Cage Transcriptome Analysis Reveals BCL2A1 Upregulation in FLT3-ITD/D835 Dual Mutated AML Cells Harboring Complex Co-Mutations

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 1264-1264
Author(s):  
Kotoko Yamatani ◽  
Tomohiko Ai ◽  
Kaori Saito ◽  
Haeun Yang ◽  
Koya Suzuki ◽  
...  
Keyword(s):  
Gene Expression ◽  
Tyrosine Kinase ◽  
Board Of Directors ◽  
Research Funding ◽  
Leukemia Cells ◽  
Genetic Mutations ◽  
Advisory Committees ◽  
Protein Levels ◽  
Significant Difference ◽  
Equity Ownership

Genetic mutations in FLT3 (fms-like tyrosine kinase-3) play an important role in the pathogenesis of acute myeloid leukemia (AML). FLT3 internal tandem duplications (FLT3-ITD) occur in approximately 25% of all AML cases and various tyrosine kinase inhibitors (TKIs) targeting FLT3-ITD such as quizartinib, crenolanib, and gilteritinib have been developed. Although these selective FLT3 inhibitors were thought to be promising, their effects turned out to be temporary due to the rapid development of resistance associated with clonal switching. Acquired FLT3 point mutations at D835 in the activation loop of tyrosine kinase domain are often accountable for clonal switching at least for Type II TKIs. In addition, adjunct mutations in other genes are also found to be associated with TKI resistance. To investigate the underlying molecular mechanism of this secondary, mutation-driven acquired resistance, we first analyzed co-occurring mutations in the leukemia cells obtained from 26 AML patients with FLT3-ITD (n=14) or FLT3-ITD/D835 dual (n=12) mutations, and performed cap analysis of gene expression (CAGE) sequencing, which identifies and quantifies the 5' ends of capped mRNA transcripts (transcription start sites) and allows investigating promoter structures necessary for gene expression. Patients with FLT3-ITD/D835 harbored a higher number of co-mutations such as ASXL1 and RUNX1 compared to AML with FLT3-ITD (FLT3-ITD/D835: 2.83 ± 0.52, FLT3-ITD: 0.49 ± 0.13, p<0.0001). Intriguingly, CAGE detected significantly higher expression of the anti-apoptotic Bcl-2 family genes BCL2 and BCL2A1 in FLT3-ITD/D835 compared to FLT3-ITD mutant primary samples. Specifically, the CAGE peak of BCL2 was highest in samples with FLT3-ITD/D835 alone (p<0.01), while the CAGE peak of BCLA1 was highest in samples with FLT3-ITD/D835 and co-mutations compared with the other samples (p=0.01). To recapitulate the observations obtained with primary human AML samples, we generated MV4;11 cells with acquired FLT3-ITD/D835 mutations (MV4;11-QR cells) by culturing FLT3-ITD MV4;11 leukemia cells in the presence of quizartinib (1.5 nM), a selective FLT3 inhibitor, for 6 months. While quizartinib (0.2 nM) suppressed the proliferation of 50% of the parental MV4;11 at 72 hours, much higher concentrations of quizartinib (10 nM) was required to suppress the proliferation of MV4;11-QR cells. Quantitative RT-PCR and immunoblot analysis revealed that MV4;11-QR cells expressed higher transcript and protein levels of BCL2A1 than MV4;11 parental cells, while BCL2 levels were similar in both cells and MCL1 and BCLxL expression were lower in the MV4;11-QR than in the parental cells. Next, to investigate the molecular properties of AML cells bearing FLT3-ITD or FLT3-ITD/D835 without other co-mutations, we created Ba/F3 cells stably expressing FLT3-ITD or FLT3-ITD/D835. Of notes, the FLT3-ITD/D835 Ba/F3 cells expressed markedly higher BCL2 transcript and protein levels with lower expression of BCLxL than in FLT3-ITD Ba/F3 cells. No significant difference of MCL1 expression was observed. The sensitivity to quizartinib was massively decreased in the FLT3-ITD/D835 Ba/F3 cells (IC50: FLT3-ITD/D835 >1000nM vs. FLT3-ITD, 0.8nM, at 48h). Finally, we examined the efficacy of the BCL-2 specific inhibitor venetoclax in FLT3-ITD/D835 dual mutated cells with or without upregulation of BCL2 or BCL2A1, the latter shown to confer resistance to venetoclax by sequestering released BIM (Esteve-Arenys, Oncogene. 2018). As expected, venetoclax caused more profound cell growth inhibition and apoptosis induction in BCL2 upregulated FLT3-ITD/D835 Ba/F3 compared to FLT3-ITD Ba/F3 cells (IC50: FLT3-ITD/D835 301nM vs. FLT3-ITD >1000 nM, 96 h). However, FLT3-ITD/D835 bearing MV4;11-QR cells with upregulated BCL2A1 were less sensitive to venetoclax than MV4;11 parental cells (IC50: MV4;11-QR, 149nM vs. MV4;11, 33 nM, 72 h). In conclusion, these results demonstrate that acquisition of D835 mutation in FLT3-ITD mutated AML is often accompanied with multiple co-occurring genetic mutations, and depends on anti-apoptotic BCL-2 associated pro-survival mechanisms. BCL2A1 upregulation might be involved in pathogenesis of acquired drug resistance of FLT3-ITD/D835 dual mutant AML cells, and is a promising new target in FLT3-ITD/D835 refractory AML with complex mutations. Disclosures Carter: Amgen: Research Funding; AstraZeneca: Research Funding; Ascentage: Research Funding. Shah:Bristol-Myers Squibb: Research Funding. Konopleva:Genentech: Honoraria, Research Funding; Ascentage: Research Funding; Kisoji: Consultancy, Honoraria; Reata Pharmaceuticals: Equity Ownership, Patents & Royalties; Ablynx: Research Funding; Astra Zeneca: Research Funding; Agios: Research Funding; Calithera: Research Funding; Stemline Therapeutics: Consultancy, Honoraria, Research Funding; Forty-Seven: Consultancy, Honoraria; Eli Lilly: Research Funding; AbbVie: Consultancy, Honoraria, Research Funding; Cellectis: Research Funding; Amgen: Consultancy, Honoraria; F. Hoffman La-Roche: Consultancy, Honoraria, Research Funding. Andreeff: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; Aptose: Equity Ownership; Eutropics: Equity Ownership; Senti Bio: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Oncoceutics: Equity Ownership; Oncolyze: Equity Ownership; Breast Cancer Research Foundation: Research Funding; CPRIT: Research Funding; NIH/NCI: Research Funding; 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; NCI-CTEP: Membership on an entity's Board of Directors or advisory committees; German Research Council: 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; NCI-RDCRN (Rare Disease Cliln Network): Membership on an entity's Board of Directors or advisory committees; CLL Foundation: Membership on an entity's Board of Directors or advisory committees; BiolineRx: Membership on an entity's Board of Directors or advisory committees.

Targeting Brouton's Tyrosine Kinase with PCI-32765 Blocks Growth and Survival of Multiple Myeloma and Waldenström Macroglobulinemia Via Potent Inhibition of Osteoclastogenesis, Cytokines/Chemokine Secretion, and Myeloma Stem-Like Cells in the Bone Marrow Microenvironment

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 883-883
Author(s):  
Yu-Tzu Tai ◽  
Betty Y Chang ◽  
Sun-Young Kong ◽  
Mariateresa Fulciniti ◽  
Guang Yang ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Bone Marrow ◽  
Tyrosine Kinase ◽  
B Cell ◽  
Board Of Directors ◽  
Research Funding ◽  
Advisory Committees ◽  
Growth And Survival ◽  
Equity Ownership ◽  
Trap Staining

Abstract Abstract 883 Specific expression of Bruton's tyrosine kinase (Btk) in osteoclasts (OC), but not osteoblasts (OB), suggests its role in regulating osteoclastogenesis. Although Btk is critical in B cell maturation and myeloid function, it has not been characterized in plasma cell malignancies including multiple myeloma (MM) and Waldenström Macroglobulinemia (WM). We here investigate effects of PCI-32765, an oral, potent, and selective Btk inhibitor with promising clinical activity in B-cell malignancies, on OC differentiation and function within MM bone marrow (BM) microenvironment, as well as on MM and WM cancer cells. We further define molecular targets of Btk signaling cascade in OCs and MM in the BM milieu. In CD14+ OC precursor cells, RANKL and M-CSF stimulate phosphorylation of Btk in a time-dependent fashion; conversely, PCI-32765 abrogates RANKL/M-CSF-induced activation of Btk and downstream PLCγ2. Importantly, PCI-32765 decreased number of multinucleated OC (>3 nuclei) by tartrate-resistant acid phosphatase (TRAP) staining and the secretion of TRAP5b (ED50 = 17 nM), a specific mature OC marker. It increased size of OCs and number of nuclei per OC, with significantly defective bone resorption activity as evidenced by diminished pit formation on dentine slices. Moreover, lack of effect of Dexamethasone on OC activity was overcome by combination of Dexamethasone with PCI-32765. PCI-32765 significantly reduced cytokine and chemokine secretion from OC cultures, including MIP1α, MIP1β, IL-8, TGFβ1, RANTES, APRIL, SDF-1, and activin A (ED50 = 0.1–0.48 nM). It potently decreased IL-6, SDF-1, MIP1α, MIP1β, and M-CSF in CD138-negative cell cultures from active MM patients, associated with decreased TRAP staining in a dose-dependent manner. In MM and WM cells, immunoblotting analysis confirmed a higher Btk expression in CD138+ cells from majority of MM patients (4 out of 5 samples) than MM cell lines (5 out of 9 cell lines), whereas microarray analysis demonstrated a higher expression of Btk and its downstream signaling components in WM cells than in CD19+ normal bone marrow cells. PCI-32765 significantly inhibits SDF-1-induced adhesion and migration of MM cells. It further blocked cytokine expression (MIP1a, MIP-1β) at mRNA level in MM and WM tumor cells, correlated with inhibition of Btk-mediated pPLCγ2, pERK and NF-kB activation. Importantly, PCI-32765 inhibited growth and survival triggered by IL-6 and coculture with BM stromal cells (BMSCs) or OCs in IL-6-dependent INA6 and ANBL6 MM cells. Furthermore, myeloma stem-like cells express Btk and PCI-32765 (10–100 nM) blocks their abilities to form colonies from MM patients (n=5). In contrast, PCI-32765 has no adverse effects on Btk-negative BMSCs and OBs, as well as Btk-expressing dendritic cells. Finally, oral administration of PCI-32765 (12 mg/kg) in mice significantly suppresses MM cell growth (p< 0.03) and MM cell-induced osteolysis on implanted human bone chips in a humanized myeloma (SCID-hu) model. Together, these results provide compelling evidence to target Btk in the BM microenvironment against MM and WM., strongly supporting clinical trials of PCI-32765 to improve patient outcome in MM and WM. Disclosures: Chang: Pharmacyclics Inc: Employment. Buggy:Pharmacyclics, Inc.: Employment, Equity Ownership. Elias:Pharmacyclics Inc: Consultancy. Treon:Millennium: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Genentech: Honoraria. Richardson:Millennium: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Johnson & Johnson: Membership on an entity's Board of Directors or advisory committees; Novartis: 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. Munshi:Millennium: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees. Anderson:Millennium Pharmaceuticals, Inc.: Consultancy; Celgene: Consultancy; Novartis: Consultancy; Onyx: Consultancy; Merck: Consultancy; Bristol-Myers Squibb: Consultancy; Actelion: Equity Ownership, Membership on an entity's Board of Directors or advisory committees.

scholarly journals CD200 Is a Stem Cell-Specific Immunosuppressive Target in AML

Blood ◽  
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.

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

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 2763-2763 ◽  
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.

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

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 2733-2733 ◽  
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.

scholarly journals High Levels of APOBEC3B Gene Expression Contribute to Poor Prognosis in Multiple Myeloma Patients

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 3897-3897
Author(s):  
Valeriy V Lyzogubov ◽  
Pingping Qu ◽  
Cody Ashby ◽  
Adam Rosenthal ◽  
Antje Hoering ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Board Of Directors ◽  
Research Funding ◽  
Poor Prognosis ◽  
Target Genes ◽  
Plasma Cells ◽  
Nuclear Fraction ◽  
Advisory Committees ◽  
Significant Difference

Abstract Introduction: Poor prognosis and drug resistance in multiple myeloma (MM) is associated with increased mutational load. APOBEC3B is a major contributor to mutagenesis, especially in myeloma patients with t(14;16) MAF subgroup. It was shown recently that presence of the APOBEC signature at diagnosis is an independent prognostic factor for progression free survival (PFS) and overall survival (OS). We hypothesized that high levels of APOBEC3B gene expression at diagnosis may also have a prognostic impact in myeloma. To consider APOBEC3B as a potential target for therapy more studies are necessary to understand how APOBEC3B expression is regulated and how APOBEC3B generates mutations. Methods: Gene expression profiling (GEP, U133 Plus 2.0) of MM patients was performed. APOBEC3B gene expression levels were investigated in plasma cells of healthy donors (HD; n=34), MGUS (n=154), smoldering myeloma (SMM; n=219), MM low risk (LR; n=739), MM high risk (HR; n=129), relapsed MM (RMM; n=74), and primary plasma cell leukemia (pPCL; n=19) samples. The samples from relapse were taken on or after the progression/relapse date but within 30 days after progression/relapse from Total Therapy trials 3, 4, 5 & 6. GEP70 score was used to separate samples into LR and HR groups. We also investigated APOBEC3B expression in different MM molecular subgroups and used logrank statistics with covariate frequency distribution to determine an optimal cut off APOBEC3B expression value. Gene expression was compared in cases with low expression of APOBEC3B (log2<7.5) and high expression of APOBEC3B (log2>10), and an optimal cut-point in APOBEC3B expression was identified with respect to PFS. To explore the role of MAF and the non-canonical NF-ĸB pathway we performed functional studies using a cellular model of MAF downregulation. TRIPZ lentiviral shRNA MAF knockdown in the RPMI8226 cell lines was used to explore MAF-dependent genes. NF-ĸB proteins, p52 and RelB, were investigated in the nuclear fraction by immunoblot analysis. Results: Expression of APOBEC3B in HD control samples (log2=10.9) was surprisingly higher than in MGUS (log2=9.51), SMM (log2=9.09), and LR (log2=9.40) and was comparable to HR (log2=10.4) and RMM (log2=10.6) groups. Expression levels of APOBEC3B were gradually increased as disease progressed from SMM to pPCL. The high expression of APOBEC3B in HD places plasma cells at risk of APOBEC induced mutagenesis where the regulation of APOBEC3B function is compromised. The correlation between APOBEC3B expression and GEP70 score in MM was 0.37, and there was a significant difference in APOBEC3B expression between GEP70 high and low risk groups (p=0.0003). An optimal cut-point in APOBEC3B expression of log2=10.2 resulted in a significant difference in PFS (median 5.7 yr vs.7.4 yr; p=0.0086) and OS (median 9.1 yr vs. not reached; p<0.0001), between high and low expression. The highest APOBEC3B expression was detected in cases with a t(14;16). We analyzed t(14;16) cases with the APOBEC mutational signature and compared them to t(14;16) cases without the APOBEC signature and found elevated MAF (2-fold) and APOBEC3B (2.7-fold) gene expression in samples with the APOBEC signature. No APOBEC signature was detected in SMM cases, including those with a t(14;16). High APOBEC3B levels in myeloma patients was associated with overexpression of genes related to response to DNA damage and cell cycle control. Significant (p<0.05) increases of NF-κB target genes was seen in high APOBEC3B cases: TNFAIP3 (4.4-fold), NFKB2 (1.7-fold), NFKBIE (1.9-fold), RELB (1.4-fold), NFKBIA (2.0-fold), PLEK (2.5-fold), MALT1 (2.5-fold), WNT10A (2.4-fold). However, in t(14;16) cases there was no significant increase of NF-κB target genes except BIRC3 (2.5-fold) and MALT1 (2.0-fold). MAF downregulation in RPMI8226 cells did not lead to changes in NF-κB target gene expression but MAF-dependent genes were identified, including ETS1, SPP1, RUNX2, HGF, IGFBP2 and IGFBP3. Analysis of nuclear fraction of NF-ĸB proteins did not show significant changes in expression of p52 and RelB in RPMI8226 cells after MAF downregulation. Conclusions: Increased expression of APOBEC3B is a negative prognostic factor in multiple myeloma. MAF is a major factor regulating expression of APOBEC3B in the t(14;16) subgroup. NF-ĸB pathway activation is most likely involved in upregulation of APOBEC3B in non-t(14;16) subgroups. Disclosures Davies: TRM Oncology: Honoraria; MMRF: Honoraria; Janssen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; ASH: Honoraria; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Abbvie: Consultancy. Morgan:Bristol-Myers Squibb: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Research Funding; Takeda: Consultancy, Honoraria.

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

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 1882-1882 ◽  
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.

ATP Dependent Efflux Transporters ABCB1 and ABCG2 Are Unlikely to Impact the Efficacy, or Mediate Resistance to the Tyrosine Kinase Inhibitor, Ponatinib

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 2745-2745 ◽  
Author(s):  
Deborah L. White ◽  
Liu Lu ◽  
Timothy P. Clackson ◽  
Verity A Saunders ◽  
Timothy P Hughes
Keyword(s):  
Tyrosine Kinase ◽  
Board Of Directors ◽  
Cell Lines ◽  
Research Funding ◽  
Kinase Inhibitor ◽  
K562 Cells ◽  
Temperature Dependent ◽  
Advisory Committees ◽  
Significant Difference

Abstract Abstract 2745 Ponatinib is a potent pan-BCR-ABL tyrosine kinase inhibitor (TKI) currently in a pivotal phase 2 clinical trial. Ponatinib (PON) was specifically designed to target both native and all mutant forms of BCR-ABL, including T315I. The phase I study of oral ponatinib in patients with refractory CML/ALL or other hematologic malignancies recently reported that 66% and 53% of patients with CP-CML achieved MCyR and CCyR respectively (Cortes et al., ASH 2011 abstract #210). While extensive modelling experiments in BaF3 cells have been performed characterising in vitro response to ponatinib, little is known about the interactions of this drug and drug transporters that impact the response of other tyrosine kinase inhibitors (TKIs). To explore this we have examined both the degree of in vitro kinase inhibition mediated by ponatinib in BCR-ABL+ cell lines, and the intracellular uptake and retention (IUR) of ponatinib achieved. The IC50 was determined by assessing the reduction in %p-Crkl in response to increasing concentrations of ponatinib in vitro. The IUR assay was performed as previously using [14-C]-ponatinib. To determine the role of ABCB1 and ABCG2, both previously implicated in the transport of other TKIs, IC50 analysis was performed on K562 cells, and variants; ABCB1 overexpressing K562-DOX and ABCG2 overexpressing K562-ABCG2. As shown in Table 1, in contrast to the results previously observed with imatinib (IM), nilotinib (NIL) and dasatinib (DAS) there was no significant difference in the IC50ponatinib between these three cell lines, suggesting neither ABCB1 nor ABCG2 play a major role in ponatinib transport. Furthermore, the addition of either the ABCB1 and ABCG2 inhibitor pantoprazole, or the multidrug resistance (MDR) inhibitor cyclosporin did not result in a significant change in the IC50ponatinib in any of the cell lines tested. In contrast the addition of either pantoprazole or cyclosporin resulted in a significant reduction in IC50IM, IC50NIL. and IC50DAS of K562-DOX cells, supporting the notion that these TKIs interact with ABCB1.Table 1:The IC50 of ponatinib (compared to IM, NIL and DAS) in K562 cells and the over-expressing variants DOX and ABCG2 in the presence of the ABC inhibitors pantoprazole and cyclosporin. n=5. *p<0.05IC50% reduction in IC50+ pantoprazole+ cyclosporinPON (nM)IM (μM)NIL (nM)DAS (nM)PONIMNILDASPONIMNILDASK5627.793751111544*NA−107NA2DOX7.919*598*100*1018*63*1655*88*ABCG26.4730025*6NA To further examine the effect of ABC transporters on ponatinib efflux we have determined the IUR of [14-C]-ponatinib in K562, DOX and ABCG2 cell lines. We demonstrate no significant difference in the IUR between these cell lines at 37°C (n=6) (K562 vs DOX p=0.6; K562 vs ABCG2 p=0.37 and DOX vs ABCG2 p=0.667 at 2uM respectively). Temperature dependent IUR experiments reveal a significant reduction in the ponatinib IUR at 4°C compared to 37°C in K562 cells (n=6) (p=0.008), DOX cells (p=0.004) and ABCG2 cells (p=0.002) supporting the likely involvement of an ATP/temperature dependent, and yet to be determined, component of ponatinib influx. There was no significant difference in the IUR between these cell lines at 4°C (p=0.824, p=0.7 and p=0.803 respectively). Importantly, these data are consistent with the IC50ponatinib findings. If ATP dependent efflux pumps (ABCB1 and ABCG2) were actively transporting ponatinib, a significant decrease in IUR in DOX and ABCG2 at 37°C compared to K562 cells would be expected, but is not observed here. Analysis of ponatinib IUR in the prototypic ABCB1 over-expressing CEM-VBL100 cells, and their parental, ABCB1 null counterparts (CCRF-CEM) further confirmed these findings. The IUR in VBL100 cells was significantly higher than that observed in CEM's (p<0.001; n=5), providing further evidence that ponatinib was not being exported from the cell actively via ABCB1. These data suggest that the transport of ponatinib is, at least in part, temperature-dependent indicating a yet to be determined ATP transporter may be involved in the transport of ponatinib into leukaemic cells. Importantly, this data suggests that ponatinib is unlikely to be susceptible to resistance via the major ATP efflux transporters (ABCB1 or ABCG2) that have been previously demonstrated to significantly impact the transport of, and mediate resistance to other clinically available TKIs. Disclosures: White: BMS: Honoraria, Research Funding; Novartis Pharmaceuticals: Honoraria, Research Funding. Clackson:ARIAD: Employment. Hughes:Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; ARIAD: Honoraria, Membership on an entity's Board of Directors or advisory committees.

E-Selectin Antagonist GMI-1271 Shows a Favorable Safety, PK and Bleeding Profile in Phase I Studies of Healthy Volunteers

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 3826-3826 ◽  
Author(s):  
Dana E. Angelini ◽  
Sumana Devata ◽  
Angela E. Hawley ◽  
Susan A. Blackburn ◽  
Satwinder Grewal ◽  
...  
Keyword(s):  
Board Of Directors ◽  
Research Funding ◽  
Bleeding Time ◽  
Phase 1 ◽  
Bleeding Risk ◽  
Employment Equity ◽  
Advisory Committees ◽  
Significant Difference ◽  
Equity Ownership ◽  
Favorable Safety

Abstract Background : Selectins, among other adhesion-mediated functions, facilitate and augment thrombosis; standard anticoagulants address thrombosis but also increase bleeding risk. Previous work in animal models showed inhibiting E-selectin decreases venous thrombosis (VTE) and vein wall inflammation without adverse bleeding events, making E-selectin inhibition a favorable therapeutic candidate for VTE. GMI-1271 is a potent, specific E-selectin antagonist. Here we report final analysis of safety, PK, biomarker and bleeding risk profile for GMI-1271 in 2 phase 1 studies of healthy subjects. Methods: The first study was a blinded single ascending dose (SAD) evaluation of 2, 5, 20, or 40mg/kg of GMI-1271 (n=4/cohort), vs placebo control saline (n=4) or active control Lovenox 1mg/kg (n=4). The second was an open-label multiple ascending dose (MAD) study of 10 (n=3) or 20mg/kg (n=3) of GMI-1271 QD d 1-5 vs Lovenox 1mg/kg (n=2) d 1-5. Assessments included safety (adverse events [AEs], clinical labs, bleeding time, PT/PTT, vitals, exam); PK (plasma and urine); and biomarkers. Biomarkers included ELISAs (CRP, D-dimer, IL-10, MPO, Prothrombin fragment 1.2, soluble E-selectin (sEsel), soluble P-selectin (sPsel), sICAM, Thrombin-antithrombin complex (TAT), Tissue Factor (TF), TNFα, VWF activity, and sCD40L); PicoGreen Assay for circulating DNA; flow cytometry (Platelet Monocyte Aggregates (PMA), Mac-1, LFA-1, and CD44) and Thromboelastography (TEG). See Table 1 for functional description. SAD remains blinded to GMI-1271 or placebo (GMI-1271/p). In SAD, we measured biomarker values at baseline, 8 and 24 h after dosing. Analysis was performed of biomarkers at each dose level and then pooled by GMI-1271/p vs Lovenox. In MAD, we measured biomarker values at baseline and day 4. Comparisons were made using unpaired t-test. Results: In total, 32 subjects enrolled and received GMI-1271/placebo (GMI-1271/p; 20), GMI-1271 (6) or Lovenox (L; 6). Safety: All subjects completed dosing uneventfully. Safety findings for GMI-1271/p were unremarkable. No moderate or serious AE were seen. AE overall were as expected in healthy volunteers. In SAD, only 1/20 GMI-1271/p subjects experienced an AE possibly related to study drug (mild transient headache); 0/6 in MAD. In the L group we saw expected mild transaminitis and injection site bruising. In all studies, GMI-1271 had no effect on bleeding time, PT, or PTT. PK: Plasma levels, Cmax, and AUC increased in a linear manner. Cl, Vz, and t ½ were not dose dependent; no accumulation was seen with multiple dosing (Fig 1 and 2). TEG: In SAD, there was a tendency to increase R, K, and decrease A and MA with no change in % lysis in L vs GMI-1271/p. In MAD, we saw a similar trend as SAD except for an increase in % lysis in L. In the pooled SAD analysis, there was a statistically significant difference between GMI-1271/p and L (higher values) for R (p<0.001) and K (p<0.001); MA was statistically higher in GMI-1271/p vs L (p<0.05). sEsel: In SAD, there was a trend for sEsel to decrease with treatment in all cohorts (combined GMI-1271/p vs L p= 0.017). In MAD, there was non-significant trend for sEsel to decrease between Day 0 and Day 4 in GMI-1271; sEsel levels trended upward with L in comparison. MPO: In SAD, there was a trend for increased values in L vs GMI-1271/p, except for the 40mg/kg cohort. When pooled, there was a significant difference between GMI-1271/p vs L (higher levels) p<0.01. In MAD, there was a non-significant trend for higher levels in L vs GMI-1271. MAC-1 In SAD, there was no change. In MAD, there was a significant decrease in MAC-1 at the 10mg/kg dose (p<0.01). No other notable trend was seen in the other biomarkers measured. Conclusion: We report a favorable safety, biomarker and bleeding profile for the E-selectin antagonist GMI-1271 in healthy subjects. There is no signal to suggest GMI-1271 increases bleeding potential based on adverse events, PT, PTT, bleeding time, and TEG values, unlike traditional anticoagulants. An additional unblinded analysis of the biomarkers will be presented at ASH. We note a trend for sE-sel to decrease with GMI-1271 treatment even in this healthy volunteer population, consistent with previous experience and as expected based on mechanism of action. A phase 1 study of GMI-1271 is currently ongoing to evaluate the safety and efficacy of E-selectin antagonism for the treatment of patients with calf vein DVT. PK Figures: Figure 1: SAD; Figure 2: MAD. Figure 1 Figure 1. Figure 2 Figure 2. Disclosures Hemmer: GlycoMimetics, Inc.: Employment, Equity Ownership. Flanner:GlycoMimetics, Inc.: Employment. Parker:GlycoMimetics, Inc.: Employment. Li:GlycoMimetics, Inc.: Employment, Equity Ownership. Froehlich:Novartis: Consultancy; Janssen: Consultancy; Pfizer: Membership on an entity's Board of Directors or advisory committees; Boehringer-Ingelheim: Membership on an entity's Board of Directors or advisory committees; Fibromuscular Disease Society of America: Research Funding; Blue Cross/Blue Shield of Michigan: Research Funding; Merck: Consultancy. Magnani:GlycoMimetics: Employment, Equity Ownership, Membership on an entity's Board of Directors or advisory committees. Thackray:GlycoMimetics: Employment, Equity Ownership. Sood:Bayer: Research Funding.

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

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 2757-2757
Author(s):  
Christopher B. Benton ◽  
Ahmed Al Rawi ◽  
Feng Wang ◽  
Jianhua Zhang ◽  
Jeffrey L. Jorgensen ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Board Of Directors ◽  
Rna Sequencing ◽  
Research Funding ◽  
Mononuclear Cells ◽  
Post Treatment ◽  
Advisory Committees ◽  
Diagnostic Samples ◽  
Equity Ownership

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.

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

Blood ◽  
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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