Hay, Annette E.

Abstract Background: Although progression-free survival (PFS) in early-stage (ES) classic Hodgkin Lymphoma (cHL) is high, there remains a subset of patients (pts) at higher risk of worse outcomes, particularly in those with unfavorable risk disease. Leveraging pt-level data from within the HoLISTIC (Hodgkin Lymphoma International STudy for Individual Care) Consortium, the E-HIPI was developed in clinical trial pts and validated in real-world registry pts to predict 2-year (y) PFS (Rodday. NEJM Evidence 2025). Continuous, objective, and readily measurable pre-treatment variables were considered, with sex, maximum tumor diameter, albumin, and hemoglobin included as significant predictors. Using the E-HIPI, interactive calculators for pt risk, comparison, and stratification were created (https://rtools.mayo.edu/holistic_ehipi/). Although the E-HIPI was developed and validated in both unfavorable and favorable ES cHL pts, the clinical predictors are primarily markers of unfavorable disease. In the development and initial validation cohorts, 2y PFS was 93.7% and 90.3%, respectively, the C-statistic was 0.63 in both cohorts, calibration was strong with slight underprediction of events in the validation cohort, and the E-HIPI outperformed the historic binary classification of patients as favorable or unfavorable using EORTC criteria. However, since pts in the original E-HIPI development and validation were primarily treated with ABVD chemotherapy protocols, additional validation is needed, particularly among pts with unfavorable ES cHL who were treated initially with more intensive chemotherapeutic platforms (e.g., BEACOPP-based). Thus, we conducted an external validation of the E-HIPI in pts treated on recent unfavorable ES cHL GHSG trials. Methods: The validation cohort included 2367 adults (ages 18 to 60y) with newly-diagnosed unfavorable cHL from randomized phase 3 trials conducted by the GHSG (HD14 von Tresckow. JCO 2012; and HD17 Borchmann. Lancet 2021). We calculated the predicted probability of 2y PFS events based on the E-HIPI model equation (scale 1-100%). Model performance was assessed using discrimination and calibration. Discrimination, which is the model's ability to separate high and low risk pts, was assessed using Harrell's C-statistic. Calibration, which is the model's ability to accurately predict absolute risk, was assessed by comparing observed and predicted probabilities of 2y PFS events within quintiles of predicted probabilities and estimating calibration-in-the-large (predicted minus observed event rate) and the calibration slope. Results: For pts treated on unfavorable GHSG trials, mean age was 33y, 95% had stage II disease, mean baseline E-HIPI score (i.e., predicted 2y PFS event rate) was 6.0% (SD=2.3%), and the observed 2y PFS rate was 96.2% (95%CI: 95.4-97.0%). The C-statistic was 0.65 (95% CI: 0.59-0.70), calibration-in-the-large was 0.02, and the calibration slope was 1.08 (95% CI: 0.52, 1.64). Within the lowest risk E-HIPI quintile, the observed 2y PFS event rate was 1.6%, compared with 6.1% in the highest risk quintile. Conclusions: In external validation of the recently published E-HIPI in pts with cHL treated on unfavorable GHSG trials using more intensive BEACOPP-based protocols, discrimination was similar to the original development and validation cohorts. Collectively, the E-HIPI is a treatment-agnostic prediction tool across varied treatment regimens for unfavorable ES cHL pts. Better 2y PFS in the GHSG trials than in the E-HIPI development and initial validation cohorts may have contributed to mild misclassification, likely reflecting differences in treatment regimens. This may be resolved with recalibration or incorporating granular treatment variables into future models. Additional research is needed to understand E-HIPI performance in pts with favorable disease who have even lower event rates as well as to more fully predict individualized pt outcomes across varied chemotherapy platforms and radiation received for all ES cHL pts.

Background: There are a lack of clinical prediction tools in early-stage Hodgkin lymphoma (E-HL). We developed and validated a modern-day model, known as E-HIPI, to predict progression-free survival (PFS) within the first 5 years (y) incorporating detailed individual patient (pt) data from international clinical trials and prospective registry data that were standardized, normalized & harmonized as part of the HoLISTIC Consortium (www.hodgkinconsortium.org). Methods: Model development utilized Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines (Moons, Ann Intern Med 2015) on a cohort of 3,080 newly-diagnosed E-HL pts treated on 4 seminal phase 3 clinical trials (NCIC: Meyer, NEJM 2012; RAPID: Radford, NEJM 2015; EORTC-GELA H9U: Ferme, E ur J Cancer 2017; EORTC/LYSA/FIL H10: Andre, JCO 2017). External validation was done in an independent cohort of 462 E-HL pts from a large registry (Princess Margaret, Toronto). Pts with classic HL, stage I or II disease, and ages 18-65 y were included. The primary outcome was PFS. Cox models were utilized with follow-up truncated at 5 years. Baseline predictors were: sex, stage, B symptoms, histology, number & location of nodal sites, and continuous data values of age, maximum tumor diameter (MTD), white blood cell count (WBC), lymphocyte count, hemoglobin, albumin & erythrocyte sedimentation rate (ESR). Multiple imputation was used for missing data. To consider possible non-linear relationships for continuous variables we examined plots & cubic splines. We used backward elimination ( P<0.1) to develop the models & internal validation with 200 bootstrap samples was conducted to estimate optimism & correct for overfitting. The final prediction equations applied optimism corrections to beta coefficients & hazard ratios (HR). Sensitivity analyses included model results stratified by PET staging (vs CT) and each HL trial. The models were evaluated in the external validation cohort & compared using c-statistics. Model scores were also grouped by tertiles for visualization in Kaplan-Meier (KM) curves. Results: Median age in the development cohort was 35y; 51% were female; 81% had nodular sclerosis & 77% stage II disease; 26% had B symptoms; 32% had mediastinal bulk (≥10cm or >1/3 diameter) and the mean MTD was 6.4 cm (interquartile range (IQR), 3.9-8.2). Median follow-up was 60 months (IQR 45-75). KM estimates at 5y was 90.7% (264 events) for PFS, and 97.1% (78 events) for overall survival (OS). After backward elimination, significant variables retained were age, sex, nodal location (cervical region), MTD & albumin. Stage, ESR, or the number of nodal sites were not significant. Age was modeled with piecewise linear splines due to its non-linear relationship. See the Table for the final prediction model and optimism-corrected HRs. Optimism-corrected c-statistics in the development model was 0.63. Sensitivity analyses of stratification by type of imaging at staging or by each clinical trial demonstrated no change in model parameters or c-statistics. Most baseline characteristics and outcomes of the external validation cohort were similar besides median follow-up time (74 months, IQR 37-129). C-statistics for the E-HIPI model in external validation was 0.63. In addition, observed 5y outcomes stratified by tertile of predicted risk in the external cohort separated pts into a low & higher-risk group, which comprised 1/3 & 2/3 of pts, respectively ( Figure). Finally, model diagnostics revealed the potential for time dependency in the retained covariates. Further assessment of differential prediction between early (<2y) vs. later (2-5y) PFS events is ongoing. Conclusion: Following TRIPOD methodology, we rigorously developed and externally validated the E-HIPI among >3,500 E-HL pts.Discrete risk groups were delineated, and we identified several novel factors, including nodal location, sex & key continuous variables that predict 5y PFS. Additional model refinement is underway, including formal calibration & external validation via other HL registries (Stanford, Mayo/Iowa SPORE). A web-based calculator to simplify application of the E-HIPI will also be presented at ASH. Future planned modeling includes integration of differing treatments & response-adapted imaging via multistate modeling to enrich individualized survival estimates and simulation modeling to predict late consequences.

CCTG CLC.2/Alliance A041202 demonstrated superior progression-free survival at 2 years with ibrutinib alone (87%; HR 0.39) or ibrutinib-rituximab (IR 88%; HR 0.38) compared to chemo-immunotherapy with bendamustine-rituximab (BR 74%) in treatment-naïve patients (pts) with chronic lymphocytic leukemia (CLL) who were 65 or older (Woyach NEJM 2018). We hypothesized that ibrutinib-based therapies would be more costly than BR but that costs would be offset by less toxicity and improved quality of life (QOL). We completed a prospective trial-based economic analysis to study the direct medical costs and quality-adjusted benefit associated with ibrutinib-based therapies compared to BR in the Canadian (CDN) subset of patients enrolled in CLC.2/Alliance 041202. All CDN pts were invited to participate in the companion analysis. Health utilities were collected using the EuroQOL EQ-5D and calculated using CDN population valuations (Bansback PLOS One 2012). Resource utilization forms were administered to collect off-protocol health care encounters. The planned analysis was a cost-utility analysis from the perspective of a public healthcare system, examining the costs and outcomes (quality-adjusted life years or QALYs) of ibrutinib-based therapy compared to BR. Unit costs were applied to resource data based on publicly available provincial/national databases; all costs were expressed in 2019 US dollars (1 CDN = 0.75 US dollar). Total and disaggregated direct medical costs are presented descriptively. Mean survival was calculated using the restricted mean survival method from randomization to the study time-horizon of 24 months; derived utilities were used to calculate QALYs. A discount rate for costs and benefits (r=0.05) was applied. The analysis was based on estimation (with bootstrapping) of an incremental cost-effectiveness ratio (ICER) and/or direct medical costs. A total of 55 pts were enrolled; two pts who did not receive any treatment were censored at day 1 and 3 after randomization and excluded from analysis. Of the 53 analysed, pt demographics were well balanced between treatments and were reflective of the entire population: mean age was 71.6 (SD 6.34) in pts receiving ibrutinib alone (n=17), 72.2 (SD 3.85) in pts receiving IR (n=18), and 71.7 (SD 4.1) in pts receiving BR (n=18). A total of 3 pts, one in each arm, had 17p deletion. Progression-free survival at 2 years for CDN pts was 94% (95% CI 65-99%) for ibrutinib, 100% (95% CI 100-100%) for IR, and 72% (95% CI 45-87%) for BR (Figure 1). At 24 months, 1 pt on the BR arm had crossed over to ibrutinib (as per protocol); there was no overall survival difference between the three arms. On-protocol costs (including protocol treatment, ambulatory care, and imaging) and off-protocol costs (including hospitalizations, concomitant medications, and ambulatory care) are highlighted in Figure 2. On-protocol costs were higher for pts receiving ibrutinib (mean $142,001 USD; SD 48,417) and IR ($164,931; SD 46,208) compared to BR ($38,509; SD 10,351), driven by higher drug acquisition costs associated with ibrutinib (list price $6422 for 420mg/30 days). In contrast, off-protocol costs were modestly higher for pts on BR (mean $3050; SD 3812) compared to the ibrutinib ($2460; SD 3863) or IR ($2890; SD 4206); hospitalizations were the key off-protocol cost drivers and were highest for pts on IR and BR. Overall mean costs over the 2-year time horizon were $144,461 (SD 47,910) for pts on ibrutinib, $167,820 (SD 46,830) for pts on IR, and $41,560 (SD 11,849) for pts on BR. Discounted QALYs were similar between the three treatment arms: 1.66 (0.16) for ibrutinib alone, 1.65 (0.24) for IR, and 1.66 (0.17) for BR. Given the similar quality-adjusted survival between arms at the time of this analysis, a formal ICER was not calculated. Direct medical costs are substantially higher for pts receiving continuous ibrutinib-based therapies, compared to chemo-immunotherapy of fixed duration, in frontline CLL management; the key cost driver is the cost of ibrutinib. The PFS benefit with ibrutinib-based therapy has not translated into an advantage in quality-adjusted survival to date; further follow-up may be required to demonstrate any cost or QOL benefits associated with fewer progression events for those on ibrutinib. Support: U10CA180821, U10CA180882; U10CA180863 and #704970 CCTG. https://acknowledgments.alliancefound.org ClinicalTrials.gov: NCT01886872 Figure Disclosures Owen: AbbVie, F. Hoffmann-La Roche, Janssen, Astrazeneca, Merck, Servier, Novartis, Teva: Honoraria. Lam:Roche: Honoraria, Speakers Bureau; Janssen: Honoraria, Speakers Bureau. Crump:Servier: Consultancy; Kite/Gilead: Consultancy; Roche: Consultancy. Sperlich:Lundbeck Canada: Honoraria. Woyach:Pharmacyclics, Janssen, Morphosys, Karyopharm, Verastem, Abbvie, Lox: Research Funding; Janssen, Pharmacyclics, AstraZeneca, Abbvie, Arqule: Consultancy; Pharmacyclics LLC, an AbbVie Company, AbbVie, Janssen, AstraZeneca, ArQule: Honoraria. Prica:astra zeneca: Honoraria; seattle genetics: Honoraria; Gilead: Honoraria. Hay:Roche: Research Funding; Janssen: Research Funding.

Background: Less-fit patients with acute myeloid leukemia (AML) or high-risk myelodysplasia (MDS) age 60 years and older constitute the majority of patients with AML/MDS but are not well represented in clinical trials. DNA-methyltransferase inhibitor (HMA) monotherapy (e.g. azacitidine or decitabine) is usual. Overall response rates (ORR) are low; improvement in overall survival relative to supportive care alone is modest, highlighting the critical need for efficient identification of effective novel therapies. Blocking programed cell death protein-1 (PD-1) signaling with nivolumab may increase the sensitivity of AML cells to azacitidine and improve outcomes and is the focus of one arm of the S1612 trial. Signaling through PD-1 contributes to tumor immune evasion and growth in AML [Chen, Cancer Biol Ther 2008]. Increased expression of PD-1 (~40% of AML), is associated with poor HMA response [Ørskov, Oncotarget 2015]. A single center azacitidine/nivolumab non-randomized phase II study in relapsed/refractory AML reported ORR of 33% (23/70), including 22% complete remission/complete remission with incomplete hematologic recovery [Daver, Cancer Discovery 2018]. About 25% of the patients developed grade 2-4 immune toxicities; nivolumab immune-related adverse events led to treatment discontinuation in nearly 1 in 7 patients. Study Design and Methods:The S1612 trial [NCT03092674] is a platform randomized phase II/III clinical trial with a common azacitidine control arm (CA) and two currently active experimental arms (EA). Therapy is intended for community setting: azacitidine/nivolumab; and azacitidine/midostaurin. The innovative design utilizes a phase II go/no-go decision comparing each EA with the CA independently when there are 100 pts/arm and 104 deaths on the EA and CA combined. EAs will proceed to phase III if the null hypothesis (HR=1) is rejected in favor of the EA (15% one-sided alpha). When the two currently active EAs complete phase II accrual, a third EA (decitabine/cytarabine) will open. The CA stays open the entire length of the trial and only concurrently randomized patients will be compared across arms. If an EA proceeds to phase III, 200 additional patients (300 total patients) will be accrued. Phase III analysis occurs 1.5 years after accrual or at 414 deaths (in the respective EA and CA), whichever comes first. The phase III tests the null hypothesis with 4.5% two-sided alpha and 83% power for each EA to detect an improvement in median OS from 10.4 to 15.6 months. Eligible pts are age ≥60 years, newly diagnosed AML with ≥ 20% blasts or myelodysplastic syndrome with excess blasts-2 (MDS-EB-2); deemed by the treating physician unfit for standard cytotoxic chemotherapy; and no prior HMA permitted. Trial in Progress Issues:Between December 2017 and October 2018, 113 patients were screened and 78 randomized to study treatment (median/range age: 75/61-86 years; median/range performance status 1/0-3). Two concerns challenged this trial: 1) required administration of 7-day azacitidine at the enrolling sites created a burden for this population and 2) an early excess grade 5 toxicity signal in the azacitidine/nivolumab arm compared with the control arm. Without a control comparison, this safety signal likely would have been missed. Strategies to address these concerns were developed by the study team. Discussion with the sponsor and US Food and Drug Administration (FDA) focused on allowance of standard-of-care protocol-directed azacitidine administration in the patient's primary care doctor's office rather than at the oncology center. Because of the toxicity concern, the trial was placed on partial clinical hold for further evaluation and possible nivolumab-arm eligibility changes, along with new surveillance and pre-emptive action including prompt steroid initiation for suspected immune-related toxicities. The S1612 trial highlights special concerns when enrolling vulnerable populations onto leukemia clinical trials, and the importance of collaborative strategies including with the FDA to preserve clinical trial integrity for patient benefit. It also demonstrates the efficiency this novel platform design has for therapeutic investigation and, importantly, very early identification of serious toxicity. Updates on accrual and resolution and of these issues will be presented. Disclosures Hay: Kite: Research Funding; Gilead: Research Funding; AbbVie: Research Funding; Janssen: Research Funding; MorphoSys: Research Funding; Seattle Genetics: Research Funding; Takeda: Research Funding; Celgene: Research Funding; Roche: Research Funding; Novartis: Research Funding. Assouline:Janssen: Consultancy, Honoraria, Speakers Bureau; F. Hoffmann-La Roche Ltd: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria, Speakers Bureau; Abbvie: Consultancy, Honoraria. Walter:Pfizer: Consultancy, Research Funding; Race Oncology: Consultancy; New Link Genetics: Consultancy; Agios: Consultancy; Amphivena Therapeutics: Consultancy, Equity Ownership; Seattle Genetics: Research Funding; Aptevo Therapeutics: Consultancy, Research Funding; Argenx BVBA: Consultancy; Astellas: Consultancy; BioLineRx: Consultancy; BiVictriX: Consultancy; Boehringer Ingelheim: Consultancy; Boston Biomedical: Consultancy; Covagen: Consultancy; Daiichi Sankyo: Consultancy; Jazz Pharmaceuticals: Consultancy; Kite Pharma: Consultancy; Amgen: Consultancy. Foran:Agios: Honoraria, Research Funding. Radich:TwinStrand Biosciences: Research Funding; Novartis: Other: RNA Sequencing. Othus:Celgene: Other: Data Safety and Monitoring Committee; Glycomimetics: Other: Data Safety and Monitoring Committee. Erba:Astellas Pharma: Consultancy; Amgen: Consultancy; Amgen: Consultancy; ImmunoGen: Consultancy, Research Funding; Novartis: Consultancy, Research Funding, Speakers Bureau; MacroGenics: Consultancy, Other: Lecture fees, Research Funding; Pfizer: Consultancy; Pfizer: Consultancy; ImmunoGen: Consultancy, Research Funding; Covance: Other: Fees for serving as chair on an independent review board for AbbVie Phase III studies; Astellas Pharma: Consultancy; Covance: Other: Fees for serving as chair on an independent review board for AbbVie Phase III studies; MacroGenics: Consultancy, Other: Lecture fees, Research Funding; AbbVie: Consultancy, Other: Chair, IRC for phase III studies, Research Funding; AbbVie: Consultancy, Other: Chair, IRC for phase III studies, Research Funding; Seattle Genetics: Consultancy; Seattle Genetics: Consultancy; Celgene: Consultancy, Other: chair, AML Registry Scientific Steering Committee, Speakers Bureau; Celgene: Consultancy, Other: chair, AML Registry Scientific Steering Committee, Speakers Bureau; Daiichi Sankyo: Consultancy, Research Funding; Daiichi Sankyo: Consultancy, Research Funding; GlycoMimetics: Consultancy, Other: Chair, data and safety monitoring board, Research Funding; GlycoMimetics: Consultancy, Other: Chair, data and safety monitoring board, Research Funding; Agios: Consultancy, Speakers Bureau; Agios: Consultancy, Speakers Bureau; Incyte: Consultancy, Speakers Bureau; Incyte: Consultancy, Speakers Bureau; Jazz Pharmaceuticals: Consultancy, Speakers Bureau; Jazz Pharmaceuticals: Consultancy, Speakers Bureau; Novartis: Consultancy, Research Funding, Speakers Bureau. Michaelis:Pfizer: Equity Ownership, Research Funding; Incyte: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Novartis: Consultancy; TG Therapeutics: Consultancy, Research Funding; JAZZ: Other: Data Safety Monitoring Board, uncompensated, Research Funding; BMS: Research Funding; Bioline: Research Funding; ASTEX: Research Funding; Janssen: Research Funding; Millenium: Research Funding; Macrogeneics: Research Funding. OffLabel Disclosure: Off label experimental combination therapies in newly diagnosed AML/MDS will be discussed, including nivolumab and azacitidine.

Abstract Introduction: In multiple myeloma, randomized trials of maintenance therapy using thalidomide or lenalidomide following autologous transplant (ASCT) demonstrated prolonged progression-free survival (PFS), but overall survival benefit was inconsistent. The MY.10 trial (n=332) demonstrated improved PFS with thalidomide-prednisone maintenance therapy, but quality of life was inferior and overall survival was not improved. As a randomized trial with a no-treatment control arm and biospecimen collection, MY.10 provides a unique opportunity to identify predictors of benefit from therapy that could be used to personalize treatment. Immunomodulatory drug action is mediated in part through binding to cereblon (CRBN) and downregulation of IRF4. Candidate single nucleotide polymorphisms (SNPs) were selected from the CRBN/IRF4 pathway and other genes previously associated with myeloma prognosis, drug response and toxicity from literature review. The aim of this study was to investigate associations between candidate SNPs and benefit from immunomodulatory compound-based maintenance therapy, as well as prognostic impact. Methods: Genomic DNA was extracted from samples preserved in Trizol¨. SNPs were genotyped using TaqMan¨ genotyping assays on the ViiA7 qPCR platform. Analysis was performed on 187 patients with available samples and clinical data (86 in treatment arm; 101 in observation arm). Cox regression models were performed using SAS clinical 2.0 for prognostic impact of a single SNP on PFS, assessed with and without adjusted covariates (age, stage, performance status and response to ASCT). In treatment benefit analysis, PFS was modeled with genotype, treatment assignment and their interaction term. Results: SNPs with significant prognostic impact for PFS in all 187 patients were found in CRBN (rs1672753) (HR 0.59, 95%CI 0.4-0.86; p=0.006) and the following SNPs which had been found to be prognostic in prior studies were validated: CYP1A1 (rs1048943) (HR 1.98, 95%CI 1.19-3.27; p=0.008), CYP1B1 (rs1056836) (HR 0.72, 95%CI 0.52-0.99; p=0.043), and trend toward significance for SNP in CYP1A2 (rs2069514) (HR 2.02, 95%CI 0.98-4.15; p=0.057). Of these, only the CYP1A1 SNP was found to be possibly predictive of greater benefit from maintenance therapy. A SNP in ABCA1 (rs363717), previously associated with thalidomide-induced neuropathy,‎ was prognostic in this study (HR 1.44, 95%CI 1.03-2.02; p=0.033) but not predictive of treatment benefit. Table 1 lists SNPs associated with a differential level of benefit from maintenance therapy, including SNPs in FAM179a and ICAM1 that were possibly associated with harm from maintenance therapy. Table 1. SNPs and treatment interaction with significant impact on PFS. HR represent hazard ratio of treatment versus observation for a given genotype group. Asterisk Indicates trend toward significance. Wildtype Heterozygote & mutant p-value interaction Gene dbSNP ID SNP HR 95% CI HR 95% CI FAM179A rs1053667 T>C 0.46 0.33, 0.66 2.28 0.78, 6.65 0.003 ICAM1 rs1799969 G>A 0.51 0.36, 0.73 1.75 0.83, 3.70 0.007 CASP3 rs1049216 A>G 0.82 0.54, 1.25 0.41 0.25, 0.69 0.029 IRF4 rs12203592 C>T 0.69 0.47, 1.02 0.38 0.21, 0.69 0.062* CYP1A1 rs1048943 A>G 0.62 0.44, 0.87 0.11 0.02, 0.52 0.079* Conclusion: Previously identified SNPs in drug metabolism and drug efflux genes were validated to be prognostic in this cohort. The prognostic but not predictive significance of a SNP in CRBN suggests a possible role for CRBN in multiple myeloma disease biology independent of immunomodulatory therapy. SNPs predictive of differing degrees of benefit from immunomodulatory maintenance were found in genes involved in immunomodulatory activation, drug action and cell adhesion mediated drug resistance; SNPs in FAM179A and ICAM1 were possibly associated with harm from thalidomide maintenance and could potentially be used to identify patients unsuitable for immunomodulatory maintenance therapy. These results may be applicable to lenalidomide and pomalidomide, since immunomodulatory compounds exhibit similar chemical structures and mechanisms of action. Validation of these SNPs in prospective clinical trials involving other immunomodulatory can test this hypothesis. Further studies are needed to confirm our findings. Disclosures Off Label Use: thalidomide and prednisone maintenance for multiple myeloma. Bahlis:Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau; Amgen: Consultancy; Johnson & Johnson: Speakers Bureau; Johnson & Johnson: Consultancy; Johnson & Johnson: Research Funding. Song:Celgene Canada: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. White:Celgene Canada: Honoraria, Research Funding. Chen:Celgene: Consultancy, Honoraria, Research Funding. Seftel:Celgene: Honoraria, Research Funding. Howsen-Jan:Celgene: Honoraria, Research Funding. Reece:Amgen: Honoraria; Bristol-Myers Squibb: Research Funding; Merck: Research Funding; Novartis: Honoraria, Research Funding; Onyx: Consultancy; Millennium Takeda: Research Funding; Lundbeck: Honoraria; Otsuka: Research Funding; Janssen-Cilag: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding. Shepherd:Celgene: Honoraria, Research Funding. Chen:Celgene: Honoraria, Research Funding. Djurfeldt:Celgene: Research Funding. Reiman:Soricimed Biopharma Inc: Consultancy, Other: Scientific Advisory Board for Soricimed.

Key Points SCHOLAR-1 is the first patient-level analysis of outcomes of refractory DLBCL from 2 large randomized trials and 2 academic databases. SCHOLAR-1 demonstrated poor outcomes in patients with refractory DLBCL, supporting a need for more effective therapies for these patients.