Distinct Gene Expression Patterns of Minimal Residual Disease (MRD) Cells in High-Risk AML Patients Identified By RNA-Sequencing

Abstract INTRODUCTION Evolving techniques have made possible the direct detection, physical isolation, and study of AML minimal residual disease (MRD) after treatment. This could allow for better identification of therapeutic vulnerabilities in AML. Prior studies have focused on cells that initiate leukemia in mouse models, known as leukemia-initiating cells (LIC), generally with a foundational CD34+CD38- immunophenotype. LIC are typically derived from diagnostic samples of untreated patients. Such stem-like cells do not necessarily represent the residual fraction of AML after treatment. Relapse may originate from non-LIC, and the presence of phenotypically and molecularly defined MRD is now firmly established as a critical prognostic factor for patients. High-risk AML is characterized by relapse, despite morphologic complete remission with initial therapy in most cases. RNA-sequencing was performed on pre- and post-treatment AML subpopulations, including MRD, from high-risk patients, to determine differences in gene expression. METHODS Matched primary AML samples were collected from marrow and peripheral blood of patients with high-risk AML (including patients with unfavorable karyotype and/or TP53 mutation) at diagnosis and after treatment. Mononuclear cells were flow-sorted for bulk (CD45dim) and LIC (Lin-CD34+CD38-CD123+) from diagnostic samples. Post-treatment samples were sorted for bulk mononuclear cells (MNC) and MRD, based on difference-from-normal/MRD immunophenotype specific for each patient as determined from established 20-marker clinical flow cytometry analysis. RNA was isolated using low-input methodology, and RNA-sequencing was performed using Illumina HiSeq 2000. Gene expression was assessed using GO-Elite, and differences between patients and subpopulations were assessed using rank product method. RESULTS Gene expression in MRD was analyzed by RNA-sequencing in comparison to diagnostic samples in eight patients with high-risk AML. Four patients had unfavorable karyotype, including two with TP53 mutations. Patients had additional high-risk features, such as FLT3-ITD or RUNX1 mutations, or secondary/therapy-related AML. Treatment consisted of chemotherapy (6/8) or hypomethylating agents (2/8), with or without other targeted drugs. Residual leukemia was detected in post-treatment samples in all study patients. Significant differences in gene expression were detected between MRD and other sorted populations, including diagnostic bulk AML and LIC. Relevant MRD pathways included those with strong interactions with the microenvironment. Anti-apoptotic mechanisms, cytoskeletal, and cell adhesion related genes, WNT/beta-catenin signaling, and TGFbeta signaling ranked among the most relevant processes in AML MRD subpopulations (Figure 1A, GO-Elite interactome of highly expressed genes in AML MRD). To identify potentially critical and unique MRD-specific genes, rank product method was applied using 1) the most highly expressed genes in AML MRD, 2) the most differential expressed genes between MRD and bulk AML at diagnosis, and 3) the most differentially expressed genes between MRD and bulk MNC after treatment. Among the top 50 scoring genes using this approach (Figure 1B), 16 genes were among the top 5% of genes expressed in MRD among all patients and 20 genes have cell surface gene-products (shown in yellow). Several potential leukemia- and cancer-related genes of interest were identified (shown in bold). CONCLUSIONS Key differences exist between the gene expression profiles of post-treatment MRD from high-risk AML patients, in comparison to other populations and subpopulations of sorted cells before and after treatment. The highlighted differences suggest that MRD relies on specific intrinsic gene expression changes and microenvironmental interactions, and therefore may be targetable after elimination of bulk AML with initial therapy. Accessible surfacesome targets are among top hits. Disclosures Konopleva: cellectis: Research Funding; Immunogen: Research Funding; abbvie: Research Funding; Stemline Therapeutics: Research Funding. Andreeff:Astra Zeneca: Research Funding; Amgen: Consultancy, Research Funding; Jazz Pharma: Consultancy; Celgene: Consultancy; Reata: Equity Ownership; SentiBio: Equity Ownership; Aptose: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; United Therapeutics: Patents & Royalties: GD2 inhibition in breast cancer ; Eutropics: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Oncolyze: Equity Ownership; Daiichi-Sankyo: Consultancy, Patents & Royalties: MDM2 inhibitor activity patent, Research Funding; Oncoceutics: Equity Ownership, Membership on an entity's Board of Directors or advisory committees.

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Identification of Gene Expression Signatures in Leukemia Stem Cells and Minimal Residual Disease Following Treatment of Adverse Risk Acute Myeloid Leukemia

Blood ◽

10.1182/blood-2019-130540 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 2717-2717

Author(s):

Ghayas C. Issa ◽

Christopher B. Benton ◽

Vakul Mohanty ◽

Yifei Shen ◽

Zoe Alaniz ◽

...

Keyword(s):

Stem Cells ◽

Board Of Directors ◽

Rna Sequencing ◽

Research Funding ◽

Mononuclear Cells ◽

Residual Disease ◽

Post Treatment ◽

Cell Cycle Checkpoints ◽

Advisory Committees ◽

Equity Ownership

Introduction: Outcomes of adverse risk acute myeloid leukemia (AML) remain dismal. Despite some morphologic remission following therapy, the majority of patients relapse and succumb to their disease. Induction chemotherapy leads to a significant reduction in tumor burden, however, resistant leukemia cells persist as minimal residual disease (MRD), the reservoir for relapse. This is likely due to the capacity of these persistent cells to hijack properties from normal hematopoietic stem cells such as self-renewal, quiescence, and recapitulation of the malignant progeny. Thus leukemia cells are functionally heterogeneous, with the majority of cells at diagnosis susceptible to chemotherapy, and a minority of resistant cells that persist despite treatment. Deeper understanding of all leukemia sub-populations is necessary in order to understand mechanisms of resistance. We hypothesized that sub-populations such as leukemia-stem cells (LSCs), and post-therapy residual cells possess identifiable, targetable characteristics that drive resistance. We performed RNA-sequencing and compared differences in gene expression between these sub-populations. Methods: We collected 47 bone marrow samples from 27 patients who met criteria for adverse risk AML by ELN 2017 risk stratification. We performed RNA-sequencing on paired pre- and post-treatment sorted samples. Mononuclear cells were flow-sorted for bulk (CD45dim) and LSCs (Lin-CD34+CD38-CD123+) from diagnostic samples. Post-treatment samples were sorted for bulk mononuclear cells and MRD, determined based on patient-specific aberrant phenotype using multi-color flow cytometry analysis (Xu J et al., Clinics in laboratory medicine 2017). Sixteen patients (59%) had mutations in TP53, 9 (33%) had mutations in FLT3, and 3 (11%) had no mutations in these genes but had other adverse risk features. RNA was isolated using low-input methodology, and RNA-sequencing was performed using Illumina HiSeq 2000. Samples with low-expression of housekeeping genes were excluded from the analysis. Differential expression was analyzed using DESeq2 and Gene Set Enrichment Analysis (GSEA) was performed using the HALLMARK gene set. Results: The median age of patients included in this cohort was 67 years (range: 35-81). Baseline characteristics, including adverse risk features, commonly mutated genes, treatments and responses are described in Figure 1A. Differentially expressed genes were compared between sub-populations. Figure 1B includes pathways with statistically significant changes (changes with q<0.1 in at least one comparison are plotted in the heat map). Up-regulation of Myc-related genes was found when comparing bulk to LSCs or to MRD regardless of the genetic context (TP53 or FLT3 mutated) (Figure 1C). Similarly, there was up-regulation of genes related to the transcription factor E2F, to cell cycle checkpoints and DNA repair pathways. In addition, up-regulation of oxidative phosphorylation was found in both LSC and post-treatment MRD. This is consistent with previous data showing dependence of LSCs and cytarabine-resistant AML cells on mitochondrial function (Lagadinou et al., Cell Stem Cell 2013; Farge et al., Cancer Discovery 2017). On the other hand, we found down-regulation of immune-related genes in LSCs compared to bulk (allograft, inflammatory response, and complement-related gene sets). This is consistent with the potential of AML LSCs to evade the immune system regardless of the genetic context in this cohort. Interestingly, post-treatment MRD, in the TP53 mutated sub-group only, had up-regulation of TNFa-signaling pathway genes. This could be a specific mechanism by which AML cells with TP53 mutations modulate and evade immune control following treatment. Conclusions: In conclusion, we show that aberrant transcriptional changes may account for resistance to therapy in adverse risk AML. The transcriptome of pre-treatment LSCs and post-treatment MRD is characterized by up-regulation of Myc-related genes, cell cycle checkpoints, DNA repair pathways, and oxidative phosphorylation. We also identified down-regulation of immune-related genes in LSCs. These findings have potential impact on future therapeutic strategies aimed at overcoming resistance in adverse risk AML. Figure 1 Disclosures Konopleva: Agios: Research Funding; AbbVie: Consultancy, Honoraria, Research Funding; Astra Zeneca: Research Funding; Cellectis: Research Funding; Eli Lilly: Research Funding; Forty-Seven: Consultancy, Honoraria; Stemline Therapeutics: Consultancy, Honoraria, Research Funding; Calithera: Research Funding; Amgen: Consultancy, Honoraria; F. Hoffman La-Roche: Consultancy, Honoraria, Research Funding; Genentech: Honoraria, Research Funding; Ablynx: Research Funding; Reata Pharmaceuticals: Equity Ownership, Patents & Royalties; Kisoji: Consultancy, Honoraria; Ascentage: Research Funding. Andreeff:Senti Bio: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Oncoceutics: Equity Ownership; BiolineRx: Membership on an entity's Board of Directors or advisory committees; CLL Foundation: Membership on an entity's Board of Directors or advisory committees; Oncolyze: Equity Ownership; Breast Cancer Research Foundation: Research Funding; German Research Council: Membership on an entity's Board of Directors or advisory committees; NCI-CTEP: Membership on an entity's Board of Directors or advisory committees; Center for Drug Research & Development: Membership on an entity's Board of Directors or advisory committees; Cancer UK: Membership on an entity's Board of Directors or advisory committees; Eutropics: Equity Ownership; Aptose: Equity Ownership; Leukemia Lymphoma Society: Membership on an entity's Board of Directors or advisory committees; NCI-RDCRN (Rare Disease Cliln Network): Membership on an entity's Board of Directors or advisory committees; CPRIT: Research Funding; NIH/NCI: Research Funding; Daiichi Sankyo, Inc.: Consultancy, Patents & Royalties: Patents licensed, royalty bearing, Research Funding; Jazz Pharmaceuticals: Consultancy; Celgene: Consultancy; Amgen: Consultancy; AstaZeneca: Consultancy; 6 Dimensions Capital: Consultancy; Reata: Equity Ownership.

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Phase II Trial of the Combination of Ixazomib, Lenalidomide, and Dexamethasone in High-Risk Smoldering Multiple Myeloma

Blood ◽

10.1182/blood-2018-99-117871 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 804-804 ◽

Cited By ~ 7

Author(s):

Mark Bustoros ◽

Chia-jen Liu ◽

Kaitlen Reyes ◽

Kalvis Hornburg ◽

Kathleen Guimond ◽

...

Keyword(s):

Multiple Myeloma ◽

Bone Marrow ◽

High Risk ◽

Disease Progression ◽

Board Of Directors ◽

Rna Sequencing ◽

Research Funding ◽

Response Rate ◽

Advisory Committees ◽

Equity Ownership

Abstract Background. This study aimed to determine the progression-free survival and response rate using early therapeutic intervention in patients with high-risk smoldering multiple myeloma (SMM) using the combination of ixazomib, lenalidomide, and dexamethasone. Methods. Patients enrolled on study met eligibility for high-risk SMM based on the newly defined criteria proposed by Rajkumar et al., Blood 2014. The treatment plan was designed to be administered on an outpatient basis where patients receive 9 cycles of induction therapy of ixazomib (4mg) at days 1, 8, and 15, in combination with lenalidomide (25mg) at days 1-21 and Dexamethasone at days 1, 8, 15, and 22. This induction phase is followed by ixazomib (4mg) and lenalidomide (15mg) maintenance for another 15 cycles. A treatment cycle is defined as 28 consecutive days, and therapy is administered for a total of 24 cycles total. Bone marrow samples from all patients were obtained before starting therapy for baseline assessment, whole exome sequencing (WES), and RNA sequencing of plasma and bone marrow microenvironment cells. Moreover, blood samples were obtained at screening and before each cycle to isolate cell-free DNA (cfDNA) and circulating tumor cells (CTCs). Stem cell collection is planned for all eligible patients. Results. In total, 26 of the planned 56 patients were enrolled in this study from February 2017 to April 2018. The median age of the patients enrolled was 63 years (range, 41 to 73) with 12 males (46.2%). Interphase fluorescence in situ hybridization (iFISH) was successful in 18 patients. High-risk cytogenetics (defined as the presence of t(4;14), 17p deletion, and 1q gain) were found in 11 patients (61.1%). The median number of cycles completed was 8 cycles (3-15). The most common toxicities were fatigue (69.6%), followed by rash (56.5%), and neutropenia (56.5%). The most common grade 3 adverse events were hypophosphatemia (13%), leukopenia (13%), and neutropenia (8.7%). One patient had grade 4 neutropenia during treatment. Additionally, grade 4 hyperglycemia occurred in another patient. As of this abstract date, the overall response rate (partial response or better) in participants who had at least 3 cycles of treatment was 89% (23/26), with 5 Complete Responses (CR, 19.2%), 9 very good partial responses (VGPR, 34.6%), 9 partial responses (34.6%), and 3 Minimal Responses (MR, 11.5%). None of the patients have shown progression to overt MM to date. Correlative studies including WES of plasma cells and single-cell RNA sequencing of the bone microenvironment cells are ongoing to identify the genomic and transcriptomic predictors for the differential response to therapy as well as for disease evolution. Furthermore, we are analyzing the cfDNA and CTCs of the patients at different time points to investigate their use in monitoring minimal residual disease and disease progression. Conclusion. The combination of ixazomib, lenalidomide, and dexamethasone is an effective and well-tolerated intervention in high-risk smoldering myeloma. The high response rate, convenient schedule with minimal toxicity observed to date are promising in this patient population at high risk of progression to symptomatic disease. Further studies and longer follow up for disease progression are warranted. Disclosures Bustoros: Dava Oncology: Honoraria. Munshi:OncoPep: Other: Board of director. Anderson:C4 Therapeutics: Equity Ownership; Celgene: Consultancy; Bristol Myers Squibb: Consultancy; Takeda Millennium: Consultancy; Gilead: Membership on an entity's Board of Directors or advisory committees; Oncopep: Equity Ownership. Richardson:Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding. Ghobrial:Celgene: Consultancy; Takeda: Consultancy; Janssen: Consultancy; BMS: Consultancy.

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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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Primary Plasma Cell Leukemia Outcomes Remain Dismal Despite Novel Agents and Hematopoietic Cell Transplantation

Blood ◽

10.1182/blood-2019-127049 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 266-266

Author(s):

Sagar Patel ◽

Saulius K. Girnius ◽

Binod Dhakal ◽

Lohith Gowda ◽

Raphael Fraser ◽

...

Keyword(s):

High Risk ◽

Board Of Directors ◽

Plasma Cell ◽

Research Funding ◽

Hematopoietic Cell Transplantation ◽

Disease Status ◽

Cell Leukemia ◽

Advisory Committees ◽

Primary Plasma Cell Leukemia ◽

Equity Ownership

Background Primary plasma cell leukemia (pPCL) is a rare plasma cell neoplasm with a high mortality rate. There have been improvements in multiple myeloma (MM) outcomes with novel induction agents and use of hematopoietic cell transplantation (HCT) with maintenance, but similar progress has not been reported for pPCL. We examined the outcomes of pPCL patients receiving novel agents with autologous (autoHCT) or allogeneic (alloHCT) approaches as reported to the Center for International Blood and Marrow Transplant Research (CIBMTR) in the modern era. Methods From 2008 to 2015, 348 pPCL pts underwent HCT (N = 277 - autoHCT and 71 - alloHCT) with 45% and 48% having research level data available, respectively. Cumulative incidences of non-relapse mortality (NRM) and relapse/progression (REL), and probability of progression-free survival (PFS) and overall survival (OS) were calculated. Cox multivariate regression was used to model survival after autoHCT only. Median follow-up in autoHCT and alloHCT was 48 and 60 months, respectively. Results AutoHCT Cohort Median age was 60 years and 93% received HCT within 12 months of diagnosis with 76% after a single line of induction (Table 1). 35% had high risk cytogenetics. 23% received bortezomib, doxorubicin, cisplatin, cyclophosphamide, and etoposide (VDPACE). Moreover, 40% received bortezomib (BTZ) and immunomodulatory drug (IMIID)-based triplets. Disease status at HCT was VGPR or better in 47%. 27% received maintenance therapy. At 4 years post-HCT, NRM was 7% (4-11%), REL 76% (69-82%), PFS 17% (13-23%), and OS 28% (22-35%) (Figures 1A, 2A, 2B). Disease status ≥VGPR at HCT and Karnofsky Performance Score >90 significantly predicted superior OS in multivariate analysis. AlloHCT Cohort Median age was 53 years and 89% received HCT within 12 months of diagnosis (Table 1). 61% received a single alloHCT, while 39% used auto-alloHCT tandem approach. 42% had high-risk cytogenetics. 61% received total body irradiation with 44% receiving myeloablative conditioning. Use of VDPACE was higher at 41% in this cohort. VGPR status at HCT was similar (48%), while maintenance was used less often (12%). Grade II-IV acute GVHD occurred in 30% and chronic GVHD in 45%. At four years post-HCT, NRM was 12% (5-21%), REL 69% (56-81%), PFS 19% (10-31%), and OS 31% (19-44%) (Figures 1A, 1B, 2A, 2B). There were no differences in outcomes based on type of HCT. A comparison of post-HCT outcomes of CIBMTR pPCL patients from 1995 to 2006 showed that PFS and OS outcomes are inferior despite lower NRM in this modern cohort (Mahindra et al. Leukemia. 2012). In addition, analysis of SEER (1995-2009) and CIBMTR databases showed that use of HCT increased from 12% (7-21%) in 1995 to 46% (34-64%) in 2009. Conclusion More newly diagnosed pPCL patients are receiving modern induction regimens translating into a higher proportion receiving HCT, but without significant further benefit post-HCT. Post-HCT relapse remains the biggest challenge and further survival in pPCL will likely need a combination of targeted and cell therapy approaches. This study provides a benchmark for future HCT studies for pPCL. Disclosures Girnius: Takeda: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Genentech: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Dhakal:Takeda: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Honoraria. Shah:University of California, San Francisco: Employment; Indapta Therapeutics: Equity Ownership; Genentech, Seattle Genetics, Oncopeptides, Karoypharm, Surface Oncology, Precision biosciences GSK, Nektar, Amgen, Indapta Therapeutics, Sanofi: Membership on an entity's Board of Directors or advisory committees; Celgene, Janssen, Bluebird Bio, Sutro Biopharma: Research Funding; Poseida: Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Nkarta: Consultancy, Membership on an entity's Board of Directors or advisory committees; Kite: Consultancy, Membership on an entity's Board of Directors or advisory committees; Teneobio: Consultancy, Membership on an entity's Board of Directors or advisory committees. Qazilbash:Amgen: Consultancy, Other: Advisory Board; Bioclinical: Consultancy; Autolus: Consultancy; Genzyme: Other: Speaker. Kumar:Celgene: Consultancy, Research Funding; Takeda: Research Funding; Janssen: Consultancy, Research Funding. D'Souza:EDO-Mundapharma, Merck, Prothena, Sanofi, TeneoBio: Research Funding; Prothena: Consultancy; Pfizer, Imbrium, Akcea: Membership on an entity's Board of Directors or advisory committees. Hari:BMS: Consultancy, Research Funding; Takeda: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria; Kite: Consultancy, Honoraria; Amgen: Research Funding; Spectrum: Consultancy, Research Funding; Sanofi: Honoraria, Research Funding; Cell Vault: Equity Ownership; AbbVie: Consultancy, Honoraria.

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CD200 Is a Stem Cell-Specific Immunosuppressive Target in AML

Blood ◽

10.1182/blood-2018-99-114818 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 2768-2768

Author(s):

Shelley Herbrich ◽

Keith Baggerly ◽

Gheath Alatrash ◽

R. Eric Davis ◽

Michael Andreeff ◽

...

Keyword(s):

Gene Expression ◽

Flow Cytometry ◽

T Cells ◽

Stem Cell ◽

Board Of Directors ◽

Research Funding ◽

Advisory Committees ◽

Cd200 Receptor ◽

Blast Cells ◽

Equity Ownership

Abstract Acute myeloid leukemia (AML) stem cells (LSC) are an extremely rare fraction of the overall disease (likely <0.3%), largely quiescent, and capable of both long-term self-renewal and production of more differentiated leukemic blasts. Besides their role in disease initiation, they are also hypothesized as the likely source of deadly, relapsed leukemia. Due to the quiescent nature of the LSCs, they are capable of evading the majority of chemotherapeutic agents that rely on active cell-cycling for cytotoxicity. Therefore, novel therapeutic approaches specifically engineered to eradicate LSCs are critical for curing AML. We previously introduced a novel bioinformatics approach that harnessed publically available AML gene expression data to identify genes significantly over-expressed in LSCs when compared to their normal hematopoietic stem cell (HSC) counterparts (Herbrich et al Blood 2017 130:3962). These datasets contain gene expression arrays on human AML patient samples sorted by leukemia stem, progenitor, and blast cells (with normal hematopoietic cell subsets for comparison). We have since expanded our statistical model to identify targets that are both significantly overexpressed in AML LSCs when compared to HSC as well as LSCs compared to their corresponding, more differentiated blast cells. Instead of traditional methods for multiple testing corrections, we looked at the intersection of genes that met the above criteria in 3 independently generated datasets. This resulted in a list of 30 genes, 28 of which appear to be novel markers of AML LSCs. From this list, we first chose to focus on CD200, a type-1 transmembrane glycoprotein. CD200 is broadly expressed on myeloid, lymphoid, and epithelial cells, while the CD200 receptor (CD200R) expression is strictly confined to myeloid and a subset of T cells. CD200 has been shown to have an immunosuppressive effect on macrophages and NK cells and correlates with a high prevalence FOXP3+ regulatory T cells (Coles et al Leukemia 2012; 26:2146-2148). Additionally, CD200 has been implicated as a poor prognostic marker in AML (Damiani et al Oncotarget 2015; 6:30212-30221). To date, we have screened 20 primary AML patient samples by flow cytometry, 90% of which are positive for CD200. Expression is significantly enriched in the CD34+/CD123+ stem cell compartment. To examine the role of CD200 in AML, we established two in vitro model systems. First, we used CRISPR/Cas9 to knockout the endogenous CD200 protein in Kasumi-1. Further, we induced CD200 in the OCI-AML3 cell line that had no expression at baseline. Both cell lines did not express the CD200 receptor before or after manipulation, negating any autocrine signaling. In both systems, CD200 manipulation did not affect the proliferation rate or viability of the cells. To examine the immune function of CD200 in AML, we performed a series of mixed lymphocyte reactions. We cultured normal human peripheral blood mononuclear cells (PBMCs) with the CD200+ or CD200- cells from each line both. Cells were incubated in the culture media for 4-48 hours before being harvested and measured by flow cytometry for apoptosis or intracellular cytokine production. The presence of CD200 on the cell surface reduced the rate of immune-specific apoptosis among these leukemia cells. The difference in cell killing was most likely attributable to a CD200-specific suppression of CD107a, a surrogate marker or cytotoxic activity. In the OCI-AML3 model, PBMCs co-cultured with CD200+ cells produced approximately 40% less CD107a when compared to the CD200- co-culture. Additionally, we characterized our new cell lines using RNA sequencing. By comparing the CD200+ to the CD200- cells within each line, we observed that CD200+ cells significantly downregulate genes involved in defining an inflammatory response as well as genes regulated by NF-κB in response to TNFα. This indicates that CD200 may have an undiscovered intrinsic role in suppressing the immune microenvironment of AML LSCs. In conclusion, we have expanded our novel bioinformatics approach for robustly identifying AML LSC-specific targets. Additionally, we have shown that one of these markers, CD200, has a potential role as a stem cell-specific immunosuppressive target by reducing immune-mediated apoptosis and transcriptionally suppressing inflammatory cell processes. We are extending our study to explore CD200 in primary patient samples using a CD200-blocking antibody. Disclosures Andreeff: SentiBio: Equity Ownership; Amgen: Consultancy, Research Funding; Oncolyze: Equity Ownership; Reata: Equity Ownership; United Therapeutics: Patents & Royalties: GD2 inhibition in breast cancer ; Jazz Pharma: Consultancy; Astra Zeneca: Research Funding; Aptose: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Daiichi-Sankyo: Consultancy, Patents & Royalties: MDM2 inhibitor activity patent, Research Funding; Eutropics: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Oncoceutics: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy. Konopleva:Stemline Therapeutics: Research Funding.

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Comparative Analysis of Independent Ex Vivo functional Drug Screens Identifies Predictive Biomarkers of BCL-2 Inhibitor Response in AML

Blood ◽

10.1182/blood-2018-99-111916 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 2763-2763 ◽

Cited By ~ 1

Author(s):

Brian S. White ◽

Suleiman A. Khan ◽

Muhammad Ammad-ud-din ◽

Swapnil Potdar ◽

Mike J Mason ◽

...

Keyword(s):

Gene Expression ◽

Board Of Directors ◽

Research Funding ◽

Drug Response ◽

Ex Vivo ◽

Predictive Performance ◽

Data Sets ◽

Advisory Committees ◽

Data Set ◽

Equity Ownership

Abstract Introduction: Therapeutic options for patients with AML were recently expanded with FDA approval of four drugs in 2017. As their efficacy is limited in some patient subpopulations and relapse ultimately ensues, there remains an urgent need for additional treatment options tailored to well-defined patient subpopulations to achieve durable responses. Two comprehensive profiling efforts were launched to address this need-the multi-center Beat AML initiative, led by the Oregon Health & Science University (OHSU) and the AML Individualized Systems Medicine program at the Institute for Molecular Medicine Finland (FIMM). Methods: We performed a comparative analysis of the two large-scale data sets in which patient samples were subjected to whole-exome sequencing, RNA-seq, and ex vivo functional drug sensitivity screens: OHSU (121 patients and 160 drugs) and FIMM (39 patients and 480 drugs). We predicted ex vivo drug response [quantified as area under the dose-response curve (AUC)] using gene expression signatures selected with standard regression and a novel Bayesian model designed to analyze multiple data sets simultaneously. We restricted analysis to the 95 drugs in common between the two data sets. Results: The ex vivo responses (AUCs) of most drugs were positively correlated (OHSU: median Pearson correlation r across all pairwise drug comparisons=0.27; FIMM: median r=0.33). Consistently, a samples's ex vivo response to an individual drug was often correlated with the patient's Average ex vivo Drug Sensitivity (ADS), i.e., the average response across the 95 drugs (OHSU: median r across 95 drugs=0.41; FIMM: median r=0.58). Patients with a complete response to standard induction therapy had a higher ADS than those that were refractory (p=0.01). Further, patients whose ADS was in the top quartile had improved overall survival relative to those having an ADS in the bottom quartile (p<0.05). Standard regression models (LASSO and Ridge) trained on ADS and gene expression in the OHSU data set had improved ex vivo response prediction performance as assessed in the independent FIMM validation data set relative to those trained on gene expression alone (LASSO: p=2.9x10-4; Ridge: p=4.4x10-3). Overall, ex vivo drug response was relatively well predicted (LASSO: mean r across 95 drugs=0.62; Ridge: mean r=0.62). The BCL-2 inhibitor venetoclax was the only drug whose response was negatively correlated with ADS in both data sets. We hypothesized that, whereas the predictive performance of many other drugs was likely dependent on ADS, the predictive performance of venetoclax (LASSO: r=0.53, p=0.01; Ridge: r=0.63, p=1.3x10-3) reflected specific gene expression biomarkers. To identify biomarkers associated with venetoclax sensitivity, we developed an integrative Bayesian machine learning method that jointly modeled both data sets, revealing several candidate biomarkers positively (BCL2 and FLT3) or negatively (CD14, MAFB, and LRP1) correlated with venetoclax response. We assessed these biomarkers in an independent data set that profiled ex vivo response to the BCL-2/BCL-XL inhibitor navitoclax in 29 AML patients (Lee et al.). All five biomarkers were validated in the Lee data set (Fig 1). Conclusions: The two independent ex vivo functional screens were highly concordant, demonstrating the reproducibility of the assays and the opportunity for their use in the clinic. Joint analysis of the two data sets robustly identified biomarkers of drug response for BCL-2 inhibitors. Two of these biomarkers, BCL2 and the previously-reported CD14, serve as positive controls credentialing our approach. CD14, MAFB, and LRP1 are involved in monocyte differentiation. The inverse correlation of their expression with venetoclax and navitoclax response is consistent with prior reports showing that monocytic cells are resistant to BCL-2 inhibition (Kuusanmäki et al.). These biomarker panels may enable better selection of patient populations likely to respond to BCL-2 inhibition than would any one biomarker in isolation. References: Kuusanmäki et al. (2017) Single-Cell Drug Profiling Reveals Maturation Stage-Dependent Drug Responses in AML, Blood 130:3821 Lee et al. (2018) A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia, Nat Commun 9:42 Disclosures Druker: Cepheid: Consultancy, Membership on an entity's Board of Directors or advisory committees; ALLCRON: Consultancy, Membership on an entity's Board of Directors or advisory committees; Fred Hutchinson Cancer Research Center: Research Funding; Celgene: Consultancy; Vivid Biosciences: Membership on an entity's Board of Directors or advisory committees; Aileron Therapeutics: Consultancy; Third Coast Therapeutics: Membership on an entity's Board of Directors or advisory committees; Oregon Health & Science University: Patents & Royalties; Patient True Talk: Consultancy; Millipore: Patents & Royalties; Monojul: Consultancy; Gilead Sciences: Consultancy, Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; Leukemia & Lymphoma Society: Membership on an entity's Board of Directors or advisory committees, Research Funding; GRAIL: Consultancy, Membership on an entity's Board of Directors or advisory committees; Beta Cat: Membership on an entity's Board of Directors or advisory committees; MolecularMD: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Henry Stewart Talks: Patents & Royalties; Bristol-Meyers Squibb: Research Funding; Blueprint Medicines: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Aptose Therapeutics: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; McGraw Hill: Patents & Royalties; ARIAD: Research Funding; Novartis Pharmaceuticals: Research Funding. Heckman:Orion Pharma: Research Funding; Novartis: Research Funding; Celgene: Research Funding. Porkka:Novartis: Honoraria, Research Funding; Celgene: Honoraria, Research Funding. Tyner:AstraZeneca: Research Funding; Incyte: Research Funding; Janssen: Research Funding; Leap Oncology: Equity Ownership; Seattle Genetics: Research Funding; Syros: Research Funding; Takeda: Research Funding; Gilead: Research Funding; Genentech: Research Funding; Aptose: Research Funding; Agios: Research Funding. Aittokallio:Novartis: Research Funding. Wennerberg:Novartis: Research Funding.

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

Blood ◽

10.1182/blood-2018-99-111239 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 2733-2733 ◽

Cited By ~ 1

Author(s):

Jorge E. Cortes ◽

Akil Merchant ◽

Catriona Jamieson ◽

Daniel A Pollyea ◽

Michael Heuser ◽

...

Keyword(s):

Gene Expression ◽

Board Of Directors ◽

Research Funding ◽

Intensive Therapy ◽

Employment Equity ◽

Advisory Committees ◽

Expression Levels ◽

Biomarker Analysis ◽

Cytokine Levels ◽

Equity Ownership

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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A Novel Prognostic Model That Is Superior to FLIPI-1 and FLIPI-2 and Integrates Clinical and Treatment Factors to Predict Progression-Free Survival and Early Treatment Failure in Patients with Follicular Lymphoma in the GALLIUM Trial

Blood ◽

10.1182/blood-2018-99-114620 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 2872-2872 ◽

Cited By ~ 2

Author(s):

Farheen Mir ◽

Andrew Grigg ◽

Michael Herold ◽

Wolfgang Hiddemann ◽

Robert Marcus ◽

...

Keyword(s):

High Risk ◽

Board Of Directors ◽

Research Funding ◽

Prognostic Model ◽

Low Risk ◽

Prognostic Variables ◽

Advisory Committees ◽

High Risk Disease ◽

Risk Patients ◽

Equity Ownership

Abstract Introduction: Progression of disease within 24 months of initial therapy (POD24) is associated with poor survival in patients with follicular lymphoma (FL). Existing prognostic models, such as FLIPI-1 and FLIPI-2, show poor sensitivity for POD24, and are derived from cohorts lacking bendamustine-treated patients. More accurate predictive models based on current standard therapies are needed to identify patients with high-risk disease. The Phase III GALLIUM trial (NCT01332968) compared the safety and efficacy of standard chemotherapy regimens plus rituximab (R) or obinutuzumab (G) in patients with previously untreated FL. Using GALLIUM data, we developed a novel risk stratification model to predict both PFS and POD24 in FL patients after first-line immunochemotherapy. Methods: Enrolled patients were aged ≥18 years with previously untreated FL (grades 1-3a), Stage III/IV disease (or Stage II with bulk), and ECOG PS ≤2, and required treatment by GELF criteria. Patients were randomized to receive either G- or R-based immunochemotherapy, followed by maintenance with the same antibody in responders. The chemotherapy arm (CHOP, CVP, or bendamustine) was selected by each study center. POD24 was defined as progressive disease or death due to disease within 24 months of randomization (noPOD24 = no progression or lymphoma-related death in that period). The most strongly prognostic variables, based on PFS hazard ratios, were estimated using penalized multivariate Cox regression methodology via an Elastic Net model. Selected variables were given equal weights, and a clinical score was formed by summating the number of risk factors for each patient. Low- and high-risk categories were determined using a cut-off that provided the best balance between true- and false-positives for PFS. PFS correlation and sensitivity to predict POD24 were assessed. The data used are from an updated GALLIUM efficacy analysis (data cut-off: April 2018; median follow-up: 57 months). Results: 1202 FL patients were enrolled. Based on data availability and biological plausibility (i.e. could reasonably be linked with high-risk disease), 25 potential clinical and treatment-related prognostic variables were entered into the Elastic Net model (Table). A model containing 11 factors was retained by the methodology and chosen as the best model (Table). Patients were categorized as 'low risk' if they scored between 0 and 3 (n=521/1000 patients with complete data) and as 'high risk' if they scored between 4 and 11 (n=479/1000 patients). At 2 years, the PFS rate was 84.5% in the whole FL population. Using our model, 2-year PFS for high-risk patients was 77% compared with 79.9% for FLIPI-1 and FLIPI-2. In low-risk patients, 2-year PFS was 92% compared with 87.9% for FLIPI-1 and 87.6% for FLIPI-2 (low-intermediate-risk patients). Our model increased the inter-group difference in 2-year PFS rate from 8% (FLIPI-1) and 7.7% (FLIPI-2) to 15%. At 3 years, the inter-group difference increased from 6.9% (FLIPI-1) and 9% (FLIPI-2) to 17% (Figure). Sensitivity for a high-risk score to predict POD24 was 73% using our model compared with 55% for FLIPI-1 and 52% for FLIPI-2 (based on 127 POD24 and 873 noPOD24 patients with complete data). Excluding patients who received CVP, which is now rarely used, resulted in an inter-group difference in PFS of 15% at 2 years and 16.8% at 3 years. A sensitivity analysis showed that inclusion of the 9 clinical factors only (i.e. removal of CVP and R treatment as variables) formed a more basic scoring system (low-risk patients, 1-3; high-risk patients, 4-9); the inter-group difference in PFS was 16.5% at 2 years and 17.6% at 3 years. However, sensitivity for POD24 decreased to 56%. Conclusion: Our clinical prognostic model was more accurate at discriminating patients likely to have poor PFS than either FLIPI-1 or FLIPI-2, and its prognostic value was sustained over time. Our model also identified the FL population at risk of POD24 with greater sensitivity. Variables such as age and bone marrow involvement were not retained by our model, and thus may not have a major impact in the current era of therapy. Factors such as sum of the products of lesion diameters were included, as this captures tumor burden more accurately than presence of bulk disease. Future studies will aim to improve the accuracy of the model by considering gene expression-based prognostic markers and DNA sequencing to form a combined clinico-genomic model. Disclosures Mir: F. Hoffmann-La Roche: Employment. Hiddemann:F. Hoffman-La Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bayer: Consultancy, Research Funding. Marcus:F. Hoffman-La Roche: Other: Travel support and lecture fees; Roche: Consultancy, Other: Travel support and lecture fees ; Gilead: Consultancy. Seymour:Genentech Inc: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Honoraria, Research Funding; Celgene: Consultancy; AbbVie: Consultancy, Honoraria, Research Funding; F. Hoffmann-La Roche Ltd: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees. Bolen:Roche: Other: Ownership interests PLC*. Knapp:Roche: Employment. Launonen:Launonen: Other: Ownership interests none PLC; Travel, accommodation, expenses; Novartis: Consultancy, Equity Ownership, Other: Ownership interests none PLC; Travel. accommodation, expenses; Roche: Employment, Other: Travel, accommodation, expenses. Mattiello:Roche: Employment. Nielsen:F. Hoffmann-La Roche Ltd: Employment, Other: Ownership interests PLC. Oestergaard:Roche: Employment, Other: Ownership interests PLC. Wenger:F. Hoffmann-La Roche Ltd: Employment, Equity Ownership, Other: Ownership interests PLC. Casulo:Gilead: Honoraria; Celgene: Research Funding.

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