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 ◽  
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.

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 ◽  
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.

scholarly journals Identification and Clinical Exploration of Individualized Targeted Therapeutic Approaches in Acute Myeloid Leukemia Patients By Integrating Drug Response and Deep Molecular Profiles

Blood ◽  
2017 ◽  
Vol 130 (Suppl_1) ◽  
pp. 854-854
Author(s):  
Disha Malani ◽  
Ashwini Kumar ◽  
Bhagwan Yadav ◽  
Mika Kontro ◽  
Swapnil Potdar ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Myeloid Leukemia ◽  
Research Funding ◽  
Drug Response ◽  
Mononuclear Cells ◽  
Ex Vivo ◽  
Targeted Drugs ◽  
Systems Medicine ◽  
Bet Inhibitors ◽  
Response Profiles

Abstract Introduction Most patients with acute myeloid leukemia (AML) are still missing effective options for targeted treatments. Here, we applied individualized systems medicine (ISM) concept1 by integrating deep molecular profiles (genomics, transcriptomics) and ex vivo drug response profiles with 521 oncology drugs in 154 AML patient samples. The aim was to identify new treatment opportunities for molecular subsets of AML patients. When feasible, ISM guided treatment opportunities were applied clinically for AML patient treatment. Serial samples were available to identify molecular alterations in response to targeted drug treatment and to monitor therapeutic success or failure. We also aimed at testing the impact of bone marrow stromal cell conditioned media on drug response profiles in AML patients2. Methods Samples from bone marrow or blood of 122 AML patients and 17 healthy donors were obtained with written consent and ethical approval (239/13/03/00/2010 and 303/13/03/01/2011) from the Hematology Clinic, Comprehensive Cancer Center, Helsinki University Hospital. The ex vivo drug sensitivity and resistance testing (DSRT) assay was performed with 521 approved oncology drugs and investigational oncology compounds as described earlier1. In this study, freshly isolated mononuclear cells were randomly resuspended either in standard mononuclear cell medium (MCM, PromoCell) or in human bone marrow stroma derived conditioned medium (CM) for drug testing. DNA samples from same mononuclear cells were subjected to whole exome and transcriptome sequencing and data were analyzed as described previsously2. Hierarchical clustering and non-parametric rank correlation were performed with drugs and samples. Wilcoxon sign ranked test was applied between wild type and mutated samples to identify significant mutation-drug associations. Results Hierarchical clustering was largely independent of clinical features such as disease status or risk class. A strong drug sub-cluster with a unique response profile was composed of that of the MDM2 antagonist idasanutlin along with BCL-2 inhibitors navitoclax and venetoclax (Figure). BET inhibitors (JQ1, I-BET151, birabresib) and MEK inhibitors (trametinib, selumetinib) were positively correlated with each other suggesting an association between bromodomain mediated epigenetic deregulation and up-regulation of the MEK pathway in a subset of patients. Comparison between patient samples profiled in CM (n=77) vs MCM medium (n=77) indicated higher efficacy of MDM2 modulator idasanutlin in MCM while BET inhibitors responded more strongly in CM. Other differences observed earlier by Karjalainen et al1 between the two media types were also validated. Furthermore, 16 chemorefractory and one diagnostic stage patients were treated with the targeted drugs suggested by this ISM approach. We observed complete remission or leukemia free state in 35% (6/17) of the AML patients given tailored treatment in an observational study. The targeted drugs used for clinical translation included ruxolitinib (in n=4 patients), temsirolimus (n=5), trametinib (n=4), sunitinib (n=7), dasatinib (n=7), sorafeninb (n=4), omacetaxine (n=3) and dexamethasone (n=5). Summary This study highlights the potential of individualized systems medicine (ISM) approach in the identification of effective treatment opportunities for individual patients with AML. Identifying molecular markers for ex vivo drug responses can help to assign treatments to the patient subgroups most likely to respond in clinical trials. Figure Figure. Disclosures Heckman: Orion Pharma: Research Funding; Novartis: Research Funding; IMI2 project HARMONY: Research Funding; Pfizer: Research Funding; Celgene: Research Funding. Porkka: Bristol-Myers Squibb: Honoraria, Research Funding; Novartis: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding.

scholarly journals Predicting Response to Dasatinib Using a Computational Model and Its Validation: A Beat AML Project Study

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 1541-1541
Author(s):  
Jeffrey W. Tyner ◽  
Brian J. Druker ◽  
Cristina E. Tognon ◽  
Stephen E Kurtz ◽  
Leylah M. Drusbosky ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Computational Biology ◽  
Board Of Directors ◽  
Research Funding ◽  
Drug Response ◽  
Drug Sensitivity ◽  
Ex Vivo ◽  
Patient Specific ◽  
Advisory Committees ◽  
Equity Ownership

Abstract Background: New prognostic factors have been recently identified in AML patient population that include frequent mutations of receptor tyrosine kinases (RTK) including KIT, PDGFR, FLT3, that are associated with higher risk of relapse. Thus, targeting RTKs could improve the therapeutic outcome in AML patients. Aim: To create a digital drug model for dasatinib and validate the predicted response in AML patient samples with ex vivo drug sensitivity testing. Methods: The Beat AML project (supported by the Leukemia & Lymphoma Society) collects clinical data and bone marrow specimens from AML patients. Bone marrow samples are analyzed by conventional cytogenetics, whole-exome sequencing, RNA-seq, and an ex vivo drug sensitivity assay. For 50 randomly chosen patients, every available genomic abnormality was inputted into a computational biology program (Cell Works Group Inc.) that uses PubMed and other online resources to generate patient-specific protein network maps of activated and inactivated pathways. Digital drug simulations with dasatinib were conducted by quantitatively measuring drug effect on a composite AML disease inhibition score (DIS) (i.e., cell proliferation, viability, and apoptosis). Drug response was determined based on a DIS threshold reduction of > 65%. Computational predictions of drug response were compared to dasatinib IC50 values from the Beat AML ex vivo testing. Results: 23/50 (46%) AML patients had somatic mutations in an RTK gene (KIT, PDGFR, FLT3 (ITD (n=15) & TKD (n=4)), while 27/50 (54%) were wild type (WT) for the RTK genes. Dasatinib showed ex vivo cytotoxicity in 9/50 (18%) AML patients and was predicted by CBM to remit AML in 9/50 AML patients with 4 true responders and 5 false positive. Ex vivo dasatinib responses were correctly matched to the CBM prediction in 40/50 (80%) of patients (Table1), with 10 mismatches due to lack of sufficient genomic information resulting in profile creation issues and absence of sensitive loops in the profile. Only 4/23 (17%) RTK-mutant patients and 5/27(19%) RTK-WT patients were sensitive to dasatinib ex vivo, indicating that presence of somatic RTK gene mutations may not be essential for leukemia regression in response to dasatinib. Co-occurrence of mutations in NRAS, KRAS and NF1 seemed to associate with resistance as seen in 10 of the 14 profiles harboring these mutations. Conclusion: Computational biology modeling can be used to simulate dasatinib drug response in AML with high accuracy to ex vivo chemosensitivity. DNA mutations in RTK genes may not be required for dasatinib response in AML. Co-occurrence of NRAS, KRAS and NF1gene mutations may be important co-factors in modulating response to dasatinib. Disclosures Tyner: Leap Oncology: Equity Ownership; Syros: Research Funding; Seattle Genetics: Research Funding; Janssen: Research Funding; Incyte: Research Funding; Gilead: Research Funding; Genentech: Research Funding; AstraZeneca: Research Funding; Aptose: Research Funding; Takeda: Research Funding; Agios: Research Funding. Druker:Third Coast Therapeutics: Membership on an entity's Board of Directors or advisory committees; Novartis Pharmaceuticals: Research Funding; Millipore: Patents & Royalties; Vivid Biosciences: Membership on an entity's Board of Directors or advisory committees; Oregon Health & Science University: Patents & Royalties; McGraw Hill: Patents & Royalties; Celgene: Consultancy; MolecularMD: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; GRAIL: Consultancy, Membership on an entity's Board of Directors or advisory committees; Bristol-Meyers Squibb: Research Funding; Amgen: Membership on an entity's Board of Directors or advisory committees; Aptose Therapeutics: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Henry Stewart Talks: Patents & Royalties; Patient True Talk: Consultancy; Blueprint Medicines: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; ARIAD: Research Funding; Fred Hutchinson Cancer Research Center: Research Funding; Beta Cat: Membership on an entity's Board of Directors or advisory committees; Cepheid: Consultancy, Membership on an entity's Board of Directors or advisory committees; Leukemia & Lymphoma Society: Membership on an entity's Board of Directors or advisory committees, Research Funding; ALLCRON: Consultancy, Membership on an entity's Board of Directors or advisory committees; Aileron Therapeutics: Consultancy; Gilead Sciences: Consultancy, Membership on an entity's Board of Directors or advisory committees; Monojul: Consultancy. Sahu:Cellworks Research India Private Limited: Employment. Vidva:Cellworks Research India Private Limited: Employment. Kapoor:Cellworks Research India Private Limited: Employment. Azam:Cellworks Research India Private Limited: Employment. Kumar:Cellworks Research India Private Limited: Employment. Chickdipatti:Cellworks Research India Private Limited: Employment. Raveendaran:Cellworks Research India Private Limited: Employment. Gopi:Cellworks Research India Private Limited: Employment. Abbasi:Cell Works Group Inc.: Employment. Vali:Cell Works Group Inc.: Employment. Cogle:Celgene: Other: Steering Committee Member of Connect MDS/AML Registry.

AML Specific Targeted Drugs Identified By Drug Sensitivity and Resistance Testing: Comparison of Ex Vivo Patient Cells with in Vitro Cell Lines

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 2163-2163
Author(s):  
Disha Malani ◽  
Astrid Murumägi ◽  
Bhagwan Yadav ◽  
Tea Pemovska ◽  
John Patrick Mpindi ◽  
...  
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Myeloid Leukemia ◽  
Research Funding ◽  
Drug Sensitivity ◽  
Ex Vivo ◽  
Cancer Cell Lines ◽  
Resistance Testing ◽  
Targeted Drugs ◽  
Drug Responses

Abstract Introduction Many drug discovery efforts and pharmacogenomic studies are based on testing established cancer cell lines for their sensitivity to a given drug or a panel of drugs. This approach has been criticized due to high selectivity and fast proliferation rate of cancer cell lines. To explore new therapeutic avenues for acute myeloid leukemia (AML) and to compare experimental model systems, we applied high-throughput Drug Sensitivity and Resistance Testing (DSRT) platform with 305 approved and investigational drugs for 28 established AML cell lines and compared their drug responses with our earlier study of 28 ex vivo AML patient samples (Pemovska et al., 2013). We then correlated drug sensitivities with genomic and molecular profiles of the samples. Methods DSRT was carried out with 305 clinical, emerging and experimental drugs and small molecule chemical inhibitors. The drugs were tested at five different concentrations over a 10,000-fold concentration range. Cell viability was measured after 72 hours using Cell Titre Glow assay. IC50 values were calculated with Dotmatics software and drug sensitivity scores (DSS, a modified area under the curve metric) were derived for each drug (Yadav et al., 2014). Nimblegen's SeqCap EZ Designs Comprehensive Cancer Design kit was used to identify mutations from 578 oncogenes in cell lines. Results The 28 established AML cell lines were in general more sensitive to the drugs as compared to the 28 ex vivo patient samples, with some important exceptions. Sensitivity towards many targeted drugs was observed in both AML cell lines and in patient samples. These included inhibitors of MEK (e.g. trametinib in 56% of cell lines and 36% of ex vivo samples), mTOR (e.g. temsirolimus in 42% and 32%) and FLT3 (quizartinib in 28% and 18%). Overall, drug responses between cell lines and ex vivo patient cells in AML showed an overall correlation coefficient of r=0.81. BCL2 inhibitors (venetoclax and navitoclax) showed more sensitivity in ex vivo patient cells than in AML cancer cell lines, whereas responses to anti-mitotic agents (docetaxel, camptothecin, vincristine) showed stronger responses in cell lines (Figure). Only 7% of AML cell lines exhibited responses to a broad-spectrum tyrosine kinase inhibitor dasatinib, in contrast to 36% patient samples. AML cell lines that carried FLT3 mutations showed high sensitivity to FLT3 inhibitors. Similarly, cell lines harbouring mutations in RAS or RAF were strongly sensitive to MEK inhibitors. MEK and FLT3 inhibitor responses were mutually exclusive, indicating alternative pathway dependencies in cell lines. However, these pharmacogenomics correlations were not as clearly seen in the clinical samples. Summary These data revealed a few important differences as well as many similarities between established AML cell lines and primary AML patient samples in terms of their response to a panel of cancer drugs. The hope is that patient-derived primary cells in ex vivo testing predict clinical response better as compared to the established cancer cell lines, which indeed seem to overestimate the likelihood of responses to many drugs. On the other hand, cancer cell line studies may also underestimate the potential of dasatinib and BCL2 inhibitors as emerging AML therapeutics. References 1. Pemovska T, Kontro M, Yadav B, Edgren H, Eldfors S, Szwajda A, et al. Individualized systems medicine strategy to tailor treatments for patients with chemorefractory acute myeloid leukemia. Cancer Discovery. 2013 Dec;3(12):1416-29 2. Yadav B, Pemovska T, Szwajda A, Kulesskiy E, Kontro M, Karjalainen R, et al. Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies. Scientific reports. 2014;4:5193. Figure: Correlation of average drug responses (n=305) between 28 AML cell lines and 28 AML ex vivo patient samples Figure:. Correlation of average drug responses (n=305) between 28 AML cell lines and 28 AML ex vivo patient samples Disclosures Heckman: Celgene: Research Funding. Porkka:BMS: Honoraria; BMS: Research Funding; Novartis: Honoraria; Novartis: Research Funding; Pfizer: Research Funding. Kallioniemi:Medisapiens: Consultancy, Membership on an entity's Board of Directors or advisory committees.

Identification Of AML Subtype-Selective Drugs By Functional Ex Vivo Drug Sensitivity and Resistance Testing and Genomic Profiling

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 482-482
Author(s):  
Tea Pemovska ◽  
Mika Kontro ◽  
Bhagwan Yadav ◽  
Henrik Edgren ◽  
Samuli Eldfors ◽  
...  
Keyword(s):  
Research Funding ◽  
Drug Response ◽  
Drug Sensitivity ◽  
Kinase Inhibitors ◽  
Ex Vivo ◽  
Resistance Testing ◽  
Clinical Use ◽  
Second Line ◽  
Flt3 Mutations ◽  
Flt3 Inhibitors

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.

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

Blood ◽  
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.

scholarly journals A Multidisciplinary Model Predicts Clinical Response in Relapsed Multiple Myeloma

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 501-501
Author(s):  
Kenneth H. Shain ◽  
Ariosto Silva ◽  
Mark B Meads ◽  
Allison Distler ◽  
Timothy Jacobson ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Partial Response ◽  
Clinical Response ◽  
Research Funding ◽  
Drug Response ◽  
Evolutionary Dynamics ◽  
Ex Vivo ◽  
Patient Specific ◽  
Tumor Reduction ◽  
The Face

Abstract The future of cancer treatment lies in personalized strategies designed to specifically recognize, target, and anticipate dynamic tumor subpopulations within an individual in response to drug. Multiple myeloma (MM) is at present an incurable malignancy of bone marrow resident plasma cells with highly variable survival as a consequence of both disease- and host-specific factors. 20% of MM patients, deemed high-risk (HRMM), have shown little benefit in the era of novel agents, with an OS of less than 2 years. Intuitive treatment strategies fail to account for the complexities and evolutionary dynamics of human tumors in the face of drugs. Intuitive treatment fails to adequately account for MM evolutionary dynamics and remains a critical barrier to successful cure or, at least, long-term disease control. Reasons for therapy failure include, but are not limited to, alternation of dominant clones with each line of therapy as a consequence of Darwinian dynamics, genomic instability leading to of tumor heterogeneity, and tumor microenvironment(TME)- mediated drug resistance. We have developed an integrated computational method accounting for phenotypic tumor heterogeneity. This novel ex vivo drug screen approach, termed EMMA (evolutionary mathematical myeloma advisor), predicts patient-specific drug response in silico from fresh bone marrow biopsies within 5 days. This method utilizes longitudinal non-destructive quantification of rate and dose responses of patient-derived MM cells to drugs in an ex vivo 3D reconstruction of the bone marrow microenvironment to provide real-time personalized predictions of treatment success (percent decrease in disease burden at 90 days). The current automated 384-well plate format allows testing of 31 different drugs or combinations against a single patient sample in 5 days. An evolutionary-based computational model uses the drug sensitivity profile obtained ex vivo to detect sub-populations and their contribution to overall clinical drug response. Each drug dose is imaged once every 30 minutes for 96h. This generates 1,920 data points per drug (or combination). From these data we characterize clonal architecture as it relates to drug sensitivity as phenotypic/functional biomarker for each drug or drug combination in each MM patient sample simultaneously. We have examined the predictive accuracy of EMMA in 26 patients to date. The Pearson correlation between ex vivo model predictions and actual tumor burden changes for the 26 patients examined generated the correlation coefficient r=0.87 (P<0.0001). Further, examination of the model predictions in terms of IMWG standards revealed that 23 out of 26 patients showed agreement between model estimation and actual clinical response (88.5% concordance). The remaining 3 patients diverged by one or two stages of response: one patient presented a very good partial response (VGPR, 98.5% reduction) while the model predicted a partial response (PR, 74.5% tumor reduction); the second patient presented a partial response (PR, 74% tumor reduction) while the model predicted a complete response (CR); and the third patient presented stable disease (SD, 12% tumor reduction) and the model predicted a minimal response (MR, 30% tumor reduction). To this end, EMMA generates patient-specific clinical response predictions to individual drugs or regimens with a high degree of clinical accuracy. Beyond testing for clinical drug response, EMMA may also be used to assess dominant cell signaling pathways. We have screened 5 patients with 25 protein kinase inhibitors (PKI) representing known signaling cascades in MM. Using heatmaps representing area under the curve (AUC) of dose-response surfaces (concentration x exposure time), we have observed both common and patient-specific sensitivities to PKIs. Together, these data demonstrate that the combination of a physiological reconstruction of the TME, a non-destructive and non-invasive cell viability assay, and mathematical models, were key to overcome the major limitations of previous predictive chemosensitivity assays. EMMA has the potential to provide precise clinical insight about treatment efficacy in a timely manner and thus become a decision support tool for oncologists based on the ever-changing clonal architecture in the face of therapy. Disclosures Baz: Karyopharm: Research Funding; Celgene Corporation: Research Funding; Millennium: Research Funding; Sanofi: Research Funding.

scholarly journals Combining Next Generation Proteomics Platforms with Drug Sensitivity Resistance Testing Allows Identification of Physiologically Distinct Sub-Clones That Can Inform Therapeutic and Drug Development Strategies

Blood ◽  
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.

scholarly journals Discovery of Novel Drug Sensitivities in T-Prolymphocytic Leukemia (T-PLL) By High-Throughput Ex Vivo Drug Testing and Genetic Profiling

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 917-917
Author(s):  
Emma I Andersson ◽  
Leopold Sellner ◽  
Malgorzata Oles ◽  
Tea Pemovska ◽  
Paavo Pietarinen ◽  
...  
Keyword(s):  
Drug Screening ◽  
Research Funding ◽  
Drug Testing ◽  
Drug Response ◽  
Drug Sensitivity ◽  
Response Pattern ◽  
Ex Vivo ◽  
Drug Responses ◽  
Ic50 Values ◽  
Stat Pathway

Abstract Introduction T-PLL is a mature post-thymic T-cell neoplasm with an aggressive clinical course (5-year overall survival 21%). Almost 75% of T-PLL cases harbor chromosome 14 translocations resulting in aberrant activation of the proto-oncogene TCL1A. Furthermore, in the majority of T-PLL cases the ATM gene is mutated or deleted, and recently it was reported that mutations in genes involved in the JAK-STAT pathway were found in 76% of T-PLL cases. Due to the rareness and aggressive nature of the disease, clinical trials are difficult to execute. By using a high-throughput ex vivo drug sensitivity and resistance testing (DSRT) platform covering 306 approved and investigational oncology drugs we systematically investigated the heterogeneity of drug responses in PLL-patients. As the impact of mutations on drug sensitivity is not well understood we aimed to identify relevant associations between the drug responses and genetic lesions in T-PLL patients. Methods Primary cells (MNCs) from seven T-PLL patients were obtained for drug screening. Samples were seeded in 384-well plates and 306 active substances were tested using a 10,000-fold concentration range resulting in a dose-response curve for each compound. Cell viability was measured after 72 h incubation and differential drug sensitivity scores (sDSS), representing leukemia-specific responses, were calculated by comparing patient samples to healthy donors. Hierarchical clustering of the drug responses was performed with Cluster 3.0 and Java Tree View. To assess the performance of the drug screening platform we also exchanged six samples with the German Cancer Research Center in Heidelberg for a comparison of results between two independent drug screening systems. To understand heterogeneous pathway dependencies, drug sensitivities were correlated with somatic genetic variants and recurrent chromosomal aberrations. Genetic characterization was performed by exome sequencing of tumor and matched healthy cells to profile known recurrent genetic variants (ATM, STAT5b, IL2RG, JAK1, JAK3) as well as CNVs (TCL1A translocations, ATM deletions, recurrent chromosomal aberrations). Results Four out of seven patient samples showed high sensitivity to small molecule BCL2 inhibitors navitoclax (IC50: 10-68nM) and ABT-199 (IC50: 14-45nM) and to HDAC inhibitors panobinostat and belinostat (IC50: 2-65nM). Intriguingly, the CDK inhibitor SNS-032 was effective in 6/7 patient samples (IC50: 7-95nM). SNS-032 inhibits Cdk2, Cdk7 and Cdk9, which control transcription of anti-apoptotic proteins including MCL1 and XIAP. As the AKT1/MTOR pathway is activated in many T-PLL patients due to expression of the TCL1A oncoprotein, it was interesting to observe that patient samples did not show any response to AKT inhibitors (MK-2206 and GDC-0068 IC50 values >1000 nM) nor to MTOR inhibitors (rapalogs temsirolimus and everolimus). Similarly, T-PLL cells were insensitive to JAK-inhibitors. Clustering of drug responses from T-PLL patients with primary AML and ALL patient samples revealed the drug response profiles to be specific for T-PLL patients (Figure). 6/7 patients clustered together while the only patient (PLL4) in our cohort with confirmed mutations in the JAK-STAT pathway genes STAT5b (P702S) and IL2RG (K315E) exhibited a non-sensitive response pattern when compared to other samples (Figure). Interestingly, exome sequencing did not reveal any JAK mutations in our PLL-cohort (n=5) nor additional STAT5b or IL2RG mutations in other patients except in this unresponsive patient. In the comparison between the platforms the correlation of the censored IC50 values from the 60 overlapping drugs was r=0.75. Similar fits of dose-response curves were seen for most drugs, although there were notable exceptions, which may be due to divergent culture conditions and day of read-out. Conclusions Ex vivo drug testing of primary patient cells has the potential to provide novel personalized drug candidates (such as BCL2, HDAC and CDK inhibitors) for T-PLL. The drug response pattern was T-PLL specific warranting further clinical testing. Drug screening, mutation analysis and RNA sequencing of additional patients is currently ongoing (n=20) to validate whether drug responses can be predicted based on the mutation profile or aberrant gene expression. Figure Clustering of T-PLL, AML and ALL patient samples based on DSRT results. Figure. Clustering of T-PLL, AML and ALL patient samples based on DSRT results. Disclosures 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. Mustjoki:Bristol-Myers Squibb: Honoraria, Research Funding; Novartis: Honoraria, Research Funding.

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 ◽  
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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