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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: Understanding the symptoms and quality of life (QOL) over time of adults with follicular lymphoma (FL) is important for treatment decision-making and clinical management. However, there are limited population-level data on the long-term QOL of adults with FL. We aim to describe the real-world, long-term QOL of adults with newly diagnosed FL up to 5/6 years after diagnosis. Methods: We used the Mayo Clinic/Iowa Molecular Epidemiology Resource (MER) and the multi-institutional Lymphoma Epidemiology of Outcomes (LEO) to identify adults with grade 1-3A FL who completed QOL surveys at baseline and follow-up. Participants with lymphoma were prospectively enrolled within 9 months of diagnosis in the MER cohort from 2002-2015 and within 6 months of diagnosis in the multi-institutional LEO cohort study across 8 cancer centers from 2015-2020. The LEO/MER cohort studies systematically collected information on disease status, QOL, health behaviors, and functional assessment. Treatments, disease relapses, and deaths were verified by medical record review. Treating physicians determined clinical management. QOL was measured using the Functional Assessment of Cancer Therapy-General (FACT-G) at years 1, 2, 3, and 5/6 post-diagnosis. A higher FACT-G total score (range 0-108) indicated better QOL across 4 subscales (range): physical (0-28), social/family (0-28), emotional (0-24), and functional (0-28) well-being. We categorized participants based on their frontline management at time of diagnosis: “observation” (surveillance), “treatment” (systemic treatment with immunotherapy +/- chemotherapy), and “local” (radiation) groups. We employed a generalized linear mixed model to evaluate and compare the changes in QOL scores (as a continuous variable) from baseline for the observation, treatment, and local groups, adjusting for sex, race/ethnicity, age, FLIPI risk, and cohort (i.e., LEO or MER). We also evaluated QOL changes by FLIPI risk. Results: Our study included 1,544 participants with FL and QOL data. At the time of enrollment, median age was 61 years (range 19-91), 88% were non-Hispanic White, and 49% were female. Based on initial management, 529 (34%) were in the observation group, 880 (57%) were in the treatment group, and 135 (9%) were in the local group. FLIPI risk was high for 17% in the observation group, 29.3% in the treatment group, and 3% in the local group. The mean (standard deviation) baseline FACT-G total score was lowest in the treatment group with the following baseline scores: 86 (13) for the observation group, 83 (14) for the treatment group, and 88 (12) for the local group (Table 1). The observation and local groups reported a greater worsening of their baseline FACT-G total score vs the treatment group at both the 2-year (-1.2, -0.8 vs +1.8, respectively) and 5/6 year (-5.4, -6 vs. -1.6, respectively) timepoints (Figure 1). This appeared to be driven by worsened physical and functional well-being in the observation (-1.3, -2.2, respectively) and local (-0.9, -2.4, respectively) groups vs the treatment group (0, -0.3, respectively). These differences were statistically significant between the observation vs treatment groups. There were no statistically significant differences in the other subscales, i.e., social/family and emotional well-being, by frontline management. Social/family well-being decreased across all groups by 5/6 years (-2.3 in observation, -2.2 in treatment, and -3 in local, p<0.05). Regardless of FLIPI risk, the total FACT-G scores decreased within each FLIPI risk group at 5/6 years (-4.4 for low, -3.8 for intermediate, and -4.9 for high, p<0.05). This was primarily due to worsened social/family well-being (-2.4 for low, -2.2 for intermediate, and -3 for high, p<0.05) at 5/6 years. There was no statistically significant difference in QOL between FLIPI risk groups. Conclusion: This is one of the first and largest studies with real-world longitudinal QOL data for FL. Our study suggests that frontline systemic treatment initially improved QOL and resulted in a lower degree of QOL decline over time. Social/family well-being decreased over time regardless of frontline management or FLIPI risk. Further research is warranted to explore the impact of timing and specific systemic treatments on QOL, incorporate the patient/caregiver experience to address social/family well-being, and identify the clinical significance of our findings.

Background: Relapsed DLBCL occurs in up to 40% of patients and has a poor prognosis. Current prognostic tools such as the international prognostic index (IPI) and newer forms (NCCN-IPI) only include baseline clinical features. PET/CT features have not yet been incorporated into prognostic risk models despite being essential for staging and response assessment. Deep learning computer algorithms have consistently demonstrated the ability to analyze imaging data and identify features that are not readily apparent to the human eye. Using a deep learning computer vision model that automatically extracted radiomic features from PET/CTs, we examined the association of changes between baseline PET and interim PET (PET2) radiomic features on survival outcomes and compared the prognostic performance with NCCN-IPI. Methods: Adult patients with newly diagnosed diffuse large B-cell lymphoma (DLBCL) between 2005 and 2015 and enrolled in the prospective MER cohort were included. Native DICOM images from FDG PET/CT scans at baseline and interim PET (PET2, after 2-3 cycles of chemotherapy) were preprocessed for regularity and then segmented using the nnU-Net architecture with an external lymphoma-specific model. Feature extraction and standard uptake value (SUV) characterization were accomplished with PyRadiomics and NiBabel. Our computational method included no manual segmentation correction. Individual radiomics features (n=115) and the % change above (Δ High) and below (Δ low) 50 th quartile from baseline to PET2 were evaluated for association with event free survival (EFS) and overall survival (OS) using a univariate Cox regression model and by Kaplan Meier analysis. EFS was defined as time from PET2 to first systemic relapse or death from any cause. OS was defined as time from PET2 to death from any cause. Additionally, Spearman and Pearson correlation matrices were used to guide feature selection while minimizing collinearity. We then assessed the association of the highest scoring radiomics features with EFS in a multivariate cox regression model. Results: A total of 506 patients had baseline and PET2 scans available for radiomic feature extraction. Median age at diagnosis was 61 years (range 18-90), 43% were females. NCCN-IPI composition and outcomes of our cohort was comparable to the previously published NCCN cohort, with patient distribution and 5-year OS as follows: Low (9%, 5-y OS 83%), Low-intermediate (38%, 5-y OS 49%), High-intermediate (41%, 5-y OS 31%), High (11%, 5-y OS 12%). At a median follow up of 81 months,there were 308 events, and272 patients had died.Several radiomics features were associated with EFS including change in tumor surface area (SA Δ) (HR: 1.75, 95% CI 1.36-2.24, p<0.005, median of 26,927 mm 2, Figure 1A) and change in total metabolic tumor volume (TMTV Δ) (HR: 1.64, 95% CI 1.27-2.10, p<0.005, median of 120,473 mm 3, Figure 1B). As a reference, NCCN-IPI univariate Cox regression analysis of EFS produced a Harrell's C-statistic (c) of 0.624. Single radiomics features of SA Δ and TMTV Δ individually produced unadjusted c-statistics of 0.616 and 0.614 respectively. Combinations of radiomics features (SA Δ, TMTV Δ and NCCN-IPI classification) demonstrated cumulative benefit (c=0.652). Conclusion: Automatic PET-CT radiomic feature extraction using deep learning models is feasible among patients with newly diagnosed DLBCL. The change in tumor SA and TMTV appear to be promising radiomic biomarkers. A low decline in either tumor SA or TMTV by PET2 is associated with higher risk of relapse and poor survival outcomes. As individual characteristics, these features are similar prognostic indicators of EFS as the NCCN-IPI. Further investigation into risk stratification when combining radiomics with clinical and genomic variables is warranted. Figure 1. Event free survival for DLBCL patients with A) a change in tumor surface area by PET2 less than 50% percentile (SA_Δ Low) and greater than 50 th percentile (SA_Δ High) and B) a change in TMTV by PET2 less than 50% percentile (TMTV_Δ Low) and greater than 50 th percentile (TMTV_Δ High).

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.

Introduction Lymphoid malignancy (LM) is a heterogenous neoplasm with respect to biology, aggressiveness, and treatment with over 100 subtypes, including Hodgkin lymphoma (HL), non-Hodgkin lymphoma (NHL), and multiple myeloma (MM). Genome-wide association studies have identified common variants associated with risk of specific LM subtypes, but less is known about the contribution of rare inherited pathogenic variants (PV) in the genetic architecture of LM risk. Rare PV have been shown to contribute to the genetic architecture of solid tumor cancer risk, many of which have been found in genes that are important to the DNA-damage repair (DDR) pathway (e.g., BRCA1, BRCA1, ATM). Here, we investigated the prevalence of PV in cancer predisposition genes regularly included on multi-gene panel testing based on NCCN guidelines and correlated with risk of LM overall and by common subtypes. Methods In this association study, cases were newly diagnosed lymphomas (excluding chronic lymphocytic leukemia) from Mayo Clinic who were enrolled between 2002 and 2015 in the Lymphoma SPORE Molecular Epidemiology Resource (MER) and MM from Mayo Clinic enrolled primarily between 1998 and 2022 in a clinical registry. Controls were from the Mayo Clinic Biobank who were enrolled between 2009 and 2016, from which we excluded individuals with a history of hematologic malignancy. All participants provided a blood sample for whole exome sequencing, performed by Regeneron on an Illumina NovaSeq panel (mean coverage of 48X). Variants were called using GATK v4 and variant annotation was performed using BioR. All loss-of-function variants (i.e., nonsense, frameshift, and consensus splice sites) and pathogenic missense in 19 cancer predisposition genes with a minor allele frequency < 0.5% were included in the analyses. Logistic regression was used to estimate odds ratio (OR) and 95% confidence intervals (CI) for the association between mutation status by gene (burden test) and risk of LM overall, and by subtype that had more than 5 mutation carriers. All analyses were adjusted for age (at diagnosis for cases and at enrollment for controls) and sex. Results A total of 6990 cases and 42432 controls were included. Median age was 63 years for both cases and controls (case range 18-94 years, control range 18-99 years). Males accounted for 58.5% (n=4090) of the cases and 41.7% (n=17762) of the controls. MM accounted for 31% (n=2138) of the cases, followed by diffuse large B-cell lymphoma (DLBCL, n=1146) and follicular lymphoma (FL, n=1132) each accounting for 16% of cases. CHEK2 (1.0%), ATM (0.4%), BRCA2 (0.4%), and BRCA1 (0.3%) had the most PV carriers in controls; these frequencies are in the range of published control populations. A total of 1816 (3.7%) individuals had a PV across the 19 genes, with 4.7% of cases and 3.5% of controls carrying any PV (OR=1.34, 95% CI: 1.18-1.51). ATM (OR=1.86, 95% CI: 1.36-2.49), CHEK2 (OR=1.74, 95% CI: 1.42-2.13) and TP53 (OR=9.07, 95% CI: 4.51-18.87) were all significantly associated with increased risk of LM overall (Figure 1). When investigating subtypes (Table 1), CHEK2 (OR=1.93, 95% CI: 1.23-2.89) and TP53(OR=10.97, 95% CI: 3.06-31.43) were significantly associated with DLBCL. ATM (OR=2.22, 95% CI: 1.13-3.90) was significantly associated with FL. ATM (OR=1.97, 95% CI: 1.16-3.12), CHEK2 (OR=1.75, 95% CI: 1.24-2.41) and TP53 (OR=7.67, 95% CI: 2.43-20.72) were significantly associated with MM and ATM (OR=8.62, 95% CI: 4.47-15.12), NBN (OR=6.94, 95% CI: 2.41-15.76) and TP53 (OR=38.30, 95% CI: 10.27-116.69) were significantly associated with mantle cell lymphoma (MCL). When subset to LM cases overall, there was no evidence of enrichment for young onset (age <60 years), male sex, or family history of hematologic malignancy between PV carriers and non-PV carriers. However, FL cases with an ATM PV tended to be less than 60 years (OR=3.21, P=0.09) compared to non- ATM PV carriers. Similarly, MM cases with a TP53 PV tended to be less than 60 years of age (OR=7.39, P=0.07) compared non-carriers. Discussion In this large case-control study, PV in established cancer predisposition genes were associated with increased risk of LM overall, demonstrating that rare inherited variants may play an important etiologic role. This study also suggests that PV in cancer predisposition genes may be subtype specific. These results will help inform LM risk for individuals who undergo genetic testing of cancer predisposition genes.

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.