Citations

Abstract Background: Although some vaccines seem to be associated with a lower risk of lymphoma, their impact on outcomes is largely unknown. Methods: Data from a large prospective lymphoma cohort study were utilized to estimate associations of self-reported history of vaccination against hepatitis A, hepatitis B, influenza, and yellow fever with event-free survival (EFS) and overall survival (OS) using Cox proportional hazard models. Results: None of the vaccines were associated with EFS [hazard ratio (HR) 0.77–1] or OS (HRs 0.96–1.04). Similarly, there were no significant associations between the vaccines and outcomes for each lymphoma subtype. Conclusions: Prior vaccination against selected viruses was not associated with EFS or OS among patients with lymphoma. Impact: We did not find evidence for an impact of prior vaccination on lymphoma prognosis.

Abstract Therapeutic options for Waldenström macroglobulinemia (WM) have expanded but improvements in overall-survival (OS) are difficult to interpret due to competing non–WM mortality. We conducted a large single-center retrospective study of 1,020 patients with WM to evaluate survival trends. Patients were stratified by era of active WM diagnosis using a two-era model (1996–2005 and post-2005) and a four-era model (pre-2001, 2001–2007, 2008–2013, and post-2013). OS did not differ significantly across eras in the overall cohort (p = 0.64 and p = 0.86 for the two- and four-era models). However, among patients aged < 65 years at the time of active WM diagnosis in the 2-era model, OS improved in the post-2006 era (median 18.8 vs 14.1 years, p = 0.018). Cause-specific-survival (CSS) improved across all eras, increasing from 15.6 years in 1996–2005 to 18.9 years post-2006 (p < 0.0001), and from 13.7 years pre-2001 to not reached in later periods in the four-era model (p < 0.0001). Competing-risk-analysis (CRA) demonstrated a reduction in WM-related mortality over time (p < 0.001). However, excess mortality persisted, with a standardized mortality ratio (SMR) of 2.79 (p < 0.001) among patients < 65 years compared with a matched U.S. population. Our findings highlight improvements in WM-specific survival, while gains in OS are observed in younger patients.

Summary Family history of haematological malignancy (FHHM) reflects inherited susceptibility to non‐Hodgkin lymphoma (NHL), but its prognostic significance remains poorly understood. We used a cohort study of 1994 NHL patients to evaluate associations between FHHM and clinical outcomes across lymphoma subtypes. We estimated associations, adjusted for sex and lymphoma‐specific prognostic index, of self‐reported FHHM in a first‐degree relative with overall survival (OS), event‐free survival (EFS) and lymphoma‐specific survival (LSS) using multivariable Cox regression models and with failure to achieve EFS at 24 months (EFS24) using logistic regression. FHHM prevalence was 12.8%, and the median follow‐up among survivors was 12.0 years. FHHM was not associated with OS. In subtype‐specific analyses, FHHM was associated with inferior LSS (hazard ratio [HR] = 1.98, 95% confidence interval [CI] 1.11–3.53) in follicular lymphoma (FL); this association and the association with EFS24 failure strengthened among those treated with front‐line immunochemotherapy (EFS24 failure odds ratio = 2.57, 95% CI 1.08–5.99; LSS HR = 2.68, 95% CI 1.09–6.58). In mantle cell lymphoma, FHHM was associated with inferior EFS (HR = 1.70, 95% CI 1.02–2.85). Interaction testing did not demonstrate significant heterogeneity of FHHM effects by lymphoma subtype. FHHM may be associated with adverse outcomes in select NHL groups, particularly FL. These findings warrant further investigation of germline and environmental factors potentially contributing to more aggressive FLs.

ABSTRACT 162 patients with relapsed secondary central nervous system lymphoma (R‐SCNSL) (median age, 65 years; male, 59.9%) including central nervous system (CNS)‐only ( n = 120) and concomitant CNS/systemic relapse ( n = 42) were retrospectively analyzed. Overall, 21.9% of patients were classified as high risk according to the CNS International Prognostic Index (CNS‐IPI). Several biological and clinical features were significantly associated with leptomeningeal involvement, including double‐ or triple‐hit (DHL/THL) status, MYC rearrangement, negative BCL6 expression by IHC, bone marrow involvement, and concomitant R‐SCNSL. Multivariable analysis showed that leptomeningeal involvement independently predicted inferior OS and was associated with a 98% increase in the hazard of death compared with parenchymal relapse (HR = 1.98, 95% CI: 1.20–3.26, p = 0.008). Based on anatomical localization, R‐SCNSL was classified into four subtypes: parenchymal‐only involvement (parenchymal‐CNS [P‐CNS], 68/42%) and parenchymal involvement plus systemic relapse (parenchymal‐concomitant [P‐concomitant], 17/10.5%); and leptomeningeal with or without parenchymal involvement (leptomeningeal‐CNS [LM‐CNS], 52/32.1%) and leptomeningeal with systemic relapse (leptomeningeal‐concomitant [LM‐concomitant], 25/15.4%). This anatomical classification significantly impacted OS and PFS ( p < 0.001). Two‐year OS and PFS were 58.2% and 29.1% for P‐CNS, 32.4% and 17.7% for P‐concomitant, 22% and 13.9% for LM‐CNS, and 7.1% and 0% for LM‐concomitant, respectively. ASCT showed a trend toward improved survival among patients with a response (CR/PR) in a 4‐month landmark analysis. These findings support the clinical application of the anatomical classification in the management of R‐SCNSL.

Abstract Over the past decade, classification systems using high-throughput genetic data have been developed to identify molecular subtypes of DLBCL. Although not a primary goal, these classifiers were also proposed as prognostic factors in their respective publications. To date, however, no study has directly compared these classifiers' ability to predict overall (OS) and event-free survival (EFS) in the same cohort. Herein, we sought to systematically compare predictive performance, benchmarked against the IPI, of several current established molecular classifiers and evaluate if any add significant independent prognostic information beyond the IPI. WES (including paired germline filter) and RNAseq data from newly diagnosed DLBCL patients (N=432; 2018 WHO criteria) in the Mayo Clinic/University of Iowa Lymphoma Molecular Epidemiology Resource (MER) were available for analysis (WES n=112, RNAseq n=40, or both n=280). The number of cases able to be classified (including as unclassified) for each classifier was as follows: IPI (432, 100%), cell-of-origin (COO, by Hans algorithm; 346, 80.1%), refined COO (by gene expression with Dark Zone Signature; 320, 100%), Haematological Malignancy Research Network (HMRN; 349, 89.0%), B-cell state (320, 100%), Lymphoma Ecotype (320, 100%), Lymphoma Microenvironment (LME; 320, 100%), Tumor microenvironment (TME26; 320, 100%), SubLymE (312, 97.5%), LymphGen (358, 91.3%), and DLBclass (223, 58.2%). OS was defined as time from diagnosis to all-cause death while EFS was defined as time from diagnosis to disease progression, initiation of second-line treatment, or all-cause death. We calculated Harrell's c-statistics for each Cox proportional hazards model evaluating the discriminative performance of each classifier in predicting OS/EFS. Using bootstrap validation, we obtained optimism-corrected c-statistics, which we report in the results. We performed likelihood ratio tests (LRT) comparing a univariable Cox model with IPI alone and models including both IPI and a given classifier to assess if a classifier provided independent prognostic information beyond the IPI. All patients were treated with immunochemotherapy (IC) with curative intent, 247 (57%) were male, median age at diagnosis was 65 years (IQR 55-72 years), 390 (96%) were White, 209 (60%) had GCB subtype by Hans, 22 (5.1%) had MYC double-hit by FISH (MYC and BCL2 or BCL6), and 162 (37.9%) had an IPI ≥ 3. The median follow-up was 7.0 years, 167 (39%) experienced an event, and 137 (32%) died. In a univariable Cox model, the c-statistic for IPI predicting OS was 0.66 (95% confidence interval [CI] 0.61-0.70). All other classifiers had c-statistics of ≤0.56. The c-statistic for IPI predicting EFS was 0.64 (95% CI 0.60-0.69), and all other classifiers had c-statistics of ≤0.58. The univariate DLBclass results for OS and EFS were not significantly impacted when restricted to cases with confidence &gt;0.7 (n=136). In Cox models of IPI alone versus IPI plus an individual classifier, the only classifier that provided statistically significant, independent information for OS was TME26 (LRT p=0.02). Adjusting for IPI, patients who were TME26-negative had significantly inferior OS compared to TME26-positive cases (HR 1.63, 95% CI 1.07-2.47; p=0.02). For EFS, DLBclass (restricted to confidence &gt;0.7) added significant prognostic information beyond IPI (LRT p=0.02). This finding was largely driven by C5 subtype; relative to C1 subtype and adjusting for IPI, C5 subtype had significantly inferior EFS (HR 5.53, 95% CI 1.81-17.0; p=0.003). In summary, we compared prognostic performance of multiple DLBCL molecular classifiers with respect to OS and EFS in a single real-world, prospective cohort of newly diagnosed, IC-treated DLBCL. Our data supports two key findings: all classifiers had modest performance for prognosis, and none individually outperformed the IPI in predicting OS nor EFS. Second, only TME26 and DLBclass (confidence &gt;0.7) provided additional prognostic information beyond that captured by the IPI for OS and EFS, respectively. This finding suggests that the tumor immune microenvironment and characteristics of C5 DLBCLs (ABC-subtype, CD79B mutations) can potentially offer significant independent information for predicting survival and response to front-line immunochemotherapy. If replicated, these findings could inform risk-adapted treatment approaches and aid in identifying high-risk patients for enrollment in genomically-informed clinical trials.

Abstract BACKGROUND: DNA methyltransferase inhibitors (DNMTi) are US FDA approved for the management of CMML. These agents are associated with an overall response rate (ORR) of 40-50%, with a true complete remission (CR) rate of &lt;20%. TET2 mutations are seen in 60% of CMML, with TET2 being a ferrous and oxoglutarate dependent DNA-dioxygenase that oxidizes 5mC to generate 5hmC, 5fC and 5caC. Ascorbic acid (AA) is critical for TET activity and can enhance TET 1/2/3 dioxygenase function by binding to the catalytic domain and when given in high doses, can result in glutathione depletion and free radical induced cell death. In a pilot study (Xie et al. Blood 2024), we demonstrated that high dose IV AA can be clinically effective and induce changes in differentially methylated regions (DMR) involving hematopoietic enhancers and improve cytopenias in TET2-mutant (mt) patients [CCUS]. We expanded this study to combine high dose IV AA with DNMTi to enhance the demethylation effect and produce durable responses in new diagnosed, treatment requiring, TET2mt CMML patients. METHODS: At Mayo Clinic, the LS1781 (NCT03418038) clinical trial was amended to include Arm E, combing high dose IV AA with standard of care decitabine in newly diagnosed TET2mt CMML patients that needed therapy. IV AA was dosed at 1 g/kg on days 1, 3 and 5 (per 28-day cycle, max dose 100 gm) through a central line, based on demonstrated safety from phase 1 studies. Standard dosing was adopted for decitabine, and patients were then asked to take oral AA, 1 gm daily from days 6-28. Key inclusion criteria included a WHO-defined diagnosis of CMML with at least one detectable TET2 mutation requiring therapy with adequate performance status (ECOG PS &lt;2) and organ function (creatinine clearance &gt; 55 mL/min). Response was evaluated by the MDS/MPN International Working Group Criteria (Savona et al. Blood 2015). We measured pre- and post-therapy (4 cycles) bone marrow variant allele fractions (VAF) through next generation sequencing assays, transcriptomic changes through RNA seq, and DNA methylation changes using the Illumina Methylation EPIC array. RESULTS: Between February 2024 and July 2025, a total of six patients were enrolled. The median age at enrollment was 73 years (range: 65-80); all Caucasian with 3 (50%) males. All patients had a diagnosis of CMML-1 with a median bone marrow (BM) blast percentage of 3 (0-10), median hemoglobin 13 (9.5-14.5) g/dL, median WBC count of 5.7 (2.1-12.6) x 109/L, absolute monocyte count 1.4 (0.7-2.7) x 109/L, and platelet count of 92.5 (32-123) x 109/L. TET2 (100%, 4 patients &gt; 1 TET2 mutation at mean VAF of 36%), SRSF2 (67%), and KRAS (33%) were the most frequent molecular abnormalities. Neutropenia (without infection/sepsis) was the only treatment-associated adverse effect seen in 3 (50%, all grade 3) patients. Response was assessed in patients who completed at least 4 cycles (n=5) of therapy, with all (100%) patients meeting criteria for either a complete response (n=2, 40%), optimal marrow response (n=1, 20%) or clinical benefit (platelet response, n=2, 40%). In five patients who had NGS assessment pre-and post-therapy, there was a &gt;50% reduction in TET2mt VAF, with one patient loosing the KRAS G12D clone in its entirety. Furthermore, we identified significant changes in DNA methylation associated with treatment with &gt;85% of the CpGs being hypomethylated post-treatment. RNA-sequencing on patients pre- and post-treatment, identified key transcriptomic changes, with a decrease in expression of leukemogenic genes. Integration of the differential methylation data with transcriptomic changes identified several genes in which changes in DNA methylation correlated with a change in gene expression. These genes include regulators of blood cell development and inflammatory response including GATA2, BCL2, and HLA-DPA. Several leukemia-associated genes were downregulated including MEIS1, TCL1A, and SETBP1. CONCLUSION: In TET2mt CMML, high dose IV AA acid enhances TET activity and synergizes with DNMTi with encouraging clinical responses in all patients and with no new safety signals. On completion of this study, we plan a randomized phase 2 trial comparing high dose IV AA with decitabine vs decitabine alone in newly diagnosed TET2/IDHmt CMML.

Abstract Background Previous studies in aggressive B-cell lymphoma demonstrate that patients with shorter diagnosis-to-treatment intervals (DTI) represent those with higher risk disease. This suggests that patients enrolled in clinical trials may carry a selection bias as they often have longer DTI than patients in the general population. This has important implications for clinical research design and the generalizability of clinical trials in aggressive lymphomas. However, the prognostic implications of DTI have not been evaluated in peripheral T-cell lymphomas (PTCL). Therefore, we examined DTI and its association with clinical factors and outcomes in a prospective observational cohort of patients with PTCL from the United States within the LEO (Lymphoma Epidemiology and Outcomes) and MER (University of Iowa and Mayo Clinic Specialized Programs of Research Excellence Molecular Epidemiology Resource). Patients and Methods We evaluated patients enrolled to the MER (2002-2015) and LEO (2015-2020) cohorts with the following forms of PTCL: PTCL-not otherwise specified (PTCL-NOS, n=217), nodal T-follicular helper (TFH) cell lymphoma (nTFHL, n=153), anaplastic large cell lymphoma (ALCL), ALK positive (ALCL, ALK+, n=74), ALCL, ALK negative (ALCL, ALK-, n=95), enteropathy associated T-cell lymphoma (n=18), and monomorphic epitheliotropic intestinal T-cell lymphoma (n=2). Associations of DTI with clinical factors and outcomes were examined using Kaplan Meier and Cox models. Event free survival (EFS) was defined as the time from start of treatment to relapse/progression, start of 2nd line therapy, or death from any cause. Overall survival (OS) was defined as the time from start of treatment until death from any cause. DTI was defined as the number of days between the first diagnostic biopsy and the start of therapy. Patients who were initially observed or had DTI &gt; 100 days were excluded. Results In 559 patients (229 MER and 330 LEO), median age was 60, 34% had IPI&gt;2 and 71% were stage III/IV. 472 (84%) patients initially received anthracycline-based chemotherapy. Median DTI was 23 days (interquartile range (IQR): 12-39) and was similar in MER (21 days, IQR: 11-33) and LEO (25 days, IQR: 14-42). 168 (30%) patients had DTI 0-14 days, 178 (32%) 15-28 days, and 213 (38%) 29-100 days. Overall in the cohort, shorter DTI was associated with a worse 5-year OS (DTI 0-14 days: 43%; DTI 15-28 days: 53%; DTI 29-100 days: 58%, p&lt;0.001) and 5-year EFS (DTI 0-14 days: 31%; DTI 15-28 days: 39%; DTI 29-100 days: 45%, p&lt;0.001). In multivariable analyses, the association of DTI with outcomes remained consistent after adjusting for age and International Prognostic Index (IPI). Shorter DTI was strongly associated with adverse clinical factors, including elevated lactate dehydrogenase levels, poor performance status, B-symptoms, and higher IPI in both cohorts (all P &lt; .003). DTI was not associated with age, sex, or distance from the LEO/MER center (all p&gt;0.3). 6% patients with DTI of 0-14 days received 1L treatment on a clinical trial, compared to 11% with DTI 15-28 days and 13% with DTI 29-100 days, underscoring that patients with lower DTI may be underrepresented in clinical trials. When examining within PTCL subtypes, the association between short DTI (0-14 days) and inferior outcomes was most prominent in PTCL, NOS (OS HR=2.13, 95% CI: 1.52-2.94; EFS HR =1.49, 95% CI: 1.19-1.85). Associations between short DTI and outcomes were weaker in other subtypes: nTFHL (OS HR=1.28, 95% CI: 0.83-2.00; EFS HR =1.22, 95% CI: 0.82-1.81), ALCL, ALK-, (OS HR=1.30, 95% CI: 0.59-2.86; EFS HR =1.32, 95% CI: 0.68-2.56), ALCL, ALK+, (OS HR=1.05, 95% CI: 0.26-4.17; EFS HR =2.12, 95% CI: 0.84-5.26). Conclusion Shorter DTI is associated adverse prognostic clinical factors (LDH, stage, high IPI) and inferior EFS and OS in newly diagnosed PTCL. Patients with DTI ≤ 14 days, which represents approximately 1/3 patients with PTCL, may be underrepresented in clinical trials. Understanding of this selection bias should inform clinical trial design and interpretation. Future studies should consider strategies to minimize barriers to enrollment for patients with low DTI.