Habermann, Thomas M.

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.

Introduction: Nanotechnology for cancer therapy aims to increase drug delivery to tumor sites while lowering toxicity. AR160 is a novel 160 nm nano-immunoconjugate consisting of commercial grade nab-paclitaxel (NP) nanoparticles non-covalently coated with rituximab (R) for targeted delivery into tissues expressing CD20. The drug delivery system and clinical grade product was developed in the laboratory of Svetomir N. Markovic, M.D.,Ph.D. Single agent rituximab at a dose of 375 mg/m 2 in relapsed patients with aggressive lymphomas is not utilized and has limited activity in patients with multiple relapses. Taxanes (Younes 1995, Wilson 1995) have modest clinical activity as a single agent with overall response rates of 23% and 17%, respectively, when administered at doses of 200 mg/m 2 and 140 mg/m 2 in previously treated patients with non-Hodgkin lymphoma (NHL). We hypothesized that utilizing the targeted ability of rituximab that the AR160 taxane nanoparticle would have activity in relapsed B-cell NHL and offer a new class of agents for relapsed B-cell NHL. Methods: A 3+3 phase I clinical trial was conducted in patients with relapsed refractory non-Hodgkin lymphoma to determine the maximum tolerated dose of AR160 administered intravenously on days 1, 8, and 15 of a 28-day cycle. Patients were required to have had prior systemic treatment with no other therapy with clinical benefit. Patients with aggressive lymphoma were required to have a stem cell transplant if transplant eligible. AR160 was prepared on-site in the Mayo Clinic Cancer Center Research Pharmacy, and the product was reconstituted by mixing the two agents delivering as one combined infusion at a specific concentration. The starting dose level (DL1) was NP at 100mg/m 2 with R at 40 mg/m 2. These doses were significantly lower than standard doses. Disease evaluations were conducted after every 2 treatment cycles using CT based criteria from the revised 2014 Lugano Classification or PET scans. Patients were treated until disease progression or intolerability. Samples were collected for pharmacokinetic (PK) studies. Results: Nine (7 males and 2 females) patients with a median age of 61 (range 55-84 years) were enrolled (2 follicular lymphoma (FL), 1 previously transformed from FL to diffuse large B-cell lymphoma (DLBCL) followed by FL relapse, 1 high-grade B-cell lymphoma with MYC and BCL2 rearrangements transformed from FL, 1 transformed FL to DLBCL, 2 high-grade B-cell lymphoma with MYC and BCL2 rearrangements, 1 high-grade B-cell lymphoma NOS and 1 mantle cell lymphoma. The median number of prior treatments was 4 (range 3-16). At dose level 1, all 3 of the first 3 patients experienced grade 3 or 4 neutropenia (C1D15, C4D1, and C1D15) with the C4D1 a grade 4 and unlikely related. After the first three patients, de-escalation to dose level -1 was implemented per protocol with NP at 75 mg/m 2 and rituximab at 30 mg/m 2. There was one patient with a grade 3 non-hematological adverse event all possibly related to treatment (anorexia, diarrhea, and enterocolitis). There were 6 partial responses (71%), 1 complete response (14%) with high-grade B-cell lymphoma with MYC and BCL2 rearrangement, 1 stable disease with mantle cell lymphoma and 1 patient had progressive disease. Six of the 9 patients were alive with subsequent therapies. PK evaluation is pending. Conclusion: AR160 therapy is a novel drug delivery system incorporating significantly lower doses of paclitaxel and rituximab that is safe and demonstrates promising clinical activity in patients with low-grade and high-grade previously treated B-cell lymphomas. This principle of treatment is applicable to other disorders. Further clinical testing is warranted in these patient populations.

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.

Background: Primary central nervous system lymphoma (PCNSL) is a rare form of non-Hodgkin lymphoma with two-year overall survival (OS) rates of 66-70% reported in prospective clinical trials. Induction therapy with high-dose methotrexate (HD-MTX) followed by consolidation has become the mainstay of treatment, however, there are few contemporary and robust prognostication indices widely available for clinical use. Some of the most used indices were developed prior to the routine use of rituximab and include the International Extranodal Lymphoma Study Group (IELSG; 1980-1999), Memorial Sloan-Kettering Cancer Center (MSKCC; 1983-2003), Nottingham/Barcelona (NB; 1986-2001), and Taipei Score (TS; 2003-2015). Herein, we describe a contemporary comparison of clinical prognostication indices using a single-center cohort of PCNSL patients treated at the Mayo Clinic. Methods: Patients with a diagnosis of PCNSL who received HD-MTX as part of induction therapy at Mayo Clinic between October 2010 and June 2022 were identified and retrospectively reviewed. Patients with prior or concurrent systemic lymphoma were excluded. Primary endpoints were progression-free survival (PFS) defined as time from diagnosis to relapse, progression, or death due to any cause; and OS defined as time from diagnosis to death due to any cause. PFS and OS were evaluated using Kaplan-Meier curves and compared by risk scores using a log-rank test. The association between baseline characteristics and survival outcome were assessed in univariate Cox regression models. Any characteristics showing significant (p-value <0.05) association with outcome were combined using a multivariate Cox regression model. All characteristics that maintained significant association with outcome were combined into a new prognostic model. We compared the performance of each prognostic index model using Harrell's C (HC). Results: A total of 148 patients were identified (50.7% female) with median age at diagnosis of 66 years (range 29-85), with 48 patients (32.4%) age >70 (Table 1). Most had multifocal disease (60.1%) and deep brain involvement (67.6%) at diagnosis with elevated CSF protein (58.1%) but no documented CSF involvement (93.9%). In total, 117 patients (79.1%) received methotrexate, rituximab, and temozolomide (MRT) induction therapy, and the remaining 31 patients (20.9%) received methotrexate and rituximab (MR) induction therapy with various consolidation strategies. The median follow-up for all patients was 4.5 years and the two-year OS was 79% (95% CI: 73-86%). This cohort was stratified using previously published prognostication indices (Table 1). Upon analysis, PFS was not accurately predicted using the IELSG (score 4-5: HR 0.7 (CI 0.3-2.0); HC 0.6), MSKCC (3: 1.3 (0.4-4.0); 0.5), NB (3: 0.9 (0.4-2.5); 0.6), or TS (3: 0.9 (0.1-7.2); 0.5) prognostication indices. Furthermore, OS was not accurately predicted using the IELSG (4-5: 1.5 (0.6-4.0); 0.6), MSKCC (3: 1.2 (0.5-3.2); 0.6), NB (3: 1.2 (0.5-3.2); 0.6), or TS (3: 1.3 (0.2-9.8); 0.6) prognostication indices. Upon multivariate analysis, no variables listed in Table 1 were associated with PFS within this cohort. The two variables predicting a worsened OS were an ECOG performance status of 2 or more (1.9 (1.01-3.6)) and an MR induction regimen instead of MRT (2.1 (1.05-4.3)) (Figure 1). The two-year OS with no risk factors was 86.1% (95% CI: 78.8-94.1%), one risk factor was 75.7% (95% CI: 63.6-90.1%), and two risk factors was 61% (95% CI: 43-86%) (Figure 1). Conclusions: This study reports overall improved outcomes in patients with PCNSL compared to prior prospective and retrospective cohorts, likely in part due to treatment advances and supportive care strategies. This single-center, retrospective cohort of rituximab-exposed patients with PCNSL were not well prognosticated using previously published indices, indicating that further study and more sophisticated approaches to prognostication are warranted. Although multivariate analysis did not demonstrate any variables associated with PFS, it did demonstrate an inferior OS with a performance score of 2 or more and in patients treated with MR rather than MRT.

Background: Primary central nervous system lymphoma (PCNSL) is a rare form of non-Hodgkin lymphoma with historically poor outcomes. Induction therapy with high-dose methotrexate (HD-MTX)-based regimens followed by consolidation with autologous stem cell transplant (auto-SCT) has become the mainstay of treatment with overall response rates (ORR) of 69-87% and two-year overall survival (OS) of 66-70% reported in prospective clinical trials. Limited data are available regarding a maintenance approach with single-agent HD-MTX, particularly in patients who are not auto-SCT candidates. Herein, we describe a comparison of outcomes in patients with PCNSL who underwent HD-MTX-based induction followed by either HD-MTX maintenance or auto-SCT consolidation at Mayo Clinic, Rochester. Methods: Patients with a diagnosis of PCNSL who received HD-MTX as part of induction therapy at Mayo Clinic between October 2010 and June 2022 were identified. Patients with prior or concurrent diagnosis of systemic lymphoma were excluded. Primary endpoints were progression-free survival (PFS) defined as time from post-induction treatment initiation to relapse, progression, or death due to any cause; and OS defined as time from post-induction treatment initiation to death due to any cause. We compared baseline characteristics by post-induction therapy using descriptive statistics. The primary endpoints PFS and OS were evaluated using Kaplan-Meier curves and compared by risk scores using a log-rank test. Results: A total of 148 patients were identified (51% female, 95% white) with median age at diagnosis of 66 years (range 29-85), with 48 patients (32%) age > 70. Most patients had multifocal disease (n=89, 60%) and deep brain involvement (n=100, 70%) at diagnosis. Few had vitreoretinal (n=15, 10%) or CSF involvement (n=9, 6%) at diagnosis. Using the Memorial Sloan Kettering (MSKCC) prognostic score for PCNSL, 35 patients (26%) scored class 3 (poor prognosis), 85 (62%) scored class 2, and 17 (12%) scored class 1. In total, 117 patients (79%) received methotrexate, rituximab, and temozolomide (MRT) induction therapy, and the remaining 31 patients (21%) received methotrexate and rituximab (MR) induction therapy (Table 1). A total of 70 patients (47%) underwent consolidation with auto-SCT and 37 patients (25%) received maintenance methotrexate. The patients who received maintenance methotrexate had higher median age of 72 versus 62 years, higher proportion of patients older than 70 (54% versus 16%, p<0.001), higher proportion of MSKCC prognostic score class 3 patients (33% versus 17%, p=0.026), and trend towards higher proportion of patients ECOG 2 or higher (41% versus 29%, p=0.109). A higher proportion of patients who underwent auto-SCT received MRT (vs MR) induction (n=64, 91%) compared to those who received maintenance methotrexate (n=24, 65%, p=0.004) (Table 1). The median follow-up for all patients was 4.5 years. At 1-, 3-, and 5-years post-induction treatment initiation, the PFS in the auto-SCT cohort was 85.8% (95% CI 77.7-94.9), 81.0% (95% CI 72.0-91.0), and 74.6% (95% CI 63.3-87.9), respectively; and the PFS in the HD-MTX maintenance cohort was 88.6% (95% CI 78.7-99.8), 72.6% (95% CI 58.8-89.8), and 72.6% (95% CI 58.8-89.8), respectively. At the same time points, the OS in the auto-SCT cohort was 94.0% (95% CI 88.4-99.9), 81.3% (95% CI 71.8-92.0), and 76.0% (95% CI 65.0-89.0), respectively; and the OS in the HD-MTX maintenance cohort was 94.3% (95% CI 86.9-100.0), 87.5% (76.8-99.8%), and 82.4% (69.0-98.4), respectively. Overall, there was no significant difference in PFS (p=0.64) or OS (p=0.95) based on post-induction treatment (Figure 1). Conclusions: Our study demonstrates comparable outcomes in PCNSL between a post-induction treatment approach with maintenance HD-MTX and consolidation auto-SCT, even though baseline characteristics indicate that patients who went on to receive auto-SCT were younger and had overall better performance status. This suggests that maintenance HD-MTX is a reasonable, time-limited strategy for patients with PCNSL responding to initial induction therapy, particularly in patients who are not candidates for auto-SCT.