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The future directions in leveraging real-world evidence (RWE) and real-world data (RWD) in the field of lymphoma, as compared to traditional experimental clinical trials, are poised to significantly impact research methodologies, treatment strategies, and patient care. Current methods of clinical trials involve a well-controlled design and patient selection bias. Integrating RWE and RWD with experimental clinical trials offers a multifaceted approach to understanding lymphoma and enhancing patient outcomes. In this review, we discuss how RWE has helped shape lymphoma clinical trials, and we compare and evaluate evidence obtained from real-world lymphoma studies/databases with that obtained from clinical trials. We also discuss methods for utilizing surrogate endpoints to facilitate clinical trials and expedite drug development. RWE can be leveraged to bridge the gap between data obtained from clinical trial populations and the broader patient population encountered in clinical practice, by highlighting differences in outcomes and the need for effective treatment strategies across diverse patient groups.

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.

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.

AbstractIntroductionStructured data capture requires defined languages such as minimal Common Oncology Data Elements (mCODE). This pilot assessed the feasibility of capturing 5 mCODE categories (stage, disease status, performance status (PS), intent of therapy and intent to change therapy).MethodsA tool (SmartPhrase) using existing and custom structured data elements was Built to capture 4 data categories (disease status, PS, intent of therapy and intent to change therapy) typically documented as free‐text within notes. Existing functionality for stage was supported by the Build. Participant survey data, presence of data (per encounter), and time in chart were collected prior to go‐live and repeat timepoints. The anticipated outcome was capture of >50% sustained over time without undue burden.ResultsPre‐intervention (5‐weeks before go‐live), participants had 1390 encounters (1207 patients). The median percent capture across all participants was 32% for stage; no structured data was available for other categories pre‐intervention. During a 6‐month pilot with 14 participants across three sites, 4995 encounters (3071 patients) occurred. The median percent capture across all participants and all post‐intervention months increased to 64% for stage and 81%–82% for the other data categories post‐intervention. No increase in participant time in chart was noted. Participants reported that data were meaningful to capture.ConclusionsStructured data can be captured (1) in real‐time, (2) sustained over time without (3) undue provider burden using note‐based tools. Our system is expanding the pilot, with integration of these data into clinical decision support, practice dashboards and potential for clinical trial matching.

PURPOSE Patients diagnosed with advanced-stage Hodgkin lymphoma (aHL) have historically been risk-stratified using the International Prognostic Score (IPS). This study investigated if a machine learning (ML) approach could outperform existing models when it comes to predicting overall survival (OS) and progression-free survival (PFS). PATIENTS AND METHODS This study used patient data from the Danish National Lymphoma Register for model development (development cohort). The ML model was developed using stacking, which combines several predictive survival models (Cox proportional hazard, flexible parametric model, IPS, principal component, penalized regression) into a single model, and was compared with two versions of IPS (IPS-3 and IPS-7) and the newly developed aHL international prognostic index (A-HIPI). Internal model validation was performed using nested cross-validation, and external validation was performed using patient data from the Swedish Lymphoma Register and Cancer Registry of Norway (validation cohort). RESULTS In total, 707 and 760 patients with aHL were included in the development and validation cohorts, respectively. Examining model performance for OS in the development cohort, the concordance index (C-index) for the ML model, IPS-7, IPS-3, and A-HIPI was found to be 0.789, 0.608, 0.650, and 0.768, respectively. The corresponding estimates in the validation cohort were 0.749, 0.700, 0.663, and 0.741. For PFS, the ML model achieved the highest C-index in both cohorts (0.665 in the development cohort and 0.691 in the validation cohort). The time-varying AUCs for both the ML model and the A-HIPI were consistently higher in both cohorts compared with the IPS models within the first 5 years after diagnosis. CONCLUSION The new prognostic model for aHL on the basis of ML techniques demonstrated a substantial improvement compared with the IPS models, but yielded a limited improvement in predictive performance compared with the A-HIPI.

Abstract Use of surrogates as primary end points is commonplace in hematology/oncology clinical trials. As opposed to prognostic markers, surrogates are end points that can be measured early and yet can still capture the full effect of treatment, because it would be captured by the true outcome (eg, overall survival). We discuss the level of evidence of the most commonly used end points in hematology and share recommendations on how to apply and evaluate surrogate end points in research and clinical practice. Based on the statistical literature, this clinician-friendly review intends to build a bridge between clinicians and surrogacy specialists.

PURPOSE Nodular lymphocyte–predominant Hodgkin lymphoma (NLPHL) is a rare cancer, and large international cooperative efforts are needed to evaluate the significance of clinical risk factors and immunoarchitectural patterns (IAPs) for all stages of pediatric and adult patients with NLPHL. METHODS Thirty-eight institutions participated in the Global nLPHL One Working Group retrospective study of NLPHL cases from 1992 to 2021. We measured progression-free survival (PFS), overall survival (OS), transformation rate, and lymphoma-specific death rate. We performed uni- and multivariable (MVA) Cox regression stratified by management to select factors for the lymphocyte-predominant international prognostic score (LP-IPS) validated by five-fold cross-validation. RESULTS We identified 2,243 patients with a median age of 37 years (IQR, 23-51). The median follow-up was 6.3 years (IQR, 3.4-10.8). Most had stage I to II (72.9%) and few B symptoms (9.9%) or splenic involvement (5.4%). IAP was scored for 916 (40.8%). Frontline management included chemotherapy alone (32.4%), combined modality therapy (30.5%), radiotherapy alone (24.0%), observation after excision (4.6%), rituximab alone (4.0%), active surveillance (3.4%), and rituximab and radiotherapy (1.1%). The PFS, OS, transformation, and lymphoma-specific death rates at 10 years were 70.8%, 91.6%, 4.8%, and 3.3%, respectively. On MVA, IAPs were not associated with PFS or OS, but IAP E had higher risk of transformation (hazard ratio [HR], 1.81; P < .05). We developed the LP-IPS with 1 point each for age ≥45 years, stage III-IV, hemoglobin <10.5 g/dL, and splenic involvement. Increasing LP-IPS was significantly associated with worse PFS (HR, 1.52) and OS (HR, 2.31) and increased risk of lymphoma-specific death (HR, 2.63) and transformation (HR, 1.41). CONCLUSION In this comprehensive study of all ages of patients with NLPHL, we develop the LP-IPS to identify high-risk patients and inform upcoming prospective clinical trials evaluating de-escalation of therapy for patients with low LP-IPS scores (<2).

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.