Cerhan, James R.

Abstract Background Mosaic chromosomal alterations (mCAs) are large-scale somatic gains, losses, or copy-neutral loss of heterozygosity (CNLOH) detectable by genotyping of peripheral blood DNA. They represent a form of clonal hematopoiesis and are observed in 0.5-5% of cancer-free individuals. The presence of any autosomal mCA has been associated with >3-fold risk of hematological malignancies. In particular, mCAs occurring in regions related to lymphoid malignancies have an 11-fold increased risk of lymphoid malignancies and a 68.6-fold risk for chronic lymphocytic leukemia (CLL). Monoclonal B-cell lymphocytosis (MBL), the precursor state to CLL, is defined by <5×10⁹/L clonal B cells in otherwise healthy individuals and is classified as low-count (<0.5x109/L) or high-count (0.5-5x10⁹/L) based on clonal B-cell count. MBL is also associated with increased risk of lymphoid malignancy (beyond CLL), with 4.3-fold and 74-fold risk in individuals with low- and high-count MBL, respectively. We previously reported a significant enrichment of autosomal mCAs in MBL, demonstrating a 1.7-fold association in those with low-count MBL and a 50.5-fold association in those with high-count MBL. Whether mCAs independently predict lymphoid malignancy risk after accounting for MBL status is unknown. Here, we evaluated the impact of mCAs on risk of incident lymphoid malignancies in individuals with MBL and in individuals screened negative for MBL. Methods CLL-type MBL (CD5 positive) was identified using flow cytometry assay on peripheral blood. Participants were age ≥40, had no prior hematologic malignancy, and were enrolled through the Mayo Clinic Biobank or the Division of Hematology at Mayo Clinic, Rochester. Participants were followed for incident lymphoid malignancy, including CLL, with diagnoses confirmed by medical record review. mCAs with cell fraction >1% were detected from array-based genotyping of whole blood DNA. Cox proportional hazards models, adjusted for age and sex, estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for risk of developing an incident lymphoid malignancy in individuals with autosomal mCA versus those without an autosomal mCA. Results In 4,331 participants with available MBL and mCA status, the median age was 67 years, and 45% were male. Flow cytometry identified 3,231 individuals who screened negative for MBL, 754 individuals with low-count MBL, and 346 individuals with high-count MBL. Autosomal mCAs were present in 6.5% of individuals without MBL, 7.6% with low-count MBL, and 71.1% with high-count MBL. Over a median follow-up of 6.9 years, 237 individuals developed an incident lymphoid malignancy. The most common types were CLL, multiple myeloma, and diffuse large B-cell lymphoma. Ignoring MBL status, the presence of an autosomal mCA was associated with increased risk of lymphoid malignancy (HR = 2.65; 95% CI: 1.45–4.86; P = 0.002), replicating previous results. However, after excluding individuals with MBL, this association attenuated; those with mCA alone did not have a significantly elevated risk (HR = 1.60; 95% CI: 0.57-4.52; P = 0.38) compared to individuals without MBL or mCA. We next examined the effect of mCAs among individuals with MBL. In those with low-count MBL, the most frequent mCA events were 13q loss (2.1%), 12 gain (1.9%), and 4q CNLOH (1.2%), and individuals with an mCA had a borderline significant risk of developing a lymphoid malignancy (HR = 2.78; 95% CI: 1.01–7.67; P = 0.049) compared to those without an mCA. Similarly, in those with high-count MBL, the most frequent mCA events were 13q loss/CNLOH (31.2%), 12 gain (13.0%), and 11q loss (4.9%), and the presence of an mCA conferred an increased risk compared to those without mCA (HR = 1.51; 95% CI: 1.09-2.08; P = 0.012). Conclusions Autosomal mCAs were more frequent in individuals with high-count MBL compared to those with low-count or no MBL. mCAs showed limited predictive value for lymphoid malignancy among individuals without MBL. In contrast, in individuals with low-count MBL, autosomal mCAs emerged as a potential biomarker for identifying those at risk of developing a lymphoid malignancy. In high-count MBL, mCAs were also a biomarker, but were driven by known chromosomal abnormalities identified through clinical testing. These findings suggest that the utility of mCAs as a predictive marker for lymphoid malignancy is contingent on the presence of MBL and may inform risk stratification and surveillance strategies in this population.

Abstract Background Primary central nervous system lymphoma (PCNSL) is a rare but aggressive non-Hodgkin lymphoma with limited prospective data to guide optimal therapy. This study evaluates real-world treatment patterns and survival outcomes using a large national cohort. Methods We analyzed data from the National Cancer Database (NCDB) for patients diagnosed with PCNSL between 2004 and 2020. Baseline demographics and treatment characteristics were analyzed using descriptive statistics. Survival outcomes were estimated using the Kaplan-Meier method and compared using log-rank tests. Multivariable Cox proportional hazards models were constructed to identify independent predictors of overall survival (OS). Results A total of 14,450 patients with PCNSL were included; 14,439 had first-line treatment data available, and 13,390 had survival data. Median age at diagnosis was 67 years (range 18-90), 49.2% were female, and 6.6% were African American. Chemotherapy alone was the modality of treatment for 81.6% (n=11,787), 12.2% (n=1,769) received chemoradiation, 3.8% (n=545) received chemotherapy and underwent autologous stem cell transplant (ASCT) but no radiation, and 2.4% (n=348) received radiation only. The median follow-up time was 73.5 months (95% CI 71.4-75.6), and the median OS for the group was 17.4 months (95% CI 16.2-18.6). Survival differed significantly by age group (median OS: <65 yrs, 46.1 months [95% CI 41.7 -50.6]; 65-79 yrs, 12.5 months [95% CI 11.2-13.7]; ≥80 yrs, 3.2 months [95% CI 2.9-3.5]; p<0.001), by year of diagnosis (median OS: 2004-2009, 11.0 months [95% CI 9.9-12.1]; 2010-2015, 20.0 months [95% CI 17.4-22.6], 2016-2020, 29.7 months [95% CI 26.0-33.4]; p<0.001), and by frontline treatment strategy (p<0.001). Patients treated with chemotherapy followed by ASCT had the longest median OS (not reached), while those receiving radiation alone had the poorest outcomes with a median OS of 3.9 months (95% CI 3.2-4.7); chemoradiation patients had a median OS of 31.8 months (95% CI 27.5-36.1), and chemotherapy only patients had a median OS of 13.9 months (95% CI 12.7-15.1)(p<0.001). African American patients had an inferior outcome with a median OS of 10.2 months [95% CI 7.2-13.1], compared to “other” races with a median OS of 36.9 months [95% CI 27.6-46.1] (p<0.001), although this is more similar to the median outcome for white patients (17.0 months [95% CI 15.8-18.3]). Treatment at an academic center (25.7 vs 11.0 months) and a high case-volume center (defined as >median number of cases per facility) (18.6 vs 10.5 months), and lower Charlson-Deyo comorbidity scores (CDS) (defined as CDS 0-1) (21.7 vs 6.8 months) were also associated with superior OS (p<0.001). In multivariable analysis, increasing age ( 65-79 y.o HR 1.60 [1.52-1.1.68], ≥80 y.o HR 2.95 [2.75-3.17]), CDS≥2 (HR 1.36 [1.28-1.44]), and African American race (HR 1.12 [1.02-1.24]) were adverse predictors of OS (all p<0.001), while treatment strategy (ASCT [HR 0.17], chemoradiation [HR 0.53], chemotherapy only [HR 0.68] compared to radiation alone), more recent year of diagnosis (2010-2015 [HR 0.81], 2016-2020 [0.70]), and treatment at an academic center [HR 0.81] remained predictors of superior OS (all p<0.001). Conclusion In this large national cohort of patients with PCNSL, treatment with chemotherapy followed by ASCT was associated with the most favorable survival outcomes. Patients treated with chemoradiation or multi-agent chemotherapy also demonstrated improved survival compared to those receiving radiation monotherapy or single-agent chemotherapy alone, respectively. Age, comorbidities, race, and treatment at academic centers were also significant independent predictors of OS. These findings highlight substantial disparities in outcomes and underscore the importance of optimizing treatment strategies, improving access to high-intensity therapies, and supporting timely referral to academic or high case-volume centers, particularly for younger and medically fit patients.

Abstract Follicular lymphoma (FL) is the most common indolent non-Hodgkin lymphoma. Although patients with FL have high response rates to therapy, most develop increasingly resistant disease. In addition, transformation into an aggressive lymphoma is associated with unfavorable outcomes. Many novel agents are under investigation, and early clinical data are encouraging. Aligning treatment with the underlying tumor biology and sequencing of therapies remain key clinical challenges. At the Lymphoma Research Foundation’s biannual 2024 Follicular Lymphoma Scientific Workshop, experts convened to discuss the role of chemotherapy in the context of new therapies, the impact of early progression on treatment sequencing, novel end points in clinical trials, disease biology and the tumor microenvironment, and new treatments on the horizon. This report focuses on updates in FL biology, first-line treatment, the role of progression of disease in 24 months, clinical trial design, and redefining cure in FL.

PURPOSE Although most patients with follicular lymphoma (FL) can expect an indolent course, progressive lymphoma remains the primary cause of death during the first decade after diagnosis. Progression of disease within 24 months (POD24) of starting first-line (1L) immunochemotherapy defines a high-risk population with poor survival, but better risk stratification at diagnosis is needed. METHODS The FLIPI24 model was developed and internally validated to predict 24-month event rates using individual data from 4,485 patients treated with 1L immunochemotherapy from 10 observational cohorts of FL. Overall and cause-specific survival was further evaluated in FLIPI24 risk groups. External validation in the 1L immunochemotherapy setting was performed using the prospective observational Lymphoma Epidemiology of Outcomes (LEO) cohort (N = 565) and three randomized phase III trials (N = 3,192); extension to all patients with FL (any 1L therapy) was performed in the LEO cohort (N = 1,445) and its Molecular Epidemiology Resource subcohort (N = 1,074). RESULTS The FLIPI24 model uses age and four blood-based variables (hemoglobin, lactate dehydrogenase, beta-2 microglobulin, and WBC count). FLIPI24 showed consistent performance across validation and extension data sets, which was superior to existing prognostic tools. Across the four external immunochemotherapy validation data sets, patients with high-risk FLIPI24 (23%-32% of patients) had significantly higher 24-month event rates (22%-35%) and inferior 5-year overall survival (77%-83%) compared with patients with low-risk FLIPI24 (29%-31% of patients, 24-month event rates: 10%-12%; 5-year OS: 96%-97%). Results were consistent when evaluating lymphoma-related death and when extended to all patients with FL. CONCLUSION The FLIPI24 model robustly stratifies, at diagnosis, patients with FL at increased risk of lymphoma-related death versus patients with very low lymphoma-related mortality during the first decade after diagnosis. FLIPI24 can be used to enrich future clinical trial designs in newly diagnosed FL.

Abstract Anaplastic large cell lymphomas (ALCLs) are CD30+ T-cell lymphomas that share pathologic features but differ in presentation, outcome, and genetics. Current classification incorporates clinical presentation and anaplastic lymphoma kinase (ALK) status but inadequately addresses molecular heterogeneity and therapeutic vulnerabilities. We studied 689 patients with ALCL in the LLMPP (Lymphoma/Leukemia Molecular Profiling Project) and performed expert consensus review, genetic subtyping (ALK, DUSP22, TP63, and triple negative), and immunohistochemistry for phosphorylated STAT3Tyr705. RNA sequencing with unsupervised gene expression profiling in 393 patients identified 2 main molecular types of ALCL that could be predicted with 91% accuracy based on the presence (type I) or absence (type II) of phosphorylated STAT3Y705 expression. Type I ALCLs included ALK+ ALCL and a subset of triple-negative ALCLs (TN-I); type II ALCLs included tumors with DUSP22 and/or TP63 rearrangements and the remaining triple-negative ALCLs (TN-II). Type I ALCLs were enriched for JAK-STAT3, whereas type II ALCLs were enriched for non–tyrosine kinase pathways, particularly epigenetic regulators such as EZH2. Immunohistochemistry showed overexpression of EZH2 and its trimethylated substrate H3K27. Prognosis in systemic ALCL was favorable for DUSP22-rearranged ALCL (5-year overall survival, 95%) and ALK+ ALCL (88%), intermediate for triple-negative ALCL (TN-I, 52% and TN-II, 37%), and poor for TP63-rearranged ALCL (0%). We introduce an integrated molecular classification that preserves currently diagnosed ALCL entities but identifies 4 molecularly distinct ALK− ALCL subtypes (DUSP22-rearranged, TP63-rearranged, TN-I, and TN-II). This classification can be easily implemented on paraffin tissue in routine practice or clinical trials, and stratifies ALCL into diagnostically, prognostically, biologically, and potentially therapeutically relevant subtypes.

Abstract Description Follicular Lymphoma (FL) is a germinal center-derived malignancy representing approximately 20% of all non-Hodgkin’s lymphomas. Despite a relatively indolent disease course, FL is a biologically and clinically heterogeneous disease which poses significant clinical challenges due to its propensity for relapse and transformation into aggressive and untreatable forms of lymphoma. Here, we have leveraged computational techniques with WES, RNA-sequencing, and immune profiling data from FL tumors to gain further insight into the biologic basis for FL heterogeneity. We performed computational reconstruction of the B cell receptor repertoire on B cell sorted RNA-sequencing data from normal lymph nodes (n = 5) and FL tumors (n = 89). To gain a better understanding of FL B cell biology at the single cell level, we further investigated the clonal architecture of dominant malignant clonotypes in FL tumors using computational analysis of paired single cell V(D)J and 5’ GEX sequencing data from FL tumors (n = 21) and non-malignant lymph nodes (n = 3), allowing us to characterize the germinal center programs in malignant versus non-malignant B cells. Our data suggest that a subset of FL tumors consist of complex polyclonal B cell populations, some of which represent a pre-malignant FL precursor. Our data provide a deepened understanding of FL biology, including precursor cells, and contributes a refined transcriptomic characterization of malignant and non-malignant B cells in Follicular Lymphoma. Funding Sources This work was supported by the National Institutes of Health (R01 CA212162-01A1 to A.J.N and J.R.C., and P50 CA097274 to J.R.C., and A.J.N) Topic Categories Computational and Systems Immunology (COMP)

Abstract Glioblastoma, IDH-wildtype (GBM), and primary central nervous system lymphoma (PCNSL) are two aggressive brain tumors with distinct treatment strategies but overlapping imaging features on MRI. Accurate preoperative differentiation remains a clinical challenge, due to their overlapping imaging features, even when advanced MRI techniques are employed. This study explored deep learning approaches leveraging multi-sequence MRI to distinguish GBM from PCNSL. Models were developed utilizing 146 PCNSL cases, which were matched to146 GBM by age, sex and year of diagnosis. A 3D-DenseNet121 convolutional neural network was trained using two analytical strategies: (i) loss-based model selection and (ii) ensemble prediction using five-fold cross-validation. Model performance was evaluated on an independent validation set of 256 GBM and 37 PCNSL from the same institution as well as 36 PCNSL cases from an external institution. All MRIs were preprocessed using the FeTS pipeline. Using the independent validation cohort, area under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CI) were computed overall and stratified by demographic characteristics. Using both T1 post-contrast (T1Gd) and T2-weighted images, the loss-based model achieved AUC=0.83 (95% CI: 0.77-0.88) and the ensemble model yielded an AUC=0.82 (95% CI: 0.76-0.89). Model performance was similar across males and females; however, model performance was better for subjects older than 50 years of age versus younger than 50. When using only T1Gd images, the loss-based model dropped to AUC=0.55 (95% CI: 0.48-0.63), whereas the ensemble model had AUC=0.79 (95% CI: 0.72-0.85). Models trained with only T2 images achieved AUCs of 0.67 (95% CI: 0.60-0.73) (loss-based) and 0.75 (95% CI: 0.69-0.82) (ensemble). These results highlight the added value of the multi-sequence input. Our results confirm the feasibility of using automated deep learning models to differentiate GBM from PCNSL. Notably, the model generalized well to external MRI data, emphasizing its potential utility as a diagnostic aid in real-world clinical workflows.

Abstract Rural health disparities in the United States remain poorly understood, in part due to inconsistent definitions of “rurality” and the failure to integrate both geographic and socially constructed dimensions of place. This commentary draws from insights shared during the panel “Addressing Diversity in Cancer Prevention, Control, and Survivorship Within Rural Communities” at the 2024 Transforming Community and Rural Healthcare (TCRH) conference. Panelists emphasized the limitations of current rural-urban classification systems, which often overlook the intersectional identities, cultural heterogeneity, and lived experiences of rural populations. They advocated for integrating community-based participatory research (CBPR) and identity-based approaches to define rurality more holistically. Such methods prioritize self-identification, social determinants of health (SDOH), and localized knowledge, allowing for a more accurate assessment of cancer disparities and the development of equitable interventions. The commentary outlines how geophysical and census-based metrics, although convenient, can misrepresent rural communities and inadvertently perpetuate disparities in cancer care access, outcomes, and policy. The authors argue for an identity- and community-centered conceptualization of rurality that is dynamic, inclusive, and responsive to the complexity of rural life.

ABSTRACT Newly diagnosed patients with non‐Hodgkin lymphoma (NHL) often have a history of other diseases, and these comorbidities can impact patient treatment and management options, as well as overall survival (OS). We developed the Lymphoma Epidemiology of Outcomes (LEO) comorbidity index (LCI) as a sum of 10 comorbidities adapted from the Self‐Report‐Generated Charlson Comorbidity Index (SRG‐CCI) for use in the LEO cohort, a national prospective study of newly diagnosed NHL. Of the 5502 participants with self‐reported comorbidity data, 3107 (56.4%) were male and the mean age at diagnosis was 60.9 years (range, 18–99 years). The LCI ranged from 0 to 6, with 48.6% having 0, 30.2% having 1, 21.2% having 2 or more comorbidities. With a median follow‐up of 62.4 months among surviving participants, 2099 patients had an event and 1219 died. Continuous LCI similarly predicted both 1‐year mortality (c‐statistic = 0.654) and OS (c‐statistic = 0.655), while it showed a weaker but still statistically significant predictive ability for lymphoma‐specific (c‐statistics = 0.617) and event‐free (c‐statistic = 0.574) survival. Participants with 1 (HR = 1.21, 95% CI 1.05–1.39) and 2+ (HR = 1.80, 95% CI 1.56–2.08) comorbidities had inferior OS compared to those with no comorbidities after adjustment for age and sex (c‐statistic = 0.654), and performance strengthened after adjustment for the International Prognostic Index (c‐statistic = 0.672). LCI predicted OS most strongly in marginal zone (c‐statistics = 0.748) and weakest in T‐cell (c‐statistic = 0.579) lymphoma. The cumulative incidence of death due to lymphoma, lymphoma treatment, and other causes all increased with increasing comorbidities, with the greatest increase observed for death due to other causes. The LCI performs comparable to other published comorbidity indices, supporting its use in the LEO cohort to better model real‐world outcomes and more generally providing an approach to implementing comorbidity indices in cancer survivorship cohorts.