Cerhan, James R.

Abstract Underrepresentation of racial and ethnic subgroups in cancer clinical trials remains a persistent challenge. Restrictive clinical trial eligibility criteria have been shown to exacerbate this problem. We previously identified that up to 24% of patients treated with standard immunochemotherapy would have been excluded from recent first-line trials in diffuse large B-cell lymphoma (DLBCL) based on 5 laboratory-based criteria. These ineligible patients had worse clinical outcomes and increased deaths related to lymphoma progression, suggesting the potential exclusion of patients who could have benefited most from the novel therapies being evaluated. Using data from the prospectively enrolled Lymphoma Epidemiology Outcomes cohort study, with demographics broadly similar to the US patients diagnosed with lymphoma, we evaluated the impact of laboratory eligibility criteria from recent first-line DLBCL trials across various racial and ethnic backgrounds. There were significant differences in the baseline laboratory values by race/ethnicity with Black/African American (AA) patients having the lowest mean hemoglobin and highest creatinine clearance. Based on recent clinical trial eligibility criteria, AA and Hispanic patients had higher rates of laboratory-based ineligibility than non-Hispanic White patients. The largest gap in the clinical outcomes between eligible and noneligible patients was noted within AA patients with an overall survival hazard ratio based on POLARIX clinical trial criteria of 4.09 (95% confidence interval, 1.83-9.14). A thoughtful approach to the utility of each criterion and cutoffs for eligibility needs to be evaluated in the context of its differential impact across various racial/ethnic groups.

Chronic lymphocytic leukemia (CLL) is characterized by multiple copy number alterations (CNAs) and somatic mutations that are central to disease prognosis, risk stratification, and mechanisms of therapy resistance. Fluorescence in situ hybridization (FISH) panels are widely used in clinical applications as the gold standard for screening prognostic chromosomal abnormalities in CLL. DNA sequencing is an alternative approach to identifying CNAs but is not an established method for clinical CNA screening. We sequenced DNA from 509 individuals with CLL or monoclonal B-cell lymphocytosis (MBL), the precursor to CLL, using a targeted sequencing panel of 59 recurrently mutated genes in CLL and additional amplicons across regions affected by clinically relevant CNAs [i.e., del(17p), del(11q), del(13q), and trisomy 12]. We used the PatternCNV algorithm to call CNA and compared the concordance of calling clinically relevant CNAs by targeted sequencing to that of FISH. We found a high accuracy of calling CNAs via sequencing compared to FISH. With FISH as the gold standard, the specificity of targeted sequencing was >95%, sensitivity was >86%, positive predictive value was >90%, and negative predictive value was >84% across the clinically relevant CNAs. Using targeted sequencing, we were also able to identify other common CLL-associated CNAs, including del(6q), del(14q), and gain 8q, as well as complex karyotype, defined as the presence of 3 or more chromosomal abnormalities, in 26 patients. In a single and cost-effective assay that can be performed on stored DNA samples, targeted sequencing can simultaneously detect CNAs, somatic mutations, and complex karyotypes, which are all important prognostic features in CLL.

7011 Background: Monoclonal B-cell lymphocytosis (MBL) is a precursor to chronic lymphocytic leukemia (CLL) but also a risk factor for other lymphoid malignancies. Clonal hematopoiesis (CH), originally defined as the presence of mutations in myeloid driver genes, is a precursor to myeloid malignancies. Recently, CH that includes genes associated with lymphoid malignancies has been reportedly associated with risk of lymphoid malignancies, and particularly CLL. Little is known about the association between CH and MBL and their joint effects on risk of hematological malignancies (HM). Methods: Study participants were from the Mayo Clinic Biobank, a large-scale biorepository of patients who provided a peripheral blood sample. CH at enrollment was determined using whole-exome sequencing (50x coverage). Myeloid-CH (M-CH) was based on mutations located in 56 genes associated with myeloid malignancies. Lymphoid-CH (L-CH) was based on 235 genes associated with lymphoid malignancies. Individuals were screened for MBL using eight-color flow cytometry in two different cohorts of Biobank participants. Cohort 1 (N=3883) included participants who had available sample for MBL screening at enrollment. Cohort 2 (N=5684) were participants recontacted to provide a sample for MBL screening. Incident HM were identified using ICD codes and confirmed via medical record review. Logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI). Cox regression was used to estimate hazard ratios (HR), with time defined as date between MBL sample and the first of incident HM, death, or 9/30/2023. Analyses were adjusted for age at MBL screening and sex. Results: In total, 9567 individuals (39% male, median age 66 years) were screened for MBL with available CH, of whom 17% were positive for MBL, 7% were positive for M-CH, and 2% were positive for L-CH. We found no evidence of an association between M-CH and MBL (OR=1.11, 95% CI:0.91-1.34) nor L-CH and MBL (OR=1.05, 95% CI:0.70-1.55). When subset to Cohort 1, where the same sample was used for both CH and MBL screening, the results held (M-CH OR=1.11, 95% CI: 0.83-1.48; L-CH OR=0.82, 95% CI:0.40-1.53). Next, we investigated the effect of each of these precursors with incident HM. Median follow-up was 4 years, and 78 individuals developed incident HM (31 myeloid, 49 lymphoid overall, 8 CLL). When modeling both precursors, both L-CH (HR=6.23, 95% CI:2.46-15.79) and MBL (HR=3.89, 95% CI:2.17-6.99) were independently associated with incident lymphoid malignancy. When excluding CLL events, the association held (L-CH: HR=5.94, 95% CI:2.11-16.77; MBL: HR=2.95, 95% CI:1.54-5.66). Only M-CH was significantly associated with incident myeloid malignancies. Conclusions: In the largest cohort with MBL and CH precursors measured, we found no evidence of an association between them. However, both MBL and L-CH were strong independent risk factors for incident lymphoid malignancies.

Abstract Objectives Heart failure (HF) impacts millions of patients worldwide, yet the variability in treatment responses remains a major challenge for healthcare professionals. The current treatment strategies, largely derived from population based evidence, often fail to consider the unique characteristics of individual patients, resulting in suboptimal outcomes. This study aims to develop computational models that are patient-specific in predicting treatment outcomes, by utilizing a large Electronic Health Records (EHR) database. The goal is to improve drug response predictions by identifying specific HF patient subgroups that are likely to benefit from existing HF medications. Materials and Methods A novel, graph-based model capable of predicting treatment responses, combining Graph Neural Network and Transformer was developed. This method differs from conventional approaches by transforming a patient's EHR data into a graph structure. By defining patient subgroups based on this representation via K-Means Clustering, we were able to enhance the performance of drug response predictions. Results Leveraging EHR data from 11 627 Mayo Clinic HF patients, our model significantly outperformed traditional models in predicting drug response using NT-proBNP as a HF biomarker across five medication categories (best RMSE of 0.0043). Four distinct patient subgroups were identified with differential characteristics and outcomes, demonstrating superior predictive capabilities over existing HF subtypes (best mean RMSE of 0.0032). Discussion These results highlight the power of graph-based modeling of EHR in improving HF treatment strategies. The stratification of patients sheds light on particular patient segments that could benefit more significantly from tailored response predictions. Conclusions Longitudinal EHR data have the potential to enhance personalized prognostic predictions through the application of graph-based AI techniques.

Abstract Patients with large B-cell lymphoma (LBCL) that fail to achieve a complete response (CR) or who relapse early after anthracycline-containing immunochemotherapy (IC) have a poor prognosis and are commonly considered to have “primary refractory disease.” However, different definitions of primary refractory disease are used in the literature and clinical practice. In this study, we examined variation in the time to relapse used to define refractory status and association with survival outcomes in patients with primary refractory LBCL in a single-center prospective cohort with validation in an independent multicenter cohort. Patients with newly diagnosed LBCL were enrolled in the Molecular Epidemiological Resource cohort (MER; N = 949) or the Lymphoma Epidemiology of Outcomes cohort (LEO; N = 2755) from September 2002 to May 2021. Primary refractory LBCL was defined as no response (stable disease [SD]) or progressive disease (PD) during, or by the end of, frontline (1L) IC (primary PD; PPD); partial response at end of treatment (EOT PR); or relapse within 3 to 12 months after achieving CR at EOT to 1L IC (early relapse). In the MER cohort, patients with PPD had inferior overall survival (OS; 2-year OS rate: 15% MER, 31% LEO) when compared with other subgroups considered in defining primary refractory disease, EOT PR (2-year OS rate: 38% MER, 50% LEO) and early relapse (2-year OS rate: 44% MER, 58% LEO). Among patients receiving 1L IC with curative intent, we identified that patients with PPD are the key subgroup with poor outcomes. We propose a definition of primary refractory LBCL as SD or PD during, or by the end of, 1L treatment.

Abstract Background: The impact of a prior history of non-hematologic malignancy (NHM) on clinical outcomes among individuals with newly diagnosed chronic lymphocytic leukemia (CLL) or monoclonal B-cell lymphocytosis (MBL), the precursor to CLL, is not well known. Methods: In this prospective study, we identified individuals with CLL/MBL who were seen in the Division of Hematology at the Mayo Clinic, Rochester. Participants provided consent within nine months of diagnosis, had no prior history of hematological malignancy, and completed a self-reported risk-factor questionnaire, including a prior history of NHM. Time to first treatment (TTFT) was defined as the time between the date of diagnosis and the earliest date of the first CLL treatment, death, or the last known treatment-free follow-up date. Overall survival (OS) was defined as the time from diagnosis to death or the last follow-up. We used Cox regression to estimate hazard ratios (HR) and 95% confidence intervals (CI) for associations between a prior history of a NHM with OS and TTFT. We adjusted for sex and the CLL International Prognostic Index (CLL-IPI), a score comprised of five key CLL prognostic factors, including age and Rai stage. Results: Among the 1,090 CLL/MBL individuals, 833 (76%) had CLL. The median age at diagnosis was 64 years (range 24-89), with 65% being men, and 230 (21%) reported a prior history of NHM. The most common tumor was non-melanoma skin cancer (NMSC, 8.9%, 96 individuals), followed by prostate, testicular, and penile tumors (7.3%, 51 individuals), and melanoma (1.9%, 20 individuals). CLL/MBL patients with a prior history of a NHM were more likely to be over 65 years old (29%) than under 65 years old (14%, p < 0.0001). After a median follow-up of 2.9 years (range 0-21), 470 CLL/MBL patients progressed, requiring CLL treatment. CLL/MBL individuals with a prior history of NHM had a longer TTFT compared to those without (HR=0.69; 95% CI: 0.53-0.90; p=0.006). Individuals with a prior history of NHM who had either intermediate or high-very high CLL-IPI score had significantly longer TTFT (HR= 0.55; 95% CI: 0.33-0.92; p=0.02, and HR=0.65; 95% CI: 0.43-0.97; p=0.03, respectively) compared to those without NHM. After a median follow-up of 8.6 years (range 0-21), 359 individuals died. CLL/MBL patients with a prior history of NHM also experienced reduced OS compared to those without (HR=1.46; 95% CI: 1.08-1.97, p=0.013), particularly among those with low (HR=1.70; 95% CI: 0.96-3.00, p=0.07) or intermediate-risk (HR=2.08; 95% CI: 1.24-3.51, p=0.006), according to CLL-IPI. After excluding NMSC, results remained constant. Conclusions: At the time of diagnosis, one out of five CLL/MBL individuals had a prior history of a NHM. These individuals had a longer time to first CLL directed treatment and reduced OS, even after accounting for other important CLL prognostic factors, which deserves additional studies. Citation Format: Bryan A. Vallejo, Cristine Allmer, Kari G. Rabe, Dennis P. Robinson, Yucai Wang, Paul J. Hampel, Esteban D. Braggio, Neil E. Kay, James R. Cerhan, Sameer A. Parikh, Susan L. Slager. The impact of prior history of non-hematologic malignancies on time to first treatment and overall survival among individuals with CLL or MBL [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7450.

Abstract Chronic Lymphocytic Leukemia (CLL) exhibits a substantial familial risk that extends beyond CLL to other B-cell lymphomas (including follicular lymphoma and diffuse large B-cell lymphoma). Monoclonal B-cell Lymphocytosis (MBL), a precursor to CLL, is also associated with a significant risk for hematologic malignancies overall and for lymphoid malignancies, specifically. Moreover, previous research has shown an association between the CLL polygenic risk score (CLL-PRS) and common lymphoma subtypes. This study aims to further evaluate CLL-PRS associations with hematological cancer overall and by lineage while also considering MBL status a strong risk factor for lymphoid malignancies. Utilizing the Mayo Clinic Biobank, we selected individuals aged 40 years or older, residing in proximal counties, who had no prior hematologic malignancies, and with available biospecimens. MBL screening was done using 8-color flow cytometry, and MBL with CLL (CD5+/CD20dim) or atypical CLL (CD5+/CD20+) immunophenotype were included. CLL-PRS was calculated from 41 known susceptibility variants, with weights being the log of the odds ratios from the largest CLL genome-wide association study. Incident hematologic malignancies were ascertained through ICD codes and confirmed via medical record review. Hazard ratios (HR) and 95% confidence intervals (CIs) for associations with hematologic malignancies were estimated through Cox regression. Of 7,332 participants, 1,150 (16%) screened positive for MBL; median age was 67 years (range 40-101), and 37% male. Median follow-up was 2.9 years (range 0-13), and 56 hematologic cancers were identified. After adjusting for age and sex, the CLL-PRS was associated with increased risk of developing hematologic (HR=1.49, 95%CI=1.12-1.98, P=0.006) which was all attributable to lymphoid cancers (HR=1.79, 95%CI=1.26-2.56, P=0.001). When adjusting for baseline MBL status in the models, the CLL-PRS associations remained significant but were attenuated for hematological (HR=1.33, 95%CI=0.99-1.79, P=0.058) and lymphoid (HR=1.56, 95%CI=1.07-2.26, P=0.020) malignancies, while MBL status was also significantly associated with risk of hematologic (HR=2.76, 95%CI=1.55-4.91, P=0.001) and lymphoid (HR=3.61, 95%CI=1.78-3.32, P<0.001) malignancies. In this prospective cohort study of individuals screened for MBL, CLL-PRS was associated with an elevated risk of hematologic malignancies overall and within the lymphoid lineage, underscoring a shared genetic etiology across many B-cell lymphomas. However, the associations between CLL-PRS and hematologic malignancies were attenuated when accounting for MBL status or when analyzing among MBL-positive individuals, highlighting the role of MBL status in these associations. Further research with expanded cohorts is needed to unravel these complex genetic and clinical interactions. Citation Format: Raphael Mwangi, Dennis P. Robinson, Sara J. Achenbach, Geffen Kleinstern, Aaron D. Norman, Kari G. Rabe, Janet E. Olson, Neil E. Kay, Rosalie Griffin, Nicholas J. Boddicker, Esteban Braggio, Sameer A. Parikh, Curtis A. Hanson, Celine M. Vachon, James R. Cerhan, Tait D. Shanafelt, Susan L. Slager. Genetic associations with hematologic malignancies: An evaluation of CLL polygenic risk score and MBL status in the Mayo Clinic Biobank Cohort [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6152.

Abstract High-count monoclonal B-cell lymphocytosis (HCMBL) is a precursor condition to chronic lymphocytic leukemia (CLL). We have shown that among individuals with HCMBL, the CLL-International Prognostic Index (CLL-IPI) is prognostic for time-to-first therapy (TTFT). Little is known about the prognostic impact of somatically mutated genes among individuals with HCMBL. We sequenced DNA from 371 individuals with HCMBL using a targeted sequencing panel of 59 recurrently mutated genes in CLL to identify high-impact mutations. We compared the sequencing results with that of our treatment-naïve CLL cohort (N = 855) and used Cox regression to estimate hazard ratios and 95% confidence intervals (CIs) for associations with TTFT. The frequencies of any mutated genes were lower in HCMBL (52%) than CLL (70%). At 10 years, 37% of individuals with HCMBL with any mutated gene had progressed requiring treatment compared with 10% among individuals with HCMBL with no mutations; this led to 5.4-fold shorter TTFT (95% CI, 2.6-11.0) among HCMBL with any mutated gene vs none, independent of CLL-IPI. When considering individuals with low risk of progression according to CLL-IPI, those with HCMBL with any mutations had 4.3-fold shorter TTFT (95% CI, 1.6-11.8) vs those with none. Finally, when considering both CLL-IPI and any mutated gene status, we observed individuals with HCMBL who were high risk for both prognostic factors had worse prognosis than patients with low-risk CLL (ie, 5-year progression rate of 32% vs 21%, respectively). Among HCMBL, the frequency of somatically mutated genes at diagnosis is lower than that of CLL. Accounting for both the number of mutated genes and CLL-IPI can identify individuals with HCMBL with more aggressive clinical course.

Mixture analysis is an emerging statistical tool in epidemiological research that seeks to estimate the health effects associated with mixtures of several exposures. This approach acknowledges that individuals experience many simultaneous exposures and it can estimate the relative importance of components in the mixture. Health effects due to mixtures may vary over space driven by to political, demographic, environmental, or other differences. In such cases, estimating a global mixture effect without accounting for spatial variation would induce bias in effect estimates and potentially lower statistical power. To date, no methods have been developed to estimate spatially varying chemical mixture effects. We developed a Bayesian spatially varying mixture model that estimates spatially varying mixture effects and the importance weights of components in the mixture, while adjusting for covariates. We demonstrate the efficacy of the model through a simulation study that varies the number of mixtures (one and two) and spatial pattern (global, one‐dimensional, radial) and magnitude of mixture effects, showing that the model is able to accurately reproduce the spatial pattern of mixture effects across a diverse set of scenarios. Finally, we apply our model to a multi‐center case‐control study of non‐Hodgkin lymphoma (NHL) in Detroit, Iowa, Los Angeles, and Seattle. We identify significant spatially varying positive and inverse associations with NHL for two mixtures of pesticides in Iowa and do not find strong spatial effects at the other three centers. In conclusion, the Bayesian spatially varying mixture model represents a novel method for modeling spatial variation in mixture effects.

Abstract Rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) is considered the standard-of-care for patients with advanced-stage diffuse large B-cell lymphoma (DLBCL), despite findings that patients with nongerminal center B-cell like (non-GCB) have significantly worse outcome with this regimen. We evaluated the prognostic significance of baseline risk factors, including cell of origin (COO) classified by the Hans algorithm, within an alternative chemoimmunotherapy program. At Memorial Sloan Kettering Cancer Center (MSK), 151 patients with DLBCL received sequential R-CHOP induction and (R)-ICE (rituximab, ifosfamide, carboplatin, and etoposide) consolidation. Outcome analysis based on COO was validated with a propensity score–matched cohort treated with R-CHOP from the Mayo Clinic component of the Molecular Epidemiology Resource (MER). Among the patients with GCB (n = 69) and non-GCB (n = 69) at MSK, event-free survival (EFS) of non-GCB was superior to that of GCB (hazard ratio [HR], 0.53; 95% confidence interval [CI], 0.29-0.98). Overall survival (OS) demonstrated an association in the same direction but was not statistically significant (HR, 0.68; 95% CI, 0.33-1.42). Propensity score–matched patients from MSK (n = 108) demonstrated a small attenuation in the HRs for EFS (HR, 0.57; 95% CI, 0.27-1.18) and OS (HR, 0.76; 95% CI, 0.33-1.79) and were no longer statistically significant. In contrast, the matched MER cohort (n = 108) demonstrated an EFS association (HR, 1.17; 95% CI, 0.70-1.95) and OS association (HR, 1.13; 95% CI, 0.64-2.00) in the opposite direction, but were also not statistically significant. R-CHOP induction and (R)-ICE consolidation may overcome the negative prognostic impact of the non-GCB phenotype, per the Hans algorithm, and can be preferentially selected for this population. This trial was registered at www.ClinicalTrials.gov as #NCT00039195 and #NCT00712582.