Maurer, Matthew J.

Introduction: Chimeric antigen receptor T-cell therapy (CAR-T) has revolutionized the treatment of non-Hodgkin lymphoma (NHL). Despite having an increased risk of NHL, patients with pre-existing autoimmune disease (AID) were excluded from pivotal CAR-T trials due to concerns that CAR-T may exacerbate AID. As a result, there are limited data on the safety and efficacy of CAR-T for NHL in patients with AID. Interestingly, there is also growing evidence that CAR-T can be therapeutically effective for AID, such as in the treatment of systemic lupus erythematosus (SLE). As CAR-T utilization for NHL increases, it is essential to characterize the safety and efficacy of this potentially curative therapy in patients with AID. This study aimed to evaluate the impact of AID on outcomes in patients treated with commercial CD19-targeted CAR-T for NHL. Methods: Following Institutional Review Board approval, we conducted a retrospective analysis of all patients who were treated with commercial CD19-targeted CAR-T for NHL and had a pre-existing AID across the Mayo Clinic Enterprise (Arizona, Florida, and Minnesota) between January 2018 and May 2023. Variables collected included age, gender, NHL type, AID type, and CAR-T product. Efficacy outcomes, including response rates at 6 and 12 months after CAR-T, were collected. Toxicities, including cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS), were collected and analyzed. Our chart review for AID included SLE, sarcoidosis, ulcerative colitis, celiac disease, pernicious anemia, Crohn's disease, psoriasis, Sjogren's syndrome, hypothyroidism (Hashimoto's disease), hyperthyroidism (Graves' disease), rheumatoid arthritis (RA), vasculitis, scleroderma, and mixed connective tissue disease (MCTD). Results: A total of 235 patients who received commercial CD19-targeted CAR-T for NHL across the Mayo Clinic Enterprise were identified (Table 1). Of these 235 patients, 27 had a pre-existing AID and 208 did not. The median age was 66 years (range 39-81) for patients with AID and 59 years (range 18-88) for patients without AID. Over half of the patients were male (59% of patients with AID and 51% of patients without AID). The most common NHL type was diffuse large B cell lymphoma (41% of patients with AID and 56% of patients without AID). Most patients received axicabtagene ciloleucel (85% of patients with AID and 84% of patients without AID). The most common AID was hypothyroidism (81%, n=22), although other conditions were also identified, including RA (11%, n=3), SLE (4%, n=1), pulmonary sarcoidosis (4%, n=1), psoriasis (4%, n=1), and MCTD (4%, n=1). Two patients had more than one AID. Median follow-up was 18 months (range 1-36) for patients with AID and 14 months (range 2-49) for patients without AID. Six months after CAR-T, the complete response (CR) rate was 77.2% and 45.1% in patients with AID and without AID, respectively (p=0.0042). Twelve months after CAR-T, the CR rate was 50% and 31.4% in patients with AID and without AID, respectively (p=0.0801). CRS of any grade occurred in 78% and 88% of patients with and without AID, respectively. ICANS of any grade occurred in 41% and 54% of patients with and without AID, respectively. Conclusion: AID did not appear to adversely impact the safety or efficacy of CAR-T for NHL. Limitations of our study include the small number of patients with AID and the fact that the most common AID was hypothyroidism. Larger studies are warranted to confirm these findings. Additional data are needed to characterize the impact of CAR-T for NHL on AID outcomes and to describe the effect of disease-modifying antirheumatic drugs on NHL outcomes after CAR-T.

Background: QOL can be affected by disease burden, side effects of treatment, and psychosocial effects of living with cancer. Although MZL is considered incurable, we previously found that patients diagnosed from 2002-2015 had a good QOL both at baseline (BL) and the first decade after diagnosis, regardless of treatment choice (Bommier et al., submitted). Here, we evaluate whether these findings can be extended to the 2015-2020 treatment era and a more diverse US cohort. Methods: Adults with newly diagnosed MZL from 8 academic centers were prospectively enrolled within 6 months of diagnosis from 2015-2020 in the LEO cohort (NCT02736357). Participants were actively followed for disease progression/relapse, retreatment and death. QOL was measured at BL, and at 1, 2, 3 and 5 years after diagnosis using the Functional Assessment of Cancer Therapy-Lymphoma (FACT-Lym) scale (range 0-164), which consists of the FACT-General (FACT-G, range 0-104) plus a lymphoma-specific scale (range 0-60). Total FACT-G measures 4 QOL domains (range): physical (0-28), social/family (0-28), emotional (0-24), and functional (0-28) well-being (WB). We also used the Trial Outcome Index (TOI; range 0-112), which consists of FACT-G physical and functional WB plus the lymphoma-specific scale and is commonly used as an endpoint in treatment trials. All MZL patients with both BL and at least one follow-up QOL questionnaire were eligible for analysis. Patients were grouped by initial management: active surveillance (observation), local/antibiotic therapy (radiation, surgery, or anti- H. pylori therapy), or systemic therapy (chemotherapy, targeted agents, or antibody therapy). Change in QOL scores over time was analyzed using mixed models for repeated measurements, and initial treatment groups were compared. Results: Of 384 patients, 60% were female, 91% were White, 6.6% were Black/African American, and 13% were Hispanic (all races). The median age was 64 years (range 19-94). For initial management, 28% (N=106) were observed, 31% (N=118) received local/antibiotic therapy, and 42% (N=160) received systemic therapy. Across treatment groups, there were no significant differences by age, gender, race, or performance status, while there were differences by Hispanic ethnicity (p=0.006), Ann Arbor stage (p<0.001) and MALT-IPI score (p<0.001) (Table). At BL, patients receiving systemic therapy had lower physical WB (p=0.008), emotional WB (p=0.04), functional WB (p=0.03), FACT-G total score (p=0.008), lymphoma subscale (p<0.001), FACT-Lym (p=0.001) and TOI (p<0.001) compared to observation and local/antibiotic therapy (Table). While FACT-G total scores were largely stable through 5 years after diagnosis, social/family WB declined from BL for patients initially receiving either local/antibiotic therapy (mean at 1-year -1.4, p<0.05; 2-year -2.1, p<0.05; 3-year -1.6, p<0.05; 5-year-2.9, p<0.05) or systemic therapy (mean at 2-year -1.2 , p<0.05; 3-year -1.4, p<0.05; and 5-year -1.2, p<0.05), while this was stable for observed patients. In patients receiving initial systemic therapy, there was a transient improvement in the lymphoma-specific scale (mean +1.7, p<0.05) and TOI (mean +3.7, p<0.05). Otherwise, QOL remained stable over time and global QoL (FACT-G, FACT-Lym), TOI, Lymphoma-specific scale (Figure), and the other FACT-G subscales (physical, emotional, or functional WB) did not differ by initial treatment strategy. Conclusions: In the diverse LEO cohort from the recent treatment era, we confirm our prior findings for newly diagnosed MZL patients that QOL is globally stable over the first 5 years after diagnosis; emotional WB is not associated with initial treatment type, providing evidence that being initially observed does not negatively impact emotional health; and there is a decline in social/family WB (support from friends and family, family acceptance of illness, family communication, close to partner), which supports the need to consider interventions for patients to address these unmet needs. In addition, this study extends these findings to the lymphoma-specific scale, FACT-Lym, and TOI, which all showing largely stable trends in the 5 years after diagnosis and suggest that recent MZL treatment strategies, including systemic therapies, neither impair nor improve QOL.

Background: Prognostic modeling allows personalized risk prediction for individual patients (pt). The A-HIPI model in advanced stage classical Hodgkin lymphoma (AS-HL), developed and validated by the HoLISTIC Consortium (www.hodgkinconsortium.org), generates the individualized probability of a progression-free survival (PFS) event or death (OS) within the first 5 years (y) from diagnosis in pts based on continuous variables (www.qxmd.com/calculate/calculator_869/a-hipi). Clinical prognostic tools in lymphoma (eg, IPS, IPI, FLIPI, etc) typically use groupings of categorical values to define risk. Grouping a continuous value often results in loss of information, and most tools are not predictive for individual pts. However, discrete groupings have clinical utility & practicality for a) defining pt populations for clinical trials & real world studies, b) stratification within clinical trials, and c) crafting treatment guidelines. We studied potential approaches for utilizing the A-HIPI model to generate risk groups with input on strengths & limitations from the HoLISTIC modeling team & clinical experts. Methods: The A-HIPI model was developed via TRIPOD guidelines on 4,022 pts treated on 8 international clinical trials for AS-HL (Rodday. JCO 2023). External validation was performed on a dataset of 1,431 pts from 4 prospective registries. The 5y PFS (PFS5) in the A-HIPI development dataset was 77% (95% CI: 76-78); the 5y OS (OS5) was 92% (95% CI: 91-93). This represented the average outcome for a pt with AS-HL pt naïve to other clinical data at presentation. The distribution of PFS5 & OS5 predictions were heavily skewed (ie, asymmetric distribution) in both the A-HIPI discovery and validation dataset. While not unexpected due to the excellent PFS & OS in this disease setting, this presents challenges in the delineation of risk groups as depicted below . Three approaches were examined for the generation of A-HIPI risk groups. Proposed cutoffs were defined using the distribution of A-HIPI risk scores and data from the model-building cohort (ie, clinical trials). Validation was done using the A-HIPI validation cohort (ie, HL registries). Results: Approach 1: Risk groups based on clinical thresholds. Clinicians were queried what estimates of PFS5 would constitute high vs low risk. The positive, right-skewed distribution of A-HIPI risk scores limited this approach ( Figure), as cutoffs of PFS5 <70 and PFS5> 90 would only identify 15% and <1% of the population, respectively. Approach 2: Risk groups based on deviation from “average” pt. The 5-y PFS was 77% (95% CI: 76-78). We explored defining “standard risk” based on this confidence interval as well as clinical boundaries, with pts above or below this classified as decreased or increased risk, respectively. Results of a +/-5% clinical boundary are presented in the Table. Approach 3: Risk groups based on “ranking” of pts. Here we ranked the A-HIPI risk scores of the 4022 AS-HL pts in the model building cohort and used the distribution of the risk scores as a benchmark. The risk profile for a future pt is then compared to this distribution (eg, how do you rank compared to your peers). Continuous results are often presented this way (eg, tertiles or quartiles). It also allows flexibility for the user to define the size of the risk groups and/or clinical threshold of interest. Application of this approach showed good alignment between the predicted model percentiles and the observed distribution of scores in the validation cohort ( Table). This is also reflected in calibration curves presented in the primary manuscript. An online application (eg, R-Shiny) will be provided at the meeting to allow users to define their own cutoffs to aid in pt prognostication as well as identify populations for clinical trial development. Conclusions: There are challenges with defining risk groups from individual risk prediction modeling in AS-HL. Different applications and purposes, the skewed distribution of events/risk estimates, as well as varying clinical definitions of high risk, make it challenging to define consensus expert-based risk groupings for AS-HL. A flexible “rank-based” approach appeared to provide the most clinical utility & data granularity, which may be leveraged for clinical trial design and pt stratification. Further analysis and discussion of how to optimally define high-risk and low-risk populations in AS-HL will be needed as new therapeutic options emerge.

Background Peripheral T-cell lymphomas (PTCLs) comprise a rare, heterogeneous group of non-Hodgkin lymphomas of mature T-cell origin. Most systemic (s) PTCL subtypes typically have poor outcomes with conventional anthracycline-based chemotherapy. Biomarker-driven and subtype-specific treatments are promising but unmet needs. Few benchmark studies exist to characterize the current real-world landscape of clinical and pathologic practice. We report the patterns of care and updated outcome data for PTCL patients enrolled in the LEO-MER multi-center prospective cohort study (ClinicalTrials.gov NCT02736357). Methods Patients aged 18 years or older with newly diagnosed PTCL were prospectively enrolled in the University of Iowa/Mayo Clinic MER cohort (2002-2015) or the expanded LEO cohort (2015-2020). Clinical, pathology, treatment, and outcome data were abstracted from medical records using a standard protocol in both cohorts. Pathology underwent expert re-review based on WHO criteria. Overall survival (OS) was calculated from date of diagnosis to date of death or last follow-up. Event-free survival (EFS) was calculated from date of diagnosis to disease progression, initiation of 2 nd line therapy, or death from any cause. EFS and OS were evaluated using Cox model and Kaplan-Meier estimator. Log-rank test was used to test the significance of difference between groups. Results LEO-MER enrolled 1132 PTCL patients during the study period (462 MER and 670 LEO). Of these, 722 were sPTCLs (281 MER and 441 LEO), including PTCL-NOS (N=253, 35%), anaplastic large cell lymphoma (ALCL; N=180, 24.9% [74 ALK+, 106 ALK-]), angioimmunoblastic T-cell lymphoma (AITL, N=164, 22.7%), extranodal NK-TCL (N=50, 6.9%), adult T-cell leukemia/lymphoma (N=33, 4.6%), enteropathy-associated TCL (N=19, 2.6%), and hepatosplenic TCL (N=13, 1.8%) ( Table 1). Median age at diagnosis was 60 years. M:F ratio was 1.5. Comparing LEO to MER, African American population was 15.4% vs 0.7%, and Hispanic was 10% vs 2.2%. Clinical characteristics were comparable between MER and LEO overall: PS≥2, 20.9%; elevated LDH, 50.7%; stage III-IV, 67.6%; and IPI 2-5, 65.7%. The most common 1 st line chemotherapy regimens overall were CHOP-based (N=506, 70%), including 60% receiving CHOP-like chemotherapy in both MER and LEO cohorts (CHOP [N=268, 37%], CHOEP [N=91, 13%] or EPOCH [N=75, 10%]), and 16% with CHOP-like in combination with novel agents in LEO cohort (BV+ [N=41, 9.3%], azacitidine+ [N=11, 2.5%], pralatrexate+ [N=10, 2.3%], lenalidomide+ [N=8, 1.9%]) ( Table 1). More patients received etoposide in LEO (40.3%) than in MER (20.3%). 74 patients (10.3%) underwent consolidative SCT following induction chemotherapy (71 auto and 3 allo). 65 patients (11.2%) received frontline therapy on clinical trials. At a median follow-up of 9.4 years in MER and 4 years in LEO, 177 (MER) and 269 (LEO) events, and 150 (MER) and 206 (LEO) deaths were observed, respectively. The 3-year EFS and 4-year OS estimates for sPTCL were 46.3% and 59.1% in MER, and 40.4% and 51.3% in LEO. EFS and OS correlated with IPI scores and differed by sPTCL subtypes ( Figure 1). ALCL had superior survival, with 3-year EFS and 4-year OS of 78.2% and 90.5% for ALK+, and 65.9% and 76.9% for ALK-. Non-ALCL subtypes including PTCL-NOS and AITL had inferior outcomes, with 3-year EFS and 4-year OS of 31.4% and 42.8% for PTCL-NOS, and 33.4% and 50.7% for AITL. In subset analysis, the addition of etoposide to CHOP chemotherapy as either CHOEP or EPOCH was associated with improved OS for ALK- ALCL (p=0.038, log-rank), but not for ALK+ ALCL (p=0.75) or non-ALCL subtypes of PTCL-NOS and AITL (p=0.88). Conclusion The LEO-MER study is the largest prospective cohort study of PTCL to date. Patterns of care in the LEO cohort begin to incorporate novel agents in frontline therapy. Outcomes continue to mature with longitudinal follow-up and ongoing accrual, which poise to shape benchmarks in the contemporary era. The lack of benefit of etoposide adding to CHOP induction and poor overall survival of non-ALCL subtypes underscores the unmet need of therapeutic breakthrough for non-ALCL frontline treatment, particularly through clinical trials with biomarker-guided approaches incorporating novel agents.

Background: Marginal Zone Lymphoma (MZL) is considered indolent but incurable, with a variety of initial treatment strategies including observation. Quality of life (QOL) can be affected by disease burden, side effects of treatment, and psychosocial effects of living with an incurable cancer. However, little is known about long-term QOL in patients with MZL. We investigated QOL at baseline (BL), and up to nine years after a diagnosis of MZL. Methods: Newly diagnosed MZL patients aged 18 years and older were prospectively enrolled within 9 months of diagnosis in the Iowa/Mayo Clinic Molecular Epidemiology Resource from 9/2002 - 6/2015. Pathology was reviewed by a hematopathologist. All MZL participants with both BL and at least one follow-up (FU) QOL questionnaire were eligible for this analysis. QOL was measured at BL and years 1-, 2-, 3-, 6- and 9-year FU with the Functional Assessment of Cancer Therapy-General (FACT-G) scale (range 0-108), which measures 4 QOL domains (range): physical (0-28), social/family (0-28), emotional (0-24), and functional (0-28) well-being (WB). Clinical data were abstracted according to a standard protocol and patients were systematically followed for events (progression, re-treatment, and death). Patients were grouped into initial management with active surveillance (observation), local/antibiotic therapy (including radiation, surgery, and anti- H. pylori therapy), or systemic therapy (including chemotherapy, targeted agents, or antibody therapy). Change in QOL scores over time was analyzed using mixed models for repeated measurements, and comparisons were made according to initial treatment group. Results: Of 324 participants in the analysis, 59% were female, 98% were White and the median age was 63 years. For initial management, 36% (N=118) were observed, 30% (N=96) received local/antibiotic therapy and 34% (N=110) received systemic therapy. Across the initial treatment groups, there were no statistically significant differences by age, gender, or performance status, while there were differences for Ann Arbor Stage IV (observation 50%, local/antibiotic 27%, systemic 67%, p<0.001) and MALT-IPI score 2+ (observation 23%, local/antibiotic 18%, systemic 28%, p<0.001). At BL, mean FACT-G total score was 85.7 (SD 13.1), and physical, social/family, emotional, and functional WB scores were 20.3 (SD 4.2), 24.4 (SD 3.9), 18.9 (SD 3.5), 22.1 (SD 5.3); these scores were similar across initial treatment groups (all p>0.05). FACT-G total score was largely stable over time by treatment group (Panel 1). However, it declined at years 6 (mean -4.0, p<0.05) and 9 (mean -3.7, p<0.05) in the observation group (Panel 2), driven by a decline in functional WB, although this change was not significantly different from the other treatment groups. Further, in comparison with other treatment groups, local therapy was associated with a short-term 1-year improvement of FACT-G total score (mean +2.8, p=0.04), driven by improvements in both functional and emotional WB. For the subscales, we identified two major trends irrespective of treatment group: an overall improvement from BL of emotional WB at years 1 (mean +0.6, p<0.05), 2 (mean +0.7, p<0.05), 3 (+1.3, p<0.05), 6 (+1, p<0.05), and 9 (+1.3, p<0.05) and an overall decline of social/family WB at years 1 (mean -1.1, p<0.05), 2 (mean -1.7, p<0.05), 3 (-2.1, p<0.05), 6 (-2.7, p<0.05), and 9 (-2.2, p<0.05). There was no notable change from BL for physical or functional WB. Conclusions: This analysis is the first to provide prospective real-world QOL data on a longitudinal cohort of MZL patients with long follow-up. At BL, initial treatment was associated with stage and MALT-IPI score, but not QOL. Except for the transient 1-year QOL improvement in patients under local/antibiotic therapy, initial treatment groups had no impact on QOL over time. Irrespective of treatment group, emotional WB showed improvement over time, suggesting psychosocial adaptation by the patient, as it was found in aggressive lymphomas (Thompson et al., 2018). Further, emotional WB over time did not vary across treatment groups, suggesting that being initially observed did not lead to more emotional stress than being actively treated. Finally, decline in social/family WB (support from friends, family, family acceptance of illness, family communication, close to partner) suggests a need to consider interventions to patients and families to address these needs.

Background: Recently, large B-cell lymphomas (LBCLs) have undergone major shifts in classification and treatment paradigm, particularly with the advent of CD19-targeted chimeric antigen receptor T cell therapy (CAR-T). Initially approved as third-line treatment in LBCL in 2017, as of early 2022 CAR-T is indicated for second-line therapy (2L) in patients with relapsed or refractory (R/R) disease within 12 months of frontline treatment or who are not candidates for autologous stem cell transplant (ASCT). Given this quickly changing landscape, we aimed to describe disease characteristics, patterns of care, and survival outcomes in patients with R/R LBCL receiving two or more lines of systemic therapy across modern treatment eras. Methods: We developed a database of patients with R/R LBCL from 8 U.S. academic centers in the Lymphoma Epidemiology of Outcomes (LEO) Cohort study (NCT02736357) and Consortium for Real World Evidence (CReWE). Unlike the SCHOLAR-1 dataset (M Crump et al, Blood 2017), our cohort included any patient receiving 2L, regardless of timing of progression/relapse. For this analysis, eligible patients were aged ≥18 years and received 2L between 2002-2022. Patients with histologic transformation, post-transplant lymphoproliferative disorder, primary CNS lymphoma, primary mediastinal B-cell lymphoma, Burkitt lymphoma, or plasmablastic lymphoma were excluded. Prognostic factors, therapy details (including intent for ASCT and/or CAR-T), and outcomes, including treatment response, event-free survival (EFS), and overall survival (OS), were abstracted for all lines of therapy. Treatment eras were defined as pre-CAR-T (2002-2010), CAR-T available via clinical trial (2011-2017), and post-FDA approval of CAR-T (2018-2022). Results: Of 1760 patients initiating 2L for R/R disease, 1523 were eligible for analysis. Median age at start of 2L was 62 (interquartile range [IQR] 53-70), and 65% were male; 11% were non-White, and 8% were Hispanic. High-grade subtypes comprised 16% of all cases. IPI was available for 1013 patients at time of 2L therapy, with 54% having IPI 3-5. Median time from diagnosis to 2L was 8.7 months (IQR 5.6-18.5), with 834 patients not having achieved complete response (CR) to 1L, 285 patients relapsing <12 months after 1L, and 404 patients relapsing ≥12 months after 1L. The median number of total lines of therapy received was 4 (range 2-18), with 586 and 347 receiving ASCT and CAR-T, respectively, at any line of treatment. At a median follow-up of 48 months from the start of 2L, 926 patients (61%) had died. Progressive lymphoma was the primary cause of death in 75% of deceased patients, with 8% of reported deaths due to therapy. 2L was received at an academic medical center in 83% cases, and 209 patients (14%) received 2L on a clinical trial. ASCT and/or CAR-T was planned at 2L for 989 patients (65%), of whom 463 ultimately received ASCT, 88 received CAR-T, and 21 received allogeneic transplant at 2L. 494 patients were not considered for ASCT or CAR-T at 2L, and 40 were unknown for ASCT/CAR-T intent. Breakdown by treatment era (n = 1518 with available treatment start date) is shown in the Table. Median EFS from start of 2L was 4.2 months (95% confidence interval [CI]: 3.8-4.8). Median OS from start of 2L was 18 months (95% CI: 17-22), and 2- and 5-year OS estimates were 46% (95% CI: 44-49%) and 35% (95% CI: 33-38%), respectively. EFS and OS improved significantly for patients initiating 2L between 2011-2017 compared to 2002-2010. However, EFS and OS in the 2018-2022 era remained similar to 2011-2017 (Figure). Conclusions: To our knowledge, this study is the first-ever large-scale attempt to describe patterns of care and outcomes in LBCL 2L in the modern era, inclusive of all treatment approaches. The rich and unique LEO CReWE dataset captures evolving practice approaches over time in terms of both intent for and receipt of cellular therapies. Likely reflecting the rapid development of many novel immune effector cell products and bispecific antibodies, we observed increased enrollment on 2L clinical trials in the 2018-2022 period, including a substantial proportion of patients receiving CAR-T in 2L. Importantly, survival increased in treatment eras during which CAR-T was available. Ongoing analyses of factors associated with improved survival will be presented at the meeting.

Background: We previously reported thatearly events in follicular lymphoma (FL), defined as progression of disease within 24 months (POD24) or early transformation to a more aggressive histology, are significantly associated with increased risk of lymphoma related death. To refine clinical management, the FLIPI24 prognostic model was developed to predict the risk of early events after starting immunochemotherapy (IC) (Maurer et al, 2022). The FLIPI24 model was found to be significantly superior to standard clinical models for prediction of both event-free survival at 24 months (EFS24) and overall survival (OS) in internal and external validation sets of patients receiving IC. We sought to evaluate the prognostic ability of the FLIPI24 model in patients with FL not receiving IC. Methods: The cohort consists of prospectively enrolled patients in the University of Iowa/Mayo Clinic SPORE Molecular Epidemiology Resource (MER) and the multicenter Lymphoma Epidemiology of Outcomes (LEO) Cohorts. Data were collected from medical records by standard protocol with all events verified by medical record review. Eligible patients were those diagnosed with grades 1-3A FL and initiated observation, rituximab monotherapy, radiation, or other non-IC therapy. The FLIPI24 model utilizes 5 continuous variables: age (linear 60-90 years with inflection at age 75), HGB (linear 8-17 g/dL), WBC (linear 4-11x10 9/L), LDH/ULN (linear 0.5-5), and B2M (linear 1-10 mg/L). FLIPI24 risk is grouped as follows: low risk (≤10%], low-average risk (10-15%], average risk (15-20%], high risk (20-40%], very high risk (>40%) for an early event. EFS was defined as time from diagnosis to progression, relapse, (re)treatment, histologic transformation, or death due to any cause. OS was defined as the time from diagnosis until death from any cause. Cox models and Kaplan Meier curves were used to evaluate association between FLIPI24 and EFS or OS. Results: 1542 patients initiating non-IC management approaches at diagnosis from 2002-2020 were identified. 51% were >60 years (IQR 52-70), and 49% were male. Most patients (86%) had FL grade 1-2. 61% of patients met any criteria for high tumor burden per GELF, BLNI, or GITMO based on available variables. Treatment approaches were 833 observation (54%), 315 rituximab monotherapy (20%), 186 radiation (12%), and 208 other non-IC approaches (14%). At median follow-up of 58 months, EFS24 was 73.8% (95% CI: 71.6%, 76.1%), 5-year OS was 90.9% (95% CI: 89.3%, 92.5%) and 253 (16%) patients died. The FLIPI24 score identified poor outcomes among patients with high/very high-risk score (FLIPI24 >20%), with median EFS of 1.8 years (95% CI, 1.3, 3.28) and 5 year OS of 65.1% (95% CI: 55.9-75.8) (Figure 1). When evaluating patient subsets, for patients with high tumor burden (defined based on available variables) and high/very high-risk score, median EFS was 1.6 years and 5 year OS was 64.6% (95% CI: 54.9%-76.1%). Among patients with low tumor burden FL and high/very high-risk score, median EFS was 2.0 years (95% CI: 0.84-NA) and 5 year OS was 66.2% (95% CI: 42.4%-100%). Patients undergoing observation with high/very high-risk score similarly had shortened median EFS of 1.53 years (95% CI: 1.25-NA) and 5 year OS of 68.9% (95% CI: 56.4%-84.1%). The FLIPI24 had superior concordance for OS (c=0.726) compared to the FLIPI (c=0.646) when evaluated in patients not treated with IC. Results were comparable in the subsets of patients observed (c=0.706 vs 0.596) and receiving rituximab monotherapy (c=0.764 vs 0.721). Conclusion: The FLIPI24 model has been previously validated to provide an individual risk score at diagnosis for the likelihood of experiencing an event within 24 months from starting IC. Here we demonstrate that a high FLIPI24 score predicts for poorer outcomes following non-IC strategies including observation, treatment with radiation, and rituximab monotherapy. The FLIPI24 has better prognostic performance for OS than FLIPI in this non-IC population. High FLIPI24 in patients not treated with IC identifies a patient population who should be considered for IC and/or novel frontline therapies when feasible.

Introduction Survival rates for people with mantle cell lymphoma (MCL) have improved in recent years. Among the potential factors accounting for this improvement is the approval of multiple new therapies over the past two decades, including bortezomib, bendamustine, ibrutinib, lenalidomide, acalabrutinib, and zanubrutinib. We hypothesized that the availability of multiple agents with non-overlapping mechanisms of action would result in significant improvements in overall survival following first-line therapy. Methods Patients diagnosed with MCL between 2002 and 2017 were identified using the SEER-Medicare linked database. Selected patients were >65 years of age at the time of diagnosis with MCL. Lines of therapy were determined using billing codes, with inpatient/outpatient claims used for traditional agents and Medicare Part D dataset used for oral agents. Next lines of therapy were defined based on claims for new anticancer agents separated from the end of the prior line by some certain number of days. Sensitivity analyses were performed varying the number of days between therapies. Overall survival (OS) was defined as initiation of initial therapy to death, while OS2 was defined as time from initiation of second-line therapy to death. Time to next therapy 1 (TTNT1) was defined as time from start of first-line therapy to death or start of second-line therapy, whichever came first. Cox models and Kaplan-Meier (KM) survival curves were generated for time-to-event analyses. Analyses were stratified by categories of years of first- and second-line therapies corresponding to eras of key drug approvals (e.g., 2002-2005, 2006-2009, 2010-2013, >2013). Results In total, 4,832 patients were included, among whom 3,840 patients (79.5%) had claims for first-line regimens and 1,648 (34.1% of total and 42.9% of patients with first-line regimens) had second-line regimens. Of the 4,832 patients, the majority were male (67.4%), White (94.6%), and non-Hispanic (94.7%), with good performance status (74.8%) and a Charlson Comorbidity Index <2 (64.8%). The median age at diagnosis was 75 (IQR 70-81). Patients with first- and/or second-line regimens did not differ significantly from the entire population of 4,832 patients with MCL in terms of baseline characteristics. In the first-line setting, use of bendamustine-rituximab (BR) increased from 2.3% of patients treated in 2006-2009 to 49.5% in 2014-2017. Similarly, use of single-agent BTK-inhibitors (BTKi) increased from nonexistent in patients treated in 2010-2013 to 13.8% in 2014-2017. In the second-line setting, use of single-agent BTKi similarly increased from 2.6% of those treated in 2010-2013 to 44.9% in 2014-2017. Median OS for the 3,840 patients with fist-line regimens was 4.4 years (95% CI, 4.1-4.5); median OS for those receiving first-line therapy in 2002-2005 was 3.5 years (3.1-3.9), compared to 3.7 (3.4-4.0), 4.5 (4.2-5.0), and not reached (4.8-NR) among those treated in 2006-2009, 2010-2013, and 2014-2017, respectively. Patients treated in 2014-2017 had significantly improved OS compared with those in 2002-2005 (log rank p<0.0001). Median TTNT1 overall was 2.5 years (2.4-2.6); median TTNT1 was 3.2 years (2.9-3.5) vs 2.3 years (2.1-2.6) for patients who received first-line therapy in 2014-2017 compared to 2002-2005 (log rank p<0.0001) ( Figure A). In addition, there was a significant difference across KM curves for OS2 stratified by categories of years of second-line therapy, with median OS2 of 2.5 (2.0-3.2) in patients treated in 2014-2017 compared to median OS2 of 2.1 (1.7-2.5) in 2010-2013 ( Figure B). Discussion In this analysis, we observed improvements survival of MCL patients over time, along with similar improvements in outcomes following first-line therapy, which may correlate with increased BR and BTKi use as first-line agents. Unexpectedly, improvements in survival following first-line therapy were considerably more modest. Potential explanations include lack of data from recent years during which we expect continued increased utilization of oral therapies vs unique impact of repeated therapies in the elderly. Overall, these data support the need for ongoing development of first- and subsequent-line therapies in MCL.

Introduction: Non-Follicular Low-Grade B-Cell Lymphomas (LGBCL) are a subset of lymphomas that share overlapping clinical and histologic features. While most cases of LGBCL have an indolent course, some experience aggressive disease with no definitive biomarkers to identify aggressive cases at diagnosis. Significant advances have been made in the molecular classification of other lymphomas, yet molecular profiling and identification of mechanisms driving aggressive disease in LGBCL remain an unmet need. We previously used a multi-omic approach to characterize the molecular and immune landscapes of LGBCL tumors and identified a transcriptomic signature associated with cell proliferation and inferior overall survival (OS) in two independent cohorts (HR 7.82; 95% CI 2.4-25.4; p < 0.001 & HR 10.07; 95% CI 2.00-50.61; p = 0.005) and cases that transformed to DLBCL (Hopper et al., 2023). Here, we identify the oncoprotein DEK as a novel transcriptional regulator of this signature and use a functional genomic approach to further interrogate the role of DEK in B-cell lymphoma. Methods: Tumors from 64 newly-diagnosed/untreated LGBCL cases enrolled in the University of Iowa/Mayo Clinic Molecular Epidemiology Resource were included in this study (SMZL = 48, NMZL = 6, LPL = 5, B-NOS = 3, EMZL = 2). Tumor RNA-seq and paired tumor-normal WES were used to assess DEK expression and identify copy number alterations (CNAs), respectively. DEK deficient (KO) cell line models were generated using CRIPSR/Cas9. DEK expression was assessed by western blot (WB) and immunofluorescence (IF) using standard techniques. Proliferation was assessed by MTT assay. Results: To identify transcriptional regulators of our previously identified signature, we used the tool MAGIC to mine publicly available ChIP-seq data and identified DEK, an oncoprotein associated with tumorigenesis and poor prognosis in several cancers, as a key regulator of genes in our signature (p = 0.008). RNA-seq indicated that high levels of DEK expression associated with inferior OS in LGBCL (HR 2.99; 95% CI 1.21-7.36; p = 0.02). Additionally, we identified DEK as a target of copy number gains/amps in LGBCL (7% of cases), which corresponded to significantly higher DEK expression (p = 0.04). The association of DEK with aggressive lymphoma was confirmed when we assessed its expression in public cell line RNA-seq data and observed the highest expression in lines derived from aggressive lymphomas, which we further validated at the protein level in a panel of B-cell lymphoma cell lines by WB and IF confocal microscopy. Additionally, we detected nuclear localization of DEK, consistent with its role as a transcriptional regulator. As a model of DEK overexpression for functional characterization, we used the SUDHL6 lymphoma cell line, which has an amplification of DEK at 6p22.3 and expresses high levels of DEK protein. Using CRISPR technology, we generated DEK deficient SUDHL6 and tested the impact of loss of DEK expression. DEK KO cells proliferated significantly slower than WT SUDHL6 cells in vitro (p < 0.0001)(Panel A), indicating DEK plays a role in regulating proliferative capacity in lymphoma. To identify mechanisms responsible for reduced proliferation in DEK KOs, we assessed baseline protein expression levels of apoptosis proteins in WT versus KO cells. We found near complete abrogation of anti-apoptotic protein Bcl-2 and reduction of Bcl-xL expression in DEK KO cells (Panel B). These results indicate DEK may contribute to tumor proliferative capacity by regulating susceptibility to apoptosis. Conclusion: DEK has been implicated in regulation of gene expression, but its precise role has remained elusive, with little known about its role in lymphoma. Using an integrative molecular and functional genomics approach, we identified DEK as a novel oncoprotein in LGBCL that is upregulated in cases with inferior clinical outcome and is regulated by copy number gains/amps. DEK plays a role in regulating tumor proliferative capacity by altering susceptibility to apoptosis through regulation of Bcl-2 and Bcl-xL. Collectively, this data suggests high-risk LGBCL tumors may enrich for cells with DEK overexpression or CNAs as a mechanism driving oncogene addiction, highlighting DEK, and its downstream targets, as potential therapeutic targets in a subset of high-risk LGBCL.

Background Diffuse large B cell lymphoma (DLBCL) is the most common aggressive lymphoid malignancy in adults. Though standard immunochemotherapy regimens can result in clinical remission and cure in a majority of patients, approximately 30% of patients are primary treatment resistant or eventually relapse (rrDLBCL), and their prognosis is very poor. Recent molecular classification by large scale genomic and transcriptomic profiling of newly diagnosed DLBCL tumors has resulted in a deeper understanding of disease drivers, however, the molecular heterogeneity of rrDLBCL is yet to be fully explored. Therefore, in this study, we used an unsupervised approach to better define transcriptional and genetic programs in rrDLBCL samples hypothesizing that molecular stratification within this high-risk group may lead to a better understanding of this disease, which remains an unmet need, and a more personalized therapeutic approach. Methods Data used in this study included RNA (n=143) and whole exome (n=126) sequencing data from available FFPE tumor samples at the time of a relapse (any line of treatment, r1-r10 relapse timepoints included in analysis, one per patient), consented to the Molecular Epidemiology Resource (n=61), banked in the Mayo Lymphoma Biobank (n=50), or consented to the CC-122-ST-001 clinical trial (n=32, NCT01421524). Unsupervised clustering was performed on protein coding gene expression values using the non-negative matrix factorization (NMF) approach. NMF was carried out by performing 200 runs on cluster sizes from 2 to 6. Cell of origin (COO) was determined using the method described by Reddy et al. The tumor microenvironment was analyzed using CIBERSORTx and Lymphoma Microenvironment Classification (LME). Genetic classification was done using LymphGen. Results In order to identify unique transcriptional programs in rrDLBCL, we implemented NMF and consensus clustering to define an ideal quantity of stable patient clusters. We identified 4 rrDLBCL patient clusters (rrC1-rrC4) as the most stable solution, based on consensus clustering selection criteria of iterative NMF runs. While relapse timepoint was not associated with individual clusters, we did observe significant differences in tumor characteristics between the clusters. COO classification found that rrC1 and rrC4 were enriched for GCB-DLBCL while rrC3 was enriched for ABC-DLBCL classification (P < .001). To explore the biologic programs in each cluster, we used pathFindR, which leverages protein-protein interaction networks to identify disease related pathways, rrC1 and rrC4 were defined by pathways involved in cell cycle and mismatch repair; oxidative phosphorylation in rrC2; and MAPK and NF-kB in rrC3. Next, LME classification was done to define patterns in the tumor microenvironment and we found a significant difference between groups (P < .001), with rrC2 and rrC4 tumors enriched for a depleted TME, while rrC1 and rrC3 had a more immune rich or inflammatory TME. This was supported by CIBERSORTx analysis identifying significant differences (P > .001) in abundance of CD8 T cells, memory CD4 T cells, and M1 macrophages between clusters, with rrC4 having the lowest abundance. We then explored genetic patterns in each cluster. Gene level mutation enrichment analysis showed BCL7A mutation exclusively in C4 (6/6, P = .03). The clusters were not associated with LymphGen classification. Lastly, our recently published high risk signature classification that is associated with early clinical failure and is characterized by metabolic dysregulation, a depleted TME, T53 alterations, and poor outcome, was significantly different between the groups (P = .03) with enrichment for high-risk cases in rrC4. In summary, we show for the first time that rrDLBCL patients can be classified into four gene expression clusters that are associated with distinct pathway, TME, and genetic programs. These clusters should now be tested to learn if they can help select patients for newer therapies for rrDLBCL such as CAR-T and bispecific antibodies.