Cerhan, James R.

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.

Abstract Prognostic indices such as the CLL International Prognostic Index (CLL-IPI) and the International Prognostic Score for Early-stage CLL (IPS-E) can predict time to first treatment (TTFT) in patients with early-stage CLL. Their ability to predict risk of infection - a leading contributor to morbidity and mortality in CLL - remains uncertain. The CLL-TIM is a machine-learning model developed in Europe that integrates clinical, laboratory, and infection-related data to predict TTFT or infection risk within 2-years of diagnosis, Receiver Operating Characteristic - Area Under the Curve (ROC-AUC) 0.74; Agius et al., Nat Comm 11, 363 2020. We conducted the first validation of CLL-TIM in a US cohort of newly diagnosed CLL patients and compared its performance with the CLL-IPI and IPS-E indices. Adults with newly diagnosed CLL (2000-2020) were identified through the Rochester Epidemiology Project using International Classification of Disease (ICD) codes; all diagnoses were confirmed. We replicated the variable selection from the original CLL-TIM model and applied it to our cohort. The primary endpoint was a 2-year composite of either TTFT or incident infection (defined as having blood cultures drawn). Model performance was evaluated using ROC-AUC consistent with the original CLL-TIM methods. CLL-IPI and IPS-E performance was assessed using time-dependent ROC-AUC; pairwise differences in discrimination were tested using DeLong. We identified 454 CLL patients with a median age of 72 years [range, 30-97], 166 (37%) were female, Rai Stage was 0-II in 399 (92%) patients. IGHV genes were unmutated in 130/305 (42%) patients and TP53 disruption (either del17p by FISH or TP53 mutation) was present in 24/329 (8%) patients. At 2-years, 55 (12%) patients received CLL therapy, and 56 (12%) patients had an infection. The 2-year composite endpoint was observed in 94 (21%) patients. The ROC-AUC was 0.74 (95% CI 0.68-0.80) for CLL-TIM, 0.69 for CLL-IPI (95% CI 0.63-0.75, p=0.11 compared to CLL-TIM) , and 0.68 for IPS-E (95% CI 0.62-0.74, p=0.02 compared to CLL-TIM). We also evaluated the ROC-AUC using all the prognostic models for each individual endpoints. The corresponding ROC-AUC for TTFT at 2 years were 0.79, 0.74, and 0.76 for CLL-TIM, CLL-IPI, and IPS-E, respectively; and for infection at 2 years were 0.68, 0.57, and 0.52, respectively. This is the first validation of the CLL-TIM in a US-based cohort of newly diagnosed CLL patients. Although the CLL-TIM model exceeded the minimally meaningful discrimination threshold (AUC >0.7) for individual patient-level risk prediction, there was no statistical difference in AUC between CLL-TIM and CLL-IPI, suggesting either model can predict the composite endpoint at 2 years. Most of the observed discrimination was driven by prediction of TTFT rather than infection, underscoring the need to develop more infection-specific risk models for newly diagnosed CLL. Citation Format: Raphael Mwangi, Tait D. Shanafelt, Soren Basnet, Emily L. West, Owen Keegan, Timothy G. Call, Yao Yuan, Bryan Alexis Vallejo, Paul J. Hampel, Lindsey E. Roeker, Yucai Wang, Saad J. Kenderian, Sara J. Achenbach, Aaron D. Norman, Kari G. Rabe, Neil E. Kay, James R. Cerhan, Curtis A. Hanson, Susan L. Slager, Sameer A. Parikh. Validation of the CLL treatment infection model (CLL-TIM) in patients with newly diagnosed chronic lymphocytic leukemia (CLL) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 4228.

Abstract Background: CLL incidence differs by ancestry, with higher rates in individuals of European ancestry (EA) than African ancestry (AA). The contribution of epigenetic variation to these disparities remains unclear. We investigated chromatin accessibility, histone modifications, and gene expression in CLL patients of EA and AA to identify ancestry-associated epigenetic signatures. Methods: ATAC-seq was performed on blood clonal B cells from 15 AA and 51 EA patients; CUT&Tag profiling of six histone marks (H3K4me1, H3K4me3, H3K27ac, H3K9me3, H3K27me3, H3K36me3) was done in 8 AA and 12 EA cases; RNA-seq was generated for all samples. Ancestry was genetically confirmed. Differential regions were linked to genes using ENCODE and Hi-C datasets. Results: ATAC-seq revealed 4,955 regions with increased and 3,000 with decreased accessibility in AA vs EA (fold change >2, FDR ≤0.05). Regions with increased accessibility mapped to 2,313 genes; 599 were upregulated and enriched in TNF-α/NF-κB (p=1.40E-53) and apoptosis pathways (p=6.90E-11). Regions with decreased accessibility linked to 1,953 genes; 317 were downregulated, enriched in interferon-γ response (p=1.04E-03) and IL-6/JAK/STAT3 signaling (p=6.41E-03). Histone profiling showed ancestry-specific changes across all marks. For H3K27ac (active enhancer), 68 genes with a decreased signal in AA were downregulated and enriched in interferon-γ (p=1.55E-06) and IL-6/JAK/STAT3 (p=9.62E-05) pathways. Increased H3K27me3 (repressive histone modification) in AA affected 399 genes enriched in IL-6/JAK/STAT3 (p=8.13E-09) and interferon-γ (p=2.48E-05) pathways. These findings suggest coordinated regulation by active and repressive chromatin states. Conclusions: Despite limited sample size, this study suggests differences between AA and EA ancestries may exist in epigenetic signatures in CLL, implicating chromatin accessibility and histone modifications in pathways relevant to tumor biology. These signatures may also contribute to the differences in CLL incidence by ancestry. Future studies with more patients and functional validation experiments are needed to clarify the role of these epigenetic differences in CLL pathogenesis. Citation Format: Zhiquan Wang, Huihuang Yan, Mingma Sherpa, Daniel Bihnam, Cristine Allmer, Chantal McCabe, Daniel O’Brien, Dennis P. Robinson, James R. Cerhan, Neil E. Kay, Sameer A. Parikh, Esteban Braggio, Susan Slager. Ancestry-associated epigenetic differences in chronic lymphocytic leukemia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 5064.

Abstract Background: Epidemiologic and other studies have identified environmental, occupational, and genetic risk factors for lymphoid and myeloid malignancies, but most studies have been conducted in Western populations. Investigations in populations with differing exposure patterns, distributions of disease subtypes, and genetic architecture are needed to fully understand hematopoietic tumor etiology. Methods: We conducted a large, multicenter, hospital-based case-control study of lymphoid and myeloid neoplasms in East Asia (AsiaLymph), including 5,671 lymphoid cases, 1,879 myeloid cases, and 3,858 controls. Participants completed a computer-assisted personal interview and provided biospecimens. Cases underwent central pathology review and were coded into the WHO Classification. Herein, we describe the study methods in detail and report association results for education, body mass index, and family history. Results: Greater BMI at age 20 but not at age 40 was associated with odds of total lymphoid neoplasm (per 5 kg/m2 increase: OR [95% CI]: 1.18 [1.09-1.27]), total myeloid neoplasm (OR [95% CI]: 1.17 [1.05-1.29]), and several subtypes. Greater educational attainment was associated with increased odds of total lymphoid neoplasm (for college vs. less than primary education: OR [95% CI]: 1.41 [1.21-1.65]) but not myeloid neoplasm (OR [95% CI]: 1.17 [0.95-1.45]; p-heterogeneity=0.02). There were also positive associations between family history of hematologic cancer in first degree relatives and odds of lymphoid neoplasm (OR [95% CI]: 1.53 [1.15-2.03]) and myeloid neoplasm (OR [95% CI]: 1.65 [1.11-2.44]) and specific subtypes. None of the evaluated risk factors were associated with NK/T-cell lymphoma, which supports a distinct etiology for this subtype. Conclusion: The AsiaLymph Study is one of the largest molecular epidemiology studies of both lymphoid and myeloid neoplasms with standardized World Health Organization classification of histopathologic subtypes and will serve as a valuable resource for etiologic investigations into risk factors for these malignancies. Citation Format: Qing Lan, Lauren M. Hurwitz, John K. Chan, Tai Hing Lam, Kexin Chen, Yok Lam Kwong, Xu Caigang, Brian CH Chiu, Raymond Liang, Ip Dennis, Wei Hu, Bryan Bassig, Mark Purdue, Jun Xu, Sarah Locke, Sophia S. Wang, James R. Cerhan, Sonja Berndt, Jonathan N. Hofmann, Jianxin Shi, Kai Yu, Shahinaz Gadalla, Lisa J. McReynolds, Rena Jones, Hongji Dai, Zhangyan Lyu, Lugui Qiu, Wei Liu, Huilai Zhang, Xianhuo Wang, Lindsay M. Morton, Stephen Chanock, Martha Linet, Melissa C. Friesen, Roel Vermeulen, Nathaniel Rothman. A multi-center hospital-based case-control study of lymphoid and myeloid neoplasms (AsiaLymph): Study design and initial findings for education, body mass index, and family history [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 5051.

Abstract Background: Mantle cell lymphoma (MCL) is an aggressive and rare B-cell non-Hodgkin lymphoma with poor prognosis, even with treatment. Despite its clinical importance, the etiology of MCL remains largely unknown. Some studies have reported a positive association with family history, suggesting that genetic factors could contribute to risk. Methods: To identify germline genetic variants associated with risk, we conducted the first genome-wide association studies (GWAS) and GWAS meta-analysis for MCL, comprising 1,163 cases and 61,271 controls. The most significant loci were taken forward for replication in three independent studies, including 576 cases and 771,773 controls. To identify potential target genes and pathways of discovered loci, we conducted F-MAGMA, colocalization, and transcriptome-wide association study (TWAS) analyses. To gain insight into underlying regulatory mechanisms, we performed integrative epigenomic analyses using DNase I hotspots and histone mark ChIP-seq data across diverse cell types applying the FORGE2 framework. Results: In the joint analyses, we identified eight genome-wide significant loci (P<5x10-8) associated with MCL risk, several of which were located near genes with known DNA repair (ATM), telomere (TERT), or RNA-binding functions (RBM20). Further analyses using F-MAGMA, which uses DNase-seq data to prioritize genes from GWAS, identified genes that were significantly associated with risk, including SP140, a chromatin reader and immune regulator of pathogen response. This gene also displayed strong colocalization with our lead variant in whole blood (COLOC posterior probability PP4=0.97). TWAS demonstrated significant associations with SP140 and ACTA2, which has been linked to cell proliferation (P<3x10-6). Enrichment for DNase I hotspots was observed in B and T lymphocytes (q-value<0.05), providing evidence for a role in immune cell-specific regulatory regions. Enrichment for enhancer-associated histone mark H3K4me1 was observed in B cells (q-value<0.01), highlighting a role for immune cell-specific enhancers. Transcription factor (TF) motif enrichment analysis pointed to TFs critical for B-cell development and differentiation, notably TCF3 and EP300 (q<0.01 for both), suggesting an association between B-cell lineage regulation and MCL risk. Finally, genome-wide SNP-based heritability was estimated to be 17%, underscoring the importance of common variants in MCL risk. Conclusion: Our study provides novel insight into the inherited susceptibility of MCL by identifying eight significant loci and estimating substantial common variant heritability. These findings highlight a role for several distinct biological pathways -specifically DNA repair, telomerase function, and B-cell differentiation- in the etiology of MCL, offering new targets for mechanistic investigation. Citation Format: Charles E. Breeze, Alyssa Clay-Gilmour, Hanla A. Park, Ólafur B. Davíðsson, Angelica Macauda, Brenda M. Birmann, Elizabeth E. Brown, Murat Güler, Michelle A. T. Hildebrandt, Alexandra Nieters, Krystle Ong, Jojo Biel-Nielsen Dietz, Karin E. Smedby, Karl Smith-Byrne, Rosalie Griffin, Sophia S. Wang, Susan Slager, James R. Cerhan, Jonathan N. Hofmann, Qing Lan, Nathaniel Rothman, Ingrid Glimelius, Henrik Hjalgrim, James Mckay, Sonja I. Berndt. Genome-wide association study of mantle cell lymphoma identifies novel loci suggesting a critical role for B-cell chromatin readers and DNA repair mechanisms [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 3602.

Abstract Introduction: The National Cancer Institute estimated almost 317,000 new breast cancer and over 13,000 new cervical cancer cases in 2025. While the National Breast and Cervical Cancer Early Detection Program has served millions of women over the past two decades, inequities in cancer screening, subsequently delayed diagnosis, and timely and appropriate treatment remain a public health challenge in the United States. Despite the substantial impact of social determinants of health (SDOH) on cancer screening, few studies have accounted for SDOH at the individual level when assessing breast and cervical cancer screening adherence. The primary objective of this study is to examine whether the individual-level SDOH, measured using HOUsing-based SocioEconomic Status (HOUSES) index, is associated with breast and cervical cancer screening non-adherence (not current on respective USPSTF recommendations for age and frequency). Methods: In this retrospective study, patients were identified as those receiving primary care in the Mayo Clinic Health System and Mayo Clinic (Rochester, MN). The Midwest Quality Metrics panel was then used to assess patients due for screening for breast or cervical cancer in July 2023. The HOUSES, using home address and publicly available data on the residence which measures and categorizes housing status into quartiles, was obtained using the electronic health records (EHRs)-linkable patient-level information. Logistic regression analyses were conducted, controlling for Census block group-level adverse social exposome from the Area Deprivation Index, age, and comorbidities. Results are presented as odds ratios (OR) with corresponding 95% confidence intervals (CI). Results: For breast cancer, among 128,462 eligible patients, 20.1% (n= 25,857) were non-adherent. Compared to the highest HOUSES quartile (Q4), lower HOUSES groups showed significantly higher odds of non-adherence: Q3 OR=1.20 (95% CI=1.15, 1.25), Q2 OR=1.45; (95% CI=1.40, 1.53), and Q1 OR=1.90 (95% CI=1.81, 1.99). For cervical cancer, among 175,712 eligible patients, 29.9% (n=52,533) were non-adherent. Compared to the highest HOUSES quartile (Q4), lower HOUSES groups showed significantly higher odds of non-adherence: Q3 OR=1.07 (95% CI=1.03, 1.10), Q2 OR=1.16; (95% CI=1.12, 1.20), and Q1 OR=1.36 (95% CI=1.31, 1.41). Conclusions: A clinically meaningful association between the HOUSES index and cancer screening adherence was observed. A lower HOUSES index was associated with higher odds of non-adherence for both breast and cervical cancer screening, even after accounting for neighborhood-level risk, age, and comorbidities. Incorporating HOUSES into EHRs can be a practical “screening-for-screening” tool that enables close monitoring of higher-risk patients without additional burden of surveys. Citation Format: Eunice Y. Park, Madison Beenken, Dave Watson, Chung Il Wi, Tufia Haddad, Gladys Asiedu, Karla BALLMAN, James R. Cerhan, Carolyn Flock, Brian Lynch, Folakemi T. Odedina, Scott H. Okuno, Young J Juhn, . HOUSES as a screening tool for cancer screening: Patient-level housing-based socioeconomic status and breast and cervical cancer screening adherence [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 7599.

Abstract Background: Digital access, health literacy, and confidence in obtaining cancer information are well-known factors that influence cancer beliefs and behaviors. However, less is understood about whether these information-related factors vary across regions within a cancer center's catchment area and how they collectively relate to cancer beliefs in large community samples. This study investigated regional differences in digital access, health literacy, and confidence in seeking information, and examined the connections between these factors and cancer risk perceptions, fatalistic beliefs, prevention confusion, and willingness to participate in cancer clinical trials. Methods: Data (N=2,334) from the 2023 Mayo Clinic Comprehensive Cancer Center’s Cancer-Focused Needs Assessment, conducted across catchment areas in Arizona, Florida, and the Midwest (Minnesota, Wisconsin, Iowa). Regional differences in digital access (Internet use and device ownership), health literacy, and confidence in obtaining cancer information were analyzed using multivariable ANCOVAs and logistic regressions, adjusting for sociodemographic covariates. Follow-up models examined whether these information precursors are associated with perceived cancer risk, fatalistic beliefs, confusion about recommendations, and willingness to participate in cancer clinical trials, adjusting for the same covariates. Results: Regions showed minimal differences in digital access and health literacy. Health literacy was instead associated with ethnicity, age, gender, and education (all p<.001). Confidence in obtaining cancer information followed similar trends, with significant influences of ethnicity and gender (p<.01). Internet use was strongly associated with sociodemographic factors (all p<.01). Additionally, the information precursors consistently correlated with cancer beliefs across the full sample: Higher health literacy and greater confidence in obtaining cancer information were associated with increased perceived cancer risk, reduced cancer fatalism, and less confusion regarding prevention guidelines (ps < .001). Internet use and device access were significantly related to a greater willingness to participate in clinical trials (ps < .01). These relationships persisted even after controlling for sociodemographic covariates. Conclusions: Regional differences in digital access and health literacy were minimal, indicating a consistent information environment. Within this context, individual sociodemographic and information-related factors played a larger role, strongly shaping cancer beliefs. These findings underscore technology access and information empowerment as key targets for enhancing cancer prevention knowledge and research engagement, and indicate that the effects of information empowerment are primarily driven at the individual rather than the geographic level. Citation Format: Tingyu Zou, Monica L. Albertie, Emelina Asto-Flores, Manisha Salinas, Farhia Omar, Adeline Abbenyi, Noreen Stephenson, Nuwanthi Heendeniya, James R. Cerhan, Kathleen J. Yost, Folakemi T. Odedina. Digital access, health literacy, and cancer beliefs: Findings from a multi-region community survey [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 7553.