Citations

Abstract Secondary central nervous system (CNS) large B-cell lymphoma (SCNSL) occurs in the de novo setting, as a CNS-isolated relapse, or synchronous (concomitant CNS and systemic) relapse. SCNSL is a devastating event without therapeutic consensus. Thus, we aimed to evaluate treatment outcomes in an international cohort. Progression-free survival (PFS), overall survival (OS), and cumulative incidence of relapse (CIR, estimated using competing-risk models) were reported. Prognostic factors were identified in a 6-month landmark multivariate analysis. Outcomes after thiotepa autologous stem cell transplant (ASCT) and chimeric antigen receptor (CAR) T-cell therapy (CAR-T) delivered at relapse were compared after propensity score matching (PSM). A total of 1139 patients were included in the analysis (de novo: 537; relapsed SCNSL: 602). Two-year PFS estimates were 40.4%, 43.9%, and 16.2% for de novo SCNSL, CNS-isolated relapse, and synchronous relapse, respectively. Patients with CNS-isolated relapse demonstrated low rates of systemic recurrence (24-month CIR, 6%). Thiotepa-ASCT correlated with longer survival in de novo SCNSL (PFS: hazard ratio [HR], 0.57; P = .005; and OS: HR, 0.62; P = .023) and CNS-isolated relapses (PFS: HR, 0.55; P = .002; and OS: HR, 0.39; P< .0001). ASCT (thiotepa or no thiotepa) also associated with improved survival in synchronous relapses (PFS: HR, 0.57; P = .023; and OS: HR, 0.48; P = .019). Higher survival with thiotepa-ASCT than CAR-T was observed after PSM (PFS: HR, 0.45; P = .005 and OS: HR, 0.41; P = .014). These data support thiotepa-ASCT in eligible patients, particularly de novo disease and CNS-isolated relapses. CNS-isolated relapse was infrequently associated with systemic recurrence, supporting treatment regimens adopted from primary CNS lymphoma.

Abstract Background Chimeric antigen receptor T-cell (CAR-T) and bispecific antibody (BsAb) therapies are both effective for patients with relapsed/refractory (R/R) follicular lymphoma (FL), but differ in treatment schedules and associated “time toxicity,” or contact days spent physically interacting with the healthcare system. While contact days can be estimated from clinical trials, real-world data provides a more nuanced depiction of treatment-related time burden, which may guide treatment decision-making. The comparative time toxicity of CAR-T vs BsAb in R/R FL is not defined. Methods We conducted a multicenter retrospective study of adults with R/R grade 1-3A FL who received standard-of-care CAR-T (axi-cel, tisa-cel, liso-cel) or BsAb (mosunetuzumab, epcoritamab) in the third-line or later setting before Jan 1, 2025. Time toxicity was defined as outpatient or inpatient days with physical health care system contact, counting multiple visits on the same day as only one day. All days after treatment initiation were counted, regardless of disease progression, therapy completion, or relation to FL therapy. Median contact days over 1-year time horizon were compared, stratified by CAR-T vs BsAb, prior lines of therapy (LOT, 2 vs 3+), and time intervals (0-1, 1-3, 3-6, 6-9, and 9-12 months). Progression-free survival (PFS) and overall survival (OS) were calculated from day of CAR-T infusion or day 1, cycle 1 for BsAb. Cox regression and propensity matching (by age, ECOG, refractory to prior LOT, POD24) were used to assess predictors and survival associations with contact days. Results Among 209 patients from 15 U.S. academic centers, median age was 64, 37% were female, 61% received CAR-T (80% axi-cel, 7% liso-cel, 13% tisa-cel) and 39% BsAb (93% mosun, 7% epcor). Most had Grade 1-2 FL (72%), advanced stage (84%), and extranodal disease (51%). Median prior LOT was 3 (range 2-15), 41% were refractory to prior LOT, and 52% had POD24. Median number of BsAb doses was 10 (range 1-36). Patients receiving BsAb were significantly older (p<.01) and had higher CIRS comorbidity scores (p<.02) in both 2 and 3+ prior LOT cohorts, and had higher ECOG scores (p=.01) and were less likely to be refractory to prior LOT (p=.029) in 3+ LOT cohort. There were no differences in POD24 rates between CAR-T and BsAb. Median PFS for the entire cohort was 3.6 yrs; median OS was not reached. In the 2 prior LOT cohort (N=75), 60% received CAR-T and 40% BsAb. With median follow-up of 1.5 years, median PFS was not reached for CAR-T and 1.6 yrs for BsAb (p<.0001). In the 3+ prior LOT cohort (N=134), 62% received CAR-T and 38% BsAb. With median follow-up of 2 yrs, median PFS was 3.6 yrs for CAR-T and 1.6 yrs for BsAb (p=.0044). In propensity matched analysis, PFS was shorter for BsAb in both 2 LOT (HR 9.0, 95% CI 1.14-71.04, p=.037) and 3+ LOT (HR 3.25, 95% CI 1.06-9.97, p=.039) cohorts as compared to CAR-T. There were no significant differences in OS between CAR-T and BsAb. Median contact days were significantly higher for CAR-T vs BsAb in the 0-1 month interval: 19 vs 8 days in 2 LOT (p<.0001) and 18 vs 7 days in 3+ LOT (p<.0001) cohorts. There were no differences in contact days between CAR-T vs BsAb in subsequent time intervals. Over 1 year, CAR-T had significantly more time toxicity than BsAb: median 51 vs 36 contact days in 2 LOT (p<.0001) and 64 vs 38 days in 3L+ LOT (p<.0001) cohorts; these remained significant in the matched analysis. B symptoms (p=.04) and Grade ≥3 cytopenias (p=.007) were associated with higher contact days in 3+ LOT cohort in univariable analysis. Higher contact days were associated with inferior PFS (HR 1.01, 95% CI 1.00-1.02, p=.032) and OS (HR 1.01, 95% CI 1.00-1.02, p=.038) in 3+ LOT cohort regardless of treatment type. Discussion In this large real-world cohort of patients with R/R FL, CAR-T therapy resulted in better PFS but more time toxicity over the first year as compared to BsAb therapy. Excess time toxicity from CAR-T was driven by more contact days in the first month of treatment. This study provides benchmark data for counseling patients about expected time burden of CAR-T (51-64 days) and BsAb (36-38 days) over the first year of these therapies. Further, with superior PFS of CAR-T in this study and minimal impact of time toxicity on survival outcomes, these data can be used to better support shared decision-making in R/R FL by addressing patient preferences regarding both contact days as well as duration of disease control.

Background: Susceptibility to cardiometabolic diseases (CDs) is shaped by genetic, socio-environmental, and lifestyle factors. We evaluated the associations of disease-specific polysocial risk scores (PSSs)— derived from social determinants of health (SDOH) and lifestyle factors—and genetic risk factors with six CDs, across self-identified race/ethnicity (SIRE) groups in the eMERGE IV cohort. Methods: The six CDs included: atrial fibrillation, coronary heart disease, chronic kidney disease, hyperlipidemia, obesity, and type 2 diabetes. Disease-specific PSSs were developed using machine-learning models based on 65 SDOH and lifestyle factors. Participants were grouped based on their SIRE as Asian, Black, Hispanic/Latino, and White. We evaluated PSS distributions across SIRE groups. Genetic risk was assessed through disease-specific polygenic risk scores (PRSs), monogenic etiologies, and family history (FamHx). High PSS and high PRS were defined as the top 5th percentile. Associations were tested using multivariable logistic regression; model performance was assessed using the C-statistic. Results: Among 20178 participants (50±15 years, 68% female, 40% non-White), the most frequently associated PSS components were physical activity, sleep, self-perceived health, and income. PSS distributions varied across SIRE groups, with Whites having the lowest and Black and Hispanic/Latinos the highest scores for most CDs (Figure 1). All PSSs were significantly associated with their respective CDs, independent of confounders (OR per 1 SD increase: 1.19–2.68; Figure 2-A). No significant interactions were observed between PSSs and PRSs or monogenic etiologies. However, significant interactions were noted between PSSs and FamHx for CKD and obesity. PSS and PRS effects on the odds of CDs were independent (Figure 2-B) and additive (Figure 2-C). Incorporating PSSs into models that included clinical and genetic risk factors improved predictive performance of risk prediction models (Figure 3). Conclusion: PSSs, reflecting socio-environmental and lifestyle disadvantage, varied significantly across SIRE groups and were associated with risk of six CDs, independent of and additive to PRS. Given the substantial variation in socio-environmental and lifestyle risks across SIRE groups, integrating these factors into risk prediction models for CDs may help address health disparities and enhance preventive efforts, especially in marginalized populations.

Background: We investigated the impact of genetic risk factors for coronary heart disease (CHD) -polygenic risk score (PRS), familial hypercholesterolemia (FH), and family history (FamHx)- on CHD risk estimates, across the age spectrum, in two diverse cohorts of US adults -eMERGE IV (eIV) and All of Us (AoU). Methods: CHD was defined as myocardial infarction, unstable angina, and coronary revascularization. Self-identified race/ethnicity (SIRE) was used as a population descriptor. We calculated a PRS for CHD (PRS CHD , PGS004698), ascertained FH as the presence of pathogenic/likely pathogenic variants in FH genes, and defined FamHx as early-onset CHD in first-degree relatives. We employed Pooled Cohort Equations (PCE) to estimate the 10y risk of CHD for adults ≥40y and modeled the association of conventional risk factors with CHD in adults <40y. AoU served as the training set, and eIV as the testing set. We analyzed the impact of PRS CHD and FamHx on CHD risk estimates by a) using multivariable logistic regression and Cox proportional hazard models, assessing discrimination and the extent of risk reclassification; and b) net benefit analysis and decision curves to assess the performance of prediction models across actionable thresholds. Results: We analyzed data for 19348 participants from eIV (age 50±15, 68% female, 41% non-White) and 239645 participants from AoU (age 55±17, 61% female, 48% non-White). Genetic risk factors were significantly associated with CHD. PRS CHD performance varied by SIRE groups, while FamHx was consistent. The effects of PRS CHD and FamHx on CHD were independent and additive (Figure 1). In adults ≥40y, incorporating PRS CHD and FamHx into PCE improved discrimination (C-statistic increased from 0.719 to 0.753; P -diff=9.1×10 -3 , Figure 2) and reclassified risk in 19% and 20% of participants at the 7.5% and 10% 10y CHD risk thresholds, respectively. Between the 7.5% and 10% 10y CHD risk thresholds, incorporating PRS CHD and FamHx into the PCE improved the net benefit of the risk prediction models across White, Black, and Hispanic/Latino groups (Figure 3). Conclusion: PRS CHD and FamHx were independently and additively associated with CHD in two large diverse cohorts in the US. Incorporating PRS CHD and FamHx into PCE improved risk discrimination, reclassified risk in a significant portion of participants, and improved net benefit of the PCE across all three major SIRE groups, motivating the addition of these factors to clinical risk calculators.

Abstract Purpose: Polatuzumab vedotin (polatuzumab) was approved for up-front treatment of diffuse large B-cell lymphoma in combination with chemoimmunotherapy (Pola-R-CHP) based on the POLARIX trial. However, when stratified by cell of origin (COO), polatuzumab seems to have greater efficacy in activated B-cell than germinal center B-cell (GCB) subtype disease. Most studies of polatuzumab used RNA expression to assess COO, whereas in routine clinical practice, the immunohistochemistry-based Hans algorithm is used. Experimental Design: To assess the impact of COO by immunohistochemistry on polatuzumab efficacy, we conducted a multicenter real-world study of adults with large B-cell lymphoma (LBCL) receiving polatuzumab from 2015 to 2024, split by receipt of polatuzumab in first-line versus relapsed/refractory settings. The primary endpoint was overall response rate (ORR) to polatuzumab-based treatment in GCB versus non-GCB relapsed/refractory LBCL. Results: Of 740 patients, 305 received polatuzumab in the first-line setting and 435 in the relapsed/refractory setting. In the relapsed/refractory cohort, the ORR in non-GCB versus GCB LBCL was 59.7% versus 36.3% [OR, 2.6; 95% confidence interval (CI), 1.77–3.84; P &lt; 0.0001], with a complete response rate of 35.7% versus 17.7% (OR, 2.6; 95% CI, 1.66–4.02; P &lt; 0.0001). Progression-free survival was longer for patients with non-GCB versus GCB COO (HR, 0.64; 95% CI, 0.5–0.83; P = 0.0006). In the first-line cohort, ORR, complete response rate, and progression-free survival were similar in non-GCB versus GCB LBCL, as hypothesized based on outcomes in the Pola-R-CHP arm of POLARIX, suggesting that the addition of polatuzumab overcomes the adverse risk of non-GCB COO in patients receiving R-CHOP. Conclusions: Based on these data, COO classification by the Hans algorithm is a strong predictor of polatuzumab efficacy in LBCL, informing real-world treatment decisions.