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Introduction Melphalan flufenamide (melflufen), is a novel peptide-drug conjugate that targets aminopeptidases and selectively delivers alkylating agents in tumors. Melflufen was recently FDA approved for the treatment of relapsed/refractory multiple myeloma (MM) patients. Considering the challenges in treating this group of patients, and the availability of several new drugs for MM, information that can support treatment selection is urgently needed. To identify potential indicators of response and mechanism of resistance to melflufen, we applied a multiparametric drug sensitivity assay to MM patient samples ex vivo and analyzed the samples by single cell RNA sequencing (scRNAseq). Ex vivo drug testing identified MM samples that were distinctly sensitive or resistant to melflufen, while differential gene expression analysis revealed pathways associated with response. Methods Bone marrow (BM) aspirates from 24 MM patients were obtained after written informed consent following approved protocols in compliance with the Declaration of Helsinki. BM mononuclear cells from 12 newly diagnosed (ND) and 12 relapsed/refractory (RR) patients were used for multi-parametric flow cytometry-based drug sensitivity and resistance testing (DSRT) evaluation to melflufen and melphalan, and for scRNAseq. Based on the results from the DSRT tests and drug sensitivity scores (DSS), we divided the samples into three groups - high sensitivity (HS, DSS > 40 (melflufen) or DSS > 16 (melphalan)), intermediate sensitivity (IS, 31 ≤ DSS ≤ 40 (melflufen) or 10 ≤ DSS ≤ 16 (melphalan)), and low sensitivity (LS, DSS < 31 (melflufen) or DSS < 10 (melphalan)). To identify genes, responsible for the general sensitivity to melphalan-based drugs we conducted differential gene expression (DGE) analyses separately for melphalan and melflufen focusing on the plasma cell populations, comparing gene expression between HS and LS samples for both drugs ("HS vs. LS melphalan" and "HS vs. LS for melflufen", respectively). In addition, to explain the increased sensitivity of RR samples, we conducted the DGE analysis for ND vs. RR samples and searched for similarities between these three datasets. Results DSRT data indicated that samples from RRMM patients were significantly more sensitive to melflufen compared to samples from NDMM (Fig. 1A). In addition, we observed that samples with a gain of 1q (+1q) were more sensitive to melflufen while those with deletion of 13q (del13q) appeared to be less sensitive, although these results lacked significance (Fig. 1A). After separating the samples into different drug sensitivity groups (HS, IS, LS), DGE analysis showed significant downregulation of the drug efflux and multidrug resistance protein family member ABCB9 in the melflufen HS group opposed to the LS group (2.2-fold, p < 0.001). A similar pattern was detected for the melphalan HS vs. LS comparison suggesting that this alteration might be a common indicator of sensitivity to melphalan-based drugs. Furthermore, in the melflufen HS group we observed downregulation of the matrix metallopeptidase inhibitors TIMP1 and TIMP2 (3-fold and 1.6-fold, p < 0.001, respectively), and cathepsin inhibitors CST3 and CSTB (3.2-fold and 1.3-fold, p < 0.001, respectively) (Fig. 1B). This effect was observed in both "ND vs. RR" and "HS vs. LS for melflufen" comparisons, but not for melphalan, suggesting that these changes are associated with disease progression and specific indicators of sensitivity to melflufen. Moreover, gene set enrichment analysis (GSEA) showed activation of pathways related to protein synthesis, as well as amino acid starvation for malignant and normal cell populations in the HS group. Conclusion In summary, our results indicate that melflufen is more active in RRMM compared to NDMM. In addition, samples from MM patients with +1q, which is considered an indicator of high-risk disease, tended to be more sensitive to melflufen. Based on differential GSEA and pathway enrichment, several synergizing mechanisms could potentially explain the higher sensitivity to melflufen, such as decreased drug efflux and increased drug uptake. Although these results indicate potential indicators of response and mechanisms of drug efficacy, further validation of these findings is required using data from melflufen treated patients. Figure 1 Figure 1. Disclosures Slipicevic: Oncopeptides AB: Current Employment. Nupponen: Oncopeptides AB: Consultancy. Lehmann: Oncopeptides AB: Current Employment. Heckman: Orion Pharma: Research Funding; Oncopeptides: Consultancy, Research Funding; Novartis: Research Funding; Celgene/BMS: Research Funding; Kronos Bio, Inc.: Research Funding.

Background: Immunoglobulin light-chain (AL) amyloidosis is a rare disease caused by plasma cell secretion of misfolded light chains that assemble as amyloid fibrils and deposit on vital organs including the heart and kidneys, causing organ dysfunction. Plasma cell directed therapeutics, aimed at preferentially eliminating the clonal population of amyloidogenic cells in bone marrow are expected to reduce production of toxic light chain and alleviate deposition of amyloid thereby restoring healthy organ function. Melphalan flufenamide ethyl ester, melflufen, is a peptidase potentiated alkylating agent with potent toxicity in myeloma cells. Melflufen is highly lipophilic, permitting rapid cellular uptake, and is subsequently enzymatically cleaved by aminopeptidases within cells resulting in augmented intracellular concentrations of toxic molecules, providing a more targeted and localized treatment. Previous data demonstrating multiple myeloma plasma cell sensitivity for melflufen suggests that the drug might be useful to directly eliminate amyloidogenic plasma cells, thereby reducing the amyloid load in patients. Furthermore, the increased intracellular concentrations of melflufen in myeloma cells indicates a potential reduction in systemic toxicity in patients, an important factor in the fragile amyloidosis patient population. To assess potential efficacy in amyloidosis patients and to explore the mechanism of action, we examined effects of melflufen on amyloidogenic plasma cells invitro and invivo. Methods: Cellular toxicity and apoptosis were measured in response to either melflufen or melphalan in multiple malignant human plasma cell lines, including the amyloidosis patient derived light chain secreting ALMC-1 and ALMC-2 cells, as well as primary bone marrow cells from AL amyloidosis patients, using annexin V and live/dead cell staining by multicolor flow cytometry, and measurement of cleaved caspases. Lambda light chain was measured in supernatant by ELISA, and intracellular levels were detected by flow cytometry. To assess efficacy of melflufen in vivo, the light chain secreting human myeloma cell line, JJN3, was transduced with luciferase and adoptively transferred into NSG mice. Cell death in response to melflufen or melphalan was measured by in vivo bioluminescence, and serum light chain was monitored. Results: Melflufen demonstrated increased potency against multiple myeloma cell lines compared to melphalan, inducing malignant plasma cell death at lower doses on established light chain secreting plasma cell lines. While ALMC-1 cells were sensitive to both melphalan and melflufen, the IC50 for melphalan at 960 nM was approximately 3-fold higher than melflufen (334 nM). However, ALMC-2 cells were relatively insensitive to melphalan (12600 nM), but maintained a 100-fold increase in sensitivity to melflufen (121 nM). Furthermore, while 40% of primary CD138+ plasma cells from patients with diagnosed AL amyloidosis responded to melflufen treatment in vitro, only 20% responded to melphalan with consistently superior IC50 values for melflufen (Figure 1). Light chain secreting cell lines and AL amyloidosis patient samples were further analyzed by single cell sequencing. We further examined differential effects on apoptosis and the unfolded protein response in vitro in response to either melflufen or melphalan. This is of particular interest in amyloidosis, where malignant antibody producing plasma cells possess an increased requirement for mechanisms to cope with the amplified load of unfolded protein and associated ER stress. As AL amyloidosis is ultimately a disease mediated by secretion of toxic immunoglobulin, we assessed the effects of melflufen on the production of light chain invitro, measuring a decrease in production of light chain in response to melflufen treatment. Finally, we took advantage of a recently described adoptive transfer mouse model of amyloidosis to assess the efficacy of melflufen and melphalan in eliminating amyloidogenic clones and reducing the levels of toxic serum light chain in vivo. Conclusions: These findings provide evidence that melflufen mediated toxicity, previously described in myeloma cells, extends to amyloidogenic plasma cells and further affects the ability of these cells to produce and secrete toxic light chain. This data supports the rationale for the evaluation of melflufen in patients with AL amyloidosis. Figure 1 Disclosures Flanagan: Oncopeptides AB: Employment. Slipicevic:Oncopeptides AB: Employment. Holstein:Celgene: Consultancy; Takeda: Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees; GSK: Consultancy; Genentech: Membership on an entity's Board of Directors or advisory committees; Sorrento: Consultancy. Lehmann:Oncopeptides AB: Employment. Nupponen:Oncopeptides AB: Employment. Heckman:Celgene: Research Funding; Novartis: Research Funding; Oncopeptides: Research Funding; Orion Pharma: Research Funding.

Targeted drug treatment strategies have significantly prolonged the overall survival rate among multiple myeloma (MM) patients. However, high relapse rates and multiple drug resistance still pose major challenges. Although, the underlying molecular features of the disease have been explored both at the genomic and transcriptomic levels, the functional role of microRNAs (miRNA) in MM disease progression and prognosis is yet to be investigated at a personalized level. In earlier studies, microRNAs have been implicated to regulate gene expression and were determined to play crucial roles in the biology of MM by acting as oncogenes or tumor suppressors. Nevertheless, considering the heterogeneity of MM, little is known about the roles of miRNAs in controlling MM disease progression and drug response at an individualized systems level. We collected bone marrow aspirates from MM patients at diagnosis (n=20) and relapse (n=25) after informed consent and following approved protocols in accordance with the Declaration of Helsinki. CD138+ plasma cells were enriched from the bone marrow samples and used for miRNA-sequencing and drug sensitivity and resistance testing (DSRT). The miRNA was prepared from the CD138+ cells and subjected to sequencing using Illumina compatible technologies. DSRT was performed and responses to 83 clinically approved drugs and investigational compounds were measured as drug sensitivity scores (DSS) as described previously (Majumder et al., Oncotarget 2017). The pairwise comparative analysis of miRNA expression and drug responses was performed using Spearman's rank-order correlations, to elucidate significant associations of miRNA expression with drug sensitivity and resistance. Additionally, using DEseq2 the differential miRNA expression was determined for the newly diagnosed and relapse samples to deconvolute the role of miRNAs in MM disease progression. The comparative analysis of the miRNA expression and drug sensitivity scores revealed statistically significant associations between miRNA expression and drug sensitivity measures with the Spearman coefficient (r) ranging from -0.71 to 0.64 (adjusted p-value ≤ 0.05) (Figure 1A). Negative associations were more prevalent, with 40 miRNAs negatively associated with ≥1 drug response from the total of 30 predicted drugs. miR-486, which is known to be an effective biomarker in diagnosis and prognosis of multiple cancer types (Jiang et al., Oncotarget 2018), was found to have significant negative correlation (r= -0.71 to -0.52, p-value ≤ 0.01) with the responses of 14 drugs. Similarly, negative correlation was observed for miR-144 with 12 drugs and miR-584 with 9 drugs. We observed that PI3K/mTOR inhibitors and HDAC inhibitors were common amongst all the significant negative correlations predicted. Specifically, the PI3K/mTOR inhibitors apitosilib, omipalisib and buparlisib were found to be negatively associated with the expression of 18, 14 and 7 miRNAs respectively. These observations can lead to the understanding of miRNA mediated regulation of molecular pathways involved in drug resistance. Differential miRNA expression analysis between newly diagnosed and relapse MM samples revealed the involvement of miRNAs in disease progression. The analysis resulted in total of 31 significant differentially expressed miRNAs with fold change ≥2 and adjusted p-value ≤ 0.1 (Figure 1B). Several miRNAs known to play crucial roles in cancer diagnosis and prognosis were found to be significantly upregulated in the relapse samples. In particular, 25 miRNAs were upregulated, including following miR-17/92 cluster members: miR-18b, miR-20a, miR-92b and miR-106a, which are known to have an oncogenic role in various cancer types (Mogilyansky & Rigoutsos, Cell Death and Differentiation 2013). Interestingly, 12/31 differentially regulated miRNAs were located on chromosome X. Although cytogenetic alteration data predicted that chromosome 1q gain is significantly prominent in the relapse samples (p-value = 0.009), only 3/31 differentially regulated miRNAs were located on chromosome 1. These results demonstrate the role of miRNAs in regulating drug response and disease progression in multiple myeloma. Monitoring miRNA expression profiles in MM patients can facilitate the assessment of treatment outcome and prognosis, and miRNAs could potentially be useful prognostic and treatment biomarkers for MM. Disclosures Silvennoinen: Amgen: Research Funding; Bristol-Myers Squibb (BMS): Research Funding; Takeda: Research Funding; Celgene: Research Funding. Heckman:Celgene: Research Funding; Novartis: Research Funding; Oncopeptides: Research Funding; Orion Pharma: Research Funding.

Novel agents have increased survival of multiple myeloma (MM) patients, however high-risk and relapsed/refractory patients remain challenging to treat and their outcome is poor. To identify novel therapies and aid treatment selection for MM, we assessed the ex vivo sensitivity of 50 MM patient samples to 308 approved and investigational drugs. With the results we i) classified patients based on their ex vivo drug response profile; ii) identified and matched potential drug candidates to recurrent cytogenetic alterations; and iii) correlated ex vivo drug sensitivity to patient outcome. Based on their drug sensitivity profiles, MM patients were stratified into four distinct subgroups with varied survival outcomes. Patients with progressive disease and poor survival clustered in a drug response group exhibiting high sensitivity to signal transduction inhibitors. Del(17p) positive samples were resistant to most drugs tested with the exception of histone deacetylase and BCL2 inhibitors. Samples positive for t(4; 14) were highly sensitive to immunomodulatory drugs, proteasome inhibitors and several targeted drugs. Three patients treated based on the ex vivo results showed good response to the selected treatments. Our results demonstrate that ex vivo drug testing may potentially be applied to optimize treatment selection and achieve therapeutic benefit for relapsed/refractory MM.

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.

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.

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.

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.

Introduction Multiple myeloma (MM) is a heterogeneous disease that eventually becomes resistant to therapy. Determining the genomic lesions driving each stage of the tumor and identifying actionable items for novel targeted drugs will improve and increase therapeutic options for the malignancy. The aim of the present work is to obtain a comprehensive catalog of driver genomic lesions for both newly diagnosed (NDMM) and refractory/relapsed MM (RRMM) patients by integrating multiple genomic data and linking these to the action of targeted therapeutic approaches. Methods Molecular cytogenetics was assessed by fluorescence in situ hybridization and somatic mutations and copy number changes were identified by performing exome sequencing of DNA from CD138+ cell and skin paired samples collected from 30 MM patients (NDMM n=12; RRMM n=18). In addition, gene expression profiles were obtained by transcriptome sequencing. The proportion of tumor clones bearing a specific mutation was inferred from the variant allele frequency. Genetic alterations involved in the tumorigenesis of each patient (named drivers) were identified by combining an in silico method aimed to score their potential for being malignant with the a priori knowledge retrieved from the identification of complementary signals of positive selection in available tumor cohorts (Tamborero et al. Nat Sci Rep 2013). Selective drug response was assessed by testing the ex vivo sensitivity of patient derived CD138+ cells to 306 oncology drugs and comparing results with responses derived from healthy bone marrow control cells. Results Overall, 0.5 translocations, 3±2.8 mutations and 4.9±2.7 copy number changes per patient were identified as putative drivers. The total number of driver alterations did not differ between NDMM and RRMM samples, and no gene reached statistical significance for being more frequently altered in the latter group. However, the only mutations in RAS genes that appeared at sub-clonal proportions occurred in diagnosed samples, pointing out their positive selection among relapsed patients in which they were present in all clones. Translocations involving IGH@ were observed in 11 (37%) patients, and interestingly 3 other samples exhibited driver alterations in the oncogenes involved in these fusions (i.e. activating mutations in FGFR3 or gene amplification plus peaked overexpression of WHSC1 and CCND1). Recurrent alterations were observed among genes previously associated with MM, including DIS3 (n=15), KRAS (n=11), CYLD (n=8), TRAF3 (n=6) and FAM46C (n=5). Other genes not previously associated with or less-known to be involved in MM pathogenesis were also identified, including the histone methyltransferase MLL, the tumor necrosis factor associated genes FAF1 and TNFRSF13B, the p53-suppressing protein phosphatase PPM1D, and several genes related with blood cell differentiation and B-lymphocyte development (e.g. SOX7, BLK and PRDM1). Overall, the pathways most frequently targeted by driver alterations were MAPK (23 (77%) samples, mostly by mutations), NF-κB (17(57%) samples, mostly by gene copy loss), cell-cycle (18 (60%) samples), and RNA-processing (17 (57%) samples). Comparison of driver lesions to drug response using data derived from ex vivo testing of the same patient samples to different targeted small molecule inhibitors (e.g. PI3K/mTOR and MEK inhibitors) indicated that alterations affecting PI3K and p53 pathways were associated with increased drug sensitivity, while alterations involving activation of FGFR3 and copy loss of TRAF3 were associated with a more resistant phenotype. Conclusions The integration of multiple genomic data by combining different predictive computational tools can comprehensively identify cancer events in individual patients. Applying these tools to genomic data from MM patients identified both known and novel driver lesions, and some of these alterations were associated with the ex vivo response to selective drugs. However, further data is required to confirm biomarkers of response to those novel therapeutics and test potential benefits in MM patients. Disclosures Silvennoinen: Janssen, Sanofi, Celgene: Honoraria; Research Funding of Government Finland, Research Funding from Janssen and Celgene: Research Funding. Porkka:Novartis: Honoraria, Research Funding; Bristol-Myers Squibb: Honoraria, Research Funding. Heckman:Celgene: Research Funding.