Search

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.

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.

We present an individualized systems medicine (ISM) approach to optimize cancer drug therapies one patient at a time. ISM is based on (i) molecular profiling and ex vivo drug sensitivity and resistance testing (DSRT) of patients' cancer cells to 187 oncology drugs, (ii) clinical implementation of therapies predicted to be effective, and (iii) studying consecutive samples from the treated patients to understand the basis of resistance. Here, application of ISM to 28 samples from patients with acute myeloid leukemia (AML) uncovered five major taxonomic drug-response subtypes based on DSRT profiles, some with distinct genomic features (e.g., MLL gene fusions in subgroup IV and FLT3-ITD mutations in subgroup V). Therapy based on DSRT resulted in several clinical responses. After progression under DSRT-guided therapies, AML cells displayed significant clonal evolution and novel genomic changes potentially explaining resistance, whereas ex vivo DSRT data showed resistance to the clinically applied drugs and new vulnerabilities to previously ineffective drugs. Significance: Here, we demonstrate an ISM strategy to optimize safe and effective personalized cancer therapies for individual patients as well as to understand and predict disease evolution and the next line of therapy. This approach could facilitate systematic drug repositioning of approved targeted drugs as well as help to prioritize and de-risk emerging drugs for clinical testing. Cancer Discov; 3(12); 1416–29. ©2013 AACR. See related commentary by Hourigan and Karp, p. 1336 This article is highlighted in the In This Issue feature, p. 1317

"Single-nucleotide polymorphisms (SNPs) in the 30 region of myosin IXB (MYO9B) gene have recently been reported to associate with different inflammatory or autoimmune diseases. We monitored for the association of MYO9B variants to multiple sclerosis (MS) in four Northern European populations. First, 18 SNPs including 6 SNPs with previous evidence for association to immune disorders, were tested in 730 Finnish MS families, but no linkage or family-based association was observed. To ensure the power to detect variants with a modest effect size, we further analyzed 10 variants in 899 Finnish cases and 1325 controls, and in a total of 1521 cases and 1476 controls from Denmark, Norway and Sweden, but found no association. Our results thereby do not support a major function of the tested MYO9B variants in MS. European Journal of Human Genetics (2009) 17, 840-843; doi: 10.1038/ejhg.2008.251; published online 14 January 2009" "Single-nucleotide polymorphisms (SNPs) in the 30 region of myosin IXB (MYO9B) gene have recently been reported to associate with different inflammatory or autoimmune diseases. We monitored for the association of MYO9B variants to multiple sclerosis (MS) in four Northern European populations. First, 18 SNPs including 6 SNPs with previous evidence for association to immune disorders, were tested in 730 Finnish MS families, but no linkage or family-based association was observed. To ensure the power to detect variants with a modest effect size, we further analyzed 10 variants in 899 Finnish cases and 1325 controls, and in a total of 1521 cases and 1476 controls from Denmark, Norway and Sweden, but found no association. Our results thereby do not support a major function of the tested MYO9B variants in MS. European Journal of Human Genetics (2009) 17, 840-843; doi: 10.1038/ejhg.2008.251; published online 14 January 2009" "Single-nucleotide polymorphisms (SNPs) in the 30 region of myosin IXB (MYO9B) gene have recently been reported to associate with different inflammatory or autoimmune diseases. We monitored for the association of MYO9B variants to multiple sclerosis (MS) in four Northern European populations. First, 18 SNPs including 6 SNPs with previous evidence for association to immune disorders, were tested in 730 Finnish MS families, but no linkage or family-based association was observed. To ensure the power to detect variants with a modest effect size, we further analyzed 10 variants in 899 Finnish cases and 1325 controls, and in a total of 1521 cases and 1476 controls from Denmark, Norway and Sweden, but found no association. Our results thereby do not support a major function of the tested MYO9B variants in MS. European Journal of Human Genetics (2009) 17, 840-843; doi: 10.1038/ejhg.2008.251; published online 14 January 2009"

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.

Large case–control genome-wide association studies primarily expose common variants contributing to disease pathogenesis with modest effects. Thus, alternative strategies are needed to tackle rare, possibly more penetrant alleles. One strategy is to use special populations with a founder effect and isolation, resulting in allelic enrichment. For multiple sclerosis such a unique setting is reported in Southern Ostrobothnia in Finland, where the prevalence and familial occurrence of multiple sclerosis (MS) are exceptionally high. Here, we have studied one of the best replicated MS loci, 5p, and monitored for haplotypes shared among 72 regional MS cases, the majority of which are genealogically distantly related. The haplotype analysis over the 45 Mb region, covering the linkage peak identified in Finnish MS families, revealed only modest association at IL7R (P = 0.04), recently implicated in MS, whereas most significant association was found with one haplotype covering the C7-FLJ40243 locus (P = 0.0001), 5.1 Mb centromeric of IL7R. The finding was validated in an independent sample from the isolate and resulted in an odds ratio of 2.73 (P = 0.000003) in the combined data set. The identified relatively rare risk haplotype contains C7 (complement component 7), an important player of the innate immune system. Suggestive association with alleles of the region was seen also in more heterogeneous populations. Interestingly, also the complement activity correlated with the identified risk haplotype. These results suggest that the MS predisposing locus on 5p is more complex than assumed and exemplify power of population isolates in the identification of rare disease alleles.

Loss-of-function mutations of DAP12 and TREM2 cause a recessively inherited disease PLOSL, manifesting in brain white matter. The genes of the DAP12-TREM2 signaling receptor are located on 19q13.12 and 6p21.1, to which linkage has been observed also in families affected by another immune-mediated demyelinating disease, MS. We have tested if allelic variation in DAP12 or TREM2 predisposes also to MS by monitoring carrier frequency of the Finnish PLOSL mutation in Finnish MS cases and by studying DAP12 and TREM2 in MS by linkage and association. To conclude, the DAP12-TREM2 complex unlikely has a role in genetic susceptibility of MS.

Reliable and efficient PCR and extension reactions using standardized procedures are key elements for successful single nucleotide polymorphism (SNP) genotyping projects. To improve the cost efficiency and overall performance of SNP genotyping we evaluated two commercial thermostable DNA polymerases used for the extension reaction in the homogeneous mass extension MassARRAY genotyping system. The aim was to study whether the quality, accuracy, and expenses of a new TERMIPol® DNA polymerase are competitive to the commonly used ThermoSequenase® DNA polymerase. We compared the enzymes by testing 96 SNPs genotyped for DNA samples of 31 unrelated individuals and one water control. The success rates, congruence between the genotypes and completeness of extension reactions support the use of TERMIPol®, especially when the amplification of the higher mass allele is difficult. Further, using TERMIPol® enabled successful genotyping (>93%) of several SNPs that failed (

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.

Introduction Adult acute myeloid leukemia (AML) exemplifies the challenges of modern cancer drug discovery and development in that molecularly targeted therapies are yet to be translated into clinical use. No effective second-line therapy exists once standard chemotherapy fails. While many genetic events have been linked with the onset and progression of AML, the fundamental disease mechanisms remain poorly understood. There is significant genomic and molecular heterogeneity among patients. Several targeted therapies have been investigated for improved second-line AML therapy but none has been approved for clinical use to date. It would be critically important to identify patient subgroups that would benefit from such therapies and to identify combinations of drugs that are likely to be effective. Methods To identify and optimize novel therapies for AML, we studied 28 samples from 18 AML patients with an individualized systems medicine (ISM) approach. The ISM platform includes functional profiling of AML patient cells ex vivo with drug sensitivity and resistance testing (DSRT), comprehensive molecular profiling as well as clinical background information. Data integration was done to identify disease- and patient-specific molecular vulnerabilities for translation in the clinic. The DSRT platform comprises 306 anti-cancer agents, each tested in a dose response series. We calculated differential drug sensitivity scores by comparing AML responses to those of control cells in order to distinguish cancer-specific drug effects. Next generation RNA- and exome-sequencing was used to identify fusion transcripts and mutations that link to drug sensitivities. Results Individual AML patient samples had a distinct drug sensitivity pattern, but unsupervised hierarchical clustering of the drug sensitivity profiles of the 28 AML patient samples identified 5 functional AML drug response subtypes. Each subtype was characterized by distinct combinations of sensitivities: Bcl-2 inhibitors (e.g. navitoclax; Group 1), JAK inhibitors (e.g. ruxolitinib) (Group 2) and MEK inhibitors (e.g. trametinib) (Groups 2 and 4), PI3K/mTOR inhibitors (e.g. temsirolimus; Groups 4 and 5), broad spectrum receptor tyrosine kinase inhibitors (e.g. dasatinib) (Groups 3, 4 and 5) and FLT3 inhibitors (e.g. quizartinib, sunitinib) (Group 5). Correlation of overall drug responses with genomic profiles revealed that RAS and FLT3 mutations were significantly linked with the drug response subgroups 4 and 5, respectively. Activating FLT3 mutations contributed to sensitivity to FLT3 inhibitors, as expected, but also to tyrosine kinase inhibitors not targeting FLT3, such as dasatinib. Hence, these data point to the potential synergistic combinatorial effects of FLT3 inhibitors with dasatinib for improved therapy outcome (Figure). Early clinical translational results based on compassionate use support this hypothesis. Therefore, by combinations of drugs we expect to see synergistic drug responses that can be translated into efficacious and safe therapies for relapsed AML cases in the clinic. Clinical application of DSRT results in the treatment of eight recurrent chemorefractory patients led to objective responses in three cases according to ELN criteria, whereas four of the remaining five patients had meaningful responses not meeting ELN criteria. After disease progression, AML patient cells showed ex vivo resistance to the drugs administered to the patients, as well as significant changes in clonal architecture during treatment response. Furthermore, we saw genomic alterations potentially explaining drug resistance, such as appearance of novel fusion genes. Summary The ISM approach represents an opportunity for improving therapies for cancer patients, one patient at the time. We show that the platform can be used to identify functional groups of AML linking to vulnerabilities to single targeted drugs and, importantly, unexpected drug combinations. This information can in turn be used for personalized medicine strategies and for creating hypotheses to be explored in systematic clinical trials, both for approved and investigational drugs. Disclosures: Off Label Use: Many of the compounds included in our DSRT platform are not indicated for AML therapy. Mustjoki:BMS: Honoraria, Research Funding; Novartis: Honoraria. Porkka:Novartis: Honoraria, Research Funding; BMS: Honoraria, Research Funding. Kallioniemi:Medisapiens: Membership on an entity’s Board of Directors or advisory committees; Roche: Research Funding.

Despite significant advances in characterizing the molecular genetics of AML, the clonal evolution of leukemic cells and the dynamic impact of genomic changes on the development of the disease and progression to drug resistance are not well understood. Here, we applied next-generation sequencing to quantify aberrant tumor subclones carrying specific mutant alleles of key cancer genes and developed a method to extract quantitative high-resolution copy number changes across the genome using exome sequencing data from matching cancer and normal DNA. Serial bone marrow (BM) samples collected from diagnosis to relapse to post-treatment drug resistance in a patient-centric manner made it possible to trace the clonal evolution of AML and to identify variants potentially involved in drug resistance. Exome sequencing from AML blast cells and normal skin biopsies was performed as part of the Finnish Hematology Registry and Biobanking (FHRB) effort. Consecutive paired samples from different patients revealed unique genetic patterns of clonal evolution and cancer progression in each patient. In a pre-resistant sample of one AML M5 patient, we identified four closely spaced insertions in the Wilm's Tumor (WT1) suppressor gene, none of which appear on the same sequence reads. This suggests the presence of multiple distinct leukemic subclones even before treatment resistance and underscores the strong selective advantage conferred by WT1 mutations. After relapse, one of the subclones was lost, and another one significantly increased suggesting that the relapse arose from the expansion of a pre-existing resistant subclone. In this patient, recurrent clones otherwise featured similar copy number changes and the same fusion genes as the primary diagnostic sample. In another AML patient developing recurrence an opposite pattern was observed: The relapsed, drug-resistant cells displayed an enormous increase of small microdeletions compared to the diagnostic, pre-treatment sample, while almost all sequence-level alterations in potential cancer genes were the same between the two samples. This suggests that a distinct type of DNA repair deficiency may have contributed to the drug resistant clone in this patient. Conclusions: Exome sequencing from paired samples of AML cells before and after relapse makes it possible to trace the clonal evolution of the disease and study the impact of therapy both at the level of sequence alterations of key cancer genes and simultaneously at the level of copy number changes inferred from exome sequence data. This analysis has highlighted multiple genomic patterns by which resistance may evolve in vivo during cancer treatment. Refined bioinformatic analysis and interpretation of exome-seq data provides a rich resource to identify genetic biomarkers of drug response and minimal residual disease. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5067. doi:1538-7445.AM2012-5067

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.