Link, Brian K.

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.

Introduction: The treatment of chronic lymphocytic leukemia (CLL) has evolved over the past two decades, from finite duration chemoimmunotherapy (CIT) to an era of targeted agents such as Bruton Tyrosine Kinase (BTK) inhibitors, BCL2 inhibitors, and anti-CD20 monoclonal antibodies. Little is known about how initial management strategy (watch and wait [W&W] vs. treatment) at diagnosis impacts patient reported outcomes (PROs) in CLL survivors over time, particularly in light of therapeutic advances over the last decade. Methods: Newly diagnosed CLL patients were prospectively enrolled within 9 months of diagnosis in the University of Iowa/Mayo Clinic SPORE Molecular Epidemiology Resource (MER1) study between 9/1/02 and 6/30/15, and within 6 months of diagnosis in the MER Phase 2 (MER2) study between 7/1/15 and 5/1/20. PROs were measured using the Functional Assessment of Cancer Therapy-General scale (FACT-G), which generates Physical (PWB, range 0-28), Social/Family (SFWB, 0-28), Emotional (EWB, 0-24), and Functional Well-Being (FWB, 0-28) scores and a Total Score (0-108), with higher scores indicating better WB. In both cohorts, FACT-G was measured at enrollment and at years 1, 2, and 3 after diagnosis, while the timing of later assessments varied slightly by study (MER1: 6 yrs and 9 yrs; MER2: 5 years). We employed a generalized linear mixed model to evaluate change from baseline between groups with groups defined by each participant's initial management strategy (W&W vs. upfront systemic treatment). Initial management strategy was determined by the treating physician. The study was approved by the Mayo Clinic IRB. Results: A total of 722 patients were identified in MER1 and 256 patients in MER2. The median age at diagnosis was 63 years in MER1 and 66 years in MER2 (P<0.05); 66% were male in MER1 and 68% were male in MER2 (P>0.05). More patients initiated upfront systemic therapy in MER1 (n=380, 53%) than in MER2 (n=110, 43%). Other baseline clinical characteristics were similar between the cohorts ( Table 1). There was no statistical difference in the mean FACT-G total score at baseline between MER1 (85.0, SD 12.5) and MER2 (85.2, SD 12.7). In the MER1 cohort, statistically significant lower baseline QOL scores were observed in patients initiating upfront treatment compared to W&W on the FACT-G total score (P=0.002) as well as the PWB (P=0.01), EWB (P=0.03), and FWB (P=0.006) subscales. We also observed that over time, EWB and FWB scores were consistently lower amongst those treated upfront compared to W&W. Further, EWB scores steadily improved over time regardless of initial management ( Figure 1), whereas the FWB and PWB subscales were largely stable over time. Mean SFWB scores dropped amongst patients who received upfront therapy at 3 years, which then steadily improved through year 9 but did not recover back to baseline. SFWB scores were stable among W&W patients. In the MER2 cohort, no statistically significant differences in any of the baseline QOL scores were noted between the W&W vs. initial treatment groups. Despite this, we observed consistently lower mean EWB scores among patients who initiated upfront treatment compared to W&W pts over time. No other statistically significant differences in mean QOL scores over time were observed between groups. Similar to MER1, we did observe a drop in mean SFWB scores amongst patients initiating upfront therapy at 3 years of follow-up that remained decreased at year 5. Conclusion: This is the largest longitudinal study of CLL survivors that spanned over 18 years and two treatment eras. We demonstrated that in the novel treatment era, QOL metrics were similar between initial management strategies (W&W vs. treatment), suggesting that initial management had little to no impact on QOL at baseline or over time. In contrast, in the CIT era, we observed differences in QOL metrics at baseline between groups. In both treatment eras, patients with CLL who required upfront therapy demonstrated a decrease in SFWB from baseline at year 3 of follow-up, and the mean EWB scores were lowest at baseline regardless of initial management strategy (W&W vs. upfront treatment) or treatment era (CIT vs. novel agents), but steadily improved over time. These findings suggest opportunities for interventions to improve QOL in patients with CLL, and additional analyses are ongoing. Further follow-up in the era of novel agents is needed.

Background: The goal of clinical trial eligibility criteria is to identify a population of patients that have high risk disease or have unmet needs with standard of care therapy. Frontline trials of diffuse large B-cell lymphoma (DLBCL) include patients with International Prognostic Index (IPI) scores of 3-5 as a way to identify a high risk population. Trials typically include patients with IPI score=2, and sometimes IPI score=1, to expand the eligible patient population. However, broader patient populations must be balanced by the potential inclusion of low-risk patients with excellent outcomes to standard of care therapy, leading to potentially underpowered trials. IPI is a sum of dichotomous variables which makes implementation easy but can result in heterogeneity in outcomes within IPI scores. This heterogeneity may be caused by unequal prognostic information across IPI variables, loss of information within IPI variables due to the use of cutoffs, as well as information captured by variables that are not included in the IPI. In prior modeling of outcomes in DLBCL (Maurer et al, AJH 2016), we identified that: i) LDH has a strong prognostic ability beyond the upper limit of normal (ULN) and ii) bulky disease is an independent variable for prognosis. Here we evaluate if the presence of either of these two variables, easily captured at diagnosis within the current standard clinical work-up, can identify a high-risk subset of patients within the population of those with IPI scores of 1-2. Methods: Patients with newly diagnosed lymphoma were enrolled within 6 months of diagnosis in the LEO Cohort at 8 academic medical centers from 2015-2020 and prospectively followed. Inclusion criteria for this analysis was aggressive B-cell lymphoma typically included in frontline trials (e.g. DLBCL, high grade B-cell lymphoma, FL3B), IPI 1-5, frontline therapy with rituximab and anthracycline-based chemotherapy, stage II-IV disease and ages 18-80 years. Event-free survival (EFS) was defined as the time from diagnosis until progression, initiation of 2 nd line therapy, or death due to any cause; EFS was evaluated at 24 months (EFS24). Very high LDH was defined as LDH>1.3xULN based on prior modeling (Maurer et al, AJH 2016) and bulky disease was defined as maximum tumor diameter (MTD) of 7cm or greater. IPI 1 and 2 were first evaluated separately and then combined due to similar outcomes in patients with very high LDH and/or bulky disease. High risk IPI1-2 was defined as the presence of either bulky disease and/or very high LDH within an IPI score of 1-2. Confirmation was performed in a similar cohort of patients enrolled in the Iowa/Mayo SPORE Molecular Epidemiology Resource (MER) from 2002-2015; bulky disease was defined using a 10cm cutoff per availability in MER data collection forms. Results: 1560 patients from LEO were analyzed; median age was 64 years (IQR: 54-71), 42% were female and 83% had stage III/IV disease; subtype was DLBCL, NOS in 1335 (86%). IPI was 1-2 in 766 patients, 3 in 496 patients and 4-5 in 298 patients ( table). 391 (51%) with IPI1-2 had either bulky disease and/or very high LDH (high risk IPI1-2) and 375 had low-risk IPI1-2. EFS24 in high risk IPI1-2 (71.6%, 95% CI: 67.2-76.3) was significantly inferior to IPI1-2 without bulky disease or very high LDH (85.2, 95% CI: 81.7-88.9 logrank p=0.0035, figure) and similar to patients with IPI3 (70.7%, 95% CI: 66.8-74.9). Consistent results were observed in 1014 patients analyzed in the MER using a higher cutoff for bulky disease (10cm) due to data availability ( table). Patients with high risk IPI1-2 had significantly shorter diagnosis to treatment interval (DTI) in both the LEO (per-week OR=0.83, 95% CI: 0.77-0.88) and MER (per-week OR=0.77, 95% CI: 0.68-0.87) cohorts compared to low risk IPI1-2, both p<0.0001). In sensitivity analysis, LEO results were similar when performed in the subset of patients with DLBCL, NOS. Conclusion: The presence of either bulky disease and/or very high LDH at presentation identifies a high-risk subset of patients with IPI 1-2 that have similar outcomes to patients with IPI 3 disease. These simple criteria can be easily implemented within existing clinical trial diagnostic work-ups. Future trial designs in frontline aggressive B-cell lymphoma should include patients with IPI 3-5 and patients with IPI 1-2 with either bulky disease and/or very high LDH to better define a high-risk population of patients for clinical trial eligibility.

Background: Event-free survival at 24 months (EFS24) after frontline immunochemotherapy (IC) is an important endpoint in diffuse large B-cell lymphoma and follicular lymphoma, and patients who achieve EFS24 have similar survival compared to age- and sex-matched general population. The role of EFS24 in predicting subsequent survival has not been established in mantle cell lymphoma (MCL), possibly due to perceived poor survival historically. In the last decade, the outcomes of MCL are improving in the evolving treatment landscape. In this international multicenter study, we investigated conditional survival and cause of death in patients with MCL who achieved EFS24 after frontline IC in the older and more recent eras. Methods: Outcomes after frontline IC were evaluated in 5 independent cohorts that included over 3000 patients in total: Mayo/Iowa MER prospective cohort, BC Cancer retrospective population-based cohort, US 12-center retrospective cohort, Swedish Lymphoma Registry, and Danish National Lymphoma Registry. For each cohort, 2 treatment eras were defined based on cohort-specific shifts in treatment patterns (Table 1). Overall survival (OS) after diagnosis and after achieving EFS24 were compared to the background age- and sex-matched general population using a standardized mortality ratio (SMR). Cumulative incidences of cause-specific deaths were analyzed using a competing risk model. Results: In the MER cohort, patients treated in Era M1 (2002-2009; n=147) and Era M2 (2010-2015; n=153) both had inferior OS compared to the general US population. A lower SMR in Era M2 vs M1 (2.25 vs 3.46) suggests a narrower gap in OS compared to the general US population. Lymphoma was the leading cause of death in both eras. In Era M1, patients who achieved EFS24 still had inferior OS compared to the general US population (SMR=2.40, 95% CI 1.76-3.19), and lymphoma remained the leading cause of death. For patients in Era M2 who achieved EFS24, the difference in OS compared to the general US population was not statistically significant with current follow-up (SMR=1.43, 95% CI 0.93-2.09), and lymphoma was no longer the leading cause of death (Table 2). In the BC cohort, patients treated in Era B2 (6/2013-2019; n=188) vs Era B1 (2003-5/2013; n=250) had a narrower gap in OS compared to the general British Columbia population (SMR 4.53 vs 6.69). Lymphoma was the leading cause of death in both eras. For patients achieving EFS24, the gap in OS was narrower in Era B2 vs B1 (SMR 3.56 vs 4.99). After achieving EFS24, lymphoma was the single leading cause of death for patients in Era B1 but not in Era B2 (Table 2). In the US 12-center cohort, patients treated in Era U1 (2002-2011; n=312) and Era U2 (2012-2016; n=417) both had inferior OS compared to the general US population (SMR 2.68 and 2.92, respectively). In patients who achieved EFS24, with current follow up, the difference in OS compared to the general US population was statistically significant in Era U1 (SMR=2.01, 95% CI 1.50-2.64) but not in Era U2 (SMR=1.44, 95% CI 0.82-2.34). Lymphoma was not the leading cause of death after achieving EFS24 (Table 2). In the Swedish cohort, the gap in OS compared to the general Swedish population was narrower in Era S2 (2013-2018; n=439) vs S1 (2006-2012; n=442), both after frontline IC (SMR 4.8 vs 5.4) and after achieving EFS24 (SMR 2.6 vs 3.4). After achieving EFS24, lymphoma was the leading cause of death for patients in Era S1 but not in Era S2 (Table 2). In the Danish cohort, patients treated in Era D2 (2014-2020; n=370) vs Era D1 (2004-2013; n=461) had a slightly narrower gap in OS compared to the general Danish population (SMR 1.90 vs 2.10). For patients who achieved EFS24, those in Era D1 still had inferior OS compared to the general Danish population (SMR=1.44, 95% CI 1.22-1.68), but the OS difference compared to the general Danish population in Era D2 was not statistically significant (SMR=1.27, 95% CI 0.92-1.72) with current follow-up (Table 2). Cause of death data were not available in this cohort. Conclusion: Survival in patients with MCL who achieved EFS24 after frontline IC improved in the more recent treatment era and moved closer to the background expected survival. After achieving EFS24, lymphoma-related mortality was no longer the leading cause of death in the more recent era. EFS24 following frontline treatment may become a critical endpoint for predicting subsequent outcomes in patients with MCL in the modern era.

Background: Shorter diagnosis to treatment interval (DTI) has been shown to be associated with more aggressive disease features and inferior outcomes in diffuse large B-cell lymphoma (DLBCL; Maurer et al, JCO 2018). Additional confirmatory studies have also shown that DTI is related to tumor volume (Alig et al, JCO 2021) and molecular phenotype (Aluaij et al, Blood 2023). Here we evaluate DTI in a large multicenter cohort of patients prospectively enrolled and followed as part of the Lymphoma Epidemiology of Outcome (LEO) cohort. Methods: Patients were enrolled within 6 months of diagnosis at one the 8 LEO cohort academic medical centers in the United States between 2015 and 2020. Patients in this analysis had aggressive B-cell lymphoma (BCL) treated with anthracycline-based chemotherapy. DTI was defined as the initial lymphoma biopsy date until the start of initial chemotherapy; patients with DTI between 0-100 days were evaluated. Short DTI was defined as DTI ≤ 14 days (DTI≤14) as previously described in prior publications (e.g. Maurer et al, JCO 2018) and confirmed via examination of functional forms via splines. Event-free survival (EFS) was defined as the time from start of treatment until progression/relapse, retreatment, or death due to any cause. Overall survival (OS) was defined as the time from start of treatment until death due to any cause. Results: 2565 patients with aggressive B-cell lymphoma were evaluated. Median age was 62 years (IQR 51-71) and 1469 (57%) were male. 297 patients (12%) were self-reported non-White race and 314 patients (12%) were self-reported Hispanic or Latino. The majority had DLBCL, NOS subtype (N=1927, 75%), while 227 (9%) had high-grade BCL with MYC and BCL2 and/or BCL6 rearrangements (HG, DH) and 90 (4%) had high-grade BCL NOS (HG, NOS). Clinical characteristics are summarized in the table. At median follow-up of 49 months (IQR 36-68), 800 patients (31%) had an event and 578 patients (23%) died. EFS at 24 months (EFS24) was 76% (95% CI: 74-77). Median DTI across all subtypes was 21 days (IQR 12-33) and 845 patients (33%) had DTI≤14. Patients with DTI≤14 were significantly younger (median age 59 years) but there was no association between DTI and gender, race, or ethnicity. Patients with DTI≤14 were more likely to have HG, DH or HG NOS subtypes, ECOG PS 2-4, advanced stage, elevated LDH, CNS involvement, B symptoms, and bulky disease ( see table). Patients with DTI≤14 were more likely to receive R-EPOCH-based or intensive therapies (41%) and less likely to receive 1L therapy on a clinical trial (3.8%) compared to DTI>14 (26% and 10.1%, respectively). Consistent with previously reported data, DTI≤14 was associated with inferior EFS (HR=1.62, 95% CI: 1.41-1.87, figure), OS (HR=1.73, 95% CI: 1.47-2.04), and EFS24 (OR=1.93, 95% CI: 1.60-2.38). The association between DTI and outcomes remained significant after stratifying for subtype (EFS HR=1.68, 95% CI: 1.46-1.94; OS HR=1.78, 95% CI: 1.50-2.10) and adjustment for IPI (EFS HR=1.43, 95% CI: 1.24-1.65; OS HR=1.47, 95% CI: 1.24-1.74). In subset analysis, DTI≤14 was significantly associated with inferior outcomes within the subtypes of HG, DH (EFS HR=2.70, 95% CI: 1.82-4.01; OS HR=2.97, 95% CI: 1.92-4.60); HG, NOS (EFS HR=2.07, 95% CI: 0.93-4.60; OS HR=2.36, 95% CI: 1.03-5.43); and DLBCL, NOS (EFS HR=1.51, 95% CI: 1.27-1.78; OS HR=1.52, 95% CI: 1.45-1.85). Analysis was limited in other subtypes due to number of events. Notably, within the subset of patients with DTI>14, there was no significant difference in EFS between subtypes for DLBCL, NOS (EFS24=80%, EFS HR=ref), HG, DH (EFS24=73%, EFS HR=1.26, 95% CI: 0.92-1.73) and HG, NOS (EFS24=83%, EFS HR =0.84, 95% CI: 0.40-1.78), logrank p=0.29; similar results were observed for OS (p=0.22). Conclusions: Patients requiring early initiation of therapy for aggressive B-cell lymphoma represent a distinct population of patients with more aggressive clinical features and inferior outcomes. In patients with longer DTI, high grade subtypes had similar outcomes to DLBCL, NOS. Efforts should be made to include patients with anticipated short DTI in clinical trials and translational studies to fully capture the spectrum of patients with aggressive B-cell lymphoma.

Background: There are a lack of clinical prediction tools in early-stage Hodgkin lymphoma (E-HL). We developed and validated a modern-day model, known as E-HIPI, to predict progression-free survival (PFS) within the first 5 years (y) incorporating detailed individual patient (pt) data from international clinical trials and prospective registry data that were standardized, normalized & harmonized as part of the HoLISTIC Consortium (www.hodgkinconsortium.org). Methods: Model development utilized Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines (Moons, Ann Intern Med 2015) on a cohort of 3,080 newly-diagnosed E-HL pts treated on 4 seminal phase 3 clinical trials (NCIC: Meyer, NEJM 2012; RAPID: Radford, NEJM 2015; EORTC-GELA H9U: Ferme, E ur J Cancer 2017; EORTC/LYSA/FIL H10: Andre, JCO 2017). External validation was done in an independent cohort of 462 E-HL pts from a large registry (Princess Margaret, Toronto). Pts with classic HL, stage I or II disease, and ages 18-65 y were included. The primary outcome was PFS. Cox models were utilized with follow-up truncated at 5 years. Baseline predictors were: sex, stage, B symptoms, histology, number & location of nodal sites, and continuous data values of age, maximum tumor diameter (MTD), white blood cell count (WBC), lymphocyte count, hemoglobin, albumin & erythrocyte sedimentation rate (ESR). Multiple imputation was used for missing data. To consider possible non-linear relationships for continuous variables we examined plots & cubic splines. We used backward elimination ( P<0.1) to develop the models & internal validation with 200 bootstrap samples was conducted to estimate optimism & correct for overfitting. The final prediction equations applied optimism corrections to beta coefficients & hazard ratios (HR). Sensitivity analyses included model results stratified by PET staging (vs CT) and each HL trial. The models were evaluated in the external validation cohort & compared using c-statistics. Model scores were also grouped by tertiles for visualization in Kaplan-Meier (KM) curves. Results: Median age in the development cohort was 35y; 51% were female; 81% had nodular sclerosis & 77% stage II disease; 26% had B symptoms; 32% had mediastinal bulk (≥10cm or >1/3 diameter) and the mean MTD was 6.4 cm (interquartile range (IQR), 3.9-8.2). Median follow-up was 60 months (IQR 45-75). KM estimates at 5y was 90.7% (264 events) for PFS, and 97.1% (78 events) for overall survival (OS). After backward elimination, significant variables retained were age, sex, nodal location (cervical region), MTD & albumin. Stage, ESR, or the number of nodal sites were not significant. Age was modeled with piecewise linear splines due to its non-linear relationship. See the Table for the final prediction model and optimism-corrected HRs. Optimism-corrected c-statistics in the development model was 0.63. Sensitivity analyses of stratification by type of imaging at staging or by each clinical trial demonstrated no change in model parameters or c-statistics. Most baseline characteristics and outcomes of the external validation cohort were similar besides median follow-up time (74 months, IQR 37-129). C-statistics for the E-HIPI model in external validation was 0.63. In addition, observed 5y outcomes stratified by tertile of predicted risk in the external cohort separated pts into a low & higher-risk group, which comprised 1/3 & 2/3 of pts, respectively ( Figure). Finally, model diagnostics revealed the potential for time dependency in the retained covariates. Further assessment of differential prediction between early (<2y) vs. later (2-5y) PFS events is ongoing. Conclusion: Following TRIPOD methodology, we rigorously developed and externally validated the E-HIPI among >3,500 E-HL pts.Discrete risk groups were delineated, and we identified several novel factors, including nodal location, sex & key continuous variables that predict 5y PFS. Additional model refinement is underway, including formal calibration & external validation via other HL registries (Stanford, Mayo/Iowa SPORE). A web-based calculator to simplify application of the E-HIPI will also be presented at ASH. Future planned modeling includes integration of differing treatments & response-adapted imaging via multistate modeling to enrich individualized survival estimates and simulation modeling to predict late consequences.

Background: Tumor mutational burden (TMB), defined as the number of nonsynonymous mutations per megabase (Mb) in the coding regions of a tumor genome, has been associated with improved response to immune checkpoint blockade treatments and more broadly with overall survival (OS) in many cancers. Limited prior studies have found that TMB is higher in diffuse large B-cell lymphoma (DLBCL) compared to many other cancers. However, only two studies have evaluated TMB and prognosis in newly diagnosed DLBCL patients. The prognostic role of TMB in diffuse large B-cell lymphoma (DLBCL) remains unclear as prior studies have been limited to panel sequencing of cancer genes and/or lacked matched germline-tumor samples for filtering. Methods: We characterized TMB from whole exome sequencing (WES) in newly diagnosed DLBCL patients with matched diagnostic tumor-normal samples prior to treatment with standard immunochemotherapy (IC). Tumor DNA was extracted from formalin-fixed, paraffin embedded tissue sections and germline DNA from peripheral blood. We evaluated the association between TMB and event-free (EFS) and OS using Cox regression adjusted for the international prognostic index (IPI) and other factors including cell of origin (COO), MYC double hit (DH) with BLC2 or BCL6 by FISH, and TP53 mutation status. EFS and OS were defined as time from diagnosis to earliest of treatment failure (EFS only), death, or last follow-up; EFS at 24 months (EFS24) was also modeled as a marker of early aggressive DLBCL. An optimal high/low TMB cutpoint was determined based on EFS using maximally selected rank statistics; sensitivity analyses used an 20/80 percentile to define the threshold. We also evaluated the association of driver tumor mutational load (driverTML) with EFS and OS, defined as the total number of mutations in 292 DLBCL driver genes. Results: In the analysis cohort of 394 DLBCL patients, the median age at diagnosis was 65 years (interquartile range: 56-73), 43% were female, 62.2% had IPI 0-2, and 57.4% germinal center B-cell COO. MYC was DH in 7.5% and TP53 was mutated in 16.2% of patients. Through a median follow-up of 7.5 years, there were 141 events and 122 deaths. EFS24 was achieved in 75.1% of the cohort; 5-year EFS was 69% (95% CI 64-74%) and 5-year OS was 75% (95% CI 71-80%). Median TMB in our cohort of DLBCL patients was 3.6 mutations/Mb, which was the fifth highest compared with 32 cancers in The Cancer Genome Atlas. As a continuous variable, increasing TMB (mutation/Mb) was associated with superior EFS (HR=0.89, 95% CI 0.81-0.98) and OS (HR=0.89, 95% CI 0.81-0.99) after adjustment for IPI. Spline analyses confirmed linear associations of TMB with EFS and OS. The optimal modeled cutpoint based on EFS was 4.5 mutations/Mb, with 31% classified as high TMB (>4.5). No significant ( P >.05) differences were observed between high and low TMB patients with clinical (i.e., age, sex, IPI category) or molecular (cell of origin, MYC DH, TP53 mutated) factors. In Cox regression, high-TMB was significantly protective for EFS and for OS compared to low-TMB after adjustment for each clinical factor ( Figure 1), with simultaneous adjustment for all these factors associated with superior EFS (HR=0.52, 95% CI 0.33-0.84) and OS (HR=0.59, 95% CI 0.36-1.00). TMB (high/low) combined with IPI (0-2, 3-5) was significantly ( P =.001) associated with EFS in Kaplan-Meier analysis ( Figure 2). Among patients with IPI 3-5, high TMB was significantly associated with superior EFS (HR=0.47, 95% CI 0.27-0.81) and OS (HR=0.57, 95% CI 0.33-0.99). Among patients with IPI 0-2, we also observed longer EFS (HR=0.62, 95% CI 0.34-1.11) and OS (HR=0.62, 95% CI 0.32-1.20) in high TMB patients, although the associations were not statistically significant ( P >.05). In sensitivity analyses, a 20/80 percentile TMB threshold found similar results for EFS (HR=0.64, 95% CI 0.41-1.03) and OS (HR=0.69, 95% CI 0.42-1.12) as in our main analyses. TMB and driverTML were significantly correlated ( R=0.65, P <10 -16) and we found similar effect sizes for EFS (HR=0.45, 95% CI 0.22-0.91) and OS (HR=0.55, 95% 0.27-1.13) as in our main analyses. Conclusions: In a large and well-defined cohort of IC treated DLBCL patients, we found DLBCL patients with higher TMB had superior EFS and OS after adjustment for other clinical and molecular prognostic factors, and the prognostic association for TMB was robust to threshold and gene set.

Background: Bendamustine and rituximab (BR) is a standard-of-care first-line (1L) therapy for older or unfit patients with mantle cell lymphoma (MCL). The SHINE trial compared BR with rituximab maintenance plus ibrutinib vs placebo in patients ≥65 years old and showed that the ibrutinib arm had significantly improved progression-free survival (PFS; median 80.6 vs 52.9 months) but similar overall survival (OS; 57% vs 55% at 7 years) compared to the placebo arm. Whether sequential treatment with BR in 1L and a Bruton's tyrosine kinase inhibitor (BTKi) in second-line (2L) can result in a similar cumulative PFS compared to 1L BR plus BTKi combination therapy is unknown. To provide insight to this question, we modeled observational data to evaluate MCL outcomes after 1L BR and 2L BTKi therapy in the BTKi era. Methods: Patients with MCL who received 1L BR with or without rituximab maintenance in 2014-2020 at one of the 27 participating centers were included. Exclusion criteria included participation in the SHINE or ECHO trials, any additional 1L therapy other than BR (with or without rituximab maintenance), and stem cell transplant consolidation after 1L BR. Baseline characteristics, treatment, and follow-up data were abstracted by chart review. Event-free survival (EFS) was defined as time from index line treatment start to the first event (progression, relapse, retreatment, or death). OS was defined as time from index line treatment start to death. Using an intention-to-treat (ITT) framework, EFS2 was defined as time from 1L BR start to progression, relapse, or retreatment following 2L BTKi treatment or death. Patients who received a non-BTKi treatment at 2L were censored for EFS2 at 2L treatment start; living patients without an event following 1L BR or 2L BTKi were censored for EFS2 at last follow-up. Results: A total of 618 patients with MCL who received 1L BR in 2014-2020 were included. The median age was 71 (IQR 65-76) years, and 447 (72%) were male. 59 (11%) patients had an ECOG PS ≥2, 566 (93%) had stage III-IV disease, and simplified MIPI was low in 105 (21%), intermediate in 200 (39%), and high in 202 (40%) patients. The median follow-up following 1L BR start was 57.4 (95% CI 53.8-63.2) months. Response data were available in 580 patients, and the best ORR was 92% (79% complete response [CR] and 13% partial response [PR]). 258 (42%) patients received rituximab maintenance. As of last follow-up, 255 patients were alive and in remission after 1L BR, 92 patients died without 2L therapy, and 271 patients received a 2L therapy. The median EFS was 34.1 (95% CI 31.0-40.0) months. The median OS was 97.8 (95% CI 81.2-NA) months, the 5-year OS rate was 58.6% (95% CI 54.4-63.2), and the 7-year OS rate was 56.7% (95% CI 52.4-61.5) (Fig 1). Among the 271 patients who started a 2L treatment, 203 (75%) received a BTKi at 2L - 101 (50%) ibrutinib, 76 (37%) acalabrutinib, and 26 (13%) zanubrutinib. The median follow-up following 2L BTKi start was 38.5 (95% CI 31.3-45.1) months. Response data were available in 171 patients, and the best ORR was 64% (36% CR, 28% PR). The median EFS was 10.7 (95% CI 7.7-14.0) months, and the median OS was 24.8 (95% CI 17.3-33.1) months with 2L BTKi therapy (Fig 2). By ITT analysis, the median EFS2 following 1L BR and 2L BTKi was 72.1 (95% CI 56.7-97.8) months (Fig 1). A subset analysis of patients aged ≥65 years (n=471; 198 [42%] received rituximab maintenance) showed similar results. The median EFS with 1L BR was 32.7 (95% CI 29.1-36.3) months. The median OS was 81.5 (95% CI 65.0-NA) months, and the 7-year OS rate was 53.3% (95% CI 48.3-58.7). 208 patients received a 2L therapy, 163 (79%) with a BTKi. The median EFS was 11.5 (95% CI 7.6-15.8) months, and the median OS was 21.0 (95% CI 14.0-29.6) months with 2L BTKi therapy. By ITT analysis, the median EFS2 following 1L BR and 2L BTKi was 58.0 (95% CI 50.2-77.0) months. Conclusion: In this multicenter retrospective study, initiation of 1L BR (with or without rituximab maintenance) resulted in a 7-year OS of 57%. Median EFS2 for sequential 1L BR and 2L BTKi was 72.1 months. In context, the SHINE study reported a median PFS of 80.6 months in the BR (with rituximab maintenance) plus ibrutinib arm and a 7-year OS of 57% in the ibrutinib arm and 55% in the placebo arm, where 39% of patients received a BTKi in 2L. Within the constraints of observational data, our results provide support for sequential use of BR in 1L and BTKi in 2L for patients with MCL.

Background: High Grade B cell Lymphoma (HGBL) with MYC and BCL2 and/or BCL6 rearrangements(R) was introduced as an entity in 2016 by the WHO revised 4 th edition. In 2022, both the WHO 5 th edition (beta version) and the International Consensus Classification (ICC) separated DHL- BCL2 (+/- BCL6-R)from DHL- BCL6 given differences in biology . However, while the ICC has maintainedDHL- BCL6 as a provisional entity, the WHO has removed the category, thus removing the requirement to FISH for BCL6-R in this setting. Clinical data on DHL- BCL6 is much more limited, as these cases represent only 10-20% of DHL and have been combined with BCL2-R cases in prior studies. Outcomes are variable in retrospective studies with no consistent data on prognosis or optimal therapeutic strategies. By retaining the category of DHL-BCL6 as a provisional entity, the ICC emphasized the need for further, multicenter, prospective studies evaluating the clinical and biological features of this disease. We herein report a comprehensive comparison of clinical characteristics and outcomes in patients with DHL- BCL6 compared to diffuse large B-cell lymphoma (DLBCL), not otherwise specified (NOS); DLBCL with MYC rearrangement only; DHL- BCL2; and HGBL, NOS in a large, multicenter, prospective cohort of patients from Lymphoma Epidemiology of Outcomes (LEO). Methods: Adult patients with newly diagnosed large B-cell or HGBL were enrolled within 6 months of diagnosis at one the 8 LEO cohort academic medical centers in the US between 2015 and 2020. Baseline characteristics were abstracted at the time of diagnosis per protocol. Based on FISH data patients were further sub mgrouped into DLBCL, NOS (without MYC rearrangement); MYC-R DLBCL, NOS; HGBL, NOS; DHL- BCL2 and DHL- BCL6. Event-free survival (EFS) was defined as the time from diagnosis until progression/relapse, retreatment, or death. Overall survival (OS) was defined as the time from diagnosis until death due to any cause. Results: A total of 1526 eligible patients were identified during this time period. All FISH data was available at the time of diagnosis and the choice of treatment was based on physician discretion. Median age at diagnosis was 63 years (IQR 53-72), with 148 (10%) patients in the AYA category, and 128 (8%) patients > 80 years. 58% (891) were male, 11% self identified as Hispanic or Latino, and 7% as Black/African American. The median diagnosis to treatment interval (DTI) was 20 days (IQR 12-32), and 33% had DTI < 14 days. The FISH-based subgroups were MYC-negative DLBCL, NOS (N=1146, 75%), MYC-R DLBCL,NOS 227 (N = 96, 6%), DHL- BCL2 (N=154, 10%), DHL- BCL6 (N=38, 3%), and HGBL, NOS (N=92, 6%). When available, COO by Hans algorithm was 92% GCB in DHL- BCL2 and 50% GCB in DHL- BCL6. Clinical characteristics can be found in the table. At a median follow-up of 49 months (IQR 36-67), 490 patients (32%) had an event and 356 patients (23%) died. EFS at 24 months (EFS24) was 75% (95% CI: 73-77). Patients with DHL- BCL6 were younger at diagnosis (median 60 years), had more extranodal site involvement (40%), more often stage III/IV disease (70%), and more often treated with a higher intensity regimen than R-CHOP (69%) compared to DLBCL,NOS and MYC-R DLBCL. DHL- BCL6 also had fewer patients that were males (47%), with DTI <=14 days (33%), NCCN IPI ≥ 4 (45%), elevated LDH (53%) than HGBL, NOS and DHL- BCL2. The 2-year EFS and OS rates were noted to be significantly better in the DHL- BCL6 (EFS 79%, 95% CI: 67-93; OS 92%, 95% CI: 84-100) as compared to DHL- BCL2 (EFS 58%, 95% CI: 50-66; OS 70%, 95% CI 63 - 78) and HGBL, NOS (EFS 74%, 95% CI: 65-84; OS 74%, 95% CI: 65-84 ), (Figure 1) but were comparable to that of DLBCL, NOS (EFS 78%, 95% CI: 76-81; OS 87%, 95% CI: 86-89). Conclusions: Our data support separating DHL- BCL6 from DHL -BCL2 as these patients form a unique subgroup with some clinical characteristics comparable to both DLBCL, NOS as well as HGBL, NOS and DHL- BCL2 subtypes. In this cohort, clinical outcomes are more comparable to DLBCL, NOS than DHL- BCL2 or HGBCL, NOS. More frequent use of intensive chemotherapy in DHL- BCL6 compared with DLBCL may account for this finding, although larger multicenter studies are needed. Our results support continued identification of DHL- BCL6 in the clinical setting to better understand optimal therapy and biology of this cohort.