Novak, Anne J.

7073 Background: The Optimized HLH Inflammatory (OHI) index (Zoref-Lorenz et al., Blood , 2022), based on serum soluble CD25 (sCD25) and ferritin, is a prognostic tool identifying inflammation in the hemophagocytic lymphohistiocytosis (HLH) spectrum and early mortality risk. Prior studies were retrospective and enriched with HLH-suspected cases. We hypothesized that the OHI index would predict outcomes in unselected diffuse large B-cell lymphoma (DLBCL). Methods: 670 newly diagnosed DLBCL patients from a prospective cohort (2002–2015) were analyzed: 335 for discovery and 335 for validation. Pre-treatment serum sCD25 and ferritin levels were quantified via ELISA, and we evaluated original thresholds (sCD25 ≥ 3,900 U/mL; ferritin ≥ 1,000 ng/mL) and developed DLBCL-optimized thresholds (using receiver operator curves) for predicting 500-day mortality. We also evaluated event-free survival (EFS), EFS at 24 months (EFS24), and overall survival (OS). Results: The median age was 64, and 51% were male. Using original thresholds, 4.2% (n=14) of patients were OHI+, with a 5.6-fold higher risk of 500-day mortality (95% CI 1.6–17; p<0.001). Optimized thresholds identified 23.6% (n=78) as OHI+ with a 9.2-fold higher 500-day mortality risk (95% CI 4.0–24; p<0.001). Optimized OHI+ also had a higher risk of EFS24 failure and inferior EFS and OS (Table). OHI+ patients had higher rates of B symptoms (35% vs. 20%; p=0.027), elevated LDH (76% vs. 34%; p<0.001), worse performance status (ECOG ≥2: 38% vs. 12%; p<0.001), and advanced-stage disease (Stage IV: 57% vs 38%; p<0.001), though IPI scores (3–5) were not significantly different (44% vs 36%; p=0.28). Adjusting for age and IPI, OHI+ independently predicted 500-day mortality (OR=5.0; CI 1.9–13; p=0.001), EFS24 failure (OR=3.2; CI 1.5–6.5; p=0.0018), and inferior long-term EFS (HR=2.2; CI 1.1–4.3; p=0.030) and OS (HR=2.0; CI 1.0–4.0; p=0.040) at a median follow up of 9.8 years in living patients. In validation, optimized OHI predicted 500-day mortality, EFS24 failure, and long-term OS and EFS (Table). Cytokine profiling revealed elevated inflammatory markers in OHI+ patients, including IL1a (p=0.008), CCL2 (p=0.03), CXCL10 (p<0.001), and CXCL9 (p<0.001), reflecting systemic hyperinflammation. Conclusions: The OHI index is a powerful predictor of early and long-term outcomes in DLBCL patients. Optimized thresholds identify a larger OHI+ group, highlighting hyperinflammation’s critical role in poor outcomes. These findings support its use in routine management and clinical trial design for novel therapies. Odds ratios or hazard ratios and p-values for optimized OHI+ and DLBCL prognosis. Outcome Discovery Validation OR for 500-day mortality 9.2; p<0.001 4.0; p=0.002 OR for EFS24 failure 5.2; p <0.001 6.3; p<0.001 HR for OS 2.3; p<0.001 1.8; p=0.006 HR for EFS 2.4; p<0.001 2.5; p<0.001

7052 Background: Secondary central nervous system lymphoma (SCNSL) is a rare but serious complication that occurs in 2-10% of patients (pts) with diffuse large B-cell lymphoma (DLBCL). The CNS-International Prognostic Index (CNS-IPI) is a validated tool used to assess the risk of CNS relapse. However, with advancements in molecular profiling and treatments, the predictors of SCNSL, including the predictive ability of CNS-IPI combined with other molecular markers and treatment regimens, have become uncertain. This study evaluates new prognostic markers for CNS relapse in DLBCL, focusing on cell-of-origin (COO), gene rearrangements, and treatment types. Methods: DLBCL pts enrolled from 2002-2015 in the Mayo Clinic and University of Iowa Lymphoma Molecular Epidemiology Resource were prospectively followed for incidence of CNS relapse. Pts with primary CNS lymphoma or CNS involvement at diagnosis, and pts treated with high-dose methotrexate containing regimens were excluded. COO classification (GCB vs non-GCB) was determined using Hans algorithm and/or NanoString data. Multivariate Cox regression models estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for predictors of SCNSL. Survival analysis was performed from time of DLBCL diagnosis using Cox proportional hazards model. Results: Among 1278 newly diagnosed DLBCL pts included (median age: 63 years; 43.6% female), SCNSL occurred in 46 (3.6%) pts. Pts with SCNSL were more likely to have intermediate/high CNS-IPI scores (80.4%) than pts without SCNSL (63.9%, p=0.02). The 3 yr SCNSL incidence was 0.016 (0.008-0.0033) in low CNS-IPI vs 0.043 (0.031-0.0060) in high CNS-IPI. Those with SCNSL were significantly more likely at baseline to have non-GCB COO (56.3% vs. 36.5%, p=0.02), and advanced stage (76.1% vs. 59.9%, p=0.03) compared to those without SCNSL. The incidence of SCNSL was not influenced by MYC, BCL2, or BCL6 rearrangements, nor by treatment with R-CHOP, R-EPOCH, or other immunochemotherapy regimens. MVA showed that intermediate CNS-IPI (HR 2.32, 95% CI 1.09-4.92, p=0.029), high CNS-IPI (HR 3.59, 95% CI 1.49-8.62, p=0.004), and non-GCB COO (HR 2.19, 95% CI 1.08-4.42, p=0.0029) were independently associated with increased SCNSL risk. After a median follow-up of 12 years (IQR 9–15), overall survival (OS) was 52.1% for the cohort, with OS significantly worse for SCNSL pts (17.4% vs. 53.4%, p<0.001). Conclusions: This large cohort with extensive follow-up highlights the role of COO as an additional prognostic factor for SCNSL while reinforcing CNS-IPI’s prognostic value. Non-GCB COO pts had a higher risk of SCNSL even when adjusted by CNS-IPI risk. Despite advancements in management, SCNSL pts continue to have poor outcomes. Strategies to reduce the incidence of CNS relapse in high-risk pts require further investigation. MER CNS relapse hazard ratios (95% CI). Characteristics Hazard Ratio 95% CI P-Value CNS-IPI Group 0-1 Low Ref Ref Ref 2-3 Intermediate 2.32 1.09 - 4.92 0.029 4+ High 3.59 1.49 - 8.62 0.004 Cell of Origin* GCB Ref Ref Ref non-GCB 2.19 1.08 - 4.42 0.029 Double Hit* non-DHL Ref Ref Ref DHL 1.08 0.14 - 8.3 0.941 Not Done/Missing 1.38 0.76 - 2.51 0.297 Double Expressor* Negative Ref Ref Ref Positive 2.36 0.71 - 7.85 0.162 Not Done/Missing 1.91 0.89 - 4.08 0.097 IC Group* R-CHOP R-EPOCH Ref 1.63 Ref 0.71 - 3.74 Ref 0.249 Other IC 1.18 0.57 - 2.46 0.660 Any EN Involvement* No Ref Ref Ref Yes 0.98 0.51 - 1.88 0.946 Albumin Group* >=Normal Ref Ref Ref <Normal 1.70 0.75 - 3.85 0.201 Head & Neck Involvement* No Ref Ref Ref Yes 0.35 0.048 - 2.63 0.310 *Adjusted by CNS-IPI risk group.

Abstract Family history of hematologic malignancy (FHHM) is an established risk factor for developing lymphoma, but studies examining associations between self-reported FHHM and lymphoma prognosis during the recent treatment era are limited. Utilizing a prospective cohort study, we evaluated associations between FHHM and prognosis for common lymphoma subtypes, including diffuse large B-cell (DLBCL), follicular (FL), marginal zone (MZL), mantle cell (MCL), Hodgkin (HL), and T-cell (TCL) lymphomas. We used the Mayo Clinic component of the Molecular Epidemiology Resource, a prospective cohort study of newly diagnosed lymphoma patients enrolled from 2002 to 2015. Self-reported FHHM (HL, NHL, leukemia, or multiple myeloma) in a first-degree relative was obtained via questionnaire. Primary outcomes were overall survival (OS) and event-free survival (EFS; time from diagnosis to disease progression, initiation of second-line therapy, or all-cause death). Secondary outcomes were failure to achieve 24 months of event-free survival (EFS24; associated with aggressive disease and poor outcomes) and lymphoma-specific survival (LSS; time from diagnosis to death from lymphoma or lymphoma treatment-related complications). We used multivariable Cox proportional hazards regression to estimate hazard ratios (HRs) and 95% confidence intervals (CI) for the association of FHHM with OS, EFS, and LSS, and logistic regression to estimate odds ratios (ORs) and 95% CI for the association with failure to achieve EFS24. All models were adjusted for sex and a lymphoma subtype specific prognostic index. The cohort consisted of 650 DLBCL, 679 FL, 291 MZL, 178 MCL, 254 HL, and 196 TCL patients. The prevalence of FHHM ranged from 9.7% in TCL to 14% in FL. FHHM was not associated with OS in any subtype, but MCL patients with FHHM showed an inferior EFS (HR=1.70, 95% CI 1.02-2.85; p=0.04). For EFS24, FHHM was associated with failure to achieve EFS24 only in FL (OR=1.57, 95% CI 0.99-2.48; p=0.05). Similarly, FHHM was associated with inferior LSS in FL patients (HR=1.98, 95% CI 1.11-3.53; p=0.02). In a treatment subgroup analysis of FL patients, the FHHM associations with failure to achieve EFS24 (OR=2.48, 95% CI 1.04-5.74; p=0.04) and inferior LSS (HR=2.68, 95% CI 1.09-6.58; p=0.03) strengthened among those treated with frontline immunochemotherapy (IC). In summary, there were no evidence of associations between FHHM and OS across lymphoma subtypes, but MCL patients with FHHM displayed inferior EFS. FL patients with FHHM were more likely to not achieve EFS24 and to ultimately die of their lymphoma or a lymphoma treatment-related complication. This finding was stronger in more aggressive FL requiring frontline IC. This novel finding would suggest that genetic or environmental factors associated with FHHM may underlie pathways that impact disease aggressiveness and early clinical failure in FL. Citation Format: George Cholack, Raphael Mwangi, Thomas M. Habermann, Verena Hinder, Rosalie Griffen, Dennis Robinson, Andrew L. Feldman, Thomas E. Witzig, Stephen M. Ansell, Brian K. Link, Yucai Wang, Carrie A. Thompson, Susan L. Slager, Matthew Maurer, Anne J. Novak, James R. Cerhan. Family history of hematologic malignancy and prognosis across lymphoma subtypes: Novel association with poor outcomes in follicular lymphoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 7344.

Abstract Background: Diffuse large B-cell lymphoma (DLBCL) is clinically heterogeneous, and gene expression profiling has identified at least two biologically distinct DLBCL subtypes defined by their cell of origin (COO): germinal center B cell (GCB) and activate B cell (ABC) or non-GCB. We evaluated a variety of putative DLBCL risk factors for etiologic heterogeneity by the COO in a clinic-based study of newly diagnosed DLBCL cases (N = 638) and frequency-matched controls (N = 2,253). Methods: The COO was determined on formalin-fixed, paraffin-embedded tumor tissue, with DLBCL classified as GCB (N = 283), non-GCB (N = 188), or undetermined/missing (N = 167; mainly because of lack of tissue). Multivariable logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI). Results: We identified heterogeneity by the COO for low socioeconomic status (SES), which was only associated with non-GCB DLBCL (OR = 1.88 for low vs. average SES; 95% CI, 1.08–3.27); alcohol use, which was only associated with GCB DLBCL (OR = 0.48 for former drinkers; 95% CI, 0.29–0.80 and OR = 0.47 for current drinkers; 95% CI, 0.32–0.71); and borderline heterogeneity for the regular use of regular/extra-strength aspirin, which was only associated with non-GCB DLBCL (OR = 0.36; 95% CI, 0.16–0.85). In contrast, there was no significant heterogeneity by the COO for family history, medical history, or other lifestyle factors. Conclusions: Although requiring confirmation, most risk factors for DLBCL did not show etiologic heterogeneity by the COO, with some notable exceptions including alcohol use, SES, and perhaps regular use of regular/extra-strength aspirin showing associations. Impact: Mechanistically, these findings suggest that most of the DLBCL risk factors evaluated here influence lymphomagenesis prior to differentiation into COO subtypes, with selected factors acting later.

Background: The Optimized Hemophagocytic Lymphohistiocytosis (HLH) Inflammatory (OHI, Zoref-Lorenz et al, Blood, 2022) index, defined by the combined elevation in serum soluble CD25 (sCD25) and ferritin, has emerged as a promising prognostic tool for patients with newly diagnosed hematologic malignancies, demonstrating superior performance in predicting 500-day mortality compared to HLH-2004 criteria. However, previous studies were retrospective, with a substantial proportion (65%) of patients suspected clinically to have HLH. We evaluated the performance of the OHI index in an unselected, prospective cohort of newly diagnosed diffuse large B-cell lymphoma (DLBCL) patients. Methods: We analyzed the predictive utility of the OHI index in 331 newly diagnosed DLBCL patients enrolled from 2002-2015 in the prospective Iowa/Mayo Molecular Epidemiology Resource cohort. Ferritin and sCD25 levels were quantified in pre-treatment serum samples using R&D ELISA kits. OHI values were also optimized based on 500-day mortality in the cohort using receiver operating curve analysis with internal validation. Patients were classified as OHI+ using the original and newly optimized thresholds for sCD25 and ferritin. The primary endpoint was 500-day survival, and the secondary endpoints were event-free survival (EFS) at 24 months (EFS24), EFS, and overall survival (OS). The predictive capability of the OHI index was evaluated using logistic regression to determine odds ratios (OR) and 95% confidence intervals (CI) for 500-day mortality and EFS24, as well as Cox regression to calculate hazard ratios (HR) and 95% CIs for EFS and OS. Results: The median age at diagnosis of the 331 patients was 63.5 years (interquartile range, 54.5-74), and 168 (51%) were male. Using the original thresholds (sCD25 3,900 U/mL and ferritin 1,000 ng/mL), 4.2% of patients were OHI+, with a more than 5-fold higher 500-day mortality risk compared to OHI- patients (OR=5.6; CI 1.6-17, p&lt;0.001). After optimizing thresholds for sCD25 (2,400 U/mL) and ferritin (250 ng/mL) to predict 500-day mortality, 23.6% were OHI+ and the OHI+ group had a 9-fold higher 500-day mortality risk (OR=9.2; CI 4.0-24, p&lt;0.001), with 31% mortality in the OHI+ vs 5% in the OHI- group. OHI+ patients exhibited more B-symptoms (35% vs 20%, p=0.027), elevated LDH levels (76% vs 34%, p&lt;0.001), poorer performance status (Eastern Cooperative Oncology Group 2+: 38% vs 12%, p&lt;0.001), and more advanced stage disease (Stage IV: 57% vs 38%, p&lt;0.001), although the frequency of a high International Prognostic Index (IPI) was not statistically different between the groups (IPI 3-5: 44% vs 36%, p=0.28). After adjusting for age and IPI, OHI+ remained a strong predictor of 500-day mortality (OR=5.0; CI 1.9-13, p=0.001). Further adjustment for the cell of origin did not impact the results. The association of OHI+ status with 500-day mortality was similar in IPI 0-2 (OR=5.8; CI 1.2-29, p=0.033) and 3-5 (OR=6.8; CI 0.6-73, p=0.11). OHI+ patients were also more likely to not achieve EFS24 (45% vs 13%), with a 3-fold greater risk after adjusting for age and IPI (OR=3.2; CI 1.5-6.5, p=0.0018). The OHI index also retained its predictive ability over longer-term follow-up (median 9.8 years for living patients) for both OS (HR=2.0; CI 1.0-4.0, p=0.040) and EFS (HR=2.2; CI 1.1-4.3, p=0.030) after adjusting for age and IPI. Cytokine profiling using multiplex ELISA identified elevated inflammatory markers in OHI+ patients, underscoring the role of systemic inflammation in prognosis. The distribution of cytokines was significantly different between the groups, with consistently higher means in the OHI+ vs OHI- group, including IL1a (1,628 vs 759 pg/ml, p=0.008), CCL2 (1,020 vs 724 pg/ml, p=0.028), CXCL10 (165 vs 53 pg/ml, p&lt;0.0001), and CXCL9 (380 vs 150 pg/ml, p&lt;0.0001). Conclusions: While the original OHI index for prediction of 500-day mortality was validated in this unselected cohort of newly diagnosed DLBCL patients, optimization of the OHI thresholds identified a larger OHI+ group (24%), enhanced the identification of hyperinflammation, improved predictive ability for early (500-day) mortality, and predicted EFS24 status and long-term OS. This underscores the clinical relevance of the OHI index, supports the need to evaluate disease-specific thresholds, and suggests its potential for use in routine management and as enrollment criteria in clinical trials for novel therapies.

Introduction: Clonal hematopoiesis (CH) is a common age-related premalignant condition defined by the presence of an acquired somatic mutation in a gene frequently mutated in myeloid malignancies characterized by a variant allele frequency (VAF) of ≥2%, without fulfillment of other diagnostic criteria for myeloid neoplasm. Incidence of CH increases with age and found in 10%-30% of people &gt; 70 years of age. CH-related mutations confer an increased risk of myeloid neoplasms, cardiovascular diseases, and are linked to inferior survival in solid tumor patients treated with chemo/radiation therapy. Such mutations can act as a driver/amplifier of inflammation, especially through myeloid cells. To distinguish true disease drivers from background genetic events related to aging or environmental factors, we need a deeper understanding of the impact of CH in different tissue microenvironments and under therapeutic pressure for better risk stratification of CH carrier patients. Recent reports suggested high prevalence of CH-related mutations in lymphoma. Current analyses aimed to investigate: i) the prevalence of CH-related mutations in two real-world lymphoma cohorts from Molecular Epidemiology Resource (MER) MER-FL and MER-DLBCL, ii) CH-related mutational landscape in DLBCL and FL, iii) the impact of CH-related mutations on clinical outcomes. Methods: Whole exome sequencing (WES) derived from blood/PBMC from lymphoma patients with paired tumor samples was analyzed to identify CH-related myeloid drivers in 42 previously reported genes. We used Sention-Tnhaplotype2 somatic pipeline with a custom panel of normals from whole blood samples. To avoid tumor contamination, we filtered variants for which VAF in the tumor was higher than that in the germline sample. Other filtering criteria included HIGH/MODERATE impact; read count &gt; 1 in both forward and reverse reads; gnomAD_AF &lt; 0.01. We used the R “survival” package Cox regression implementation for outcome and other statistical analysis. Results: Genomic and clinical data of 507 patients from MER-FL and 383 patients from MER-DLBCL cohorts, with median age of 60 and 64 respectively, were available for analysis. CH-related mutations were present in 17.16% (87/507) in FL and 18.28% (70/383) in DLBCL cohorts with a VAF cut off &gt;2%. The most common CH-related mutations were DNMT3A (5%), CREBBP (2%), TET2 (1%), ASXL1 (1%) and PPM1D (1%) in MER-FL while DNMT3A (5%), TET2 (3%), TP53 (3%), ASXL1 (2%) and PPM1D (1%) in MER-DLBCL cohort. Both the prevalence of CH-related mutations and their average VAF increased with age, consistent with literature. Finally, cox regression analysis suggested that CH-related mutations were associated with significantly inferior overall survival (OS) in both FL and DLBCL cohorts (HR 1.6 p=0.028 and HR 2.3 p=&lt;0.001 respectively); however only in DLBCL this association was significant after age correction (HR 1.8 p=0.008). When separated by individual gene mutations, genes of interest with inferior OS included TP53 (n=4, HR 3.1, p=0.057) in FL and PPM1D (n=3 HR 2.6 p=0.1), TP53 (n=8 HR 2.1 p=0.1) and DNMT3A (n=14, HR=2.0, p=0.048) in DLBCL; however, these estimates did not quite reach statistical significance. Conclusions: Taken together, our exploratory analysis indicates that CH-related mutations associate with worse OS in lymphoma patients, supporting the rationale for evaluation of CH for the risk assessment in lymphoma. Furthermore, DLBCL displays of larger size effect, which may be related to different treatment regimes. Additional studies will be needed to elucidate the interaction between high dose chemotherapy regimes and CH-associated adverse events.

Splenic marginal zone lymphoma (SMZL) is a B-cell malignancy with a typically indolent course. However, some patients have more aggressive disease and initial treatment can range from observation to chemoimmunotherapy. The reason for this spectrum of disease behavior is incompletely understood but the tumor microenvironment (TME) likely plays a role as recent studies have shown that an optimized antitumor immune response in SMZL patients would allow the immune system to better target malignant B cells and irradicate the disease. Regulatory CD4+T cells (Tregs) are crucial in maintaining TME homeostasis by suppressing effector T cells and malignant B cells and in this study, we therefore explored the key factors influencing the function and development of intratumoral Tregs in SMZL patients. We identified two distinct Treg subsets in the TME of SMZL patients: CD26+Tregs and CD161+Tregs, each with unique phenotypic profiles, functions, and prognostic significance. CD161+Tregs suppress CD8+T cells and malignant B cells more effectively than CD26+Tregs, which only inhibit CD8+T cells. Clinically, patients with inferior outcomes have significantly higher numbers of CD26+Tregs and fewer CD161+Tregs. To delineate differences between these subsets, we performed Cellular Indexing of Transcriptomes and Epitopes Sequencing (CITE-seq) on biopsies from 7 SMZL patients. We found that cytokine-inducible SH2-containing protein (CISH) is the most highly expressed gene in CD26+Tregs compared to CD161+Tregs, aside from FOXP3. CISH has been reported to be a negative regulator of immune effector cell activation in tumor models. Although CISH deficiency leads to a loss of Foxp3 expression and reduced Treg suppressive function, the mechanism by which CISH modulates Treg function, especially immune suppression in hematologic malignancies, remains largely unknown. To further investigate the mechanism of CISH in Treg function in MZL patients, we conducted CITE-seq analysis on a larger cohort of MZL patients (n=12), divided into groups based on outcomes (EFS24 failed, n=4; EFS24 achieved, n=8). The CITE-seq analysis results showed significantly higher CISH expression in Tregs from patients with inferior outcomes (P=1.07E-06). We then explored factors influencing CISH expression and found that TCR engagement and treatment with cytokines (IL-15, IL-2, and IL-7) elevated CISH expression in Tregs. While we previously observed that lymphoma B cells could enhance Foxp3 expression in CD4+CD25-T cells, this study we found that B cells could also enhance CISH and CD26 expression in CD4+CD25-T cells in vitro, indicating that a high frequency of B cells in the environment may lead to increased CISH expression in Treg cells leading to an unfavorable outcome in patients. We further investigated the role of CISH in the function of Treg cells. Following CISH knockdown in Treg cells, we observed decreased CD26 expression and reduced suppressive function on CD8+ T cell proliferation. Using CRISPR-Cas9, we knocked out CISH in the Karpas 299 cell line, a human T cell lymphoma line that constitutively expresses CD25, Foxp3, and CISH. CISH-KO Karpas 299 cells showed significantly less apoptosis and Foxp3 expression compared to CISH-WT cells. Bulk RNA-seq analysis of CISH-KO and CISH-WT Karpas 299 cells revealed that CISH-KO cells showed enhanced DNA replication and cell cycle pathway activity. Taken together, our results suggest that high expression of CISH in Tregs may predict an unfavorable outcome in SMZL patients. Malignant B-cells in the TME of SMZL patients may promote expression of CD26 and CISH in Tregs, thereby modulating Treg function. Targeting CISH could decrease the immunosuppressive activity of Tregs in the TME, potentially optimizing antitumor immunity in SMZL patients.

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&lt;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&lt;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: 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 (&gt;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&lt;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&lt;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&lt;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.