Link, Brian K.

AbstractTo address the current and long‐term unmet health needs of the growing population of non‐Hodgkin lymphoma (NHL) patients, we established the Lymphoma Epidemiology of Outcomes (LEO) cohort study (NCT02736357; https://leocohort.org/). A total of 7735 newly diagnosed patients aged 18 years and older with NHL were prospectively enrolled from 7/1/2015 to 5/31/2020 at 8 academic centers in the United States. The median age at diagnosis was 62 years (range, 18–99). Participants came from 49 US states and included 538 Black/African‐Americans (AA), 822 Hispanics (regardless of race), 3386 women, 716 age <40 years, and 1513 rural residents. At study baseline, we abstracted clinical, pathology, and treatment data; banked serum/plasma (N = 5883, 76.0%) and germline DNA (N = 5465, 70.7%); constructed tissue microarrays for four major NHL subtypes (N = 1189); and collected quality of life (N = 5281, 68.3%) and epidemiologic risk factor (N = 4489, 58.0%) data. Through August 2022, there were 1492 deaths. Compared to population‐based SEER data (2015–2019), LEO participants had a similar distribution of gender, AA race, Hispanic ethnicity, and NHL subtype, while LEO was underrepresented for patients who were Asian and aged 80 years and above. Observed overall survival rates for LEO at 1 and 2 years were similar to population‐based SEER rates for indolent B‐cell (follicular and marginal zone) and T‐cell lymphomas, but were 10%–15% higher than SEER rates for aggressive B‐cell subtypes (diffuse large B‐cell and mantle cell). The LEO cohort is a robust and comprehensive national resource to address the role of clinical, tumor, host genetic, epidemiologic, and other biologic factors in NHL prognosis and survivorship.

Introduction: The use of standardized, evidence-based classification systems is crucial for the accurate diagnosis and treatment of diseases. Moreover, standardized classification facilitates research and epidemiologic studies and promotes consistency in communication among healthcare professionals. Since 2016, the revised 4 th edition of the World Health Organization classification (WHO-HAEM4R) has been the global standard for diagnosis of lymphoid malignancies. With the emergence of new data, 2 new classification systems were developed and published in 2022: the 5 th edition of the WHO Classification (WHO-HAEM5) and the International Consensus Classification (ICC). Both WHO-HAEM5 and ICC maintain a shared fundamental concept of disease classification that integrates clinical, pathologic, and molecular data. However, they differ on nomenclature, establishment of new entities, and/or diagnostic criteria for some disease categories. To evaluate the impact of these differences on real-world diagnosis of non-Hodgkin lymphoma (NHL), we examined the diagnostic classification of NHL in the Lymphoma Epidemiology of Outcomes (LEO) Cohort Study (NCT02736357). Methods: Initiated in 2015, LEO is an ongoing prospective observational study of patients aged ≥18 years with newly diagnosed NHL. Patients are enrolled at 8 major U.S. medical centers. Clinical, epidemiologic, pathologic, and treatment data are abstracted at baseline and active follow-up is conducted for all patients. Lymphoma subtype is coded for each case based on expert hematopathology re-review of the diagnostic pathology slides, the pathology report, biomarker data, and clinical data. Of note, NHL subtype distribution is similar to SEER data. We studied all patients enrolled in LEO between 7/1/2016 (the date LEO pathology review started using WHO-HAEM4R diagnostic codes) and 5/31/2020. WHO-HAEM4R diagnoses and additional clinicopathologic data, when relevant, were used to map cases into the corresponding WHO-HAEM5 and ICC diagnoses. Differences between WHO-HAEM5 and ICC were annotated for each case as: None - same disease entity; Minor - difference in nomenclature with similar diagnostic criteria; Major - difference in disease category and/or diagnostic criteria; or Unevaluable - insufficient data to assign WHO-HAEM5 and/or ICC diagnosis. Results: LEO enrolled 6143 patients during the study period. Of these, comparison between WHO-HAEM5 and ICC was evaluable in 5730 (93.3%). Unevaluable cases included those without a specific WHO-HAEM4R diagnosis at enrollment (N=384; e.g., patients with a low-grade B-cell lymphoma that could not be definitively sub-classified) and those with a specific WHO-HAEM4R diagnosis, but with insufficient clinicopathologic data to assign a specific WHO-HAEM5 and/or ICC diagnosis (N=29). Of the 5730 evaluable cases, 5376 (93.8%) showed no difference between WHO-HAEM5 and/or ICC diagnosis, 311 (5.4%) showed minor differences (nomenclature only), and 43 (0.8%) showed major differences (Table 1). The 43 major differences all involved B-cell NHLs; 20 (46.5%) were attributable to different approaches to classifying double-hit lymphomas, 21 (48.9%) to classification of splenic/leukemic B-cell lymphomas, and 2 (4.6%) to classification of B-cell lymphomas occurring at specific anatomic sites (Table 2). Conclusion: In a large, prospective lymphoma cohort reflecting real-world clinical practice at 8 major U.S. medical centers, major diagnostic differences in the classification of NHL using WHO-HAEM5 or ICC classification criteria were seen in 0.8%, whereas the remaining 99.2% of diagnoses were either the same or showed differences in nomenclature only. The existence of 2 concurrent classification systems presents potential for discrepancies in pathologic diagnosis, clinical practice, clinical trials, and other lymphoma research. Furthermore, some of the differences between WHO-HAEM5 and ICC are clinically and potentially therapeutically significant, and their resolution requires further study. Nevertheless, our findings argue that the proportion of patients affected would be small in real-world practice settings. This appears largely related to incidence rates of specific lymphoma subtypes, with major differences between the 2 classifications predominantly affecting rare entities, while there is general concordance on the most common forms of NHL.

Introduction: PET/CT is the standard-of-care for staging and therapy response assessment in lymphoma. Fluorodeoxyglucose (FDG)-avidity in extranodal marginal zone lymphoma (EMZL) remains controversial with some variability by extranodal location. The current Lugano classification recommends the use of CT scans only in staging and follow-up for EMZL. However, emerging data from small studies have challenged this suggestion. In this study we aimed to determine the ability of PET/CT to detect FDG-avid EMZL during staging work-up. Methods: First, we retrospectively analyzed the University of Miami (UM) MZL database (period 2/2016-1/2023) searching for patients who underwent staging PET/CT. Images were reviewed by expert radiologists to ascertain avidity of tumor and not surrounding tissue, with specific attention to ocular and GI locations; if scans were not available (n=15), we retrieved data from reports (results presented in part at ICML2023). Next, we aimed to validate the initial UM results additionally analyzing data from imaging reports for patients prospectively enrolled in the Lymphoma Epidemiology of Outcomes (LEO) MZL cohort (period 7/2015-5/2020), excluding LEO patients from Miami to avoid overlap. Patients with high-grade transformation at diagnosis were excluded. For patients with &gt;1 extranodal site, each location was counted independently. We considered FDG-avid disease if SUVmax was ≥2 as this value is commonly observed between mediastinal blood pool (BP) and liver background. To normalize data, we calculated ratios of lymphoma SUVmax versus both BP and liver background, as available. Patients without measurable disease were excluded from analysis. Results: Among 210 patients with EMZL in the UM cohort, 152 had staging PET/CT and were analyzed. Twenty-two patients demonstrated &gt;1 extranodal site providing a total of 187 locations. The LEO cohort included 411 patients from 7 centers, with 235 having available imaging reports. Fifty-two patients in this cohort demonstrated &gt;1 extranodal site providing a total of 306 locations. Disease distribution is depicted in Table 1. Among 187 EMZL locations in the UM cohort, the most common were gastric (n=33, 17.6%), ocular (n=31, 16.6%), lung (n=30, 16%), skin and soft tissue (n=16, 8.6%, each). Similarly, lung (n=63; 20.6%), soft tissue (n=46; 15%), gastric and ocular (n=34 each; 11%) were common locations in the LEO cohort. EMZL demonstrated FDG-avidity (SUVmax ≥2) across all locations with identical values in both cohorts (UM= 78.1% & LEO= 76.5%). FDG-avidity by location is shown in Table 1A & B with salivary gland (100% & 100%), lung (93.3% & 95.2%), soft tissue (93.8% & 100%), and airways (90.9% & 100%) demonstrating high avidity in both cohorts. Contrarily, skin was commonly non-FDG avid (93.8% & 81%). The median size of FDG-avid lesions was 2.4cm (range, 0.7-17.4) in UM and 2.9cm (range, 0.1-12) in LEO cohorts. In patients with &gt;1 extranodal site FDG-avidity was nearly universal across sites (UM=93% & LEO=100%). We observed a significant correlation between lymphoma size and FDG-avidity for UM (r=0.33; P=0.001), and for LEO (r=0.55; P&lt;0.0001). In patients with background FDG-avidity data in the UM cohort (n=150), a BP index and liver index ≥1 detected lymphoma in 79.3% & 71.5% cases, respectively. We were unable to calculate indexes in LEO cohort since only limited data on BP (n=26) and liver (n=19) background was available in imaging reports. Conclusions: This study represents the largest analysis evaluating accuracy of PET/CT to determine initial tumor burden in 387 patients with EMZL. We demonstrate that EMZL is largely an FDG-avid disease across two cohorts, and PET/CT should be included in the staging of these patients while acknowledging possible limited sensitivity in GI locations due to physiologic uptake. Furthermore, we observed that patients with multiple extranodal locations demonstrate uniform FDG-avidity across all sites except for skin involvement. Based on our results we recommend the incorporation of PET/CT into standard staging of EMZL in planned revised Lugano classification guidelines. In those demonstrating initial FDG-avidity, a lower SUVmax threshold of SUVmax ≥2, comparison to mediastinal BP rather than to liver background activity and evaluating for resolution of focal FDG-avidity should be considered to assess treatment response in routine care and clinical trials.

Introduction: Molecular classifications have been developed to characterize the heterogeneity of diffuse large B cell lymphoma (DLBCL), with activation of BCL6 and Notch signaling pathways acting as driving pathogenic features in C1/BN2 subtype tumors. Among C1/BN2 defining variants are truncating/inactivating SPEN mutations. SPEN regulates the Notch signaling pathway by initiating formation of a repressive complex that facilitates transcriptional repression of Notch target genes. Our group has identified SPEN mutations enriched in DLBCL cases failing to achieve event-free status at 24 months (EFS24). This contrasted with generally favorable outcomes in C1/BN2 tumors, suggesting a potential high-risk molecular feature with unexplored significance. Presenting an opportunity to expand underlying mechanisms and risk stratification in C1/BN2 tumors, we aimed to use a functional genomic approach, combined with clinical data, to characterize the alternative biology stemming from SPEN truncating mutations. Methods: FFPE tumor biopsies from newly diagnosed DLBCL cases (N=444) enrolled in the Mayo Clinic/Iowa Lymphoma Molecular Epidemiology Resource (MER) program were analyzed by WES (n=404) and RNA-seq (n=321). Biologic and clinical variables were annotated for all cases. CRISPR/Cas9 introduced truncating mutations to the endogenous SPEN locus in OCI-LY3 & SU-DHL-2 ABC-type human cell lines to establish models of SPEN depletion. Functional in vitro analysis utilized standard methods for RNA-seq, cell proliferation, western blot, and flow cytometry. Results: WES identified 29/404 ndDLBCL (7.2%) harboring SPEN mutations; n=17 missense and n=12 nonsense or frameshift insertions/deletions (truncating variants - [trunc]), the later considered as the focal point of our study. Against all cases, SPEN trunc cases displayed enrichment for high-risk factors including non-GCB COO (p=0.06, OR=3.7, 95% CI [0.9, 22.2]) and primary refractory disease (PTR; p=0.02, OR=5.0, 95% CI [1.05, 19.9]), and demonstrated inferior OS (p=0.06, HR=2.2), where a majority of clinical progression events occurred within 12 months of diagnosis. Leveraging RNA-seq, we observed 11 of 11 SPEN trunc cases exhibiting a double-hit negative gene expression signature, 9/11 cases with inflammatory or depleted microenvironment (LME), and 8/11 with a signature associating with intermediate/high risk of EFS24 failure. Applying the LymphGen algorithm, 5/12 SPEN trunc cases received the BN2 classification, observing co-occurring mutations with CD70 (p&lt;0.01, OR=9.68), BRCA2 (p=0.03, OR=5.84), TET2 (p=0.02, OR=4.91), IRF4 (p=0.02, OR=7.27), and PTPRD (p&lt;0.01, OR=8.31) genes, and were mutually exclusive with ARID1A, BCL2, EZH2, FAS, HLA-B, IRF8, and TMEM30A mutations ( Figure 1A). Isolating SPEN trunc against BN2 cases without SPEN variants (n=14), despite similar clustering of non-GCB COO (p=0.7), PTR was exclusively observed in SPEN trunc cases, accompanied again by inferior OS (p=0.07, HR=4.2; Figure 1B). In the OCI-LY3 SPEN depletion model, GSEA analysis from RNA-seq revealed activation of E2F (NES=2.7; p=9.4E-20) and MYC target genes (NES=2.3; p=4.1E-6), while both OCI-LY3 and SU-DHL-2 models featured depletion of antigen presentation genes (NES= -1.8, p=5.7E-4; NES= -2.2, p=3.8E-7, respectively). A decrease of MHC class II proteins (HLA-DR, HLA-DP) was confirmed by flow cytometry in both models (p&lt;0.01). Hypothesizing loss of SPEN enhances reliance on the BCL6-HDAC3 axis featured in BN2 tumors, we treated both SPEN depletion models with a class-I HDAC inhibitor highly selective for HDAC3, BRD-3308, and indeed observed a 1.4 and 2.4-fold increase in sensitivity compared to WT cells (p=0.07, p&lt;0.01, respectively). Significance: We find SPEN trunc mutations in poor acting DLBCL cases with clinical and biologic characteristics that diverge from C1/BN2 biology. The enhanced rate of clinical progression highlights urgency to better characterize these tumors. Our data suggests loss of SPEN regulatory mechanisms may act cooperatively with C1/BN2 biology to facilitate tumor immune evasion and drive progression through Notch-mediated pathways. Importantly, in vitro evidence shows SPEN deficient lymphoma cells are susceptible to targeted therapeutics against dependent pathways, providing rationale for advancing individualized care strategies for affected patients.

Background: Although marginal zone lymphoma (MZL) typically exhibits an indolent course, transformation to diffuse large B-cell lymphomas (DLBCL) is associated with a poorer outcome and remains a clinical challenge in managing patients with MZL. The limited existing literature reports various cumulative incidence rates, ranging from 2.5% and 4.7% (Finnish Cancer Registry, 1995-2018) to 6.6% and 8.4% (Florida Cancer Registry, 1995-2016) at 5 and 10-years, respectively, likely in part driven by varying study designs, ascertainment, and study population. Similarly, reported outcomes after transformed MZL (tMZL) have been variable. We evaluated the cumulative incidence of histological transformation (HT) to LBCL in a well-characterized prospective cohort of MZL patients from the upper Midwest, USA, and overall survival (OS) after transformation. Methods: Patients ≥ 18 years old with newly diagnosed MZL were prospectively enrolled in the University of Iowa/Mayo Clinic Molecular Epidemiology Resource (MER) cohort from 2002-2015. Clinical and treatment data were abstracted from medical records using a standard protocol. Patients were actively followed for retreatment, transformation, and death through 2022. Pathology was classified according to WHO 4/4R, including all transformations. The cumulative incidence of HT to LBCL was determined using death as a competing risk. Time to HT (TTHT) was defined as time from the date of MZL diagnosis to the date of transformation. We evaluated potential predictors of HT using a stepwise approach, and derived hazard ratios (HR) and 95% confidence intervals (CI) from Cox proportional hazards models. To assess predictors of OS after HT, patients ≥ 18 years old with newly diagnosed tMZL (into DLBCL) at Mayo Clinic between 1995 and 2002 (N=7) were combined with the MER cases. Results: We identified 524 patients with MZL in the MER cohort: 362 extranodal (EMZL; lung 18%, stomach 16%, orbit 13%, parotid 10%, skin 10%), 84 splenic (SMZL), and 78 nodal (NMZL). The median age at diagnosis was 63 years (range 18-92); 56% were female, and 83% were non-Hispanic white. During a median follow-up of 18.8 years (95% CI 16.7, NA), there were 25 HT (including 8 patients under observation) and 158 deaths. Eight Ten-year OS was 67% and 77% in the groups with and without HT, respectively. The overall cumulative incidence of HT was 1.5% at 2 years after diagnosis, 2.9% at 5 years, 4.5% at 10 years, and 5.7% at 15 years (Panel 1). The cumulative incidence of HT varied by subtype (e.g., at 5 years: 2%, 4% and 6%, in EMZL, NMZL and SMZL, respectively; Panel 2). The median time to HT also varied by subtype: 76 months (interquartile range (IQR) 43, 81) for EMZL, 30 months (IQR 16, 52) for SMZL, and 18 months (IQR 14, 23) for NMZL. In univariable analysis, predictors of HT were Stage III/IV (HR=3.4, 95% CI 1.36-8.53, p=0.009), elevated LDH (HR=2.98, 95% CI 1.25-7.12, p=0.014), and presence of ≥2 extranodal sites (HR=2.69, 95% CI 1.22-5.94, p=0.014). In multivariable analysis, elevated LDH was the only non-composite predictor of HT (HR=2.65, 95% CI 1.08-6.52, p=0.034). MALT-IPI score 2-3 (HR=3.61, 95% CI 1.06-12.25, p=0.040) and FLIPI score 3-5 (HR=2.19, 95% CI 1.22-8.22, p=0.018) also predicted HT. We analyzed OS after HT in 32 cases of tMZL (21 tEMZL, 7 tSMZL, 4 tNMZL). At HT, median age was 68 years (range 39-89); 24 (80%) patients presented with stage III/IV disease, 11 (58%) with elevated LDH, 12 (55%) with hemoglobin &lt;12g/dL, 3 (14%) with platelet count &lt;100x10 9/L and 9 (39%) with biopsy-proven bone marrow involvement. Twenty-six (81%) were treated with a systemic therapy, mainly CHOP-backbone therapy (R-CHOP n=13, CHOP n=4, R-EPOCH n=3, lenalidomide-R-CHOP n=1). Among these patients, the overall response rate was 85% (n=22/26). Three of the 4 non-responders died within 1 year. At last follow-up, 18/32 (56%) patients had died, and 6/13 (46%) alive patients had relapsed (MZL 3/6, DLBCL 3/6). After HT, 2-, 4- and 10-year OS were 76%, 55% and 55%, respectively. In univariable analysis, the only predictor of OS at HT was age ≥70 (HR 3.57 ,95% CI 1.34-9.48, p=0.011). Conclusion: In this North American prospective cohort of mainly non-Hispanic White patients, we found a low cumulative incidence of HT in MZL of 2.9% and 4.5% at 5- and 10-year, which was consistent with the Finnish findings (Kalashnikov et al., 2023). At HT, age was associated with OS and there was a high response rate to CHOP-backbone therapies.

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 &lt;70 and PFS5&gt; 90 would only identify 15% and &lt;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: 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&lt;0.001) and MALT-IPI score 2+ (observation 23%, local/antibiotic 18%, systemic 28%, p&lt;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&gt;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&lt;0.05) and 9 (mean -3.7, p&lt;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&lt;0.05), 2 (mean +0.7, p&lt;0.05), 3 (+1.3, p&lt;0.05), 6 (+1, p&lt;0.05), and 9 (+1.3, p&lt;0.05) and an overall decline of social/family WB at years 1 (mean -1.1, p&lt;0.05), 2 (mean -1.7, p&lt;0.05), 3 (-2.1, p&lt;0.05), 6 (-2.7, p&lt;0.05), and 9 (-2.2, p&lt;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 &lt;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 (&gt;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 &gt;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 &gt;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 &gt;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, &gt;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 &lt;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&lt;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&lt;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.