Biomarkers of Overall Survival and Response to Glasdegib and Intensive or Non-Intensive Chemotherapy in Patients with Acute Myeloid Leukemia

Abstract Background: In a previously reported Phase 2 randomized study of patients with acute myeloid leukemia (AML), addition of the investigational agent glasdegib (PF-04449913) to low-dose cytarabine (LDAC) improved overall survival (OS) when compared with LDAC alone. In a non-randomized study arm, glasdegib together with 7+3 chemotherapy was well tolerated and associated with clinical activity. We used a comprehensive biomarker analysis, evaluating gene expression, circulating cytokine levels, and gene mutations, to identify molecular drivers that predict overall response (OR) and OS. Methods: In this Phase 2 multicenter study (NCT01546038), patients with AML who were suitable for non-intensive therapy were randomized (2:1) to LDAC + glasdegib 100 mg QD or LDAC alone, and patients suitable for intensive therapy were assigned 7+3 plus glasdegib 100 mg QD. Whole blood, serum, and bone marrow aspirate samples were collected at baseline, and used to assess 19 genes for expression analysis, 38 analytes for circulating cytokine levels, and 109 genes for mutation analysis. Gene expression was analyzed using TaqMan Low Density Array Cards (TLDCs), cytokine levels were analyzed using quantitative, multiplexed immunoassays (Myriad RBM), and mutation analysis was performed using the Illumina® MiSeq instrument (San Diego, CA). All correlations were performed either for OS or for OR. For gene expression and cytokine analysis, a cut-off value above or below the median expression level for each treatment arm was used to separate samples into two subgroups (< or ≥ the median value) to explore the relationship of expression levels with OS data. Criteria for significance in the non-intensive cohort required one subgroup to have a p-value of <0.05 in the between-treatment arms comparison and the HR difference between the two subgroups to be ≥2 fold. Responses were defined as patients with a complete remission (CR), CR with incomplete blood count recovery (CRi), morphologic leukemia-free state, partial remission (PR), or PRi. For response correlations, genes or cytokines were considered to be differentially expressed if they had a p-value <0.05 and were differentially expressed by ≥2-fold. Results: Within the non-intensive arm (LDAC + glasdegib, n=68; LDAC alone, n=30), expression levels of several genes correlated with improved OS with glasdegib plus LDAC. Lower levels of expression of FOXM1 and MSI2, and higher expression levels of BCL2 and CCND2 correlated with improved OS with the combination. Additionally, lower levels of the cytokines 6CKINE (CCL21), ICAM-1, MIP-1α, and MMP-3 correlated with improved OS. An analysis of correlations of gene expression and cytokine levels with OR could not be completed due to the low number of responders in the LDAC only group (n=2). In the intensive treatment arm (glasdegib and 7+3, n=59), higher PTCH1 expression correlated with improved OS (p=0.0219, median OS 10.8 versus 39.5 months). In this cohort, lower levels of IL-8 (p=0.0225) and MIP-3β (p=0.0403) correlated with lower OS. Expression levels of no genes or cytokines significantly correlated with OR in this arm. We also examined correlations between gene mutation status and OS in both study arms. In the non-intensive arm (LDAC + glasdegib, n=58; LDAC alone, n=25), no genes mutated in at least 5 patients correlated with OS. In the intensive treatment arm (n=47), mutations in FLT3, TP53, CEP170, NPM1, and ANKRD26 correlated with OS (all p<0.05). Patients in this arm with FLT3 mutations responded better than patients with wild type FLT3 (p=0.0336, median OS of 13.1 months versus unreached for FLT3 mutant). Conclusions: In this biomarker analysis, we found that expression levels of a select number of genes and circulating cytokines implicated in AML correlated with OS in the non-intensive and the intensive arms. The improved response for patients with FLT3 mutations and high PTCH1 expression levels in the intensive arm deserves further investigation. These findings need to be verified in larger controlled studies, which are ongoing. Disclosures Cortes: Novartis: Consultancy, Research Funding; Daiichi Sankyo: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Astellas Pharma: Consultancy, Research Funding; Arog: Research Funding. Pollyea:Argenx: Consultancy, Membership on an entity's Board of Directors or advisory committees; Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead: Consultancy; Celyad: Consultancy, Membership on an entity's Board of Directors or advisory committees; AbbVie: Consultancy, Research Funding; Curis: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Agios: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Heuser:Astellas: Research Funding; Daiichi Sankyo: Research Funding; Sunesis: Research Funding; Tetralogic: Research Funding; Bayer Pharma AG: Consultancy, Research Funding; StemLine Therapeutics: Consultancy; Janssen: Consultancy; Pfizer: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Research Funding; BergenBio: Research Funding; Karyopharm: Research Funding. Chan:Pfizer: Employment, Equity Ownership. Wang:Pfizer: Employment, Equity Ownership. Ching:Pfizer Inc: Employment, Equity Ownership. Johnson:Pfizer Inc: Employment, Equity Ownership. O'Brien:Pfizer Inc: Employment, Equity Ownership.

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Baseline and on-Treatment Bone Marrow Microenvironments Predict Myeloma Patient Outcomes and Inform Potential Intervention Strategies

Blood ◽

10.1182/blood-2018-99-113169 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 1882-1882 ◽

Cited By ~ 1

Author(s):

Samuel A Danziger ◽

Mark McConnell ◽

Jake Gockley ◽

Mary Young ◽

Adam Rosenthal ◽

...

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

Bone Marrow ◽

Board Of Directors ◽

Research Funding ◽

Expression Profiles ◽

Cell Types ◽

Employment Equity ◽

Advisory Committees ◽

Equity Ownership

Abstract Introduction The multiple myeloma (MM) tumor microenvironment (TME) strongly influences patient outcomes as evidenced by the success of immunomodulatory therapies. To develop precision immunotherapeutic approaches, it is essential to identify and enumerate TME cell types and understand their dynamics. Methods We estimated the population of immune and other non-tumor cell types during the course of MM treatment at a single institution using gene expression of paired CD138-selected bone marrow aspirates and whole bone marrow (WBM) core biopsies from 867 samples of 436 newly diagnosed MM patients collected at 5 time points: pre-treatment (N=354), post-induction (N=245), post-transplant (N=83), post-consolidation (N=51), and post-maintenance (N=134). Expression profiles from the aspirates were used to infer the transcriptome contribution of immune and stromal cells in the WBM array data. Unsupervised clustering of these non-tumor gene expression profiles across all time points was performed using the R package ConsensusClusterPlus with Bayesian Information Criterion (BIC) to select the number of clusters. Individual cell types in these TMEs were estimated using the DCQ algorithm and a gene expression signature matrix based on the published LM22 leukocyte matrix (Newman et al., 2015) augmented with 5 bone marrow- and myeloma-specific cell types. Results Our deconvolution approach accurately estimated percent tumor cells in the paired samples compared to estimates from microscopy and flow cytometry (PCC = 0.63, RMSE = 9.99%). TME clusters built on gene expression data from all 867 samples resulted in 5 unsupervised clusters covering 91% of samples. While the fraction of patients in each cluster changed during treatment, no new TME clusters emerged as treatment progressed. These clusters were associated with progression free survival (PFS) (p-Val = 0.020) and overall survival (OS) (p-Val = 0.067) when measured in pre-transplant samples. The most striking outcomes were represented by Cluster 5 (N = 106) characterized by a low innate to adaptive cell ratio and shortened patient survival (Figure 1, 2). This cluster had worse outcomes than others (estimated mean PFS = 58 months compared to 71+ months for other clusters, p-Val = 0.002; estimate mean OS = 105 months compared with 113+ months for other clusters, p-Val = 0.040). Compared to other immune clusters, the adaptive-skewed TME of Cluster 5 is characterized by low granulocyte populations and high antigen-presenting, CD8 T, and B cell populations. As might be expected, this cluster was also significantly enriched for ISS3 and GEP70 high risk patients, as well as Del1p, Del1q, t12;14, and t14:16. Importantly, this TME persisted even when the induction therapy significantly reduced the tumor load (Table 1). At post-induction, outcomes for the 69 / 245 patients in Cluster 5 remain significantly worse (estimate mean PFS = 56 months compared to 71+ months for other clusters, p-Val = 0.004; estimate mean OS = 100 months compared to 121+ months for other clusters, p-Val = 0.002). The analysis of on-treatment samples showed that the number of patients in Cluster 5 decreases from 30% before treatment to 12% after transplant, and of the 63 patients for whom we have both pre-treatment and post-transplant samples, 18/20 of the Cluster 5 patients moved into other immune clusters; 13 into Cluster 4. The non-5 clusters (with better PFS and OS overall) had higher amounts of granulocytes and lower amounts of CD8 T cells. Some clusters (1 and 4) had increased natural killer (NK) cells and decreased dendritic cells, while other clusters (2 and 3) had increased adipocytes and increases in M2 macrophages (Cluster 2) or NK cells (Cluster 3). Taken together, the gain of granulocytes and adipocytes was associated with improved outcome, while increases in the adaptive immune compartment was associated with poorer outcome. Conclusions We identified distinct clusters of patient TMEs from bulk transcriptome profiles by computationally estimating the CD138- fraction of TMEs. Our findings identified differential immune and stromal compositions in patient clusters with opposing clinical outcomes and tracked membership in those clusters during treatment. Adding this layer of TME to the analysis of myeloma patient baseline and on-treatment samples enables us to formulate biological hypotheses and may eventually guide therapeutic interventions to improve outcomes for patients. Disclosures Danziger: Celgene Corporation: Employment, Equity Ownership. McConnell:Celgene Corporation: Employment. Gockley:Celgene Corporation: Employment. Young:Celgene Corporation: Employment, Equity Ownership. Schmitz:Celgene Corporation: Employment, Equity Ownership. Reiss:Celgene Corporation: Employment, Equity Ownership. Davies:MMRF: Honoraria; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; TRM Oncology: Honoraria; Abbvie: Consultancy; ASH: Honoraria; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Honoraria. Copeland:Celgene Corporation: Employment, Equity Ownership. Fox:Celgene Corporation: Employment, Equity Ownership. Fitch:Celgene Corporation: Employment, Equity Ownership. Newhall:Celgene Corporation: Employment, Equity Ownership. Barlogie:Celgene: Consultancy, Research Funding; Dana Farber Cancer Institute: Other: travel stipend; Multiple Myeloma Research Foundation: Other: travel stipend; International Workshop on Waldenström's Macroglobulinemia: Other: travel stipend; Millenium: Consultancy, Research Funding; European School of Haematology- International Conference on Multiple Myeloma: Other: travel stipend; ComtecMed- World Congress on Controversies in Hematology: Other: travel stipend; Myeloma Health, LLC: Patents & Royalties: : Co-inventor of patents and patent applications related to use of GEP in cancer medicine licensed to Myeloma Health, LLC. Trotter:Celgene Research SL (Spain), part of Celgene Corporation: Employment, Equity Ownership. Hershberg:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Dervan:Celgene Corporation: Employment, Equity Ownership. Ratushny:Celgene Corporation: Employment, Equity Ownership. Morgan:Takeda: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Research Funding.

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SGN-CD33A Plus Hypomethylating Agents: A Novel, Well-Tolerated Regimen with High Remission Rate in Frontline Unfit AML

Blood ◽

10.1182/blood.v126.23.454.454 ◽

2015 ◽

Vol 126 (23) ◽

pp. 454-454 ◽

Cited By ~ 14

Author(s):

Amir T. Fathi ◽

Harry P Erba ◽

Jeffrey E Lancet ◽

Eytan M Stein ◽

Roland B. Walter ◽

...

Keyword(s):

Board Of Directors ◽

Research Funding ◽

Remission Rate ◽

Intensive Therapy ◽

Advisory Board ◽

Hypomethylating Agents ◽

Employment Equity ◽

Advisory Committees ◽

Low Intensity ◽

Equity Ownership

Abstract Background Older patients with AML who are not candidates for intensive therapy are typically treated with hypomethylating agents (HMAs) or other low intensity therapy. HMAs have been shown to upregulate CD33 and to increase sensitivity to cytotoxic chemotherapy by decreasing apoptotic threshold in tumor cells. SGN-CD33A (or 33A) is a CD33-directed antibody conjugated to 2 molecules of a pyrrolobenzodiazepine (PBD) dimer. Upon binding, 33A is internalized and transported to the lysosomes where PBD dimer is released via proteolytic cleavage of the linker, crosslinking DNA, and leading to cell death. In preclinical studies combining 33A with an HMA (azacitidine and decitabine), synergy has been demonstrated in multidrug resistant AML models (Sutherland ASH 2014). Methods A combination cohort in a phase 1 study (NCT01902329) was designed to evaluate the safety, tolerability, pharmacokinetics (PK), and anti-leukemic activity of 33A in combination with an HMA. Eligible patients (ECOG 0-1) must have previously untreated CD33-positive AML, and have declined intensive therapy. A single dose level of 33A, 10 mcg/kg, was administered outpatient IV every 4 weeks on the last day of HMA (azacitidine or decitabine [5 day regimen], standard dosing). Patients with clinical benefit may continue treatment until relapse or unacceptable toxicity. Investigator assessment of response is per IWG criteria; CRi requires either platelet count of ≥100,000/µL or neutrophils of ≥1,000/µL (Cheson 2003). Results To date, 24 patients (63% male) with a median age of 77 years (range, 66-83) have been treated with the combination therapy. 42% of patients had adverse cytogenetics (MRC), 23 patients were treatment naïve and 1 patient had received prior low intensity therapy for MDS. At baseline, patients had a median of 60% BM blasts (range, 2%-90%) and a median of WBC of 2.2 (range, 1-132). At the time of this interim analysis, patients were on treatment for a median of 13.5+ weeks with 17 patients continuing treatment; no DLTs have been reported. Grade 3 or higher adverse events (AE) reported in >20% of patients were fatigue (54%), febrile neutropenia (46%), anemia (25%), neutropenia (25%), and thrombocytopenia (21%). Other treatment-emergent AEs regardless of relationship to study treatment reported in ˃20% of patients were nausea (29%), decreased appetite (25%), and constipation (21%). Thirty- and 60-day mortality rates are 0% and 4% respectively with no treatment-related deaths reported. Fifteen of the 23 efficacy evaluable patients (65%) achieved CR (5) or CRi (10). Remissions were generally obtained after 2 cycles of treatment and were observed in many patients with adverse risk including underlying myelodysplasia (6/8, 75%) and adverse cytogenetics (8/9, 89%). Median OS has not been reached with 20 patients alive at the time of this data cut. Conclusions The combination of 33A with HMA appears to be well-tolerated, active, and has no identified off-target toxicities. Activity with the combination compares favorably with historical experience with HMAs alone in this patient population. The CR+CRi rate of 65% in AML patients with poor risk factors with the observed low 60-day mortality (4%) are particularly encouraging. These promising data warrant further evaluation in future trials. Disclosures Fathi: Agios Pharmaceuticals: Other: Advisory Board participation; Merck: Other: Advisory Board participation; Seattle Genetics: Other: Advisory Board participation, Research Funding. Off Label Use: SGN-CD33A is an investigational agent being studied in patients with CD33-positive AML. SGN-CD33A is not approved for use.. Erba:GlycoMimetics; Janssen: Other: Data Safety & Monitoring Committees; Sunesis;Pfizer; Daiichi Sankyo; Ariad: Consultancy; Millennium/Takeda; Celator; Astellas: Research Funding; Seattle Genetics; Amgen: Consultancy, Research Funding; Novartis; Incyte; Celgene: Consultancy, Patents & Royalties. Lancet:Seattle Genetics: Consultancy; Pfizer: Research Funding; Boehringer-Ingelheim: Consultancy; Kalo-Bios: Consultancy; Amgen: Consultancy; Celgene: Consultancy, Research Funding. Stein:Seattle Genetics, Inc.: Membership on an entity's Board of Directors or advisory committees; Agios: Membership on an entity's Board of Directors or advisory committees. Walter:Pfizer, Inc.: Consultancy; Covagen AG: Consultancy; AstraZeneca, Inc.: Consultancy; CSL Behring: Research Funding; AbbVie, Inc.: Research Funding; Amgen: Research Funding; Amphivena Therapeutics, Inc.: Consultancy, Research Funding; Seattle Genetics, Inc.: Consultancy, Research Funding. DeAngelo:Incyte: Consultancy; Amgen: Consultancy; Pfizer: Consultancy; Ariad: Consultancy; Bristol Myers Squibb: Consultancy; Novartis: Consultancy; Celgene: Consultancy; Agios: Consultancy. Faderl:Celator: Research Funding; Ambit: Research Funding; BMS: Research Funding; Astellas: Research Funding; Karyopharm: Consultancy, Research Funding; Seattle Genetics, Inc.: Research Funding; JW Pharma: Consultancy; Celgene: Consultancy, Research Funding, Speakers Bureau; Pfizer: Research Funding; Onyx: Speakers Bureau. Jillella:Seattle Genetics, Inc.: Research Funding. Bixby:Seattle Genetics, Inc.: Research Funding. Kovacsovics:Seattle Genetics, Inc.: Research Funding. O'Meara:Seattle Genetics, Inc: Employment, Equity Ownership. Kennedy:Seattle Genetics, Inc.: Employment, Equity Ownership. Stein:Amgen: Speakers Bureau.

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Crowdsourced High-Risk Classifiers for Multiple Myeloma Patients Commonly Identify PHF19 As a Robust Progression Biomarker

Blood ◽

10.1182/blood-2019-127362 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 4370-4370

Author(s):

Michael J Mason ◽

Carolina D. Schinke ◽

Christine Eng ◽

Fadi Towfic ◽

Fred Gruber ◽

...

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

High Risk ◽

Disease Progression ◽

Board Of Directors ◽

Research Funding ◽

Plasma Cells ◽

Employment Equity ◽

Advisory Committees ◽

Equity Ownership

Multiple myeloma (MM) is a hematological malignancy of terminally differentiated plasma cells residing within the bone marrow with 25,000-30,000 patients diagnosed in the United States each year. The disease's clinical course depends on a complex interplay chromosomal abnormalities and mutations within plasma cells and patient socio-demographic factors. Novel treatments extended the time to disease progression and overall survival for the majority of patients. However, a subset of 15%-20% of MM patients exhibit an aggressive disease course with rapid disease progression and poor overall survival regardless of treatment. Accurately predicting which patients are at high-risk is critical to designing studies with a better understanding of myeloma progression and enabling the discovery of novel therapeutics that extend the progression free period of these patients. To date, most MM risk models use patient demographic data, clinical laboratory results and cytogenetic assays to predict clinical outcome. High-risk associated cytogenetic alterations include deletion of 17p or gain of 1q as well as t(14;16), t(14;20), and most commonly t(4,14), which leads to juxtaposition of MMSET with the immunoglobulin heavy chain locus promoter, resulting in overexpression of the MMSET oncogene. While cytogenetic assays, in particular fluorescence in situ hybridization (FISH), are widely available, their risk prediction is sub-optimal and recently developed gene expression based classifiers predict more accurately rapid progression. To investigate possible improvements to models of myeloma risk, we organized the Multiple Myeloma DREAM Challenge, focusing on predicting high-risk, defined as disease progression or death prior to 18 months from diagnosis. This effort combined 4 discovery datasets providing participants with clinical, cytogenetic, demographic and gene expression data to facilitate model development while retaining 4 additional datasets, whose clinical outcome was not publicly available, in order to benchmark submitted models. This crowd-sourced effort resulted in the unbiased assessment of 171 predictive algorithms on the validation dataset (N = 823 unique patient samples). Analysis of top performing methods identified high expression of PHF19, a histone methyltransferase, as the gene most strongly associated with disease progression, showing greater predictive power than the expression level of the putative high-risk gene MMSET. We show that a simple 4 feature model composed of age, stage and the gene expression of PHF19 and MMSET is as accurate as much larger published models composed of over 50 genes combined with ISS and age. Results from this work suggest that combination of gene expression and clinical data increases accuracy of high risk models which would improve patient selection in the clinic. Disclosures Towfic: Celgene Corporation: Employment, Equity Ownership. Dalton:MILLENNIUM PHARMACEUTICALS, INC.: Honoraria. Goldschmidt:Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; John-Hopkins University: Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Mundipharma: Research Funding; Amgen: Consultancy, Research Funding; Chugai: Honoraria, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Molecular Partners: Research Funding; MSD: Research Funding; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive Biotechnology: Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Research Funding; Dietmar-Hopp-Stiftung: Research Funding; John-Hopkins University: Research Funding. Avet-Loiseau:takeda: Consultancy, Other: travel fees, lecture fees, Research Funding; celgene: Consultancy, Other: travel fees, lecture fees, Research Funding. Ortiz:Celgene Corporation: Employment, Equity Ownership. Trotter:Celgene Corporation: Employment, Equity Ownership. Dervan:Celgene: Employment. Flynt:Celgene Corporation: Employment, Equity Ownership. Dai:M2Gen: Employment. Bassett:Celgene: Employment, Equity Ownership. Sonneveld:SkylineDx: Research Funding; Takeda: Honoraria, Research Funding; Karyopharm: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; BMS: Honoraria; Amgen: Honoraria, Research Funding. Shain:Amgen: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; AbbVie: Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees; Sanofi Genzyme: Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Consultancy. Munshi:Abbvie: Consultancy; Takeda: Consultancy; Oncopep: Consultancy; Celgene: Consultancy; Adaptive: Consultancy; Amgen: Consultancy; Janssen: Consultancy. Morgan:Bristol-Myers Squibb, Celgene Corporation, Takeda: Consultancy, Honoraria; Celgene Corporation, Janssen: Research Funding; Amgen, Janssen, Takeda, Celgene Corporation: Other: Travel expenses. Walker:Celgene: Research Funding. Thakurta:Celgene: Employment, Equity Ownership.

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Clustering of Transcriptomic Signatures in Newly Diagnosed Diffuse Large B-Cell Lymphoma Identifies Two High-Risk Subgroups Which Increase in Prevalence at Relapse

Blood ◽

10.1182/blood-2019-122756 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 923-923

Author(s):

Maria Ortiz ◽

Kerstin Wenzl ◽

Matthew Stokes ◽

C. Chris Huang ◽

Matthew J. Maurer ◽

...

Keyword(s):

Gene Expression ◽

High Risk ◽

B Cell ◽

Board Of Directors ◽

Research Funding ◽

Newly Diagnosed ◽

Employment Equity ◽

Advisory Committees ◽

Genetics Research ◽

Equity Ownership

Background: DLBCL has traditionally been classified by cell of origin (COO) subcategories based on tumor gene expression profiles which include Activated B-Cell (ABC) and Germinal Center B-Cell (GCB). Recently, using tumor samples from patients treated with RCHOP, new classification models have focused on DNA alterations. However, a comprehensive integrative approach using a large transcriptomic data set across both newly diagnosed (nd) and relapsed/refractory (r/r) DLBCL is yet to be accomplished. A robust clustering of this type will allow for identification of biologically driven DLBCL patient subgroups and may predict patient outcome and inform treatment approaches. Methods: RNAseq was performed on a total of 882 DLBCL tumor FFPE biopsies from 2 ndDLBCL cohorts (cohort 1 and 2) and 2 r/r DLBCL cohorts (cohorts 3 and 4). Cohort 1 (N=267) was commercially sourced and served as the discovery cohort. Cohort 2 (N=340) was from the Mayo/Iowa Lymphoma SPORE Molecular Epidemiology Resource (MER) and served as the replication cohort. Cohort 3 (N=189) was from the CC-122-ST-001 and CC-122-DLBCL-001 clinical trials (NCT01421524 and NCT02031419), and cohort 4 (N=86) was from r/r patients from the MER. Clustering input consisted of gene expression data, gene set variation analysis (GSVA) scores computed from the hallmark gene sets of MSigDB gene sets, as well as immune cell abundance estimates from a DLBCL-specific deconvolution method. An integrative clustering method iClusterPlus was applied to the input data to identify patient subgroups. A multinomial generalized linear model classifier was trained on the discovery dataset and applied to cohorts 2, 3, and 4 to assess patterns of gene expression and clinical features among the subgroups. Results: Integrative clustering identified 8 subgroups of ndDLBCL patients (DLBCL1-8; D1-D8) in cohort 1. Classifiers trained on cohort 1 were applied to cohort 2 and the same 8 clusters were identified. Among RCHOP treated patients in cohort 2, subgroups D4 (p<0.01) and D8 (p<0.0001) had significantly worse survival outcomes than the rest of the population. D4 comprised 21% of the MER ndDLBCL replication cohort (cohort 2) with a median event-free survival (mEFS) of 38.2 months and a median overall survival (mOS) of 80.3 months. D8 comprised 5% of the cohort with a mEFS of 7.5 months and a mOS of 12.1 months. The remaining 6 subgroups were standard risk, with mEFS ranging from 82.1 months to not reached, and none reaching mOS. The subgroups were not uniquely defined by previously known molecular classification methods such as COO or double hit signature (DHITsig), nor by clinical risk factors such as age or international prognostic index (IPI). Within D4, 92% of patients were ABC, representing a high risk subset of ABC patients. The mEFS in D4 ABCs was 38.2 months, while mEFS of non-D4 ABCs was not reached (p<0.005). Transcriptomic analysis revealed a lower abundance of immune infiltration. D4 was associated with high IPI, with 49% of D4 having IPI>2, compared to 33% of non-D4 with IPI>2 (p<0.05). D8 represented a high-risk subset which was 73% GCB. The mEFS of D8 GCBs was 5.4 months, while mEFS of non-D8 GCBs was not reached (p<0.0001). Transcriptomic analysis revealed low expression of immune response and cytokine signaling pathways, consistent with the low abundance of immune cells in D8. This subgroup consisted of 63% DHITsig positive patients. Although only 20% of all DHITsig positive patients were in D8, these D8 DHITsig patients showed significantly worse survival than non-D8 DHITsig patients (mEFS 11.3 months vs. not reached, p<0.0001). In the r/r DLBCL setting, D1-D8 were all present, with an increased prevalence of D4 and D8 in Cohort 3 (30% and 17%, respectively) and Cohort 4 (30% and 14%) compared to the newly diagnosed setting. Mutational data for these cohorts has been collected and is being interpreted in the context of the discovered subgroups. Conclusion: A novel integrative clustering of transformed gene expression data revealed 8 biologically homogeneous groups, two of which had inferior outcomes when treated with RCHOP therapy. Furthermore, these two subgroups were more prevalent in r/r DLBCL. This classification allows for the transcriptomic identification of high-risk patients underserved by RCHOP therapy. *Ortiz, Wenzl and Stokes contributed equally **Gandhi and Novak contributed equally Disclosures Ortiz: Celgene Corporation: Employment, Equity Ownership. Stokes:Celgene Corporation: Employment, Equity Ownership. Huang:Celgene Corporation: Employment, Equity Ownership. Maurer:Celgene: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding. Towfic:Celgene Corporation: Employment, Equity Ownership. Hagner:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Ratushny:Celgene Corporation: Employment, Equity Ownership. McConnell:Celgene Corporation: Employment, Equity Ownership. Danziger:Celgene Corporation: Employment, Equity Ownership. Stong:Celgene Corporation: Employment, Equity Ownership. Lata:Celgene Corporation: Employment, Equity Ownership. Kamalakaran:Celgene Corporation: Employment, Equity Ownership. Mavrommatis:Celgene Corporation: Employment, Equity Ownership. Trotter:Celgene Corporation: Employment, Equity Ownership. Czuczman:Celgene Corporation: Employment, Equity Ownership. Ansell:Seattle Genetics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding. Cerhan:NanoString: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding. Nowakowski:Genentech, Inc.: Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Curis: Research Funding; Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding; MorphoSys: Consultancy, Research Funding. Gandhi:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Novak:Celgene Coorperation: Research Funding.

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Genomic Predictors of Progression-Free Survival Among Patients with Relapsed or Refractory Multiple Myeloma Treated with Carfilzomib and Dexamethasone or Bortezomib and Dexamethasone in the Phase 3 Endeavor Trial

Blood ◽

10.1182/blood.v130.suppl_1.839.839 ◽

2017 ◽

Vol 130 (Suppl_1) ◽

pp. 839-839

Author(s):

Robert J Pelham ◽

Xuguang Hu ◽

Philippe Moreau ◽

Albert Oriol ◽

Hang Quach ◽

...

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

Board Of Directors ◽

Research Funding ◽

Rank Statistic ◽

Employment Equity ◽

Advisory Committees ◽

Kaplan Meier ◽

Equity Ownership ◽

The Cross

Abstract Background: In the phase 3 ENDEAVOR trial, treatment with carfilzomib administered at 56 mg/m2 twice weekly in combination with dexamethasone (Kd56) significantly improved progression-free survival (PFS) compared to treatment with bortezomib and dexamethasone (Vd) in patients with relapsed or refractory multiple myeloma (RRMM) (Dimopoulos MA, et al. Lancet Oncol . 2016;17:27-38). In this substudy of ENDEAVOR, we used whole transcriptome RNA sequencing (RNA-seq) to identify genes whose baseline expression levels in CD138+ cells were predictive of PFS in patients treated with Kd56 or Vd. The objective of this study was to develop a genomic classifier that could be used to stratify patients for benefit with Kd56 or Vd therapy. Methods: Patients were randomized to receive Kd56 or Vd at a 1:1 ratio. Patients who consented for this biomarker study and provided samples (Kd56, n = 155; Vd, n = 148) were included. CD138+ cells were isolated from bone marrow aspirate collected at baseline. Sequencing libraries for isolated RNA samples were prepared using an Illumina TruSeq RNA library construction kit and sequenced on an Illumina HiSeq 2500 platform. Sequencing reads were aligned against the human reference genome GRCh38 using STAR RNA-seq aligner and annotated with GENCODE v24 at the gene level. Expression counts were estimated using RSEM software and converted to counts per million for subsequent analyses using the edgeR package. Cox proportional hazard regression analysis with LASSO was used to model the relationship between patients' baseline gene expression and PFS. A classifier was established and its predictive performance was assessed using the cross-validation scheme outlined by Simon et al (Brief Bioinform . 2011;12:203-214). The statistical significance of the cross-validated Kaplan-Meier curves and corresponding log-rank statistic was estimated by generating an approximate null distribution of the cross-validated log-rank statistic through 500 random permutations. For each permutation, the patients' baseline gene expression profiles and treatment assignments were randomly re-shuffled against patients' survival times and event indicators, and the same cross-validation procedures used in the model performance assessment were repeated to compute the cross-validated log-rank statistic for the permuted data. Results: Among the 303 Kd56 or Vd patients included in this biomarker study, patients in the Kd56 arm had a 58% reduced risk of progression or death compared with patients in the Vd arm (hazard ratio [HR]: 0.42; 95% confidence interval [CI]: 0.30-0.59; P= 4.5 x 10-7). We developed a linearized classifier using patients' baseline gene expression (n = 303) to stratify patients for PFS benefit from Kd56 or Vd therapy. The cross-validated Kaplan-Meier curves and log-rank statistic for the classifier were statistically significant at P < 0.001. A 13-gene classifier derived from the whole data set could separate patients from the Kd56 arm (n = 155) into two distinct subgroups, in which one with 113 (73%) patients had a PFS benefit over the other with 42 (27%) patients (HR: 0.13; 95% CI: 0.06-0.26; P= 3.3 x 10-13). When these 42 patients were excluded from the Kd56 arm, the PFS benefit for the Kd56 arm (n = 113) over the Vd arm (n = 148) was improved by 52% (HR: 0.20; 95% CI: 0.12-0.31; P= 2.0 x 10-14). The classifier was unable to stratify patients in the Vd arm for high or low PFS benefit. The 13 genes included in the classifier were ACOXL, CLEC2B, CLIP4, COCH, FRK, IGHD, ITPRIPL2, NAP1L5, RNASE6, SH3RF3, SHROOM3, TCF7, and UGT3A2 . Several genes in this classifier, including CLIP4, IGHD, and SH3RF3, have been previously implicated in myeloma biology and in vitroresistance to proteasome inhibitors. Individually, each gene showed similar ability to stratify patients from the Kd56 arm, but the cross-validated Kaplan-Meier curves for the individual genes were not significant at P < 0.05. Conclusions: We identified a classifier with a set of genes whose baseline expression could potentially be used to stratify RRMM patients for greater treatment benefit with Kd56. As only one patient cohort was used for this study, the classifier identified here should be validated in prospective studies and with independent sets of patient cohorts. Further study of this group of genes may provide additional insights into the biology of multiple myeloma and how mechanism of action differs between carfilzomib and bortezomib. Disclosures Pelham: Amgen: Employment, Equity Ownership. Hu: Amgen: Employment, Equity Ownership. Moreau: Novartis: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Millennium: Consultancy, Honoraria; Bristol-Myers Squibb: Honoraria; Amgen: Honoraria; Takeda: Honoraria; Janssen: Consultancy, Honoraria; Celgene, Janssen, Takeda, Novartis, Amgen, Roche: Membership on an entity's Board of Directors or advisory committees; Onyx Pharmaceutical: Consultancy, Honoraria. Oriol: Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: sponsored symposia, Speakers Bureau; Celgene: Speakers Bureau; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: sponsored symposia; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: sponsored symposia, Speakers Bureau. Quach: Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Honoraria; Takeda: Honoraria. Kovacsovics: Seattle Genetics: Research Funding; Celgene: Consultancy; Flexus: Research Funding. Keats: Amgen: Research Funding. Feng: Amgen: Employment, Equity Ownership. Kimball: Amgen: Employment, Equity Ownership. Dimopoulos: Novartis: Consultancy, Honoraria; Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Onyx Pharmaceuticals, an Amgen subsidiary, Takeda Oncology: Consultancy, Honoraria, Other: Advisory Committee: Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Onyx Pharmaceuticals, an Amgen subsidiary, Takeda Oncology; Genesis Pharma: Research Funding.

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Rapid and Robust Mobilization of CD34+ HSCs without G-CSF Following Administration of Mgta-145 Alone or in Combination with Plerixafor

Blood ◽

10.1182/blood-2019-127523 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 1961-1961

Author(s):

John F. DiPersio ◽

Jonathan Hoggatt ◽

Steven Devine ◽

Lukasz Biernat ◽

Haley Howell ◽

...

Keyword(s):

Single Dose ◽

Adverse Events ◽

Board Of Directors ◽

Preliminary Data ◽

Research Funding ◽

Fold Change ◽

Employment Equity ◽

Advisory Committees ◽

Cd34 Cells ◽

Equity Ownership

Background Granulocyte colony-stimulating factor (G-CSF) is the standard of care for mobilization of hematopoietic stem cells (HSCs). G-CSF requires 4-7 days of injections and often multiple aphereses to acquire sufficient CD34+ cells for transplant. The number of CD34+ HSCs mobilized can be variable and patients who fail to mobilize enough CD34+ cells are treated with the combination of G-CSF plus plerixafor. G-CSF use is associated with bone pain, nausea, headaches, fatigue, rare episodes of splenic rupture, and is contraindicated for patients with autoimmune and sickle cell disease. MGTA-145 (GroβT) is a CXCR2 agonist. MGTA-145, in combination with plerixafor, a CXCR4 inhibitor, has the potential to rapidly and reliably mobilize robust numbers of HSCs with a single dose and same-day apheresis for transplant that is free from G-CSF. MGTA-145 plus plerixafor work synergistically to rapidly mobilize HSCs in both mice and non-human primates (Hoggatt, Cell 2018; Goncalves, Blood 2018). Based on these data, Magenta initiated a Phase 1 dose-escalating study to evaluate the safety, PK and PD of MGTA-145 as a single agent and in combination with plerixafor. Methods This study consists of four parts. In Part A, healthy volunteers were dosed with MGTA-145 (0.0075 - 0.3 mg/kg) or placebo. In Part B, MGTA-145 dose levels from Part A were selected for use in combination with a clinically approved dose of plerixafor. In Part C, a single dose MGTA-145 plus plerixafor will be administered on day 1 and day 2. In Part D, MGTA-145 plus plerixafor will be administered followed by apheresis. Results MGTA-145 monotherapy was well tolerated in all subjects dosed (Table 1) with no significant adverse events. Some subjects experienced mild (Grade 1) transient lower back pain that dissipated within minutes. In the ongoing study, the combination of MGTA-145 with plerixafor was well tolerated, with some donors experiencing Grade 1 and 2 gastrointestinal adverse events commonly observed with plerixafor alone. Pharmacokinetic (PK) exposure and maximum plasma concentrations increased dose proportionally and were not affected by plerixafor (Fig 1A). Monotherapy of MGTA-145 resulted in an immediate increase in neutrophils (Fig 1B) and release of plasma MMP-9 (Fig 1C). Neutrophil mobilization plateaued within 1-hour post MGTA-145 at doses greater than 0.03 mg/kg. This plateau was followed by a rebound of neutrophil mobilization which correlated with re-expression of CXCR2 and presence of MGTA-145 at pharmacologically active levels. Markers of neutrophil activation were relatively unchanged (<2-fold vs baseline). A rapid and statistically significant increase in CD34+ cells occurred @ 0.03 and 0.075 mg/kg of MGTA-145 (p < 0.01) relative to placebo with peak mobilization (Fig 1D) 30 minutes post MGTA-145 (7-fold above baseline @ 0.03 mg/kg). To date, the combination of MGTA-145 plus plerixafor mobilized >20/µl CD34s in 92% (11/12) subjects compared to 50% (2/4) subjects receiving plerixafor alone. Preliminary data show that there was a significant increase in fold change relative to baseline in CD34+ cells (27x vs 13x) and phenotypic CD34+CD90+CD45RA- HSCs (38x vs 22x) mobilized by MGTA-145 with plerixafor. Mobilized CD34+ cells were detectable at 15 minutes with peak mobilization shifted 2 - 4 hours earlier for the combination vs plerixafor alone (4 - 6h vs 8 - 12h). Detailed results of single dose administration of MGTA-145 and plerixafor given on one day as well as also on two sequential days will be presented along with fully characterized graft analysis post apheresis from subjects given MGTA-145 and plerixafor. Conclusions MGTA-145 is safe and well tolerated, as a monotherapy and in combination with plerixafor and induced rapid and robust mobilization of significant numbers of HSCs with a single dose in all subjects to date. Kinetics of CD34+ cell mobilization for the combination was immediate (4x increase vs no change for plerixafor alone @ 15 min) suggesting the mechanism of action of MGTA-145 plus plerixafor is different from plerixafor alone. Preliminary data demonstrate that MGTA-145 when combined with plerixafor results in a significant increase in CD34+ fold change relative to plerixafor alone. Magenta Therapeutics intends to develop MGTA-145 as a first line mobilization product for blood cancers, autoimmune and genetic diseases and plans a Phase 2 study in multiple myeloma and non-Hodgkin lymphoma in 2020. Disclosures DiPersio: Magenta Therapeutics: Equity Ownership; NeoImmune Tech: Research Funding; Cellworks Group, Inc.: Membership on an entity's Board of Directors or advisory committees; Karyopharm Therapeutics: Consultancy; Incyte: Consultancy, Research Funding; RiverVest Venture Partners Arch Oncology: Consultancy, Membership on an entity's Board of Directors or advisory committees; WUGEN: Equity Ownership, Patents & Royalties, Research Funding; Macrogenics: Research Funding, Speakers Bureau; Bioline Rx: Research Funding, Speakers Bureau; Celgene: Consultancy; Amphivena Therapeutics: Consultancy, Research Funding. Hoggatt:Magenta Therapeutics: Consultancy, Equity Ownership, Research Funding. Devine:Kiadis Pharma: Other: Protocol development (via institution); Bristol Myers: Other: Grant for monitoring support & travel support; Magenta Therapeutics: Other: Travel support for advisory board; My employer (National Marrow Donor Program) has equity interest in Magenta. Biernat:Medpace, Inc.: Employment. Howell:Magenta Therapeutics: Employment, Equity Ownership. Schmelmer:Magenta Therapeutics: Employment, Equity Ownership. Neale:Magenta Therapeutics: Employment, Equity Ownership. Boitano:Magenta Therapeutics: Employment, Equity Ownership, Patents & Royalties. Cooke:Magenta Therapeutics: Employment, Equity Ownership, Patents & Royalties. Goncalves:Magenta Therapeutics: Employment, Equity Ownership, Patents & Royalties. Raffel:Magenta Therapeutics: Employment, Equity Ownership. Falahee:Magenta Therapeutics: Employment, Equity Ownership, Patents & Royalties. Morrow:Magenta Therapeutics: Employment, Equity Ownership, Patents & Royalties. Davis:Magenta Therapeutics: Employment, Equity Ownership.

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Treatment Patterns and Outcomes in Elderly Patients with Newly Diagnosed Multiple Myeloma: Results from the Connect® MM Registry

Blood ◽

10.1182/blood-2019-126205 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 3129-3129

Author(s):

Hans C. Lee ◽

Sikander Ailawadhi ◽

Cristina Gasparetto ◽

Sundar Jagannath ◽

Robert M. Rifkin ◽

...

Keyword(s):

Board Of Directors ◽

Research Funding ◽

Causes Of Death ◽

Age Groups ◽

Treatment Patterns ◽

Age Group ◽

Newly Diagnosed ◽

Employment Equity ◽

Advisory Committees ◽

Equity Ownership

Background: Multiple myeloma (MM) is common among the elderly, with 35% of patients (pts) diagnosed being aged ≥75 years (y). With increasing overall life expectancy, the incidence and prevalence of newly diagnosed and previously treated MM patients ≥80 y is expected to increase over time. Because elderly pts are often excluded from clinical trials, data focused on their treatment patterns and clinical outcomes are lacking. The Connect® MM Registry (NCT01081028) is a large, US, multicenter, prospective observational cohort study of pts with newly diagnosed MM (NDMM) designed to examine real-world diagnostic patterns, treatment patterns, clinical outcomes, and health-related quality of life patient-reported outcomes. This analysis reviews treatment patterns and outcomes in elderly pts from the Connect MM Registry. Methods: Pts enrolled in the Connect MM registry at 250 community, academic, and government sites were included in this analysis. Eligible pts were adults aged ≥18 y with symptomatic MM diagnosed ≤2 months before enrollment, as defined by International Myeloma Working Group criteria; no exclusion criteria were applied. For this analysis, pts were categorized into 4 age groups: <65, 65 to 74, 75 to 84, and ≥85 y. Pts were followed from time of enrollment to the earliest of disease progression (or death), loss to follow-up, or data cutoff date of February 7, 2019. Descriptive statistics were used for baseline characteristics and treatment regimens. Survival outcomes were analyzed using Cox regression. Time to progression (TTP) analysis excluded causes of death not related to MM. Results: Of 3011 pts enrolled (median age 67 y), 132 (4%) were aged ≥85 y, and 615 (20%) were aged 75-84 y at baseline. More pts aged ≥85 y had poor prognostic factors such as ISS stage III disease and reduced hemoglobin (<10 g/dL or >2 g/dL <LLN) compared with other age groups, although no notable differences between creatinine and calcium levels were observed across age groups (Table). A lower proportion of elderly pts (75-84 and ≥85 y) received triplet regimens as frontline therapy. More elderly pts received a single novel agent, whereas use of 2 novel agents was more common in younger pts (Table). The most common frontline regimens among elderly pts were bortezomib (V) + dexamethasone (D), followed by lenalidomide (R) + D, whereas those among younger pts included RVD, followed by VD and CyBorD (Table). No pt aged ≥85 y, and 4% of pts aged 75-84 y received high-dose chemotherapy and autologous stem cell transplant (vs 61% in the <65 y and 37% in the 65-74 y age group). The most common maintenance therapy was RD in pts ≥85 y (although the use was low) and R alone in other age groups (Table). In the ≥85 y group, 27%, 10%, and 4% of pts entered 2L, 3L, and 4L treatments respectively, vs 43%, 23%, and 13% in the <65 y group. Progression-free survival was significantly shorter in the ≥85 y age group vs the 75-84 y age group (P=0.003), 65-74 y age group (P<0.001), and <65 y age group (P<0.001; Fig.1). TTP was significantly shorter in the ≥85 y group vs the <65 y group (P=0.020); however, TTP was similar among the 65-74 y, 75-84 y, and ≥85 y cohorts (Fig. 2). Overall survival was significantly shorter in the ≥85 y group vs the 75-84 y, 65-74 y, and <65 y groups (all P<0.001; Fig. 3). The mortality rate was lowest (46%) during first-line treatment (1L) in pts aged ≥85 y (mainly attributed to MM progression) and increased in 2L and 3L (47% and 54%, respectively); a similar trend was observed in the younger age groups. The main cause of death was MM progression (29% in the ≥85 y vs 16% in the <65 y group). Other notable causes of death in the ≥85 y group included cardiac failure (5% vs 2% in <65 y group) and pneumonia (5% vs 1% in <65 y group). Conclusions: In this analysis, elderly pts received similar types of frontline and maintenance regimens as younger pts, although proportions varied with decreased use of triplet regimens with age. Considering similarities in TTP across the 65-74 y, 75-84 y, and ≥85 y cohorts, these real-world data support active treatment and aggressive supportive care of elderly symptomatic pts, including with novel agents. Additionally, further clinical studies specific to elderly patients with MM should be explored. Disclosures Lee: Amgen: Consultancy, Research Funding; GlaxoSmithKline plc: Research Funding; Sanofi: Consultancy; Daiichi Sankyo: Research Funding; Celgene: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; Janssen: Consultancy, Research Funding. Ailawadhi:Janssen: Consultancy, Research Funding; Takeda: Consultancy; Pharmacyclics: Research Funding; Amgen: Consultancy, Research Funding; Celgene: Consultancy; Cellectar: Research Funding. Gasparetto:Celgene: Consultancy, Honoraria, Other: Travel, accommodations, or other expenses paid or reimbursed ; Janssen: Consultancy, Honoraria, Other: Travel, accommodations, or other expenses paid or reimbursed ; BMS: Consultancy, Honoraria, Other: Travel, accommodations, or other expenses paid or reimbursed . Jagannath:AbbVie: Consultancy; Merck & Co.: Consultancy; Bristol-Myers Squibb: Consultancy; Karyopharm Therapeutics: Consultancy; Celgene Corporation: Consultancy; Janssen Pharmaceuticals: Consultancy. Rifkin:Celgene: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees. Durie:Amgen, Celgene, Johnson & Johnson, and Takeda: Consultancy. Narang:Celgene: Speakers Bureau. Terebelo:Celgene: Honoraria; Jannsen: Speakers Bureau; Newland Medical Asociates: Employment. Toomey:Celgene: Consultancy. Hardin:Celgene: Membership on an entity's Board of Directors or advisory committees. Wagner:Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; American Cancer Society: Other: Section editor, Cancer journal. Omel:Celgene, Takeda, Janssen: Other: Patient Advisory Committees. Srinivasan:Celgene: Employment, Equity Ownership. Liu:TechData: Consultancy. Dhalla:Celgene: Employment. Agarwal:Celgene Corporation: Employment, Equity Ownership. Abonour:BMS: Consultancy; Celgene: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; Janssen: Consultancy, Research Funding.

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Impact of Modified Thalidomide Dosing in Bortezomib/Thalidomide/Dexamethasone for Patients with Newly Diagnosed Multiple Myeloma Who Are Transplant-Eligible: A Matching-Adjusted Indirect Comparison

Blood ◽

10.1182/blood-2019-122775 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 4739-4739

Author(s):

Pieter Sonneveld ◽

Maria-Victoria Mateos ◽

Adrián Alegre ◽

Thierry Facon ◽

Cyrille Hulin ◽

...

Keyword(s):

Peripheral Neuropathy ◽

Board Of Directors ◽

Research Funding ◽

Indirect Comparison ◽

Employment Equity ◽

Advisory Committees ◽

Post Transplant ◽

Post Induction ◽

Adjusted Indirect Comparison ◽

Equity Ownership

Introduction: For patients with newly diagnosed multiple myeloma (NDMM) who are transplant-eligible, bortezomib/thalidomide/dexamethasone (VTd) is a standard of care (SoC) for induction and consolidation therapy. Clinical practice has evolved to use a modified VTd dose (VTd-mod; 100 mg thalidomide daily), which is reflected in recent treatment guidelines. As VTd-mod has become a real-world SoC, a matching-adjusted indirect comparison (MAIC) of the VTd-mod dose from recent clinical trials versus the dose included in the label (VTd-label; ramp up to 200 mg thalidomide daily) was performed to understand the effect on efficacy of modified VTd dosing for patients with NDMM who are transplant-eligible. Methods: For each outcome (overall survival [OS], progression-free survival [PFS], overall response rates [ORR] post-induction and post-transplant, and rate of peripheral neuropathy), a naïve comparison and a MAIC were performed. Data for VTd-label were obtained from the phase 3 PETHEMA/GEM study (Rosiñol L, et al. Blood. 2012;120[8]:1589-1596). Data for VTd-mod were pooled from the phase 3 CASSIOPEIA study (Moreau P, et al. Lancet. 2019;394[10192]:29-38) and the phase 2 NCT00531453 study (Ludwig H, et al. J Clin Oncol. 2013;31[2]:247-255). Patient-level data for PETHEMA/GEM and CASSIOPEIA were used to generate outcomes of interest and were validated against their respective clinical study reports; aggregate data for NCT00531453 were extracted from the primary publication. Matched baseline characteristics were age, sex, ECOG performance status, myeloma type, International Staging System (ISS) stage, baseline creatinine clearance, hemoglobin level, and platelet count. Results: Patients received VTd-mod (n = 591) or VTd-label (n = 130). After matching, baseline characteristics were similar across groups. For OS, the naïve comparison and the MAIC showed that VTd-mod was non-inferior to VTd-label (MAIC HR, 0.640 [95% CI: 0.363-1.129], P = 0.121; Figure 1A). VTd-mod significantly improved PFS versus VTd-label in the naïve comparison and MAIC (MAIC HR, 0.672 [95% CI: 0.467-0.966], P = 0.031; Figure 1B). Post-induction ORR was non-inferior for VTd-mod versus VTd-label (MAIC odds ratio, 1.781 [95% CI: 1.004-3.16], P = 0.065). Post-transplant, VTd-mod demonstrated superior ORR in both the naïve comparison and MAIC (MAIC odds ratio, 2.661 [95% CI: 1.579-4.484], P = 0.001). For rates of grade 3 or 4 peripheral neuropathy, the naïve comparison and MAIC both demonstrated that VTd-mod was non-inferior to VTd-label (MAIC rate difference, 2.4 [⁻1.7-6.49], P = 0.409). Conclusions: As naïve, indirect comparisons are prone to bias due to patient heterogeneity between studies, a MAIC can provide useful insights for clinicians and reimbursement decision-makers regarding the relative efficacy and safety of different treatments. In this MAIC, non-inferiority of VTd-mod versus VTd-label was demonstrated for OS, post-induction ORR, and peripheral neuropathy. This analysis also showed that VTd-mod significantly improved PFS and ORR post-transplant compared with VTd-label for patients with NDMM who are transplant-eligible. A limitation of this analysis is that unreported or unobserved confounding factors could not be adjusted for. Disclosures Sonneveld: Takeda: Honoraria, Research Funding; SkylineDx: Research Funding; Janssen: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; BMS: Honoraria; Amgen: Honoraria, Research Funding; Karyopharm: Honoraria, Research Funding. Mateos:Janssen, Celgene, Takeda, Amgen, Adaptive: Honoraria; AbbVie Inc, Amgen Inc, Celgene Corporation, Genentech, GlaxoSmithKline, Janssen Biotech Inc, Mundipharma EDO, PharmaMar, Roche Laboratories Inc, Takeda Oncology: Other: Advisory Committee; Janssen, Celgene, Takeda, Amgen, GSK, Abbvie, EDO, Pharmar: Membership on an entity's Board of Directors or advisory committees; Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Takeda Oncology.: Speakers Bureau; Amgen Inc, Janssen Biotech Inc: Other: Data and Monitoring Committee. Alegre:Celgene, Amgen, Janssen, Takeda: Membership on an entity's Board of Directors or advisory committees. Facon:Takeda: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Amgen: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Hulin:celgene: Consultancy, Honoraria; Janssen, AbbVie, Celgene, Amgen: Honoraria. Hashim:Ingress-Health: Employment. Vincken:Janssen: Employment, Equity Ownership. Kampfenkel:Janssen: Employment, Equity Ownership. Cote:Janssen: Employment, Equity Ownership. Moreau:Janssen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Takeda: Consultancy, Honoraria.

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A Phase I Trial of Janus Kinase (JAK) Inhibition with INCB039110 in Acute Graft-Versus-Host Disease (aGVHD)

Blood ◽

10.1182/blood.v128.22.390.390 ◽

2016 ◽

Vol 128 (22) ◽

pp. 390-390 ◽

Cited By ~ 8

Author(s):

Mark A. Schroeder ◽

H. Jean Khoury ◽

Madan Jagasia ◽

Haris Ali ◽

Gary J. Schiller ◽

...

Keyword(s):

Board Of Directors ◽

Research Funding ◽

Janus Kinase ◽

Cell Transplant ◽

Employment Equity ◽

Advisory Committees ◽

Daily Dose ◽

Equity Ownership ◽

Grade 3 ◽

Steroid Refractory

Abstract Background: Corticosteroids are considered standard first-line systemic therapy for patients with aGVHD, but this approach is effective in only approximately half of all cases. For patients who progress or do not respond to corticosteroids, no specific agent has been identified as standard, and regimens are typically selected based on investigator experience and patient co-morbidities. In preclinical models, JAK inhibition has been shown to impair production of cytokines as well as the differentiation and trafficking of T cells implicated in the pathogenesis of aGVHD. Retrospective studies have suggested that JAK1/JAK2 inhibition with ruxolitinib treatment provides clinical benefit in patients with steroid-refractory GVHD (Zeiser et al, Leukemia 2015;29:2062-2068). Herein, we report preliminary safety results from a prospective randomized, parallel-cohort, open-label phase 1 trial evaluating the potent and selective JAK 1 inhibitor INCB039110 in patients with aGVHD. Methods: Male or female patients 18 years or older who underwent their first allo-hematopoietic stem cell transplant (HSCT) from any donor source and developed grades IIB-IVD aGVHD were eligible for the study. Patients were randomized 1:1 to either a 200 or 300 mg oral daily dose of INCB039110 in combination with corticosteroids, and were stratified based on prior treatment status (treatment-naive [TN] versus steroid-refractory [SR]). The primary endpoint of the study was safety and tolerability; secondary endpoints included overall response rate at Days 14, 28, 56, and 100, non-relapse mortality, and pharmacokinetic (PK) evaluations. Patients were assessed through Day 28 for dose-limiting toxicities (DLTs) and response. A Bayesian approach was used for continuous monitoring of DLTs from Days 1-28. Treatment continued until GVHD progression, unacceptable toxicity, or withdrawal from the study. Acute GVHD was graded according to MN-CIBMTR criteria; adverse events (AEs) were graded according to NCICTCAE v 4.03. Results: Between January and June 2016, 31 patients (TN, n=14; SR, n= 17) were randomized. As of July 25, 2016, data were available from 30 patients who received an oral daily dose of 200 mg (n=14) or 300 mg (n=16) INCB039110 in combination with 2 mg/kg methylprednisolone (or equivalent dose of prednisone). The median durations of treatment were 60.8 days and 56.5 days for patients receiving a daily dose of 200 mg and 300 mg INCB039110, respectively. One DLT of Grade 3 thrombocytopenia was reported. The most frequently reported AEs included thrombocytopenia/platelet count decrease (26.7%), diarrhea (23.3%), peripheral edema (20%), fatigue (16.7%), and hyperglycemia (16.7%). Grade 3 or 4 AEs occurred in 77% of patients and with similar frequency across dose groups and included cytomegalovirus infections (n=3), gastrointestinal hemorrhage (n=3), and sepsis (n=3). Five patients had AEs leading to a fatal outcome, including multi-organ failure (n=2), sepsis (n=1), disease progression (n=1), and bibasilar atelectasis, cardiopulmonary arrest, and respiratory distress (n=1); none of the fatal events was attributed to INCB039110. Efficacy and PK evaluations are ongoing and will be updated at the time of presentation. Conclusion: The oral, selective JAK1 inhibitor INCB039110 can be given safely to steroid-naive or steroid-refractory aGVHD patients. The safety profile was generally consistent in both dose groups. Biomarker evaluation, PK, and cellular phenotyping studies are ongoing. The recommended phase 2 dose will be selected and reported based on PK studies and final safety data. Disclosures Schroeder: Incyte Corporation: Honoraria, Research Funding. Khoury:Incyte Corporation: Membership on an entity's Board of Directors or advisory committees, Research Funding. Jagasia:Incyte Corporation: Research Funding; Therakos: Research Funding; Janssen: Research Funding. Ali:Incyte Corporation: Research Funding. Schiller:Incyte Corporation: Research Funding. Arbushites:Incyte Corporation: Employment, Equity Ownership. Delaite:Incyte Corporation: Employment, Equity Ownership. Yan:Incyte Corporation: Employment, Equity Ownership. Rhein:Incyte Corporation: Employment, Equity Ownership. Perales:Merck: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Incyte Corporation: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Chen:Incyte Corporation: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding. DiPersio:Incyte Corporation: Research Funding.

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Role of Remission Status and Prior Transplant in Optimizing Survival Outcomes Following Allogeneic Hematopoietic Stem Transplantation (HSCT) in Patients Who Received Inotuzumab Ozogamicin (INO) for Relapsed / Refractory (R/R) Acute Lymphoblastic Leukemia (ALL)

Blood ◽

10.1182/blood.v130.suppl_1.886.886 ◽

2017 ◽

Vol 130 (Suppl_1) ◽

pp. 886-886

Author(s):

Partow Kebriaei ◽

Matthias Stelljes ◽

Daniel J. DeAngelo ◽

Nicola Goekbuget ◽

Hagop M. Kantarjian ◽

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Keyword(s):

Board Of Directors ◽

Survival Probability ◽

Research Funding ◽

Employment Equity ◽

Inotuzumab Ozogamicin ◽

Advisory Committees ◽

Allogeneic Hsct ◽

Post Transplant ◽

Equity Ownership

Abstract Introduction: Attaining complete remission (CR) prior to HSCT is associated with better outcomes post-HSCT. Inotuzumab ozogamicin (INO), an anti-CD22 antibody conjugated to calicheamicin, has shown significantly higher remission rates (CR/CRi and MRD negativity) compared with standard chemotherapy (SC) in patients (pts) with R/R ALL (Kantarjian et al. N Engl J Med. 2016). Pts treated with INO were more likely to proceed to HSCT than SC, which allowed for a higher 2-yr probability of overall survival (OS) than patients receiving SC (39% vs 29%). We investigated the role of prior transplant and proceeding directly to HSCT after attaining remission from INO administration as potential factors in determining post-HSCT survival to inform when best to use INO in R/R ALL patients. Methods: The analysis population consisted of R/R ALL pts who were enrolled and treated with INO and proceeded to allogeneic HSCT as part of two clinical trials: Study 1010 is a Phase 1/2 trial (NCT01363297), while Study 1022 is the pivotal randomized Phase 3 (NCT01564784) trial. Full details of methods for both studies have been previously published (DeAngelo et al. Blood Adv. 2017). All reference to OS pertains to post-HSCT survival defined as time from HSCT to death from any cause. Results: As of March 2016, out of 236 pts administered INO in the two studies (Study 1010, n=72; Study 1022, n=164), 101 (43%) proceeded to allogeneic HSCT and were included in this analysis. Median age was 37 y (range 20-71) with 55% males. The majority of pts received INO as first salvage treatment (62%) and 85% had no prior SCT. Most pts received matched HSCTs (related = 25%; unrelated = 45%) with peripheral blood as the predominant cell source (62%). The conditioning regimens were mainly myeloablative regimens (60%) and predominantly TBI-based (62%). Dual alkylators were used in 13% of pts, while thiotepa was used in 8%. The Figure shows post-transplant survival in the different INO populations: The median OS post-HSCT for all pts (n=101) who received INO and proceeded to HSCT was 9.2 mos with a 2-yr survival probability of 41% (95% confidence interval [CI] 31-51%). In patients with first HSCT (n=86) the median OS post-HSCT was 11.8 mos with a 2-yr survival probability of 46% (95% CI 35-56%). Of note, some patients lost CR while waiting for HSCT and had to receive additional treatments before proceeding to HSCT (n=28). Those pts who went directly to first HSCT after attaining remission with no intervening additional treatment (n=73) fared best, with median OS post-HSCT not reached with a 2-yr survival probability of 51% (95% CI 39-62%). In the latter group, 59/73 (80%) attained MRD negativity, and 49/73 (67%) were in first salvage therapy. Of note, the post-HSCT 100-day survival probability was similar among the 3 groups, as shown in the Table. Multivariate analyses using Cox regression modelling confirmed that MRD negativity during INO treatment and no prior HSCT were associated with lower risk of mortality post-HSCT. Other prognostic factors associated with worse OS included older age, higher baseline LDH, higher last bilirubin measurement prior to HSCT, and use of thiotepa. Veno-occlusive disease post-transplant was noted in 19 of the 101 pts who received INO. Conclusion: Administration of INO in R/R ALL pts followed with allogeneic HSCT provided the best long-term survival benefit among those who went directly to HSCT after attaining remission and had no prior HSCT. Disclosures DeAngelo: Glycomimetics: Research Funding; Incyte: Consultancy, Honoraria; Blueprint Medicines: Honoraria, Research Funding; Takeda Pharmaceuticals U.S.A., Inc.: Honoraria; Shire: Honoraria; Pfizer Inc.: Consultancy, Honoraria, Research Funding; Novartis Pharmaceuticals Corporation: Consultancy, Honoraria, Research Funding; BMS: Consultancy; ARIAD: Consultancy, Research Funding; Immunogen: Honoraria, Research Funding; Celgene: Research Funding; Amgen: Consultancy, Research Funding. Kantarjian: Novartis: Research Funding; Amgen: Research Funding; Delta-Fly Pharma: Research Funding; Bristol-Meyers Squibb: Research Funding; Pfizer: Research Funding; ARIAD: Research Funding. Advani: Takeda/ Millenium: Research Funding; Pfizer: Consultancy. Merchant: Pfizer: Consultancy, Research Funding. Stock: Amgen: Consultancy; Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Consultancy, Membership on an entity's Board of Directors or advisory committees. Wang: Pfizer: Employment, Equity Ownership. Zhang: Pfizer: Employment, Equity Ownership. Loberiza: Pfizer: Employment, Equity Ownership. Vandendries: Pfizer: Employment, Equity Ownership. Marks: Pfizer: Consultancy, Honoraria, Speakers Bureau; Amgen: Consultancy, Honoraria, Speakers Bureau.

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