically significant (P=0.26). The other 13 ALK− cases with non-ALK TK fusions were either localized ALCLs or did not have outcome data available. Of 20 TP63 fusions, 17 (85%) were TBL1XR1::TP63 and 3 were X::TP63. As previously reported, TP63 fusions were associated with poor OS (median OS, 10 months; not reached for other ALK− ALCLs; P=0.007). Four ALCLs had a novel NCL::UBTF fusion involving genes encoding the nucleolar proteins nucleolin and nucleolar (upstream binding) transcription factor-1. Four cases each had PARG::BMS1 and TNK1::GPS2 (the latter seen only in Type I ALCLs). Conclusions: This large consortium-based analysis of coding fusions further elucidates the molecular distinction between Type I and Type II ALCLs. Both ALK and non-ALK TK fusions were seen exclusively in Type I ALCLs, which we have shown previously are enriched for JAK-STAT3 pathway genes and correlate strongly with pSTAT3Y705 positivity by immunohistochemistry. These fusions were not identified in Type II ALCLs, which previous data suggest are associated with epigenetic alterations. ALK+ ALCLs with NPM1::ALK and X::ALK showed differences in gene expression, suggesting biological differences. Furthermore, ALCLs with non-ALK TK fusions shared gene expression features with ALK+ ALCL. Clinical fusion detection could enhance molecular classification of Type I and Type II ALCLs and may guide precision therapy.

Introduction: HIV-associated diffuse large B-cell lymphoma (HIV-DLBCL) is a significant complication in HIV-positive individuals, presenting unique clinical challenges and a poorer prognosis compared to patients with de novo DLBCL. Epstein-Barr Virus (EBV) status is a critical factor influencing the pathogenesis, clinical behavior, and therapeutic responses in these lymphomas. Plasmablastic lymphomas (PBL) have shared some overlapping genomic abnormalities with HIV-DLBCL including MYC rearrangements. This study aims to elucidate the differences in the genetic profiles and pathway activations between EBV(+) and EBV(-) HIV-DLBCL through comprehensive genomic profiling. Methods: Cases of DLBCL were identified in patients living with HIV from a multi-institute cooperative group. Cases of Burkitt lymphoma and PBL were excluded. We performed whole exome sequencing (WES) on 106 tumors of HIV-DLBCL and RNA sequencing on 39 tumors and mapped the reads to hg38 reference genome using BWA and STAR, respectively. EBV status of cases was determined by in situ hybridization and/or reads mapped on EBV genome in sequencing data. Somatic mutations were identified using variant callers (Strelka2, Mutect2) with WES data. For those mutated genes in COSMIC, allele frequency cutoff was set at 0.03. Mutations with an artifact score larger than 0.7 in SOBDetector and those located in segmental duplications were further removed. They were annotated with Ensembl Variant Effect Predictor, manually checked, and plotted with maftools. RNAseq read counts aligned to each gene were obtained by featureCounts and DEseq2 was used for differential expression (DE) analysis between EBV positive and negative groups. Pathway analysis with the DE genes was performed using GSEA. FISH analysis was performed using MYC, BCL2, and BCL6 break-apart probes. Results: There were 41 cases of EBV(+) HIV-DLBCL and 65 cases of EBV(-) HIV-DLBCL. FISH of EBV(+) HIV-DLBCL identified 42% (3/7) with MYC rearrangement (MYCr), none with BCL2 rearrangements (BCL2r) (0/6), and 33% (2/6) positive for BCL6 rearrangement (BCL6r) with 1 tumor having a MYCr/BCL6r. For EBV(-) HIV-DLBCL, there were 35% with a MYCr (6/17), 18% with BCL2r (2/11), and 27% with a BCL6r (3/11) with 2 tumors with MYCr/BCL6r, and 1 tumor with MYCr/BCL2r/BCL6r. The most frequent mutations seen in EBV(+) HIV-DLBCL were STAT3 (32%), DDX3X (22%), and TP53 (15%). For EBV(-) HIV-DLBCL, the most frequent mutations were KMT2D (25%), TP53 (23%), GNA13 (22%), and TET2 (22%). Pathways that were most commonly associated with EBV(+) HIV-DLBCL were JAK/STAT (42%) (STAT3, JAK1) and NF-kB (39%)(DDX3X, TNFAIP3) while EBV(-) HIV-DLBCL was associated with epigenetic modifiers (62%)(KMT2D, TET2, EP300), JAK/STAT (45%)(SOCS1, JAK3) and PI3K/mTOR (40%)(GNA13, MTOR). Significant disparities between EBV(+) and EBV(-) were KMT2D (24% vs 50%, P=0.0025), TET2 (10% vs 26%, P=0.008), SOCS1 (29% vs 39%, P=0.04), DDX3X (15% vs 5%, P=0.03), and GNA13 (5% vs 24%, P=0.001). RAS mutations (KRAS, NRAS) were infrequent in HIV-DLBCL (EBV(+), 12%; EBV(-), 6%), in contrast to prior studies of PBL. Gene expression profiling showed enrichment of MYC targets, oxidative phosphorylation, and the mTORC pathway in EBV(+) as compared to EBV(-) HIV-DLBCL. Conclusion: EBV(+) HIV-DLBCL show a distinct mutational profile compared to EBV(-) HIV-DLBCL, with EBV(+) cases showing frequent mutations with STAT3 and DDX3X in contrast to EBV(-) cases with frequent mutations associated with epigenetic modification pathways (KMT2D, TET2). These genetic profiles underscore the need for tailored therapeutic approaches depending on EBV status. Although the analysis is limited and requires confirmation in a larger cohort, this is the largest study at present evaluating these tumors.

Introduction: Diffuse large B cell lymphoma (DLBCL) patients experience favorable outcomes following frontline immunochemotherapy, however, 30-40% of patients will exhibit primary refractory disease or relapse (rrDLBCL). Recent studies have expanded upon well-defined characteristics of newly diagnosed DLBCL (ndDLBCL) to elucidate genomic landscapes underlying rrDLBCL; yet, high-throughput examination of resistance and relapse mechanisms within rrDLBCL subtypes, including the newly defined dark zone signature (DZsig) addition to cell of origin (COO), have yet to be described. To expand upon the current tumor etiology and define subtype-specific driving mechanisms, we describe a multi-omic analysis leveraging clinical and biologic variables in rrDLBCL. Methods: Tumor biopsies (N=228) collected at the time of relapse were obtained from n=85 participants in the Mayo Clinic/University of Iowa Lymphoma Molecular Epidemiology Resource (MER), n=73 in the Mayo Clinic Biobank, and n=38 from CC-122-DLBCL-001 or n=32 CC-122-ST-001 trials. Tumor samples were analyzed by WES (n=158) or WGS (n=70) and by RNAseq (n=145). Comparative analysis against ndDLBCL MER samples (N=444) utilized WES (n=404) and RNAseq (n=321). Chi Square or Fisher's exact test was used for enrichment analysis of categorical variables, while Wilcoxon or Kruskal-Wallis test was used for continuous variables. Results: rrDLBCL samples were enriched for ABC (46% vs 33% in ndDLBCL, p=0.01), and fewer GCB (27% vs 40%, p=0.006). Amongst rrDLBCL patients, variance in overall survival (from time of original diagnosis; log-rank p=0.001) was observed between COO subtypes, with poor outcomes in patients with DZsig (median 15.0 mo) and GCB (36.5 mo) compared to ABC tumors (74.4 mo). Patients with progression or relapse within 6 months featured a more even COO distribution (ABC - 22%, GCB - 34%, DZsig - 28%) than patients with early (6-24 mo; 51%, 22%, 22%) or late relapses (>24 mo; 62%, 19%, 4%), which were predominantly ABC. Comparing mutation frequency within subtypes, rrDLBCL GCB tumors were enriched for B2M (41% vs 15% in ndDLBCL, p=0.002), EP300 (19% vs 7%, p=0.059), CIITA (19% vs 5%, p=0.02), PRKCB (11% vs 2%, p=0.035), CD79B (11% vs 0%, p=0.004), and CD83 (8% vs 1%, p=0.049) mutations, and CNV events including gains at 8q24.21 (MYC locus; 48% vs 14%, p<0.001) and 18q21.33 (BCL2, TCF4 locus; 36% vs 15%, p=0.021), and losses at 17p13.1 (TP53 locus; 40% vs 19%, p=0.035). rrDLBCL DZsig tumors featured enrichment for BCL6 (22% vs 3%, p=0.025), and SPEN (17% vs 3%, p=0.062) mutations and 2q22.2 losses (LRP1B locus; 20% vs 0%, p=0.019). Despite differential mutation and CNV profiles, gene expression analysis revealed minimal variance within COO subtypes in rrDLBCL and ndDLBCL, however upregulation of lymphoma driving genes including MYBPC2 (LogFC=1.68, FDR<0.001), MYOM2 (LogFC=1.33, FDR=0.007), MYC (LogFC=0.61, FDR=0.04), and CELSR2 (LogFC=0.53, FDR=0.02) were observed in ABC rrDLBCL. Increased frequency of LymphGen classifier A53 was observed in ABC (43% vs 17%, p=0.005) and GCB (40% vs 14%, p=0.009) rrDLBCL. Using CIBERSORTx to infer lymphoma microenvironment (LME) cell type abundance, an LME-depleted class was evident in rrDLBCL tumors, including reduction of CD8+ T cells (effect size (r)=-0.78, p<0.001) and T follicular helper cells (r=-0.48, p=0.012) in GCB rrDLBCL and reduction of T follicular helper cells (r=-0.43, p=0.023) and Tregs (r=-0.32, p=0.065) in DZsig rrDLBCL. Significance: We present an update to the genomic landscape of rrDLBCL, highlighting GCB and DZsig tumors with differential biology and advanced clinical course. Our data supports increased implementation of sequencing efforts in rrDLBCL to more broadly evaluate relapse mechanisms, with optimism that a more detailed understanding of r/r disease may guide trials for targeted treatment strategies. For example, rrDLBCL patients may be strong candidates for MYC pathway targeting agents (BETi), or a Lenalidomide-based regimen (R2 or Tafa-Len), as Lenalidomide has been shown to reprogram the LME via PD-L1 expression in addition to downregulation of MYC and MYC target genes. Or, due to enrichment of variants affecting anti-apoptotic proteins including BCL6 or BCL2 in DZsig and GCB rrDLBCL, a venetoclax-based therapeutic may be considered. Further targeted strategies should be considered as development of a more personalized approach remains an unmet need.

Introduction: Brexucabtagene Autoleucel (brexu-cel) is the first CD19-targeting chimeric antigen receptor (CD19.CAR) T-cell therapy approved for the treatment of relapsed/refractory (r/r) B-cell acute lymphoblastic leukemia (B-ALL) in adult (≥18 yo) patients (pts) based on results from the ZUMA-3 trial. However, data on the safety and efficacy of CAR-T cells in older pts (≥60 yo) remains limited, with only 8 pts >65 yo enrolled in the ZUMA-3 trial. We evaluated the treatment characteristics and outcomes with use of brexu-cel amongst patients ≥60 yo in the Real-World Outcomes Collaborative for CAR-T in ALL (ROCCA) consortium database. Methodology: ROCCA is a consortium of over 30 U.S.-based institutions with a database of adult pts with B-ALL who received brexu-cel as standard of care therapy. We identified pts ≥60 yo and further stratified them into two cohorts: 60-69 yo and ≥70 yo. Outcomes of interest included OS, PFS, ORR, and toxicities (CRS and ICANS). Both OS and PFS were calculated from the day of brexu-cel infusion. Response assessment occurred on day +28, and MRD evaluation was per institutional practices. CRS and ICANS were graded according to the American Society for Transplantation and Cellular Therapy criteria. Results: Of 280 brexu-cel recipients in the ROCCA database, 73 (26%) were ≥60 yo, of which 58 (79%) were between 60-69 yo and 15 (21%) were ≥70 yo. Among pts ≥60 yo, the median age at infusion was 65 (range 60-81). Pts were heavily pretreated with a median of 3 prior lines of therapy (range 2-9). Prior therapies included: allogeneic HSCT (38%), inotuzumab ozogamicin (41%), and blinatumomab (62%). Forty pts (55%) had Ph-negative B-ALL, 24 (33%) had Ph+ B-ALL and 9 (12%) had Ph-like B-ALL. When grouped by HCT-CI equal to 0, 1-2 or ≥3, a higher proportion of pts ≥60 yo had HCT-CI ≥3 (19% vs. 7% in <60 yo; p=0.004). Pre-apheresis, 33 pts (45%) had active disease (>5% marrow blasts or extramedullary disease), 34 (47%) were in CR (of whom 14 were MRD-negative) and 6 had unknown disease status. Following brexu-cel, 43 (59%) achieved MRD-negative CR, 20 (27%) had MRD + or CR with unknown MRD status, 6 (8%) had refractory disease and 5 had no disease evaluation available. The ORR was similar between pts 60-69 and ≥70 yo (MRD-negative CR rate: 58% vs 61%, r/r to brexu-cel: 9% vs 6%) and was comparable to pts <60 yo (MRD-negative CR 66%; r/r disease of 9%). The rate of all grade ICANS was higher in pts ≥70 (77%) than in pts 60-69 (47%) or pts <60 (49%), with grade 1-2 ICANS occurring at a significantly higher rate in pts ≥70 (44% vs. 20% in 60-69 and 20% in <60, p=0.049). Grade 3-4 ICANS were similar between the groups: 33%, 27% and 29% in the ≥70, 60-69, and <60 groups, respectively. Conversely, rates of CRS were similar across age groups (80-84%). There was no statistically significant difference in the rate of grade 3-4 CRS which was 17% in pts ≥70, 4% in pts 60-69, and 11% in pts <60 yo. With a median follow-up of 332 days (range 8-936 days) from infusion, pts ≥60 yo had a median PFS of 395 days compared to 547 days in pts <60 yo. Median PFS was lower in the ³70 yo (231 days) compared to 677 in the 60-69 yo cohort. No statistically significant difference was found between the <60 and ≥60groups in 6- and 12-month PFS rates (6 months PFS: 66% vs 64%, p=0.77 and 12 months PFS: 57% vs 57%, p=1.00). When pts ≥60 yo were stratified by age, pts ≥70 yo had similar 6-month PFS compared to other age groups but significantly lower 12-month PFS (38%; vs. 64% in pts 60-69and 57% in pts <60, p=0.001). Median OS was not reached in any of the age groups, and there was no statistically significant difference in 6-month OS (72% in ≥ 70 vs. 82% in the 60-69 vs. 82% in <60, p=0.14) or 12-months OS (61% in ≥ 70 vs. 69% in 60-69 vs. 73% in <60, p=0.18). Conclusion: This is the largest study to date of older B-ALL pts receiving CAR-T cells. We observed that older pts, particularly those aged 60-69, have similar outcomes to younger pts. However, pts ≥70 yo had lower median and 12-month PFS when compared to younger patients. Additionally, pts ≥70 yo had higher rates of grades 1-2 ICANS. An important limitation of this analysis is the potential selection bias among clinicians when selecting older patients for this treatment. Longer follow-up, including inclusion of additional pts ≥70 yo is needed to confirm these results, and further studies are needed to identify the optimal approach for use of CAR T-cell therapy in patients in this age group.

Background: A donor search prognosis score based on HLA type and race/ethnicity can classify patients into 3 groups based on the likelihood of finding an HLA-matched (8/8) unrelated donor (MUD): Very Likely (44% of the population), Likely (41%) and Very Unlikely (15%). Previous studies showed that >90% of Very Likely, 26% of Likely and <10% of Very Unlikely patients used a MUD for their hematopoietic cell transplants (HCT), with the rest using other donor sources (mismatched unrelated donor, cord blood, or haploidentical relative). For some patients, prolonged efforts to identify a MUD might be futile and delay HCT, leading to increased morbidity, greater chance for relapse and disease resistance, and worse HCT outcomes. BMT CTN 1702 (NCT03904134) was the first national study to track the entire donor search identification process. Designed as a biologic assignment study, the goal was to estimate and compare overall survival between two arms: patients who are Very Likely to find a MUD versus those who are Very Unlikely to find a MUD. It tested whether provision of a donor search prognosis score to centers could prevent prolonged futile searches for the Very Unlikely group and improve their transplant rates and outcomes, compared to the Very Likely group. Methods: Eligible diagnoses included acute leukemia, myelodysplastic syndromes, lymphoma, acquired aplastic anemia and sickle cell disease. Patients could be consented at any time after diagnosis until an unrelated donor search was started. Consented patients became evaluable only after it was determined that no appropriate HLA-identical siblings were available. The primary endpoint was survival time after the patient was declared evaluable, regardless of whether HCT occurred, and was compared between the Very Unlikely and Very Likely groups. The target enrollment was 1732 evaluable patients. Because this was a biologic assignment trial and not a randomized trial, formal comparisons of overall survival between the Very Likely and Very Unlikely groups were done using a covariate-adjusted Cox proportional hazards model. The following covariates were a priori specified for adjustment in the model, according to the statistical analysis plan: sex, age, Karnofsky Performance Score (KPS), race/ethnicity, disease, disease status, and interval from consent to evaluability. Results: 1751 evaluable patients were enrolled at 47 centers: 44% female, median age 59.4 years (range 0.6-81.3 years), 73% were non-Hispanic White, 10% Hispanic White, 8% Black, 5% Asian/Native Hawaiian/Pacific Islander, 0.5% American Indian/Alaska Native. Among the evaluable patients, 1181 received a HCT, 827 died, and 924 are still alive with or without HCT, with a median of 25 months of follow up (range=6-54 months [mos], inter-quartile range [IQR]=25-34 mos). The distribution was: 958 Very Likely, 517 Less Likely, and 276 Very Unlikely. Individuals in the Very Unlikely group were younger and were less likely to be non-Hispanic White. The unadjusted cumulative incidences of undergoing HCT by 6 mos and 2 years [yrs], in the Very Unlikely group were 51% and 61% vs. 60% and 69% in the Very Likely Group; after adjustment, there was not a significant difference in the likelihood of undergoing HCT (Dehn et al, submitted). There was no difference in median time from evaluability to HCT: 3.4 mos Very Unlikely and 3.3 mos Very Likely. Unadjusted survival rates from the time of evaluability for the Very Unlikely and the Very Likely groups were: 6 mos (85% vs 84%), 1 yr (69% vs. 69%) and 2 yrs (55% vs. 56%). In the multivariate Cox model, there was no significant difference in the risk of death (HR = 1.07 for Very Unlikely vs. Very Likely, 95% confidence interval 0.86-1.33, p=0.56) adjusting for significant clinical covariates: age, KPS, disease status, and time from consent to evaluability were all significantly associated with overall survival. There was no significant center effect and no interaction between donor search prognosis group and clinical covariates. Conclusion: In this large multicenter BMT CTN study, using the donor selection strategy described, there was no difference in adjusted time to HCT or survival for patients Very Likely versus Very Unlikely to find an 8/8 MUD. These results suggest that patients Very Unlikely to identify a MUD should proceed to HCT using the best available alternative donor rather than delaying HCT to try to identify a MUD.