Scheinberg, Phillip

Abstract Myelodysplastic syndromes (MDS) are clinically and biologically diverse disorders, emphasizing the need for personalized treatment approaches. The International Working Group for Prognostication of MDS (IWG_PM) recently introduced a molecular classification, referred to as the MDS taxonomy, that categorizes patients into 16 subgroups based on 21 gene mutations, 6 cytogenetic abnormalities, and loss of heterozygosity (LOH) at TP53 and TET2 loci. This study sought to validate and enhance the clinical relevance of the MDS taxonomy by analyzing a large retrospective cohort ( n = 5136) and transcriptomic data from a prospective cohort ( n = 477). The taxonomy successfully identified subgroups with distinct clinical characteristics and disease progression patterns. However, incorporating gene interactions from taxonomy subgroups did not improve the prognostic performance of the Molecular International Prognostic Scoring System (IPSS‐M). We further assessed whether the taxonomy could guide management in patients receiving disease‐modifying therapies. Except for the “ TP53 ‐complex” subgroup, taxonomy classifications were not predictive of hypomethylating agent response or transplant outcomes. Nonetheless, they correlated with overall survival, suggesting that while both IPSS‐M and the taxonomy capture disease biology, other non‐genetic factors may influence treatment response. RNA sequencing confirmed the biological distinctiveness of the taxonomy groups. Transcriptomic profiling of CD34+ bone marrow cells revealed unique, homogeneous gene expression patterns, particularly within the AML‐like, bi TET2 , SF3B1 , and TP53 ‐complex subgroups. Further integration of multi‐omics data may refine MDS classification, improving clinical decision‐making and guiding the development of targeted therapies.

PURPOSE The combination of the Bruton tyrosine kinase inhibitor ibrutinib with bendamustine-rituximab for first-line treatment of mantle cell lymphoma (MCL) prolonged progression-free survival (PFS), but without improvement in overall survival (OS), likely because of toxicity. Acalabrutinib was shown to be efficacious and less toxic than ibrutinib in a head-to-head trial in chronic lymphocytic leukemia and therefore might lead to better outcomes in MCL. METHODS Patients 65 years and older with previously untreated MCL received acalabrutinib (100 mg twice daily) or placebo (until disease progression or unacceptable toxicity), plus six cycles of bendamustine (90 mg/m 2 once daily; days 1 and 2) and rituximab (375 mg/m 2 as a single dose; day 1) followed by rituximab maintenance in responding patients for 2 years. Crossover to acalabrutinib at disease progression was permitted. The primary end point was PFS per the independent review committee; overall response rate and OS were secondary end points. RESULTS In total, 598 patients were randomly assigned, with 299 in each arm. At a median follow-up of 49.8 months using the reverse Kaplan-Meier method, the median PFS was 66.4 months in the acalabrutinib arm and 49.6 months in the placebo arm (hazard ratio [HR], 0.73 [95% CI, 0.57 to 0.94]; P = .0160). Benefit was seen across all subgroups, including those with high-risk features. Overall response/complete response rates were 91.0%/66.6% and 88.0%/53.5% in the acalabrutinib and placebo arms, respectively. OS was not significantly different (HR, 0.86 [95% CI, 0.65 to 1.13]; P = .27). Grade 3 or greater adverse events were reported in 88.9% and 88.2% in the acalabrutinib and placebo arms, respectively. CONCLUSION The combination of acalabrutinib with bendamustine-rituximab significantly improved PFS. Clinical benefit of acalabrutinib with bendamustine-rituximab was achieved with manageable toxicity.

Background. The inclusion of gene mutations and chromosomal abnormalities in the 2022 WHO and ICC Classifications of MDS has enhanced diagnostic precision and is expected to improve clinical decision-making process. Although these two systems share similarities, clinically relevant discrepancies still exist and potentially cause inconsistency in their adoption in a clinical setting. In this study on behalf of the International Consortium for MDS (icMDS), we adopted a data-driven approach to provide a harmonization roadmap between the 2022 WHO and ICC classification for MDS. A modified Delphi Process consensus approach is currently ongoing among icMDS experts to finalize a harmonized MDS classification scheme. Methods. We analyzed retrospective international cohorts of patients with a diagnosis of MDS (n=7017) and AML (n=1002) according to WHO 2016 criteria. Hierarchical Dirichlet Processes were applied to define clusters capturing broad dependencies among all gene mutations and cytogenetic abnormalities. To investigate the features of importance and their impact on the clustering process, we employed the SHapley Additive exPlanations approach (SHAP). This allowed to define harmonized labels for each clinical entity. The clinical relevance of the unsupervised clustering was assessed through the analysis of phenotypic features and outcomes among each group. ( Blood 2022;140: 9828-9830) Results. Patients' characteristics are summarized in Table 1. We identified 9 clusters, defined by specific genomic features. The cluster of highest hierarchical importance was characterized by biallelic inactivation of TP53 (biTP53). According to SHAP analysis, bi TP53 was defined as 2 or more TP53 mutations, or 1 mutation with copy number loss or cnLOH. Most patients assigned to bi TP53 cluster had TP53 VAF>10% (77.9%) and complex karyotype (70.1%). Assignment to bi TP53 cluster was irrespective of blast count. Patients with monoallelic TP53 mutation segregated into other clusters. Hierarchically, the second cluster included patients with del(5q). SHAP analysis highlighted 5q deletion alone, or with one other chromosomal abnormality other than -7, and absence of bi TP53, as the most relevant features. Most of these patients had blast counts <5% (88.1%). The third distinct cluster included patients with SF3B1 mutations (in the absence of concurrent del(7q), abn3q26.2, complex karyotype or RUNX1 mutation). Most patients with MDS and SF3B1 mutation had <5% blasts (94.2%). Common co-mutated variants in the SF3B1 cluster included mutant DNMT3A (25.2%) and TET2 (38.3%). Morphologically defined MDS cases (i.e., not meeting criteria for bi TP53, del(5q) or SF3B1) were preferentially assigned to the following additional clusters: SF3B1 and concurrent higher-risk mutations (e.g., RUNX1 and ASXL1); SRSF2 and concomitant TET2 mutations; U2AF1 mutations with del(20q), del(7q) or -7; SRSF2 with TET2 mutations and co-mutational patterns including RUNX1 and ASXL1; and AML-like genomic signatures. Our analyses suggest that morphologically defined MDS is characterized by a large heterogeneity in terms of mutation profiles, not entirely captured by the presence of unilineage versus multilineage dysplasia, percentage of bone marrow blasts, and presence of hypocellularity and fibrosis. To better investigate the continuum between high risk MDS (i.e., patients with ≥10% blasts) and AML, an exploratory comparison was made using a cohort of AML (defined according to WHO 2016) patients analyzed using the same statistical methods. Only a partial overlap in genetic signatures was observed between MDS with ≥10% blasts and AML. However, similarities were observed between the AML-like MDS clusters (characterized by mutant NPM1, bZIP CEBPA, and Core Binding Factor abnormalities) and AML clusters defined by the same genetic signature, thus supporting the classification of these entities as AML, irrespective of blast count. Conclusion. Our study demonstrated the utility of a data-driven approach based on advanced statistical methods to generate a harmonized classification for MDS. Table 2 shows a provisional, hierarchical classification algorithm. Further refinement of entity labels and classification criteria is the subject of the ongoing modified Delphi Process consensus approach among icMDS experts.

7514 Background: Relapse is expected in treated indolent lymphoma patients, and an unmet need exists to prolong remission with effective therapies. Lenalidomide + rituximab (R2) may improve the efficacy of next treatment by their unique mechanisms. Methods: The AUGMENT phase III study evaluated patients with R/R FL gr 1-3a (82%) and MZL (18%) after ≥ 1 prior systemic therapy (not rituximab refractory). Randomization was 1:1 to R2 (lenalidomide PO 20 mg/d, d1-21/28 X12 cycles [c] + rituximab [R] IV 375 mg/m2/wk, c1, d1, 8, 15, 22 and c2-5, d1) and R/placebo (same dosing schedule). The primary endpoint was PFS by 2007 IWG; secondary/exploratory analyses were time to next antilymphoma/chemotherapy treatment (TTNLT/TTNCT) and response to next treatment. Per regulatory guidance, PFS2 was defined as time from randomization to first PD or death from any cause after next antilymphoma treatment, or initiation of a third antilymphoma treatment. Results: Median PFS was superior for R2 over R/placebo (39.4 vs 14.1 mo; HR = 0.46; P < 0.0001). As of 22June2018, median TTNLT, TTNCT, and PFS2 were not reached for R2, and were significantly longer than R/placebo (HR = 0.54, 0.50, and 0.52, respectively). For 49/178 (28%) R2 and 80/180 (44%) R/placebo patients receiving next antilymphoma therapy, response was generally higher with R2 (57% ORR; 31% CR) than R/placebo (36% ORR; 16% CR; Table). Conclusions: These analyses suggest that R2 (vs R/placebo) prolonged time to subsequent treatment and is associated with longer PFS2, enabling greater response to next therapy. Although patient numbers were modest, it is hypothesized that patients who received R2 were generally more sensitive to subsequent therapy than those treated with R/placebo. Clinical trial information: NCT01938001. [Table: see text]

Abstract Background: Indolent non-Hodgkin lymphoma (iNHL), including follicular lymphoma (FL) and marginal zone lymphoma (MZL), is typically responsive to initial chemoimmunotherapy, but relapse is expected. Single-agent rituximab is FDA approved and frequently administered for patients with relapsed/refractory (R/R) low-grade or follicular CD20-positive B-cell NHL. Lenalidomide is an immunomodulatory agent with preclinical and clinical antilymphoma activity alone and in combination with rituximab. We compared the efficacy and safety of lenalidomide plus rituximab (R2) to rituximab (plus placebo) in patients with R/R iNHL. Methods: AUGMENT (NCT01938001) is a multicenter, double-blind, randomized phase III study of R2 vs rituximab/placebo (control) in patients with FL grade 1-3a or MZL who were previously treated with ≥ 1 prior systemic therapy with documented relapsed or refractory disease but not refractory to rituximab (refractory was defined as < partial response to rituximab or rituximab-chemotherapy OR disease progression < 6 months after last rituximab dose). Patients were stratified by prior rituximab treatment (yes vs no), time since last antilymphoma therapy (≤ 2 vs > 2 years), and histology (FL vs MZL), and then randomized 1:1 to R2 or control for up to 1 year. R2 patients received oral lenalidomide 20 mg/day (d), d1-21/28 for 12 cycles plus rituximab IV 375 mg/m2 weekly in cycle 1 and d1 of cycles 2-5. Control patients received rituximab and placebo on the same schedule. Dose modifications were pre-specified in the protocol to manage toxicities. The primary endpoint was progression-free survival (PFS) per 2007 IWG criteria without PET as assessed by Independent Review Committee (IRC; central review). Secondary endpoints included overall response rate (ORR), complete response (CR), duration of response (DOR), time-to-next antilymphoma treatment (TTNLT), overall survival (OS), and safety. Results: A total of 358 patients were randomized (n = 178 R2; n = 180 control), median age was 63 years (range, 26 - 88), 34% FLIPI score ≥ 3, and histologies of 82% FL/18% MZL. Median number of prior systemic treatments was 1 (range, 1 - 12); 84% received prior rituximab and 51% had prior antilymphoma therapy within 2 years of enrollment. At median follow-up of 28.3 months, the study met its primary endpoint of PFS with HR = 0.46 (95% CI, 0.34 - 0.62; P < 0.0001) (Figure 1). Median PFS was 39.4 months for R2 vs 14.1 months for control. IRC-assessed ORR for R2 was 78% vs 53% for control (P < 0.0001). CR was 34% for R2 vs 18% for control (P = 0.001). Median DOR was 36.6 and 21.7 months for R2 and control arms, respectively. TTNLT was improved for R2 vs control with HR = 0.54 (95% CI, 0.38 - 0.78; P = 0.0007). OS data were not mature with 16 deaths reported in the R2 arm vs 26 deaths in control (HR = 0.61 [95% CI, 0.33 - 1.13]). Selected all-grade treatment-emergent adverse events (TEAEs) of interest more common in the R2 vs control arm (≥ 10% difference) were infections (63% vs 49%), cutaneous reactions (32% vs 12%), constipation (26% vs 14%), thrombocytopenia (15% vs 4%), and tumor flare reaction (11% vs 1%). Grade 3/4 TEAEs were reported in 69% R2 and 32% control patients. More frequent grade 3/4 TEAEs in the R2 vs control arm were primarily attributable to increased neutropenia (50% vs 13%) and leukopenia (7% vs 2%). Grade 5 TEAEs were reported in 2 patients in each arm. TEAEs leading to discontinuation of lenalidomide occurred in 9% of patients vs 5% for rituximab + placebo. Neutropenia was the only TEAE leading to discontinuation of lenalidomide in > 1 patient (n = 5). Seventy-one percent of R2 patients completed all 12 cycles of planned treatment vs 61% of control. Disease progression was the leading reason for discontinuation of lenalidomide/placebo (n = 21 R2, n = 54 control). Conclusions: R2 demonstrated superior efficacy over rituximab monotherapy (plus placebo) as measured by the primary endpoint of progression-free survival as well as secondary endpoints of ORR, CR, DOR, and TTNLT in patients with R/R FL grade 1-3a and MZL. At this early analysis, fewer deaths have been observed in the R2 arm. Despite additional hematologic toxicity, greater efficacy of the R2 regimen (and fewer early progressions) allowed more patients to complete the planned therapy and delayed the need for subsequent treatment. R2 represents an important new treatment option in patients with previously treated FL/MZL, with meaningful advantages over single-agent rituximab. Disclosures Leonard: Celgene: Consultancy; Karyopharm: Consultancy; ADC Therapeutics: Consultancy; MEI Pharma: Consultancy; Juno: Consultancy; Bayer: Consultancy; AstraZeneca: Consultancy; Sutro: Consultancy; Biotest: Consultancy; United Therapeutics: Consultancy; Pfizer: Consultancy; Novartis: Consultancy; Genentech/Roche: Consultancy; Gilead: Consultancy; BMS: Consultancy. Trněný:F. Hoffman-La Roche Ltd: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Advisory board, Research Funding; Incyte: Membership on an entity's Board of Directors or advisory committees, Other: Advisory board; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Advisory board; Abbvie: Honoraria, Research Funding; Gilead: Honoraria; Janssen: Membership on an entity's Board of Directors or advisory committees, Other: Advisory board; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Advisory board; Morphosys: Membership on an entity's Board of Directors or advisory committees, Other: Advisory board; Sandoz: Honoraria. Izutsu:Otsuka: Honoraria; Bristol- Myers Squibb: Honoraria; Nihon Medi-Physics: Honoraria; Novartis: Honoraria; Mundhi: Honoraria; HUYA Bioscience International: Research Funding; Kyowa Hakko Kirin: Honoraria; Takeda: Honoraria, Research Funding; Abbvie: Honoraria, Research Funding; Amgen: Research Funding; Bayer: Consultancy, Honoraria, Research Funding; Gilead Sciences: Honoraria; Eisai: Honoraria, Research Funding; Chugai: Honoraria, Research Funding; Daiichi Sankyo: Honoraria, Research Funding; Astellas: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Zenyaku: Research Funding; Celltrion: Research Funding; MSD: Honoraria; Ono: Honoraria, Research Funding; Symbio: Research Funding; Celgene: Consultancy, Research Funding; Solasia: Research Funding; Sanofi: Research Funding; Meiji Seika: Honoraria; Shionogi: Honoraria; Asahi Kasei: Honoraria. Fowler:Janssen: Consultancy, Research Funding; Pharmacyclics: Consultancy, Research Funding. Zhu:Beijing Cancer Hospital (Peking University Cancer Hospital): Employment. Scheliga:INCA - Instituto Nacional Do Cancer, Brazil: Employment. Pinto:Servier: Consultancy; BMS: Honoraria, Research Funding; MSD: Honoraria; Roche: Honoraria; Takeda: Honoraria; Celgene: Honoraria; Gilead: Honoraria. Scheinberg:Novartis: Consultancy, Speakers Bureau; Janssen: Honoraria, Research Funding; Pfizer: Speakers Bureau. Flinn:Trillium: Research Funding; Takeda: Research Funding; Calithera: Research Funding; Incyte: Research Funding; Verastem: Research Funding; ArQule: Research Funding; Janssen: Research Funding; Pharmacyclics: Research Funding; Seattle Genetics: Research Funding; Forty Seven: Research Funding; Agios: Research Funding; BeiGene: Research Funding; Kite: Research Funding; Portola: Research Funding; Verastem: Consultancy, Research Funding; Celgene: Research Funding; Pfizer: Research Funding; Forma: Research Funding; Merck: Research Funding; Novartis: Research Funding; Constellation: Research Funding; Curis: Research Funding; Infinity: Research Funding; TG Therapeutics: Research Funding; Genentech: Research Funding; Gilead: Research Funding. Moreira:Instituto Português de Oncologia do Porto FG, EPE, Porto, Portugal: Employment. Liu:Celgene: Employment, Equity Ownership. Kalambakas:Celgene: Employment, Equity Ownership. Fustier:Celgene: Employment, Equity Ownership. Wu:Celgene: Employment, Equity Ownership. Gribben:Cancer Research UK: Research Funding; Unum: Equity Ownership; Novartis: Honoraria; Abbvie: Honoraria; Wellcome Trust: Research Funding; Acerta Pharma: Honoraria, Research Funding; Kite: Honoraria; NIH: Research Funding; Roche: Honoraria; Janssen: Honoraria, Research Funding; Medical Research Council: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; TG Therapeutics: Honoraria; Pharmacyclics: Honoraria.

Background:Lenalidomide plus rituximab (R2) has established efficacy and a tolerable safety profile in patients with previously untreated and relapsed/refractory (R/R) indolent NHL. This includes patients with marginal zone lymphoma (MZL) who are generally treated in a similar manner to follicular lymphoma (FL).Aims:Primary results for the AUGMENT study in R/R FL grade 1–3a and MZL showed significantly improved progression‐free survival (PFS) for R2 over R/placebo in the overall population and in the subgroup analyses, with the exception of the MZL subgroup. Given the small sample size for MZL patients, post hoc analyses of baseline characteristics and univariate/multivariate analyses of PFS were conducted to examine possible explanations for these results.Methods:The phase III AUGMENT study randomized patients 1:1 to R2 (lenalidomide PO 20 mg/day (d), d1–21/28 X12 cycles plus rituximab IV 375 mg/m2 weekly in cycle 1 and d1 of cycles 2–5) or rituximab‐placebo (R/placebo; same dosing schedule). PFS by 2007 IWG (without PET) was the primary endpoint. Post hoc Cox regression models were used for univariate analysis of one risk factor and multivariate analyses including treatment arm and significant risk factors (P < 0.05) from the univariate analyses.Results:A total of 63/358 (18%) MZL patients were part of the overall study population, including n = 14/16 MALT, n = 9/6 splenic, and n = 8/10 nodal MZL subtypes for respective R2 and R/placebo arms. Numerical differences between treatment arms for baseline demographics and disease characteristics for MZL patients favoring the R/placebo arm were: fewer R2 patients had an ECOG score of 0 (55% R2 vs 72% R/placebo), and more R2 (vs R/placebo) patients were older (≥65 y: 68% vs 59%; ≥70 y: 42% vs 38%), were refractory to the last prior regimen (13% vs 3%), had Ann Arbor stage IV disease at enrollment (65% vs 41%), high‐risk MALT‐IPI score (50% vs 19%), elevated LDH (29% vs 19%), B symptoms (13% vs 3%), and high tumor burden per GELF criteria (65% vs 56%). The most common grade 3/4 AEs for MZL patients receiving R2 vs R/placebo, respectively, were neutropenia (47% vs 16%), pneumonia (3% vs 13%), and leukopenia (10% vs 0). A total of 5 deaths occurred in the R2 arm (most associated with progression of disease; 2 deaths occurred early at 3 and 13 days after randomization [one prior to receiving R2 and one 2 days after treatment initiation]) and 2 in the R/placebo arm. Despite worse prognostic features, best response was improved with R2 (vs R/placebo) with ORR = 65% vs 44% (P = 0.13) and CR = 29% vs 13% (P = 0.13), although neither were statistically significant. Improved response rates did not translate into a survival advantage for R2. Median PFS for MZL patients was 20.2 mo R2 vs 25.2 mo R/placebo (HR = 1.00; 95% CI, 0.47–2.13; P = 1.0). Univariate analyses showed that several baseline factors were prognostic for MZL patients (P < 0.05): Ann Arbor stage IV, elevated LDH, and unfit for chemotherapy (Table). Multivariate analyses adjusting for the imbalance in these 3 significant prognostic factors showed an adjusted PFS HR of 0.51 (95% CI, 0.20–1.28) favoring the R2 arm, similar to the PFS HR in the overall population (HR = 0.46; 95% CI, 0.34–0.63). The univariate and multivariate analyses suggested that the imbalance in baseline prognostic factors impacted PFS results for MZL patients.Summary/Conclusion:Overall, these findings suggest that the PFS results in MZL patients were negatively impacted by the imbalance in baseline prognostic factors and more aggressive disease in the R2 over the R/placebo arm.image