Citations

Abstract Twenty percent of patients with follicular lymphoma relapse early with poor outcomes; however, the molecular mechanisms underlying this aggressive behavior are unknown. Using a multiomic approach, we show that patients with follicular lymphoma with elevated IRF4 expression (IRF4hi) have increased transformation risk, dysregulated immune signaling, and a suppressive tumor microenvironment. Loss- and gain-of-function experiments in IRF4hi lymphoma cells, along with chromatin profiling, demonstrate that IRF4 impairs their interaction with T cells by repressing antigen presentation and co-receptor gene modules while promoting the expression of cytokines that antagonize T-follicular helper cell and regulatory T-cell functions. Additionally, IRF4 rewires tumor metabolism and restricts glucose availability to immune cells. Silencing of IRF4 inhibits tumor cell growth and restores immune surveillance mechanisms, thus representing a promising target for therapy. Our data suggest that IRF4hi lymphoma cells co-opt a developmental mechanism used to exit the germinal center response in promoting a more aggressive cancer via engagement of multiple immune-evasive mechanisms. Significance: We show that IRF4 controls immune and metabolic signaling with functional effects on the cross-talk between B and T cells, resulting in a suppressive immune microenvironment when IRF4 is overexpressed in lymphoma. This underscores the importance of IRF4 as a pivotal therapeutic target to restore antitumor immunosurveillance for IRF4-associated lymphomas.

e13622 Background: Chimeric antigen receptor T-cell (CAR-T) therapy has revolutionized treatment of hematologic malignancies; however, severe toxicities like cytokine release syndrome (CRS) remain a limitation to broader utilization. Using a large dataset of patients treated with CAR-T, we developed a machine learning (ML) model leveraging multi-model patient characteristics to predict the risk of CRS onset with clinically meaningful lead times. Methods: A prospectively maintained dataset of CAR-T patients treated at Mayo Clinic was used. Baseline disease/patients characteristics, labs, ECG, echocardiogram, and vital signs (including data from remote patient monitoring program) were collected. A 24-hour rolling window was used to generate daily averages, maximum, minimum, and deltas for each parameter. ECG and echocardiogram data were forward-imputed; vital signs and labs were not imputed. Each day-post-infusion served as a potential event (CRS onset vs. no CRS). A gradient boosting classifier was trained and tested on a subset of patients (n = 351) using 1,000 bootstrap iterations, evaluating 24-, 48-, and 72-hour lead times. Shapley-based feature importance guided simplification to an 11-feature model, externally validated on an independent cohort (n = 51). Models’ performance assessed using the area under the receiver operating characteristic curve (AUC). Results: A total of 402 patients treated with CAR-T were examined. Median age was 64 years and 36% were female. Primary diagnosis was B-cell lymphoma (n = 240) or multiple myeloma (n = 162). A total of 226,332 vital sign measurements and 372,871 lab values, as well as ECG and echocardiogram data, were collected for 30 days post-infusion. Overall, 79.8% of patients experienced CRS within 30 days. The full ML model demonstrated AUCs of 0.90 (95% CI 0.86–0.93), 0.83 (95% CI 0.77–0.88), and 0.82 (95% CI 0.77–0.87) for 24-, 48-, and 72-hour predictions of CRS, respectively. Including “day of CRS onset post CAR-T” as a feature improved the 72-hour AUC to 0.87 (95% CI 0.83–0.90). A model restricted to vital signs alone showed AUCs of 0.88, 0.75, and 0.71 for 24-, 48-, and 72-hour horizons. An 11-feature model—focusing on temperature, blood pressure, fibrinogen, CRP, serum sodium level, QRS interval, and lymphocyte counts—maintained high accuracy at 24 hours (AUC 0.90, 95% CI 0.86–0.93) and was validated externally using an independent dataset with an AUC of 0.92. Conclusions: Our ML model provides a reliable approach for predicting if/when CRS will occur, with up to three days of lead time before clinical manifestation, offering an opportunity for proactive management. The robust performance of a parsimonious 11-feature model supports practical adoption across clinical settings and real-time risk stratification to enhance patient safety. Prospective validation and integration into clinical workflows will be critical to translating these findings into practice.

e13738 Background: Persistent racial/ethnic and regional disparities in neoplasm mortality remain significant public health challenges. This study evaluates long-term trends in neoplasm mortality by race/ethnicity at the county level in the United States and examines the relationship between hospital density and mortality. Methods: Using data from the Global Burden of Disease Study (2000-2019), we analyzed age-standardized mortality rates (ASR) for neoplasms by ethnicity/raceHispanic (all races) and non-Hispanic American Indian or Alaska Native (AIAN), Asian or Pacific Islander (API), Black, and White. A joinpoint regression analysis was used to identify significant changes in the mortality trends, summarized as average annual percent change (AAPC). Correlation analysis evaluated the relationship between county-level hospital density and neoplasm mortality. Results: Neoplasm mortality rates declined across all racial/ethnic groups from 2000 to 2019, but disparities persisted. ASR per 100,000 population decreased from 240.5 (95% UI: 214.3–269.9) to 203.0 (180.8–228.3) for AIAN, 149.5 (144.9–153.9) to 115.3 (111.7–118.7) for API, 314.5 (312.1–317.0) to 218.7 (217.1–220.3) for Black, 168.4 (164.6–172.0) to 133.8 (131.0–136.4) for Latino, and 241.2 (240.5–242.0) to 187.5 (186.9–188.2) for White. AAPC from 2000 to 2019 ranged from -0.94% (AIAN) to -1.90% (Black). Gender-specific analyses revealed similar trends, with males showing slightly greater declines. Southeast states experienced slower declines compared to the Northeast. Some counties exhibited worsening mortality trends, particularly for AIAN, API, and Latino. The county-level hospital density (table) showed weak but statistically significant correlations in 2019 for AIAN (r = -0.35; p < 0.0001), White (r = -0.15; p < 0.0001), and Black (r = -0.18; p < 0.0001). No inverse correlation was observed for API (r = 0.07; p = 0.071) and Latino (r = 0.06; p = 0.012). Conclusions: While significant progress has been made in reducing neoplasm mortality, racial/ethnic and regional disparities persist, and health care access appears to contribute to this at least in part. Future analyses should evaluate mortality by cancer site and also account for broader clinical and public health infrastructure, as well as social determinants of health to better understand and address underlying disparities. The correlation between hospital density and age-standardized mortality rate from neoplasms by race/ethnicity in 2019. Correlation coefficient with mortality and hospital density AIAN API Black Latino White 2019 -0.35(-0.43 – -0.27) 0.069(-0.0058–0.14) -0.18(-0.23 –-0.13) 0.064(0.014–0.11) -0.15(-0.19 – -0.12)

7075 Background: Hepatosplenic T-cell lymphoma (HSTCL) is a rare, aggressive peripheral T-cell lymphoma arising primarily from γδ T-cells. It carries a poor prognosis and resists conventional chemotherapy. Most reports on HSTCL are case-based. This study comprehensively analyzes a large cohort, evaluating treatment strategies and survival outcomes. Methods: This retrospective study included patients (pts) with pathologically confirmed HSTCL diagnosed between 2000-2024, consecutively seen at Mayo Clinic MN. Clinical, pathological, genomic, and treatment-related data were extracted when available. Descriptive statistics were used to summarize baseline characteristics. Time-to-event analyses, including Kaplan-Meier estimates, median overall survival (OS), and survival time estimates were conducted from the date of diagnosis. Results: A total of 20 patients with newly diagnosed HSTCL were included, with a median age of 57 years (range: 35-71). The cohort was predominantly male (70%) and non-Hispanic (93%). Molecular data was available for five patients, revealing abnormalities in STAT5B, MLL3 deletion, TP53, EZH2, TERT, and NF1 E291D . The median follow-up was 27.6 months (m) with a median OS of 17.6 m (95% CI: 11.2 - NA). First-line treatment was anthracycline-based in 68% of pts and non-anthracycline-based in 32%, with higher response rates in the latter group (50% vs.83%). Although non-anthracycline regimens showed a trend toward improved 3-year OS (100% vs. 29%) the difference was not statistically significant (p = 0.16). Achieving a complete response to first-line therapy was also associated with a trend towards a better 3-year OS compared to refractory disease (80% vs. 50%, p = 0.17). Most pts (79%) underwent hematopoietic stem cell transplant (HSCT), primarily allogeneic, with only one receiving autologous HSCT. First-line therapy before HSCT was evenly distributed between anthracycline (55%) and non-anthracycline (45%) regimens. HSCT recipients had significantly higher 3-year OS than non-recipients (83% vs. 33%, p = 0.017). Notably, the patient with a TP53 mutation has remained in remission for over a year post-allogeneic HSCT. Conclusions: HSTCL predominantly affects younger pts, with nearly half dying within a year. Allogeneic HSCT, rarely used in other NHL subtypes, improved survival. Non-anthracycline regimens and achieving CR trended toward better outcomes. Our study, leveraging a sizable cohort, highlights the need for targeted research and novel therapies to improve HSTCL management. Summary of survival outcomes in HSTCL. Characteristics Median OS (y) 3-Year OS (95% CI) 1.48 [0.93- NA] 46% [0.26 - 0.81] Transplant Transplant NA [NA - NA] 83% [0.58 - 1.00] No Transplant 1.02 [0.93 - NA] 33% [0.07 - 1.00] Treatment Anthracycline Based 1.02 [0.36 - NA] 29% [0.11 - 0.73] Non-Anthracycline Based 4.61 [NA- NA] 100% [1.00 - 1.00]