Phosphoproteomic Analysis of Primary Myeloma Patient Samples Identifies Distinct Phosphorylation Signatures Correlating with Chemo-Sensitivity Profiles in an Ex Vivo Drug Sensitivity Testing Platform

Abstract Introduction: Multiple myeloma (MM) is characterized by the clonal expansion of plasma cells in the bone marrow resulting in end-organ damage. Despite an extensive increase in the five-year survival rate in recent years, MM is still considered an incurable disease as patients will repeatedly relapse and develop resistance to current chemotherapies. A key focus for the personalization of myeloma therapy is understanding the biological mechanisms of drug resistance and identifying clinically useful biomarkers of therapeutic response. Highly efficient techniques for the enrichment of phosphorylated peptides followed by high resolution mass spectrometry facilitates the quantitation of thousands of site-specific phosphorylation events. Here, we have performed a phosphoproteomic analysis on MM cell lysates stratified based on their ex vivo drug response profiles to advance our understanding of drug resistance mechanisms. Materials and Methods: CD138 + plasma cells were isolated from 20 adult MM patient bone marrow aspirates at diagnosis (n=7) or relapse (n=13). Samples were grouped based on ex vivo drug sensitivity and resistance testing (DSRT) as follows: highly sensitive (Group I), sensitive (Group II), resistant (Group III), highly resistant (Group IV) [1]. For the phosphoproteomic analysis, peptides were generated and purified using the filter aided sample preparation (FASP) protocol. Peptide tandem mass tag (TMT) labelling, Fe 3+ immobilized metal ion affinity chromatography (IMAC), synchronous precursor selection (SPS), and triple stage tandem mass spectrometry (MS3) was performed. Nonenriched peptides were used for proteomic analysis. Resulting data was analysed using MaxQuant, followed by normalization of phosphosite intensities using the internal reference scale (IRS) method, and statistical analysis using Perseus. Functional enrichment and kinase enrichment analyses were performed on significant phosphoproteins using g:profiler and KEA2, respectively. Results: Our quantitative MS-based phosphoproteomic analysis identified 2,945 phosphorylation sites on 2,232 phosphopeptides from 690 phosphoproteins. Of these phosphorylation sites, 176 were significantly changed between all four DSRT groups and 267 were significantly changed between Group I and Group IV (False Discovery Rate (FDR) < 0.05). Hierarchical clustering was performed to highlight the distinct phosphoproteomic profiles associated with each DSRT group, of which the very sensitive (Group I) and very resistant (Group IV) subgroups demonstrated a well-defined separation (Fig. 1A, 1B). KEGG pathway analysis and gene ontology (GO) analysis of significantly increased phosphorylated proteins in Group IV compared to Group I MM patients demonstrated an increased phosphorylation of proteins associated with tight junctions, the Rap1 signalling pathway and the phosphatidylinositol signalling system; indicating an upregulation of cell adhesion associated processes in drug resistant MM. Phosphoproteins increased in abundance in Group I compared to Group IV MM patients revealed an increased phosphorylation of proteins involved in translation and RNA processing including the spliceosome, RNA transport and RNA binding pathways (Fig. 1C). We identified filamin A serine 2152, RAS guanyl-releasing protein 2 serine 576 and proto-oncogene tyrosine-protein kinase Src serine 17 as increased in Group IV MM, and nuclease-sensitive element-binding protein 1 (YBX1) serine 165, CD44 serine 697 and Bcl2-associated agonist of cell death (BAD) serine 99 as increased in Group I MM. KEA of the upregulated phosphoproteome in Group IV revealed an enrichment of cyclin dependent kinase 1 (CDK1) and ribosomal s6 kinases (RPS6K) whereas casein kinase 2 (CK2) and the glycolysis-associated kinases were enriched in Group I (Fig. 1D). Conclusion: Our study has generated a phosphoproteomic dataset demonstrating distinct phosphorylation signatures associated with drug sensitivity in clinical MM plasma cells. The identification of phosphorylation events associated with drug resistance provides a basis for further exploration of these events and associated signalling pathways to further understand drug resistance mechanisms in MM and identify potential biomarkers of therapeutic response and targets for drug re-sensitization in MM. References: [1] M. M. Majumder et al., Oncotarget 8(34), 56338 (2017) Figure 1 Figure 1. Disclosures Heckman: Novartis: Research Funding; Orion Pharma: Research Funding; Celgene/BMS: Research Funding; Oncopeptides: Consultancy, Research Funding; Kronos Bio, Inc.: Research Funding.

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Stratification of Multiple Myeloma Patients Based on Ex Vivo Drug Sensitivity and Identification of New Treatments for Patients with High-Risk Relapsed/Refractory Disease

Blood ◽

10.1182/blood.v126.23.3006.3006 ◽

2015 ◽

Vol 126 (23) ◽

pp. 3006-3006

Author(s):

Muntasir Mamun Majumder ◽

Raija Silvennoinen ◽

Pekka Anttila ◽

David Tamborero ◽

Samuli Eldfors ◽

...

Keyword(s):

Multiple Myeloma ◽

Research Funding ◽

Drug Testing ◽

Drug Response ◽

Drug Sensitivity ◽

Plasma Cells ◽

Ex Vivo ◽

Resistant Group ◽

Sensitive Group

Abstract Introduction Response to treatment for multiple myeloma (MM) patients is variable and often unpredictable, which may be attributed to the heterogeneous genomic landscape of the disease. However, the effect of recurrent molecular alterations on drug response is unclear. To address this, we systematically profiled 50 samples from 43 patients to assess ex vivo sensitivity to 308 anti-cancer drugs including standard of care and investigational drugs, with results correlated to genomic alterations. Our results reveal novel insights about patient stratification, therapies for high-risk (HR) patients, signaling pathway aberrations and ex-vivo-in-vivo correlation. Methods Bone marrow (BM) aspirates (n=50) were collected from MM patients (newly diagnosed n=17; relapsed/refractory n=33) and healthy individuals (n=8). CD138+ plasma cells were enriched by Ficoll separation followed by immunomagnetic bead selection. Cells were screened against 308 oncology drugs tested in a 10,000-fold concentration range. Drug sensitivity scores were calculated based on the normalized area under the dose response curve (Yadav et al, Sci Reports, 2014). MM selective responses were determined by comparing data from MM patients with those of healthy BM cells. Clustering of drug sensitivity profiles was performed using unsupervised hierarchical ward-linkage clustering with Spearman and Manhattan distance measures of drug and sample profiles. Somatic alterations were identified by exome sequencing of DNA from CD138+ cells and skin biopsies from each patient, while cytogenetics were determined by fluorescence in situ hybridization. Results Comparison of the ex vivo chemosensitive profiles of plasma cells resulted in stratification of patients into four distinct subgroups that were highly sensitive (Group I), sensitive (Group II), resistant (Group III) or highly resistant (Group IV) to the panel of drugs tested. Many of the drug responses were specific for CD138+ cells with little effect on CD138- cells from the same patient or healthy BM controls. We generated a drug activity profile for the individual drugs correlating sensitivity to recurrent alterations including mutations to KRAS, DIS3, NRAS, TP53, FAM46C, and cytogenetic alterations del(17p), t(4;14), t(14;16), t(11;14), t(14;20), +1q and -13. Cells from HR patients with del(17p) exhibited the most resistant profiles (enriched in Groups III and IV), but were sensitive to some drugs including HDAC and BCL2 inhibitors. Samples from patients with t(4;14) were primarily in Group II and very sensitive to IMiDs, proteasome inhibitors and several targeted drugs. Along with known recurrently mutated genes in myeloma, somatic mutations were identified in genes involved in several critical signaling pathways including DNA damage response, IGF1R-PI3K-AKT, MAPK, glucocorticoid receptor signaling and NF-κB signaling pathways. The predicted impact of these mutations on the activity of the pathways often corresponded to the drug response. For example, all samples bearing NF1 (DSS=21±7.9) and 67% with NRAS (DSS=15±4.35) mutations showed higher sensitivity to MEK inhibitors compared to healthy controls (DSS=5±.21). However, sensitivity was less predictable for KRAS mutants with modest response only in 47% samples (DSS=7±2.14) . One sample bearing the activating V600E mutation to BRAF showed no sensitivity to vemurafenib, which otherwise has good activity towards V600E mutated melanoma and hairy-cell leukemia. Comparison of the chemosensitive subgroups with survival showed patients in Groups I and IV had high relapse rate and poor overall survival. The ex vivo drug sensitivity results were used to decide treatment for three HR patients with results showing good ex vivo -in vivo correlation. Summary Our initial results suggest that ex vivo drug testing and molecular profiling of MM patients aids stratification. Grouping of patients based on their ex vivo chemosensitive profile proved extremely informative to predict clinical phenotype and identify responders from non-responders. While some molecular markers could be used to predict drug response, others were less predictive. Nevertheless, ex vivo drug testing identified active drugs, particularly for HR and relapsed/refractory patients, and is a powerful method to determine treatment for this group of patients. Disclosures Silvennoinen: Genzyme: Honoraria; Sanofi: Honoraria; Janssen: Research Funding; Celgene: Research Funding; Research Committee of the Kuopio University Hospital Catchment Area for State Research Funding, project 5101424, Kuopio, Finland: Research Funding; Amgen: Consultancy, Honoraria. Porkka:Bristol-Myers Squibb: Honoraria; Celgene: Honoraria; Novartis: Honoraria; Pfizer: Honoraria. Heckman:Celgene: Honoraria, Research Funding.

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Combining Next Generation Proteomics Platforms with Drug Sensitivity Resistance Testing AllowsÂ Identification of Physiologically Distinct Sub-ClonesÂ That Can Inform Therapeutic and Drug Development Strategies

Blood ◽

10.1182/blood-2018-99-115910 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 1901-1901

Author(s):

Despina Bazou ◽

Muntasir M Majumder ◽

Ciara Tierney ◽

Sinead O'Rourke ◽

Pekka Anttila ◽

...

Keyword(s):

Mass Spectrometry ◽

Board Of Directors ◽

Research Funding ◽

Drug Sensitivity ◽

Plasma Cells ◽

Ex Vivo ◽

Resistance Testing ◽

Proteomics Analysis ◽

Advisory Committees ◽

Group I

Abstract Introduction: A hallmark of Multiple Myeloma (MM) is the sequel development of drug resistant phenotypes, which may be present initially or emerge during the course of treatment. These drug resistant phenotypes reflect the intra-tumor and inter-patient heterogeneity of this cancer. Most MM cells are sensitive to proteasome inhibitors (PIs), which have become the standard of care in the treatment of newly diagnosed and relapsed MM. However, resistance develops (intrinsic/acquired). Although several novel drugs have recently been approved or are in development for MM, there are few molecular indicators to guide treatment selection. To address this limitation we have combined mass spectrometry-based proteomics analysis together with ex vivo drug response profiles and clinical outcome to elucidate a best possible accurate phenotype of the resistant sub-clones, thus yielding a theranostic profile that will inform therapeutic and drug development strategies. Methods: We performed mass spectrometry-based proteomics analysis on plasma cells isolated from 38 adult MM patient bone marrow aspirates (CD138+). Samples were obtained at diagnosis or prior to commencing therapy. The participating subjects gave written informed consent in accordance with the Declaration of Helsinki that was approved by local ethics committees. For the proteomics analysis, peptides were purified using the filtered aided sample preparation (FASP) method. Subsequently, samples were prepared for label-free liquid chromatography mass spectrometry (LC-MS/MS) using a Thermo Scientific Q-Exactive MS mass spectrometer. Proteins were analysed using the MaxQuant and Perseus software for mass-spectrometry (MS)-based proteomics data analysis, UniProtKB-Swiss Prot database and KEGG Pathway database. In parallel, we undertook a comprehensive functional strategy to directly determine the drug dependency of myeloma plasma cells based on ex vivo drug sensitivity and resistance testing (DSRT)as previously described (1). Results: Our initial proteomic analysis was generated by examining MM patient plasma cells, grouped based on DSRT to 142 anticancer drugs including standard of care and investigational drugs. Each of the 142 drugs was tested over a 10,000-fold concentration range, allowing for the establishment of accurate dose-response curves for each drug in each patient. MM patients were stratified into four distinct subgroups as follows: highly sensitive (Group I), sensitive (Group II), resistant (Group III) or highly resistant (Group IV) to the panel of drugs tested. We then performed blinded analysis on the 4 groups of CD138+ plasma cells divided based on the ex vivo sensitivity profile, identifying a highly significant differential proteomic signature between the 4-chemosensitivity profiles, with Cell Adhesion Mediated-Drug Resistance (CAM-DR) related proteins (e.g. integrins αIIb and β3) significantly elevated in the highly resistant phenotype (Group IV). In addition our results showed that Group I patients displayed significant upregulation of cell proliferation proteins including: MCM2, FEN1, PCNA and RRM2. Furthermore, Group I patients have shorter Progression Free Survival (PFS) as well as Overall Survival (OS) compared to the other subgroups. Figure 1 shows the Heatmap summarizing the expression of proteins (log2 fold change) in the four distinct MM patient subgroups. Conclusions:Our findings suggest that combining a proteomics based study together with drug sensitivity and resistance testing allows for an iterative adjustment of therapies for patients with MM, one patient at a time, thus providing a theranostic approach. Our results suggest that the disease driving mechanisms in the patient subgroups are distinct, with highly resistant patients exhibiting cell adhesion mediated cytoprotection, while highly sensitive patients show an increased cell proliferation protein profile with shorter PFS and OS. Our study aims to guide treatment decisions for individual cancer patients coupled with monitoring of subsequent responses in patients to measure and understand the efficacy and mechanism of action of the drugs. Future work will include the establishment of flow cytometry-based screening assays to identify the different resistant phenotypes at diagnosis/relapse. References: (1) M. M. Majumder et al., Oncotarget 8(34), 56338 (2017) Disclosures Anttila: Amgen: Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees. Silvennoinen:Amgen: Honoraria, Research Funding; Takeda: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; BMS: Honoraria, Research Funding. Heckman:Orion Pharma: Research Funding; Celgene: Research Funding; Novartis: Research Funding.

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Integration of Ex Vivo Drug Testing and in-Depth Molecular Profiling Reveals Oncogenic Signaling Pathways and Novel Therapeutic Strategies for Multiple Myeloma

Blood ◽

10.1182/blood.v124.21.2046.2046 ◽

2014 ◽

Vol 124 (21) ◽

pp. 2046-2046

Author(s):

Muntasir Mamun Majumder ◽

Raija Silvennoinen ◽

Pekka Antilla ◽

David Tamborero ◽

Samuli Eldfors ◽

...

Keyword(s):

Signaling Pathways ◽

Research Funding ◽

Drug Response ◽

Drug Sensitivity ◽

Ex Vivo ◽

Oncogenic Signaling ◽

Group I ◽

Signal Transduction Inhibitors ◽

Response Profiles ◽

Response Groups

Abstract Introduction New drugs have improved survival for multiple myeloma (MM) patients, however, patient outcome remains highly variable, unpredictable and often very poor. To identify novel treatments and potential biomarkers, we applied high throughput ex vivo drug sensitivity testing combined with exome and transcriptome sequencing to samples collected from newly diagnosed and relapsed MM patients. Integration of results from the different platforms indicated several oncogenic signaling pathways driving drug response and highlighted the importance of a multi-targeted approach for treatment. Methods Bone marrow (BM) aspirates (n=48) were collected from MM patients (newly diagnosed n=14; relapsed/refractory n=26) and healthy individuals (n=8). CD138+ plasma cells were enriched by Ficoll separation followed by immunomagnetic bead selection. Cells were screened against 306 oncology drugs with the drugs tested in a 10,000-fold concentration range. Drug sensitivity scores were calculated based on the normalized area under the dose response curve (Yadav et al, Sci Reports, 2014). Importantly, MM selective responses were determined by comparing data from MM patients with those of healthy BM cells. Clustering of drug sensitivity profiles was performed using unsupervised hierarchical ward-linkage clustering with Spearman and Manhattan distance measures of drug and sample profiles. Somatic mutations were identified by exome sequencing of DNA from CD138+ cells and skin biopies from each patient, while gene expression profiles were derived from RNA sequencing of CD138+ cells. Results Cluster analysis of drug response profiles segregated the samples into four MM specific groups (Figure). Group I patients (n=12) were highly sensitive to many drugs, including several signal transduction inhibitors such as those targeting PI3K-AKT, MAPK and IGF pathways, as well as HSP90 and BCL2 inhibitors plus epigenetic/chromatin modifiers such as BET and HDAC inhibitors. Group II (n=15) showed a more modest response profile and were moderately sensitive to signal transduction inhibitors and epigenetic modifiers. Group III (n=9) were largely insensitive to most drugs in the panel except for BCL2 and proteasome inhibitors, while group IV (n=3) were resistant to all drugs except BCL2 inhibitors. Many samples were selectively sensitive to navitoclax (55%), dual PI3K/mTOR inhibitors (45%) and aminopeptidase inhibitors (20%), which had little effect on healthy control or MM CD138- cells. Only 33% of the samples responded to glucocorticoids. The majority of samples including healthy BM controls were sensitive to proteasome and CDK inhibitors, suggesting low selective cytotoxicity. However, drug sensitivity profiles of healthy control and CD138- cell populations were distinct from MM CD138+ samples indicating that observed CD138+ drug responses were specific for malignant plasma cells. In addition, we observed that drugs with overlapping target profiles tended to cluster together, indicating sample responses were similar to related drugs. Diagnostic and relapse samples were spread across the different response groups. Samples with mutations to genes involved in PI3K and NF-κB signaling tended to cluster in group I, while most samples with t(4;14) fell in Group II. Samples with RAS mutations were present in all response groups and no correlation with MEK inhibitor sensitivity was observed. 17p deletion samples were also found in all response groups, however, those with additional TP53 mutation tended to have increased drug sensitivity. Summary Our results indicate that PI3K/mTOR, MAPK, IGF1R, NF-κB and cell survival (e.g. BCL2, BCLXL) signaling are important pathways mediating MM ex vivo drug response. This matched with genomic and transcriptomic data, which identified alterations of genes involved in these pathways. Although additional work is needed to correlate ex vivo drug sensitivity with in vivo treatment response, our initial results suggest the possibility that MM patients could be subjected to stratified treatment based on combined ex vivo drug testing and molecular profiling. In addition, these results highlight the multiple signaling pathways active in MM and emphasize the need for improved combination strategies for treatment. Figure: Subgrouping of MM patient samples (I-IV) based on selective drug response profiles. H/D/R denotes healthy, diagnostic and relapse, respectively. Figure:. Subgrouping of MM patient samples (I-IV) based on selective drug response profiles. H/D/R denotes healthy, diagnostic and relapse, respectively. Disclosures Silvennoinen: Research Funding of Finland Government, Research Funding from Janssen-cilag, research funding from Celgene: Research Funding; Janssen-Cilag, Sanofi, Celgene: Honoraria. Wennerberg:Pfizer: Research Funding. Kallioniemi:Medisapiens: Consultancy, Membership on an entity's Board of Directors or advisory committees. Porkka:Bristol-Myers Squibb: Honoraria, Research Funding; Novartis: Honoraria, Research Funding. Heckman:Celgene: Research Funding.

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Characterization of Synergistic Selinexor Combinations of Dexamethasone, Pomalidomide, Elotuzumab and Daratumumab in Primary MM Samples Ex Vivo

Blood ◽

10.1182/blood-2020-140485 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 29-30

Author(s):

Kenneth H. Shain ◽

Rafael Renatino-Canevarolo ◽

Mark B. Meads ◽

Praneeth Reddy Sudalagunta ◽

Maria D Coelho Siqueira Silva ◽

...

Keyword(s):

Drug Resistance ◽

Board Of Directors ◽

Nuclear Export ◽

Research Funding ◽

Drug Sensitivity ◽

Ex Vivo ◽

Single Agent ◽

Gene Mutations ◽

Drug Combinations ◽

Advisory Committees

Introduction. Multiple myeloma (MM) is an incurable plasma cell malignancy with a growing list of anti-MM therapeutics. However, the development of predictive biomarkers has yet to be achieved for nearly all MM therapeutics. Selinexor (SELI), a nuclear export inhibitor targeting exportin 1 (XPO1), has been approved with dexamethasone (DEX) in penta-refractory MM. Clinical studies investigating promising SELI- 3 drug combinations are ongoing. Here, we have investigated potential synergistic combinations of SELI and anti-MM agents in terms of ex vivo sensitivity, as well as paired RNAseq and WES to identify companion biomarkers. Methods. MM cells isolated from fresh bone marrow aspirates were tested for drug sensitivity in an organotypic ex vivo drug sensitivity assay, consisting of co-culture with stroma, collagen matrix and patient-derived serum. Single agents were tested at 5 concentrations, while two-drug combinations were tested at fixed ratio of concentrations. LD50 and area under the curve (AUC) were assessed during 96h-exposure as metrics for drug resistance. Drug synergy was calculated as a modified BLISS model. Matching aliquots of MM cells had RNAseq and WES performed through ORIEN/AVATAR project. Geneset enrichment analysis (GSEA) was conducted using both AUC and LD50 as phenotypes for single agents and combinations. Both curated pathways (KEGG and cancer hallmarks) and unsupervised gene clustering were used as genesets. Student t-tests with multiple test correction were used to identify non-synonymous mutations in protein coding genes associated with single agent or combination AUC. Results. For this analysis, a cohort of specimens from 103 patients (48% female, 4% Hispanic, 11% African American) was tested with SELI and/or DEX. with a median of 2 lines of therapy (0-12). A smaller cohort of 37 have been examined with SELI, pomalidomide (POM), elotuzumab (ELO) and daratumumab (DARA). Within this cohort we observed synergy between SELI and DEX, POM and ELO as shown in Figure 1. The volcano plot illustrates the number of samples, maximum drug concentration, as well as magnitude (x- axis) and significance (y- axis) of synergy. Although the SELI-DARA combination trended toward synergy, statistical significance was not achieved. To identify molecular mechanisms and biomarkers associated with sensitivity to SELI and SELI- combinations, we investigated paired RNAseq and WES with ex vivo sensitivity. Initially, we conducted GSEA on two cohorts of primary MM samples tested with SELI alone at 5µM (n=53) and 10µM (n=50). Cell adhesion (KEGG CAMS), inflammatory cytokines (KEGG ASTHMA), and epithelial mesenchymal transition (HALLMARK EMT) were associated with resistance in both cohorts, while the HALLMARK MYC TARGETS was associated with sensitivity (FWER p<0.05). Mutational analysis identified 46 gene mutations associated with SELI resistance and 100 associated with sensitivity at 5µM, and 87 and 27 mutations associated with SELI resistance and sensitivity, respectively, at 10µM. Two gene mutations were identified in both cohorts: BCL7A, involved in chromatin remodeling, was associated with sensitivity and CEP290, a microtubule binding protein, associated with resistance (p<0.05). Analysis of both gene sequences (NetNES 1.1) identified nuclear export signal (NES) residues suggesting these may be XPO1 cargo. Additionally, translocation t(11;14) was associated with SELI resistance in the 5µM cohort (p=0.037). The completed set of 50 specimens ex vivo, RNAseq and WES analysis will be mature and updated for the potential presentation at ASH. Conclusions. We observed ex vivo synergy between SELI and DEX, POM and ELO. Molecular analysis of matched ex vivo drug sensitivity, transcriptome and mutational profile identified environment-mediated drug resistance pathways positively correlated with SELI single agent resistance, as well as MYC regulated genes associated with ex vivo sensitivity. We also identified a list of mutations associated with SELI drug resistance and sensitivity, with special emphasis on two novel NES-containing genes, CEP290 and BCL7A. The next step of this project is to analyze transcriptional and mutational patterns associated with ex vivo synergy in the combinations here described, as putative biomarkers for future clinical investigation. Disclosures Shain: Amgen: Speakers Bureau; Adaptive: Consultancy, Honoraria; Karyopharm: Research Funding, Speakers Bureau; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; GlaxoSmithKline: Speakers Bureau; Janssen: Honoraria, Speakers Bureau; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Sanofi/Genzyme: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda: Honoraria, Speakers Bureau; AbbVie: Research Funding. Kulkarni:M2GEN: Current Employment. Zhang:M2GEN: Current Employment. Hampton:M2GEN: Current Employment. Argueta:Karyopharm: Current Employment. Landesman:Karyopharm Therapeutics Inc: Current Employment, Current equity holder in publicly-traded company. Siqueira Silva:AbbVie: Research Funding; NIH/NCI: Research Funding; Karyopharm: Research Funding.

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Identification of Dual PI3K/mTOR and BCL2 Inhibitors for the Treatment of High Risk Multiple Myeloma

Blood ◽

10.1182/blood.v124.21.646.646 ◽

2014 ◽

Vol 124 (21) ◽

pp. 646-646

Author(s):

Raija Silvennoinen ◽

Muntasir Mamun Majumder ◽

David Tamborero ◽

Pekka Anttila ◽

Samuli Eldfors ◽

...

Keyword(s):

Bone Marrow ◽

High Risk ◽

Risk Stratification ◽

Research Funding ◽

Drug Sensitivity ◽

Plasma Cells ◽

Ex Vivo ◽

Treatment Options ◽

Risk Markers ◽

Nras Mutation

Abstract Introduction Multiple myeloma (MM) is an incurable malignant plasma cell disease with the highest incidence occurring at 65-70 years of age while 10% of patients are diagnosed below 55 years of age. The International Myeloma Working Group recently proposed new risk stratification standards for MM patients: high-risk (HR), standard (SR) and low-risk (LR) groups (Leukemia 2014, 28, 269−77). Although a median overall survival of LR patients is > 10 years from the diagnosis, new drugs and therapeutic innovations are urgently needed for HR patients (20%) who have a median overall survival of only two years. To identify new treatment options for MM patients, we compared ex vivo drug sensitivity data from primary CD138+ cells to standard risk stratification markers. Ex vivo responses indicated a number of investigational drugs as potential novel options for HR MM patients with links to risk markers. Methods Bone marrow aspirates were collected from newly diagnosed (n=14) and relapsed/refractory (n=21) MM patients. Cytogenetics were determined by fluorescence in situ hybridization (FISH) and the patients stratified based on the presence or absence of adverse FISH markers (t(4;14) and 17p del). Plasma cells (CD138+) were enriched from freshly isolated bone marrow samples and exome sequencing performed using DNA extracted from the CD138+ cells and matched skin biopsies. Ex vivo drug sensitivity was assessed by measuring the viability of the cells after 3-day incubation with 306 different oncology drugs in a 10,000-fold concentration range. Drug sensitivity scores were calculated based on the normalized area under the dose response curve (Scientific Reports 2014, 4, 5193) and select sensitivities determined by comparing results to healthy bone marrow cells. Based on drug sensitivities, the patients were classified in four different groups (sensitive, moderately sensitive, resistant and highly resistant). Results Of the 35 patients included in this study, 11 were classified as HR (31%) and 24 as SR/LR (69%). In the HR group 6/11 (55%) had t(4;14) and 5/11 patients (45%) had 17p13 del. In the SR/LR group common abnormalities included 13 monosomy/13q del (10/24), 1q gain (10/24) and K/NRAS mutation (11/24). Within the HR group, other co-occurring abnormalities included 1q gain (9/11), 13 monosomy/13q del (6/11), K/NRAS mutation (5/11), and TP53 mutation (2/11). Based on overall ex vivo drug sensitivity profiles of all patients, the majority of HR patients were classified as moderately sensitive (8/11; 73%) while SR/LR patients had diverse responses from sensitive to highly resistant. In the HR group, the highest select sensitivities were to BH3 mimetics and PI3K/mTOR inhibitors. While the t(4;14) is predicted to lead to upregulation and increased activity of the FGFR3, which could be targeted by FGFR inhibitors, none of the t(4;14) samples showed sensitivity to these drugs. However, with the exception of one t(4;14) sample, the rest all showed good sensitivity to dual PI3K/mTOR inhibitors, but not to rapalogs, suggesting that inhibition of PI3K and the mTORC1/2 complexes is required to inhibit t(4;14) cell growth rather than mTORC1 alone. Of the 17p del patients, 3/5 were classified as moderately sensitive, 1/5 sensitive and 1/5 highly resistant based on ex vivo drug response of CD138+ cells. All showed select sensitivity to BH3 mimetics/BCL2 inhibitors (navitoclax/ABT-263 and venetoclax/ABT-199/GDC-0199), while response to other drugs varied. Therefore, blocking cell survival signaling is likely essential for this group of HR MM patients. Conclusion By assessing the ex vivo sensitivity of primary plasma cells to a large collection of oncology drugs and comparing these data to standard risk stratification markers for MM, we have been able to identify potential new treatment options for high risk MM patients including dual PI3K/mTOR and BCL2- inhibitors. Although a larger cohort of patients is required to support the correlation between specific drug sensitivities and risk markers, these preliminary data indicate that currently used risk markers may be useful to predict the use of novel treatments. Disclosures Silvennoinen: Janssen-Cilag: Research Funding; Celgene: Research Funding; Janssen-Cilag: Honoraria; Sanofi: Honoraria; Celgene: Honoraria. Porkka:BMS: Honoraria; BMS: Research Funding; Novartis: Honoraria; Novartis: Research Funding; Pfizer: Research Funding. Heckman:Celgene: Research Funding.

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Myeloma Drug Sensitivity Testing to Optimize Retreatment with Anti-CD38 Monoclonal Antibodies in Daratumumab-Refractory Patients

Blood ◽

10.1182/blood-2020-137819 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 37-38

Author(s):

Olivia Perez De Acha ◽

Beau M Idler ◽

Zachary J Walker ◽

Peter A Forsberg ◽

Tomer M Mark ◽

...

Keyword(s):

Research Funding ◽

Drug Sensitivity ◽

Drug Class ◽

Ex Vivo ◽

Partner Choice ◽

Sensitivity Testing ◽

Drug Sensitivity Testing ◽

Cd38 Expression ◽

Cell Subpopulations ◽

20 Nm

Background: Daratumumab-refractory multiple myeloma (MM) patients have limited treatment options and a dire prognosis. Daratumumab (Dara) targets the overexpressed myeloma antigen CD38, and its mechanism of resistance has been partially associated with downregulation of CD38 expression (Nijhof et al. Blood, 2016). Dara is a key agent in the relapsed setting and is being integrated into upfront treatment. In addition, isatuximab (Isa) has become the second FDA-approved anti-CD38 monoclonal antibody (mAb). Thus, when and how patients can be retreated with this important drug-class has become a critical question. We developed a platform for measuring drug efficacy ex vivo, including mAbs, termed Myeloma Drug Sensitivity Testing (My-DST) (Walker et al. Blood Advances, 2020). Here, we addressed whether My-DST could determine if retreatment with the anti-CD38 mAbs Dara and Isa can be effective in patients that had previously developed Dara-resistance. Methods: Bone marrow aspirates were obtained from the hematologic tissue bank (HTB) from patients at the University of Colorado Blood Cancer Program after informed consent and IRB approval. Mononuclear cells (MNCs) were isolated by Ficoll density gradient centrifugation, cells incubated for 48 h with 20 nM Dara, Isa and untreated controls in triplicate wells. Flow cytometry was performed on a BD FACSCelesta. To identify the viable MM population, samples were stained with fluorophore-conjugated mAbs to CD38, CD138, CD45, CD19, CD56 and CD46. Stained samples were washed and resuspended in Live/Dead dye Near-IR. Results were analyzed through FlowJo and Graphpad Prism software. Results: My-DST for Dara (20 nM) cytotoxicity has been performed in 67 patients, 52 of which were Dara-naïve and 15 of which were clinically Dara-refractory. Of Dara-naïve patients, 38/52 (73%) showed >20% reduction in viable MM cells whereas only 3/15 (20%) of the Dara-refractory patients showed >20% decrease (Fig 1A). Provocatively, the patients that showed potential "re-sensitization" had been off Dara for >12 months, and there was a significant correlation between response to Dara and months off treatment (r= -0.5096, p= 0.0457, Fig 1B). Furthermore, we evaluated two timepoints in one of the responders (HTB1749), and the later sample (HTB1749.3) showed a deeper response, further supporting the correlation. Surprisingly, in Dara-refractory patients the median CD38 expression was not significantly different between the three ex vivo responders and those who did not respond (p = 0.439, Fig 1C). Isa was tested in seven of the Dara-refractory patient samples, showing >20% decrease in viable MM cells in five, of which three did not respond ex vivo to Dara (Fig 1D). Interestingly, the Isa responders were only off Dara treatment for an average of 7.5 months. We further investigated HTB1059.3 and HTB1749.2, which responded to Dara, and found that these patients had multiple distinct MM cell subpopulations with different levels of CD38 expression, with the subpopulation with higher CD38 MFI accounting for the decreased viability (Fig 1E-F). When we compared HTB1059.3 with a prior sample available, these differential populations were not present before Dara treatment (data not shown), indicating these subpopulations evolved after treatment. Likewise, the same phenomenon of subpopulations with differential CD38 expression was observed in the Dara-refractory patients who responded to Isa (Fig 1G-H). Conclusion: These data support the possibility of retreatment with anti-CD38 mAbs in patients who once became refractory to Dara. Although our findings need to be confirmed with additional samples, they suggest that Isa may have efficacy earlier in this setting, supporting an approach to switch agents when retreating with this drug class. Anti-CD38 mAb sensitivity in the Dara-refractory population appears to be heavily influenced by the different CD38 expression levels on the heterogeneous MM cell subpopulations that emerge when a patient is off Dara for a period of time. Furthermore, My-DST with anti-CD38 mAbs may be applied to help guide the treatment approach in this population. Still, the presence of CD38-low subpopulations in these patients may represent resistant cells that shorten the remission times on retreatment. Thus, the combination drug partner choice will likely be critical to successfully optimizing depth and duration of response in Dara-refractory patients. Figure 1 Disclosures Forsberg: Genentech, Inc., Sanofi, Karyopharm, Abbvie: Research Funding; Celgene: Speakers Bureau. Mark:Bristol-Myers Squibb: Research Funding; Janssen: Research Funding; Celgene: Consultancy; Amgen: Consultancy; Kayopharm: Consultancy; Janssen: Consultancy; Sanofi: Consultancy; Takeda: Consultancy. Sherbenou:Oncopeptides Inc.: Research Funding.

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Development of a Cancer Pharmacopeia-Wide Ex-Vivo Drug Sensitivity and Resistance Testing (DSRT) Platform: Identification of MEK and mTOR As Patient-Specific Molecular Drivers of Adult AML and Potent Therapeutic Combinations with Dasatinib

Blood ◽

10.1182/blood.v118.21.2487.2487 ◽

2011 ◽

Vol 118 (21) ◽

pp. 2487-2487

Author(s):

Mika Kontro ◽

Caroline Heckman ◽

Evgeny Kulesskiy ◽

Tea Pemovska ◽

Maxim Bespalov ◽

...

Keyword(s):

Bone Marrow ◽

Research Funding ◽

Drug Response ◽

Drug Sensitivity ◽

Ex Vivo ◽

Molecular Profiling ◽

Resistance Testing ◽

Patient Specific ◽

Targeted Drugs ◽

Response Profiles

Abstract Abstract 2487 Introduction: The molecular drivers of adult AML as well as the determinants of drug response are poorly understood. While AML genomes have recently been sequenced, many cases do not harbor druggable mutations. Treatment options are particularly limited for relapsed and refractory AML. Due to the molecular heterogeneity of the disease, optimal therapy would likely consist of individualized combinations of targeted and non-targeted drugs, which poses significant challenges for the conventional paradigm of clinical drug testing. In order to better understand the molecular driver signals, identify individual variability of drug response, and to discover clinically actionable therapeutic combinations and future opportunities with emerging drugs, we established a diagnostic ex-vivo drug sensitivity and resistance testing (DSRT) platform for adult AML covering the entire cancer pharmacopeia as well as many emerging anti-cancer compounds. Methods: DSRT was implemented for primary cells from adult AML patients, focusing on relapsed and refractory cases. Fresh mononuclear cells from bone marrow aspirates (>50% blast count) were screened in a robotic high-throughput screening system using 384-well plates. The primary screening panel consisted of a comprehensive collection of FDA/EMA-approved small molecule and conventional cytotoxic drugs (n=120), as well as emerging, investigational and pre-clinical oncology compounds (currently n=90), such as major kinase (e.g. RTKs, checkpoint and mitotic kinases, Raf, MEK, JAKs, mTOR, PI3K), and non-kinase inhibitors (e.g. HSP, Bcl, activin, HDAC, PARP, Hh). The drugs are tested over a 10,000-fold concentration range resulting in a dose-response curve for each compound and with combinations of effective drugs explored in follow-up screens. The same samples also undergo deep molecular profiling including exome- and transcriptome sequencing, as well as phosphoproteomic analysis. Results: DSRT data from 11 clinical AML samples and 2 normal bone marrow controls were bioinformatically processed and resulted in several exciting observations. First, overall drug response profiles of the AML samples and the controls were distinctly different suggesting multiple leukemia-selective inhibitory effects. Second, the MEK and mTOR signaling pathways emerged as potential key molecular drivers of AML cells when analyzing targets of leukemia-specific active drugs. Third, potent new ex-vivo combinations of approved targeted drugs were uncovered, such as mTOR pathway inhibitors with dasatinib. Fourth, data from ex-vivo DSRT profiles showed excellent agreement with clinical response when serial samples were analyzed from leukemia patients developing clinical resistance to targeted agents. Summary: The rapid and comprehensive DSRT platform covering the entire cancer pharmacopeia and many emerging agents has already generated powerful insights into the molecular events underlying adult AML, with significant potential to facilitate individually optimized combinatorial therapies, particularly for recurrent leukemias. DSRT will also serve as a powerful hypothesis-generator for clinical trials, particularly for emerging drugs and drug combinations. The ability to correlate response profiles of hundreds of drugs in clinical ex vivo samples with deep molecular profiling data will yield exciting new translational and pharmacogenomic opportunities for clinical hematology. Disclosures: Mustjoki: Novartis: Honoraria; Bristol-Myers Squibb: Honoraria. Porkka:Novartis: Honoraria, Research Funding; Bristol-Myers Squibb: Honoraria, Research Funding. Kallioniemi:Abbot/Vysis: Patents & Royalties; Medisapiens: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Bayer Schering Pharma: Research Funding; Roche: Research Funding.

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High-Throughput Ex Vivo Drug Sensitivity and Resistance Testing (DSRT) Integrated with Deep Genomic and Molecular Profiling Reveal New Therapy Options with Targeted Drugs in Subgroups of Relapsed Chemorefractory AML

Blood ◽

10.1182/blood.v120.21.288.288 ◽

2012 ◽

Vol 120 (21) ◽

pp. 288-288

Author(s):

Caroline A Heckman ◽

Mika Kontro ◽

Tea Pemovska ◽

Samuli Eldfors ◽

Henrik Edgren ◽

...

Keyword(s):

Bone Marrow ◽

Transcriptome Sequencing ◽

Research Funding ◽

Drug Response ◽

Drug Sensitivity ◽

Ex Vivo ◽

Molecular Profiling ◽

Resistance Testing ◽

Response Patterns ◽

Serial Samples

Abstract Abstract 288 Introduction: Recent genomic analyses of acute myeloid leukemia (AML) patients have provided new information on mutations contributing to the disease onset and progression. However, the genomic changes are often complex and highly diverse from one patient to another and often not actionable in clinical care. To rapidly identify novel patient-specific therapies, we developed a high-throughput drug sensitivity and resistance testing (DSRT) platform to experimentally validate therapeutic options for individual patients with relapsed AML. By integrating the results with exome and transcriptome sequencing plus proteomic analysis, we were able to define specific drug-sensitive subgroups of patients and explore predictive biomarkers. Methods: Ex vivo DSRT was implemented for 29 samples from 16 adult AML patients at the time of relapse and chemoresistance and from 5 healthy donors. Fresh mononuclear cells from bone marrow aspirates (>50% blast count) were screened against a comprehensive collection of cytotoxic chemotherapy agents (n=103) and targeted preclinical and clinical drugs (n=100, later 170). The drugs were tested over a 10,000-fold concentration range resulting in a dose-response curve for each compound and each leukemia sample. A leukemia-specific drug sensitivity score (sDSS) was derived from the area under each dose response curve in relation to the total area, and comparing leukemia samples with normal bone marrow results. The turnaround time for the DSRT assay was 4 days. All samples also underwent deep exome (40–100×) and transcriptome sequencing to identify somatic mutations and fusion transcripts, as well as phosphoproteomic array analysis to uncover active cell signaling pathways. Results: The drug sensitivity profiles of AML patient samples differed markedly from healthy bone marrow controls, with leukemia-specific responses mostly observed for molecularly targeted drugs. Individual AML patient samples clustered into distinct subgroups based on their chemoresponse profiles, thus suggesting that the subgroups were driven by distinct signaling pathways. Similarly, compounds clustered based on the response across the samples revealing functional groups of compounds of both expected and unexpected composition. Furthermore, subsets of patient samples stood out as highly sensitive to different compounds. Specifically, dasatinib, rapalogs, MEK inhibitors, ruxolitinib, sunitinib, sorafenib, ponatinib, foretinib and quizartinib were found to be selectively active in 5 (31%), 5 (31%), 4 (25%), 4 (25%), 3 (19%), 3 (19%), 2 (13%), 2 (13%), and 1 (6%) of the AML patients ex vivo, respectively. DSRT assays of serial samples from the same patient at different stages of leukemia progression revealed patterns of resistance to the clinically applied drugs, in conjunction with evidence of dynamic changes in the clonal genomic architecture. Emergence of vulnerabilities to novel pathway inhibitors was seen at the time of drug resistance, suggesting potential combinatorial or successive cycles of drugs to achieve remissions in an increasingly chemorefractory disease. Genomic and molecular profiling of the same patient samples not only highlighted potential biomarkers reflecting the ex vivo DSRT response patterns, but also made it possible to follow in parallel the drug sensitivities and the clonal progression of the disease in serial samples from the same patients. Summary: The comprehensive analysis of drug responses by DSRT in samples from human chemorefractory AML patients revealed a complex pattern of sensitivities to distinct inhibitors. Thus, these results suggest tremendous heterogeneity in drug response patterns and underline the relevance of individual ex vivo drug testing in selecting optimal therapies for patients (personalized medicine). Together with genomic and molecular profiling, the DSRT analysis resulted in a comprehensive view of the drug response landscape and the underlying molecular changes in relapsed AML. These data can readily be translated into the clinic via biomarker-driven stratified clinical trials. Disclosures: Mustjoki: Bristol-Myers Squibb: Honoraria, Research Funding; Novartis: Honoraria. Kallioniemi:Roche: Research Funding; Medisapiens: Membership on an entity's Board of Directors or advisory committees. Porkka:Bristol-Myers Squibb: Honoraria, Research Funding; Novartis: Honoraria, Research Funding.

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DNA Damage Repair Pathway Alterations in Multiple Myeloma Predict Poor Prognosis, but Correlate with Sensitivity to IGF1R-PI3K-mTOR and HDAC Inhibitors

Blood ◽

10.1182/blood.v128.22.198.198 ◽

2016 ◽

Vol 128 (22) ◽

pp. 198-198

Author(s):

Muntasir M Majumder ◽

David Tamborero ◽

Pekka Anttila ◽

Raija Silvennoinen ◽

Samuli Eldfors ◽

...

Keyword(s):

Dna Damage ◽

Research Funding ◽

Drug Response ◽

Ex Vivo ◽

Hdac Inhibitors ◽

Resistance Mechanisms ◽

Newly Diagnosed ◽

Advisory Committees ◽

Damage Repair ◽

Somatic Alterations

Abstract Introduction Although several novel drugs have recently been approved or are in development for multiple myeloma (MM), there are few molecular indicators to guide treatment selection. In addition, the impact of recurrent myeloma alterations on drug response is often unclear. To address these limitations and elucidate genotype to phenotype relationships in myeloma, we comprehensively analyzed 100 MM samples and compared genomic, transcriptomic, and cytogenetic information to ex vivo drug response profiles and clinical outcome of individual MM patients. Our results reveal novel insights on i) drug response and resistance mechanisms, ii) biomarkers for drug response, and iii) potential treatment combinations to overcome drug resistance. Methods Bone marrow aspirates were collected from MM patients (n=100; newly diagnosed n=34; relapsed/refractory n=66) and healthy individuals (n=14). CD138+ plasma cells were enriched from the mononuclear cell fraction by immunomagnetic bead selection. Cells were screened against 142 oncology drugs tested in a 10,000-fold concentration range and 12 different drug combinations Somatic alterations were identified by exome sequencing of DNA from CD138+ cells and skin biopsies from each patient (n=85). RNA sequencing derived read counts from CD138+ cells of MM samples (n=67) were used for differential gene expression. Karyotype was determined by fluorescence in situ hybridization. Results For most drugs tested, no significant difference in response was observed between samples from newly diagnosed and relapsed refractory patients except for signal transduction inhibitors targeting IGF1R-PI3K-mTOR, MAPK and HSP90. A positive correlation was observed between mutational burden and sensitivity to targeted therapies. The median number of somatic alterations was 118 in sensitive compared to 50 in resistant samples. 14% of the samples exhibited a multidrug resistant phenotype and were resistant to proteasome inhibitors, immunomodulatory drugs and glucocorticoids. 30% of the resistant samples were from del(17p) patients. In addition, gene expression analysis revealed elevated expression of cell adhesion and integrin signaling molecules including ITGB3, ITGA2B, VCL, TLN1, MMP8, MMP9, plus ABCC3, which encodes a transporter protein shown to be associated with multidrug resistance. A combination of the protein kinase C inhibitor bryostatin-1 and pan-BCL2 inhibitor navitoclax was highly effective against the resistant samples. 26% of the patient samples harbored mutations in genes involved in DNA damage repair signaling, namely TP53, TP73, ATM and BAX, in a mutually exclusive pattern. In addition, patients with these mutations had a high relapse rate and poor overall survival (HR=7.2,95%CI 3.2-16.08). Interestingly, CD138+ cells from these patients showed activation of IGF1R-PI3K-mTOR signaling and were highly susceptible to inhibitors targeting this signaling axis. These samples were also highly sensitive to HDAC inhibitors. While no strong correlation between RAS pathway mutations (NRAS, KRAS, NF1, BRAF) and MEK inhibitor sensitivity was observed, samples with clonal RAS mutations tended to be more sensitive to MEK inhibitors compared to samples with subclonal mutations. Summary Our results suggest that drug resistance in myeloma may occur either via accumulation of somatic alterations or via cell adhesion mediated cytoprotection. Driver alterations in DNA damage signaling pathways were found to contribute to poor prognosis, but samples with these mutations showed enhanced sensitivity to IGF1R-PI3K-mTOR and HDAC inhibitors. Using genomic and transcriptomic data we identified molecular events that may shape the drug response landscape and found drug combinations that can overcome resistance mechanisms. Our results demonstrate that molecular information and ex vivo drug profiling may be useful to develop tailored treatment strategies and guide treatment decision, especially for relapsed/refractory myeloma patients. Disclosures Silvennoinen: Sanofi: Honoraria, Other: Lecture fee; Takeda: 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, Other: Lecture fee; Janssen: Honoraria, Research Funding; Celgene: Honoraria, Research Funding. Porkka:Bristol-Myers Squibb: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding; Novartis: Honoraria, Research Funding. Heckman:Pfizer: Research Funding; Celgene: Research Funding.

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Combined Targeting of BET Family Proteins and BCL2 Is Synergistic in Acute Myeloid Leukemia Cells Overexpressing S100A8 and S100A9

Blood ◽

10.1182/blood-2018-99-118890 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 2634-2634

Author(s):

Riikka Karjalainen ◽

Minxia Liu ◽

Ashwini Kumar ◽

Alun Parsons ◽

Liye He ◽

...

Keyword(s):

Gene Expression ◽

Cell Lines ◽

Drug Combination ◽

Calcium Binding ◽

Research Funding ◽

Drug Sensitivity ◽

Ex Vivo ◽

High Expression ◽

Bet Inhibitor ◽

Combination Studies

Abstract Background The 5-year survival rate for acute myeloid leukemia (AML) remains poor with most patients succumbing to relapse or refractory disease. Recently, the BCL2 specific inhibitor venetoclax has shown promising anti-leukemia activity in high-risk AML patients. Most patients, however, ultimately develop resistance to monotherapy and novel combination treatments with venetoclax are needed for patients with no other therapy options available. In this study we identified high expression of calcium binding protein S100A8/S100A9 to be associated with venetoclax resistance and looked for drug combinations to overcome the resistance in AML patient samples overexpressing S100A8 and S100A9. Methods Gene expression was assessed by RNA sequencing of AML patient samples and validated by qPCR. Gene enrichment analysis was performed on differentially expressed genes between venetoclax highly sensitive (n=3) and resistant (n=4) samples. Sensitivity of AML patient derived mononuclear cells was assessed to 304 different small molecule inhibitors by measuring cell viability after 72 h incubation with 5 different concentrations (1-10,000 nM) using the CellTiter-Glo (CTG) assay. A drug sensitivity score (DSS) was calculated based on a modified area under the dose response curve calculation. Drug combination studies were performed using AML cell lines resistant to venetoclax and confirmed with primary patient cells (n=15). Data of the drug combination studies were analyzed with the Zero Interaction Potency (ZIP) model by considering a dose-response matrix where two drugs are tested at 8 concentrations in a serially diluted manner. Statistical dependence between gene expression and drug sensitivity data was assessed by Pearson's correlation coefficient modelling. Results Venetoclax resistant AML patient samples were found to overexpress genes related to immune responses including inflammatory related proteins S100A8 and S100A9. The expression of S100A8 and S100A9 was upregulated in a sub-group of AML patients with somatic mutations in DNA methylation genes IDH2 and TET2 and chromatin modifier ASXL1. Functional studies with AML cell lines validated high expression of the S100 proteins in cells insensitive to venetoclax (NOMO-1, SKM-1 and SHI-1) whereas sensitive cell lines (MOLM-13, Kasumi-1 and ML-2) did not express the proteins. Integrated analysis of S100A8 and S100A9 expression and ex vivo drug sensitivity data indicated positive correlation of S100 expression with sensitivity to BET inhibitor (birabresib), PI3K inhibitor (TG100-115) and MEK1/2 inhibitor (AZD8330). In contrast, sensitivity to venetoclax and the FLT3 inhibitor quizartinib negatively correlated with S100 gene expression. Subsequently, we combined positively correlating drugs with venetoclax and tested the efficacy of these combinations in AML cell lines and patient samples. From the drug combination studies we found that BET inhibitor birabresib was able to overcome resistance to venetoclax treatment. The BCL2/BET inhibitor combination was synergistic in venetoclax resistant cell lines NOMO-1 and SKM-1, which express high-levels of S100A8 and S100A9 (Figure 1A). Efficacy of the combination on primary AML patient samples correlated with the expression level of the S100 genes. Nine of 11 high expression samples were sensitive to the venetoclax/birabresib combination (Figure 1B-C), whereas no synergy was observed in 3 of 4 samples with a low level of S100 expression. Conclusions The calcium binding proteins S100A8 and S100A9 are abundant in myeloid cells and are associated with poor prognosis in AML (Edgeworth et al, J Biol Chem. 1991; Nicolas et al, Leukemia 2011). From ex vivo and in vitro analyses of AML, we found that high expression of S100A8 and S100A9 is highly correlative with resistance to the BCL2 inhibitor venetoclax. In contrast, high S100A8 and S100A9 expression correlates with sensitivity to BET inhibitor birabresib. Interestingly, our studies showed that these two drugs act synergistically in venetoclax resistant AML cell lines and AML patient samples and may be a beneficial novel combination that should be further confirmed in preclinical and clinical investigations. Disclosures Porkka: Celgene: Honoraria, Research Funding; Novartis: Honoraria, Research Funding. Heckman:Orion Pharma: Research Funding; Novartis: Research Funding; Celgene: Research Funding.

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