Cerhan, James R

Background: Perinatal and early-life factors may influence non-Hodgkin lymphoma (NHL) risk, but study results have been mixed and exposure data for childhood ages are limited. Herein, we investigated associations of these exposures with risk of NHL and common subtypes. Methods: This case–control study included 2280 NHL cases and 2253 controls, enrolled from 2002 to 2014 at the Mayo Clinic Rochester. Self-reported perinatal and early-life exposures included maternal age at birth, birth order, birthweight, time breastfed, height and weight at ages 7, 12, and 18 years relative to peers, and age and weight when growth ceased. We used logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (CIs), adjusting for design variables and potential confounders. Linear trend tests were performed for ordinal variables, and heterogeneity tests were performed to evaluate whether associations varied across four NHL subtypes. Results: After multivariable adjustment, greater birth weight (OR = 1.15 for quartile 4 vs. 1; p-trend = 0.04), weight at age 7 relative to peers (compared to average, OR = 1.05 for heavy and OR = 0.87 for thin; p-trend = 0.002), and weight when growth ceased (OR = 1.30 for quartile 4 vs. 1; p-trend < 0.001) were associated with increased NHL risk. An inverse association was observed for breastfeeding duration with NHL risk (OR = 0.77 for >6 months vs. never; 95% CI 0.61–0.97). Associations did not significantly vary by major NHL subtype (p-heterogeneity > 0.05). Other perinatal and early-life exposures were not associated with NHL risk. Conclusions: Greater body weight at birth and through childhood may be associated with increased NHL risk, potentially extending the life-course perspective on adiposity and lymphomagenesis, with implications for prevention.

Abstract Germinal center (GC)-derived lymphomas arise within a specialized immune ecosystem, yet whether malignant cells direct its remodeling remains unclear. Integrating genomic, transcriptomic, spatial, and functional analyses across follicular lymphoma and diffuse large B-cell lymphoma, we define a developmental framework linking malignant B-cell differentiation state to tumor microenvironment (TME) organization. Rather than segregating by histology, lymphomas align along a shared differentiation continuum in which proliferative dark zone (DZ) states associate with immune-depleted TMEs, whereas post-GC/memory B-cell (MBC) states drive inflamed but immunosuppressed TMEs. Spatial profiling reveals collapse of GC architecture and emergence of suppressive myeloid niches along this trajectory. Single-cell lineage reconstruction uncovers a DZ-to-MBC developmental axis underlying lymphoma evolution. Genetic reprogramming in vivo demonstrates that malignant differentiation state is sufficient to instruct TME remodeling. Collectively, these findings establish malignant differentiation state as a causal determinant of immune architecture across GC-derived lymphomas and identify lineage-directed immune reprogramming as a potential therapeutic strategy.

BACKGROUND: Anthracyclines are a widely used and effective class of chemotherapy. However, a major limitation for their use is cardiotoxicity, manifesting as systolic dysfunction, congestive heart failure, and arrhythmias. The objective of this study was to identify genes and genetic risk variants for doxorubicin-induced arrhythmia. METHODS: We performed genome-wide association studies and meta-analysis across 2 independent nonoverlapping, genetically homogenous data sets from the Molecular Epidemiology Resource (cases N=77, controls N=1184) and Tapestry (cases N=13, controls N=172). Doxorubicin-related arrhythmia was the primary cardiac outcome, followed by investigation of a broader phenotype of cardiac events. Effects of doxorubicin on top-associated genes were assessed by quantitative polymerase chain reaction on RNA extracted from human cardiomyocytes following treatment with doxorubicin and by echocardiography in a zebrafish model of doxorubicin-induced cardiomyopathy. RESULTS: In the meta-analysis, rs12151014 at the KCTD15 locus was associated with doxorubicin-related arrhythmia at the genome-wide significance (minor allele frequency in Molecular Epidemiology Resource cases 0.195 versus 0.069 in controls; Tapestry cases 0.192 versus 0.079 in controls) P =9.01×10 −9 ; odds ratio, 3.61. MMP 16 rs1094199 was associated with the broader phenotype of arrhythmia and heart failure (minor allele frequency in Molecular Epidemiology Resource cases 0.079 versus 0.019 in controls; Tapestry cases 0.038 versus 0.015) P =1.98×10 − 8 ; odds ratio=6.15. In human cardiomyocytes, MMP16 and KCTD15 gene expression was significantly downregulated by doxorubicin, and in a zebrafish MMP16 knockout was protective of doxorubicin-induced decline in left ventricular ejection fraction. CONCLUSIONS: Genetic variants at KCTD15 and MMP16 are associated with doxorubicin-related arrhythmia and cardiac events. MMP16 is a potential cardioprotective therapeutic target.

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 One quarter of patients with diffuse large B‐cell lymphoma (DLBCL) who achieve a complete response following front‐line therapy will eventually experience disease relapse. Existing prognostic models use baseline factors to predict outcomes from the start of initial therapy and are not intended for use following completion of treatment. We developed a conditional event‐free survival (cEFS) tool that predicts a patient's risk of lymphoma recurrence after completing front‐line therapy taking into consideration baseline risk characteristics plus the time the patient has been in remission. Model development used pooled and harmonized data from two observational cohorts and four randomized trials of patients with newly diagnosed DLBCL treated with immunochemotherapy and either achieving a complete response or not experiencing an event within 12 weeks of end of therapy. Cox proportional‐hazards regression modeling with backwards stepwise selection was utilized for model development. External validation was performed using a large independent data registry. In total, data from 2757 patients were used for model development. The risk of experiencing an event at a time point after having survived a period of time could be computed parsimoniously using four basic predictors: age, ECOG performance status grade, Ann Arbor stage, and lactate dehydrogenase level. C‐index in the model development cohort was 0.65 (95% CI, 0.63–0.68), compared to 0.64 (95% CI, 0.62–0.66) in the external validation cohort. We believe that this dynamic risk prediction tool will not only aid clinicians in providing data‐driven recommendations for surveillance, but also assist patients themselves with key decisions, such as family planning.

Background/Objectives: Racial/ethnic and regional disparities in neoplasm-related mortality remain a significant public health challenge. In this study, we aimed to evaluate long-term trends in county-level neoplasm-related mortality rates by race/ethnicity in the United States and examine associations with social determinants of health. Methods: We conducted a cross-sectional ecological study using population-based data from the Global Burden of Disease Study, including individuals residing in 50 states of the United States and the District of Columbia from 2000 to 2019. We analyzed age-standardized neoplasm-related mortality rates by ethnicity/race. Joinpoint regression analysis was used to identify significant changes in mortality trends, summarized as average annual percentage change. County-level correlations between mortality and key social determinants of health were also assessed. Results: Neoplasm-related mortality rates declined across all racial/ethnic groups from 2000 to 2019; however, disparities persisted. The age-standardized neoplasm-related mortality rates per 100,000 population decreased in all racial/ethnic subgroups. The average annual percentage change ranged from −0.94% (Hispanic and non-Hispanic American Indian or Alaska Native) to −1.90% (Black). Sex-specific analyses revealed similar trends. Southeastern states experienced slower declines than Northeastern states did. County-level smoking and poverty rates were positively correlated, whereas the primary care physician-to-population ratio, excessive alcohol consumption rate, mammography screening rate, and median household income were inversely correlated with neoplasm-related mortality rate, varying by race/ethnicity. Conclusions: Targeted, community-specific interventions are required to reduce inequities in cancer outcomes.

Abstract Glioblastoma (GBM), isocitrate dehydrogenase wild-type (IDHwt) and central nervous system diffuse large B-cell lymphoma (CNS-DLBCL) are aggressive brain tumors with overlapping MRI features, yet distinct treatment approaches. Noninvasive tools are needed to aid in differential diagnosis. Deep learning on T1 postcontrast and T2-weighted MRI sequences were used to differentiate GBM and CNS-DLBCL. A three-stage temporal study design was utilized. Model development was performed on 146 patients with CNS-DLBCL and 146 age-matched, sex-matched, and MRI year–matched patients with GBM diagnosed at Mayo Clinic between 1998 and 2019. Models were tested on independent temporal test cohorts. Initial testing included 240 independent GBM diagnosed at Mayo Clinic between 1998 and 2019. The prospective test cohort included 37 patients with CNS-DLBCL and 256 patients with GBM diagnosed at Mayo Clinic after January 1, 2020, and 36 patients with CNS-DLBCL diagnosed at an external institution. Of the patients diagnosed at Mayo Clinic, 47% had MRIs generated from non-Mayo institutions. Two different model approaches were compared: (i) ensemble approach using area under the receiver operating characteristic curve (AUC) and cross-validation for model selection and (ii) loss approach minimizing cross-entropy loss and cross-validation to evaluate prediction performance. The AUCs on the prospective test cohort were 0.84 [95% confidence interval (CI), 0.78–0.90] and 0.83 (95% CI, 0.77–0.88) for the ensemble and loss approaches, respectively. Stability of ensemble prediction improved with the increasing number of models. Stratified AUC analysis demonstrated consistent performance across sex and age. We utilized a robust temporal study design and applied 2 different analytic approaches to develop a classification model. The findings confirm the feasibility of using MRI-based deep learning models to differentiate GBM from CNS-DLBCL. Significance: GBM, IDHwt and CNS-DLBCL are aggressive brain tumors with overlapping MRI features, yet distinct treatment approaches. Noninvasive tools are needed to aid in differential diagnosis. We developed MRI-based deep learning models to differentiate GBM, IDHwt from CNS-DLBCL using a rigorous three-stage temporal design that included prospective validation. The model AUC on a prospective cohort was 0.84.

Abstract A short diagnosis-to-treatment interval independently predicts inferior outcomes in follicular lymphoma. Using international discovery and validation cohorts, we demonstrate its prognostic value beyond established prognostic indices, with implications for risk stratification, clinical trial design, and interpretation.