Davies, Andrew

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.

Background and Significance When patients (pts) with follicular lymphoma (FL) present with a high tumor burden, they require systemic treatment with rituximab (R) in combination with chemotherapy (chemo). Given the known cumulative toxicities of chemotherapy and the older age of pts at the time of diagnosis, novel chemo-free treatment approaches are desired by pts as well as physicians. R-lenalidomide (R-Len), a chemo-free alternative to R-based chemoimmunotherapy, has recently been listed as a chemo-free option in the National Comprehensive Cancer Network® (NCCN) guidelines. Golcadomide (GOLCA) is a potential first-in-class oral cereblon E3 ligase modulator (CELMoD™) agent purposefully designed for the treatment of lymphoma. GOLCA co-opts cereblon to induce targeted degradation of the transcription factors Ikaros/Aiolos, inducing deep, rapid, and extensive tumor killing, independent of cell of origin, and enhanced immunostimulatory activity. Compared with immunomodulatory (IMiD) agents like lenalidomide, GOLCA more efficiently drives the cereblon complex to the closed active state resulting in more potent degradation (Watson ER et al, Science 2022), which is expected to translate into improved clinical benefit. In addition, the phase 1/2 study CC-99282-NHL-001 showed that GOLCA alone or in combination with R (GOLCA-R) was well tolerated and showed promising activity in pts with heavily pre-treated relapsed/refractory (R/R) FL, including in those with prior Len-based therapies and/or T cell-redirecting therapies (Chavez et al, EHA 2024). Based on this, GOLSEEK-2 (NCT06425302) is a randomized phase 2 trial assessing the efficacy and safety of GOLCA-R as a chemo-free option in pts with newly diagnosed advanced-stage FL. Study Design and Methods After a screening period of ≤ 5 weeks, approximately 90 pts with newly diagnosed advanced-stage FL will be randomized 1:1:1 between GOLCA plus rituximab at a dose of 0.2 mg or 0.4 mg once daily from day 1 to 14 every 28 days for 12 cycles or investigator's choice R-chemo (R-CHOP or R-bendamustine). R maintenance could be considered (as per pt and physician decision) for pts who achieve a partial response or better after completion of initial therapy for up to one year in GOLCA-R arms and up to one and a half years in the R-chemo arm. Pts with histologically confirmed diagnosis of grade 1, 2, 3a FL, or classic FL according to WHO classification would be eligible for the study. Other key inclusion criteria include no prior systemic treatment for FL, stage II-IV disease, the requirement for treatment as per investigator's judgment, and measurable disease as defined by Lugano 2014 criteria. Key exclusion criteria include other lymphoma subtypes, clinical evidence of transformed lymphoma, and documented or suspected central nervous system involvement. The primary endpoint is complete metabolic response (CMR) during the GOLCA plus rituximab combination treatment period, assessed by the investigator and based on Lugano 2014 Response Criteria. Secondary endpoints include progression-free survival, overall survival, overall response rate, duration of response, complete response rate at 30 months, CMR rate at 6 months and 12 months, and safety outcomes.

Abstract With the introduction of large-scale molecular profiling methods and high-throughput sequencing technologies, the genomic features of most lymphoid neoplasms have been characterized at an unprecedented scale. Although the principles for the classification and diagnosis of these disorders, founded on a multidimensional definition of disease entities, have been consolidated over the past 25 years, novel genomic data have markedly enhanced our understanding of lymphomagenesis and enriched the description of disease entities at the molecular level. Yet, the current diagnosis of lymphoid tumors is largely based on morphological assessment and immunophenotyping, with only few entities being defined by genomic criteria. This paper, which accompanies the International Consensus Classification of mature lymphoid neoplasms, will address how established assays and newly developed technologies for molecular testing already complement clinical diagnoses and provide a novel lens on disease classification. More specifically, their contributions to diagnosis refinement, risk stratification, and therapy prediction will be considered for the main categories of lymphoid neoplasms. The potential of whole-genome sequencing, circulating tumor DNA analyses, single-cell analyses, and epigenetic profiling will be discussed because these will likely become important future tools for implementing precision medicine approaches in clinical decision making for patients with lymphoid malignancies.

Abstract Despite the effectiveness of immuno-chemotherapy, 40% of patients with diffuse large B-cell lymphoma (DLBCL) experience relapse or refractory disease. Longitudinal studies have previously focused on the mutational landscape of relapse but fell short of providing a consistent relapse-specific genetic signature. In our study, we have focused attention on the changes in GEP accompanying DLBCL relapse using archival paired diagnostic/relapse specimens from 38 de novo patients with DLBCL. COO remained stable from diagnosis to relapse in 80% of patients, with only a single patient showing COO switching from activated B-cell–like (ABC) to germinal center B-cell–like (GCB). Analysis of the transcriptomic changes that occur following relapse suggest ABC and GCB relapses are mediated via different mechanisms. We developed a 30-gene discriminator for ABC–DLBCLs derived from relapse-associated genes that defined clinically distinct high- and low-risk subgroups in ABC–DLBCLs at diagnosis in datasets comprising both population-based and clinical trial cohorts. This signature also identified a population of <60-year–old patients with superior PFS and OS treated with ibrutinib–R-CHOP as part of the PHOENIX trial. Altogether this new signature adds to the existing toolkit of putative genetic predictors now available in DLBCL that can be readily assessed as part of prospective clinical trials.

Abstract Since the publication of the Revised European-American Classification of Lymphoid Neoplasms in 1994, subsequent updates of the classification of lymphoid neoplasms have been generated through iterative international efforts to achieve broad consensus among hematopathologists, geneticists, molecular scientists, and clinicians. Significant progress has recently been made in the characterization of malignancies of the immune system, with many new insights provided by genomic studies. They have led to this proposal. We have followed the same process that was successfully used for the third and fourth editions of the World Health Organization Classification of Hematologic Neoplasms. The definition, recommended studies, and criteria for the diagnosis of many entities have been extensively refined. Some categories considered provisional have now been upgraded to definite entities. Terminology for some diseases has been revised to adapt nomenclature to the current knowledge of their biology, but these modifications have been restricted to well-justified situations. Major findings from recent genomic studies have impacted the conceptual framework and diagnostic criteria for many disease entities. These changes will have an impact on optimal clinical management. The conclusions of this work are summarized in this report as the proposed International Consensus Classification of mature lymphoid, histiocytic, and dendritic cell tumors.

Background: Although diffuse large B cell lymphoma (DLBCL) can be cured using immuno-chemotherapy, 40% of patients experience relapse or refractory disease. Large-scale profiling studies have mainly focused on DLBCL at diagnosis, resolving different outcome groups based on gene expression (e.g. cell-of-origin (COO) or molecular high grade), MYC/BCL2 translocations (double-hit lymphoma) or gene mutations and copy number aberrations (Schmitz et al, NEJM 2018; Chapuy et al, NatureMedicine 2018). In comparison, longitudinal studies have been hindered by the limited availability of sequential biopsy samples. To date, the relapse-specific gene mutations identified are limited and inconsistent across studies. In our study, we have focussed attention on the changes in gene expression profile (GEP) accompanying DLBCL relapse. Methods: We retrospectively collected archival paired diagnostic/relapse formalin fixed paraffin embedded tumor biopsies from 38 de novo DLBCL patients collected from multiple UK sites treated with rituximab-based immuno-chemotherapy, where partial or complete remission was reported following treatment. COO classification was performed by the Lymph2Cx assay on NanoString to distinguish activated B-cell-like (ABC) and germinal center B-cell-like (GCB) subtypes. The Ion AmpliSeq™ Transcriptome Human Gene Expression Kit was used to measure the expression levels of > 20,000 genes on the paired samples. Results: COO remained stable from diagnosis to relapse in 17 ABC-ABC pairs, 11 GCB-GCB pairs and 4 unclassified (UNC)-UNC pairs. Frank COO switching was observed in 6 cases (1 ABC-GCB, 2 ABC-UNC, 2 GCB-UNC, 1 UNC-ABC). Pairs with stable COO were taken forward for further analysis. Gene expression analysis using the limma R package identified 163 and 136 genes as differentially expressed (DE) (p <= 0.01 and absolute log2FC > 1) between the diagnostic and relapse biopsies in ABC and GCB tumors respectively, with only a one gene overlap. Gene Set Enrichment Analysis further suggested that ABC and GCB relapses are mediated via different mechanisms, with tumor growth and proliferation signatures enriched in ABC relapses, whilst adaptive immunity-related signatures accompanied GCB relapses. Next, we aimed to utilise our relapse-specific genes to identify outcome predictors at diagnosis using publicly available GEP datasets. In order to increase our discovery power and accuracy, a larger set of DE genes from the paired differential analysis (796 genes in ABC pairs and 387 from GCB pairs) were selected (p <= 0.05) and subsequently used in a training cohort (GEP from Reddy et al, Cell 2017). The Prediction Analysis for Microarrays R (PAMR) algorithm identified a 30-gene signature within DE genes from ABC pairs (Fig1.A), capable of separating the 249 ABC cases into 136 low and 113 high-risk cases with significantly inferior overall survival (Hazard Ratio (HR)=1.89, log-rank p=0.0017, measure of goodness-of-fit C-index=0.71; Fig1.B). No equivalent signature was found in the GCB cases using this approach. The prognostic significance of this 30-gene discriminator was successfully validated using a linear predictor in two independent GEP datasets: 1) a population-based cohort (Lenz et al, NEJM 2008) with 93 R-CHOP-treated ABC cases identifying 47 low and 46 high-risk cases (HR=1.92, p=0.046, C-index=0.77; Fig1.C) and 2) a clinical trial dataset (REMoDL-B, Davies et al, Lancet Oncol 2019) with 255 ABC cases identifying 110 low and 145 high-risk ABC cases (HR=1.95, p=0.0051, C-index=0.70; Fig1.D). Conclusions: Here we describe a 30-gene discriminator in ABC-DLBCL, derived from genes differentially expressed between diagnosis and relapse, that allowed the definition of clinically distinct high and low risk subgroups in ABC-DLBCLs at diagnosis. The clinical translation of such a tool may be useful to guide therapy for this unfavourable subgroup of ABC-DLBCLs. Validation of this signature is currently underway in additional datasets and further study is required to understand the contribution of these genes in DLBCL pathology. Disclosures Korfi: Roche: Consultancy. Burton:Celgene: Membership on an entity's Board of Directors or advisory committees; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees. Rule:TG Therapeutics: Consultancy, Honoraria; Napp: Consultancy; Kite: Consultancy; Pharmacyclics: Consultancy, Honoraria; Gilead: Consultancy, Honoraria; Sunesis: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Research Funding; Roche: Consultancy, Honoraria, Research Funding; Astra-Zeneca: Consultancy, Honoraria; Celgene: Consultancy, Honoraria. Crosbie:Janssen: Honoraria. Scott:Celgene: Consultancy; Janssen: Consultancy, Research Funding; NanoString: Patents & Royalties: Named inventor on a patent licensed to NanoSting [Institution], Research Funding; Roche/Genentech: Research Funding. Rimsza:NanoSting: Patents & Royalties: Named inventor on a patent licensed to NanoSting [Institution]. Davies:Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Honoraria, Research Funding; Bayer: Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Honoraria, Research Funding; Karyopharma: Membership on an entity's Board of Directors or advisory committees, Research Funding; GSK: Research Funding; Acerta Pharma: Honoraria, Research Funding; ADCT Therapeutics: Honoraria, Research Funding; BioInvent: Research Funding; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; MorphoSys AG: Honoraria, Membership on an entity's Board of Directors or advisory committees. Gribben:Abbvie: Consultancy, Honoraria, Research Funding; Acerta/Astra Zeneca: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding. Okosun:Gilead Sciences: Honoraria, Research Funding. Johnson:Epizyme: Honoraria, Research Funding; Novartis: Honoraria; Kite: Honoraria; Janssen: Consultancy, Honoraria, Research Funding; Bristol-Myers Squibb: Honoraria; Boehringer Ingelheim: Honoraria; Takeda: Honoraria; Genmab: Honoraria; Celgene: Honoraria; Incyte: Honoraria. Fitzgibbon:Epizyme: Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead: Speakers Bureau.

Abstract Introduction: Diffuse large B-cell lymphoma (DLBCL) is an aggressive non-Hodgkin’s lymphoma (NHL) comprising 3 molecular subtypes: germinal center B-cell (GCB), activated B-cell (ABC) and class III. ABC patients (pts) have a poor prognosis. The immunomodulatory drug lenalidomide (Len) produces durable responses in pts with aggressive NHL (Witzig 2011), with preferential activity reported in non-GCB DLBCL (Hernandez-Ilizaliturri 2011). Methods: This randomized, multicenter, open-label, phase 2/3 study was conducted to determine the efficacy and safety of single-agent Len vs single-agent investigator’s choice (IC) in relapsed/refractory DLBCL pts who received ≥2 prior therapies, or were ineligible for stem cell transplantation or further combination chemotherapy. DLBCL subtype (GCB vs non-GCB) was determined by a central pathology lab using immunohistochemistry (IHC) per the Hans method (Hans 2004). Pts were stratified by subtype, then randomized 1:1 to receive Len (25 mg/day, 21 days of 28-day cycle) or IC (gemcitabine, rituximab, etoposide, or oxaliplatin) until progressive disease (PD), unacceptable toxicity, or voluntary withdrawal. In the event of radiologically confirmed PD, pts in the IC arm were allowed to cross over to Len. The primary endpoint for Stage 1 was overall response rate (ORR), as determined by an Independent Response Assessment Committee. Progression-free survival (PFS), overall survival (OS) and subtype analysis using gene expression profiling (GEP) were exploratory endpoints. Concordance of GEP and IHC was evaluated from 3 separate laboratories. Results: IHC subtyping agreement rate among the 3 laboratories ranged from 87.5%-97.9%, and sensitivity of IHC to detect ABC or GCB subtypes vs GEP ranged from 92.3%-100.0%. By IHC, 102 DLBCL pts (GCB, n=48; non-GCB, n=54) were treated with ≥1 dose of Len or IC (modified intent-to-treat population) in Stage 1. In this heavily pretreated population, >90.0% of pts received ≥2 prior systemic chemotherapies; 25 pts in Len and 32 pts in IC received ≥3 prior systemic chemotherapy regimens. Median age was 65 y in the IC arm vs 69 y in the Len arm. Twenty-nine pts crossed over from IC to Len after confirmed PD. All pts, regardless of subtype or therapy group, experienced ≥1 treatment-emergent adverse event, with neutropenia, anemia, and thrombocytopenia being the most common. Efficacy data are presented in the Table. Pts with GCB or non-GCB DLBCL (per IHC) treated with Len had similar ORR, but the data suggested greater improvements in PFS and OS with Len vs IC in the non-GCB pts. In an exploratory analysis of pts subtyped by GEP, ABC pts treated with Len vs IC-treated showed greater improvements in ORR, PFS, and OS compared with GCB pts. Prespecified criterion to advance to Stage 2 was a 2-sided 15% significance level in ORR in favor of Len based on IHC-defined subtype. The data did not fulfill this requirement, and Stage 2 was not opened. Conclusion: Len monotherapy showed clinical activity in heavily pretreated pts with DLBCL. The data suggest improved ORR, PFS, and OS with Len vs IC in the non-GCB population as defined by IHC, and the difference appears to be more pronounced in the ABC population as defined by GEP. Subtyping by GEP is warranted in further studies of Len in DLBCL. Abstract 628. Table 1.Table. Efficacy DataBy IHCBy GEPOverallGCBNon-GCBGCBABCLen(n=51)IC(n=51)Len(n=23)IC(n=25)Len(n=28)IC(n=26)Len(n=14)IC(n=16)Len(n=11)IC(n=16)ORR, % (95% CI)27.5 (15.9-41.7)11.8 (4.4-23.9) 26.1 (10.2-48.4)12.0 (2.5-31.2)28.6 (13.2-48.7)11.5 (2.4-30.2)21.4 (4.7-50.8)12.5 (1.6-38.3)45.5 (16.7-76.6)18.8 (4.0-45.6)P Value .079 .279 .179 .642 .206PFS, med wk (95% CI)13.6 (8.6-17.7)7.9 (6.3-9.0) 10.1 (8.3-22.3)29.0 (6.3-20.6)15.1 (8.3-24.1)7.1 (5.3-8.4)13.2 (8.3-24.9)7.1 (6.0-20.6)82.0 (7.3-NA)6.2 (4.3-10.1)P Value .041 .550 .021 .506 .105HR (95% CI) 0.64 (0.41-0.99) 0.82 (0.43-1.57) 0.50 (0.27-0.92) 0.77 (0.35-1.68) 0.44 (0.15-1.23)OS, med wk (95% CI)31.0 (16.6-41.3)24.6 (12.7-33.9) 30.0 (14.9-44.4)24.9 (13.7-58.3)32.3 (15.9-48.1)20.4 (10.3-33.9)30.0 (18.0-34.6)20.1 (13.7-36.9)108.4 (9.6-108.4)18.6 (6.6-48.0)P Value .673 .526 .253 .767 .144HR (95% CI) 0.91 (0.59-1.41) 1.23 (0.65-2.34) 0.70 (0.38-1.30) 1.12 (0.52-2.42) 0.47 (0.17-1.33) Abbreviations: CI, confidence interval; HR, hazard ratio; med, median; NA, not applicable/not available. Disclosures Czuczman: Celgene: Consultancy. Off Label Use: This abstract describes a clinical trial of lenalidomide, which is an orally-available immunomodulatory agent under investigation for treating patients with diffuse large B-cell lymphoma.. Davies:GlaxoSmithKlein: Research Funding; Hoffman La Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Research Funding. Wagner-Johnston:Celgene: Research Funding. Gascoyne:Celgene: Consultancy, Research Funding. Salles:Pfizer: Honoraria; Gilead: Honoraria; Jansen: Honoraria; Hoffman La Roche: Honoraria; Celgene: Honoraria; Mundipharma: Honoraria. Witzig:Celgene Corporation: Membership on an entity's Board of Directors or advisory committees, Research Funding. Zinzani:Mundipharma: Honoraria; Pfizer: Honoraria; Gilead: Honoraria, Membership on an entity's Board of Directors or advisory committees; Roche: Honoraria; Millennium Takeda: Honoraria; Celgene: Honoraria; Teva: Membership on an entity's Board of Directors or advisory committees. Wright:Celgene: Research Funding. Staudt:Celgene Corporation: Research Funding. Repici:Celgene: Employment. Song:Celgene: Employment. Manzke:Celgene: Employment.

Abstract Purpose: Randomized, multicenter, open-label, phase 2/3 trial investigating lenalidomide versus investigator's choice (IC) in relapsed/refractory diffuse large B-cell lymphoma (DLBCL). Experimental Design: Patients with DLBCL who received ≥2 prior therapies were stratified by DLBCL subtype [germinal center B-cell (GCB) vs. non-GCB; determined by immunohistochemistry (IHC)] and then randomized 1:1 to lenalidomide (25 mg/day, 21 days of 28-day cycle) or IC (gemcitabine, rituximab, etoposide, or oxaliplatin). Crossover to lenalidomide was permitted for IC-treated patients with radiologically confirmed progressive disease. The primary endpoint was overall response rate (ORR). Progression-free survival (PFS), overall survival, and subtype analysis [GCB vs. activated B-cell (ABC)] using gene expression profiling (GEP) were exploratory endpoints. Results: Stage 1: 102 DLBCL patients (by IHC: non-GCB, n = 54; GCB, n = 48) received ≥1 dose of lenalidomide or IC. Hematologic treatment-emergent adverse events with lenalidomide versus IC included neutropenia (42.6%; 36.4%), anemia (33.3%; 47.3%), thrombocytopenia (24.1%; 43.6%), and leukopenia (5.6%; 12.7%), respectively. Overall, lenalidomide-treated patients had an ORR of 27.5% versus 11.8% in IC (ORRs were similar regardless of IHC-defined DLBCL subtype). Median PFS was increased in patients receiving lenalidomide (13.6 weeks) versus IC (7.9 weeks; P = 0.041), with greater improvements in non-GCB patients (15.1 vs. 7.1 weeks, respectively; P = 0.021) compared with GCB (10.1 vs. 9.0 weeks, respectively; P = 0.550). Conclusions: The clinical benefit of lenalidomide monotherapy in DLBCL patients was more evident in the non-GCB subtype. Exploratory analyses suggest that this preferential benefit was more pronounced in the GEP-defined ABC population, demonstrating a need for additional studies of lenalidomide in DLBCL using GEP subtyping. Clin Cancer Res; 23(15); 4127–37. ©2017 AACR.