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

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

Blood ◽

10.1182/blood.v118.21.2487.2487 ◽

2011 ◽

Vol 118 (21) ◽

pp. 2487-2487

Author(s):

Mika Kontro ◽

Caroline Heckman ◽

Evgeny Kulesskiy ◽

Tea Pemovska ◽

Maxim Bespalov ◽

...

Keyword(s):

Bone Marrow ◽

Research Funding ◽

Drug Response ◽

Drug Sensitivity ◽

Ex Vivo ◽

Molecular Profiling ◽

Resistance Testing ◽

Patient Specific ◽

Targeted Drugs ◽

Response Profiles

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

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

Blood ◽

10.1182/blood.v120.21.288.288 ◽

2012 ◽

Vol 120 (21) ◽

pp. 288-288

Author(s):

Caroline A Heckman ◽

Mika Kontro ◽

Tea Pemovska ◽

Samuli Eldfors ◽

Henrik Edgren ◽

...

Keyword(s):

Bone Marrow ◽

Transcriptome Sequencing ◽

Research Funding ◽

Drug Response ◽

Drug Sensitivity ◽

Ex Vivo ◽

Molecular Profiling ◽

Resistance Testing ◽

Response Patterns ◽

Serial Samples

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

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A bispecific antibody enhances cytokine-induced killer-mediated cytolysis of autologous acute myeloid leukemia cells

Blood ◽

10.1182/blood.v81.5.1333.1333 ◽

1993 ◽

Vol 81 (5) ◽

pp. 1333-1341 ◽

Cited By ~ 20

Author(s):

T Kaneko ◽

Y Fusauchi ◽

Y Kakui ◽

M Masuda ◽

M Akahoshi ◽

...

Keyword(s):

Bone Marrow ◽

Acute Myeloid Leukemia ◽

Myeloid Leukemia ◽

Bispecific Antibody ◽

Mononuclear Cells ◽

Ex Vivo ◽

Autologous Bone Marrow Transplantation ◽

Interleukin 2 ◽

Colony Assay ◽

Acute Myeloid

Abstract An anti-CD3 Fab' x anti-CD13 Fab' bispecific antibody (BsAb) was generated. This BsAb reacted with both CD3+ T cells and CD13+ acute myeloid leukemia (AML) cells. We investigated whether cytokine- stimulated peripheral blood mononuclear cells (PBMC) could lyse patient AML cells after addition of the BsAb. When interleukin-2 (IL-2)- stimulated PBMC were assayed for their cytotoxicity against 51Cr- labeled allogeneic and autologous CD13+ AML cells, their activity was markedly enhanced by the addition of the BsAb. PBMC stimulated with IL- 2 plus anti-CD3 monoclonal antibody (MoAb) showed higher proliferative ability and higher cytotoxicity if this was expressed as lytic units per culture. IL-7-stimulated PBMC also exhibited enhanced cytotoxicity against CD13+ AML cells after addition of the BsAb. Ultrastructurally, CD13+ AML cells incubated with IL-2 plus anti-CD3 MoAb-stimulated PBMC and the BsAb showed apoptotic morphologic changes. A colony assay for AML blast progenitors showed that the colony formation of CD13+ AML cells was inhibited by the addition of autologous IL-2 plus anti-CD3 MoAb-stimulated PBMC, and that this inhibition was further enhanced by the addition of the BsAb. A colony assay for normal bone marrow progenitor cells showed that the addition of autologous IL-2 plus anti- CD3 MoAb-stimulated PBMC and the BsAb inhibited the formation of granulocyte-macrophage colonies and mixed-cell colonies. However, the degree of inhibition was smaller than that for the AML blast colonies. Taken together, these findings suggest that this BsAb may be useful for ex vivo purging of CD13+ AML cells in autologous bone marrow transplantation.

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

Blood ◽

10.1182/blood.v124.21.2163.2163 ◽

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.

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Identification of Effective Combinations of Targeted Drugs for Hematologic Malignancies

Blood ◽

10.1182/blood.v128.22.836.836 ◽

2016 ◽

Vol 128 (22) ◽

pp. 836-836 ◽

Cited By ~ 1

Author(s):

Stephen E Kurtz ◽

Christopher A. Eide ◽

Andy Kaempf ◽

Vishesh Khanna ◽

Samantha L. Savage ◽

...

Keyword(s):

Histone Deacetylase ◽

Myeloid Leukemia ◽

Research Funding ◽

Kinase Inhibitors ◽

Kinase Inhibitor ◽

Ex Vivo ◽

Single Agent ◽

Hematologic Malignancies ◽

Targeted Drugs ◽

Equity Ownership

Abstract Introduction: Translating the genetic and epigenetic heterogeneity underlying human cancers into therapeutic strategies represents an ongoing challenge. Large-scale sequencing efforts have identified that many hematologic malignancies, such as acute myeloid leukemia (AML), are driven by a spectrum of mutations and may require combinations of targeted agents to be treated effectively. In addition, the emergence of genetically heterogeneous subclones leading to relapse, rescue signals in the microenvironment, and tumor-intrinsic feedback pathways further necessitate combinatorial therapies. To identify combinations of targeted drugs for AML and other hematologic malignancies, we performed ex vivo profiling of pairs of small-molecule inhibitors for sensitivity against primary patient samples. Methods: Freshly isolated primary mononuclear cells from patients (n=122) with various hematologic malignancies (AML n=58, CLL n=42, ALL n=12, and MPN or MDS/MPN n=10) were cultured in the presence of a panel of 48 drug combinations in equimolar dose series encompassing different classes of compounds, including kinase inhibitors, bromodomain inhibitors, BH3 mimetics, and histone deacetylase inhibitors. For comparison, cells were also tested against graded concentrations of each inhibitor alone, and sensitivity was assessed by MTS-based viability assay. IC50 and AUC values were derived using a probit regression model. Efficacy of each combination relative to its single agents was calculated as a Combination Ratio (CR) value, defined as the combination IC50 or AUC divided by the lowest single agent IC50 or AUC value. A CR value < 1 indicates the combination is more effective relative to the single agent. Associated clinical characteristics were obtained where possible. For the 2 largest diagnostic groups, AML and CLL, expanded panels of clinical, prognostic, mutational, cytogenetic, and surface antigen data were compiled for comparisons according to CR values for each combination. Results: Unsupervised hierarchical clustering of CR values revealed several distinct clusters (Figure 1). Myeloid leukemia patient samples were enriched within a cluster of sensitivity to combinations pairing the Bcl-2 inhibitor venetoclax with select tyrosine kinase inhibitors (dasatinib, doramapimod, sorafenib, or idelalisib). A subset of samples within this cluster showed sensitivity to combinations involving the MEK inhibitor trametinib and a second kinase inhibitor (idelalisib, palbociclib, or quizartinib). In contrast, a discrete subcluster of predominantly lymphoid leukemia patients showed sensitivity to combinations of the histone deacetylase inhibitor panobinostat in tandem with either the JAK inhibitor ruxolitinib or the multi-kinase inhibitor sorafenib. Importantly, apart from venetoclax which as a single agent demonstrated potent and selective efficacy in CLL patient samples, the single agent efficacies do not align selectively to a combination efficacy-derived cluster (Figure 1). Comparison of CR values within each of the 4 diagnostic groups revealed partial overlap in statistically significant effective combinations, while also highlighting unique sensitivities by group, such as idelalisib-quizartinib for AML and ibrutinib-quizartinib for CLL. Further relevant clinical and genetic features were compared within each of the 2 largest groups, AML and CLL. Among AML samples, patients harboring mutations in NPM1 or DNMT3A demonstrated significant sensitivity to combinations of JQ1 and sorafenib (median CR: 0.357) or JQ1 and palbociclib (median CR: 0.119), respectively. AML patients featuring surface expression of CD11b (Integrin aM) or CD58 (LFA-3) were sensitive to combinations of venetoclax and JQ1 or venetoclax and doramapimod, respectively. Among CLL samples, patients harboring deletion of 13q showed significant sensitivity to combinations of palbociclib with either venetoclax or trametinib (median CR: 0.267 and 0.116, respectively). Conclusions: The data reveal multiple specific patterns of ex vivo drug combination efficacy beyond that of either single agent, which are associated with select, actionable diagnostic and genetic subsets, warranting their evaluation in the clinic. These findings highlight the heuristic value of an integrated approach for identifying novel treatment strategies for improved disease control and patient outcomes. Figure 1 Figure 1. Disclosures Druker: Agios: Honoraria; Ambit BioSciences: Consultancy; ARIAD: Patents & Royalties, Research Funding; Array: Patents & Royalties; AstraZeneca: Consultancy; Blueprint Medicines: Consultancy, Equity Ownership, Other: travel, accommodations, expenses ; BMS: Research Funding; CTI: Equity Ownership; Curis: Patents & Royalties; Cylene: Consultancy, Equity Ownership; D3 Oncology Solutions: Consultancy; Gilead Sciences: Consultancy, Other: travel, accommodations, expenses ; Lorus: Consultancy, Equity Ownership; MolecularMD: Consultancy, Equity Ownership, Patents & Royalties; Novartis: Research Funding; Oncotide Pharmaceuticals: Research Funding; Pfizer: Patents & Royalties; Roche: Consultancy. Tyner:Constellation Pharmaceuticals: Research Funding; Agios Pharmaceuticals: Research Funding; Takeda Pharmaceuticals: Research Funding; Inctye: Research Funding; Genentech: Research Funding; Aptose Biosciences: Research Funding; Seattle Genetics: Research Funding; Array Biopharma: Research Funding; AstraZeneca: Research Funding; Leap Oncology: Consultancy; Janssen Research & Development: Research Funding.

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

Blood ◽

10.1182/blood-2018-99-115678 ◽

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.

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The Pro-Inflammatory Cytokine Interleukin-1 Is Essential for Clonal Expansion and Disease Progression in Acute Myeloid Leukemia

Blood ◽

10.1182/blood.v126.23.866.866 ◽

2015 ◽

Vol 126 (23) ◽

pp. 866-866

Author(s):

Alyssa Carey ◽

Megan M Cleary ◽

David K Edwards ◽

Christopher A. Eide ◽

Elie Traer ◽

...

Keyword(s):

Bone Marrow ◽

Disease Progression ◽

Board Of Directors ◽

Myeloid Leukemia ◽

Research Funding ◽

Ex Vivo ◽

Clonal Expansion ◽

Advisory Committees ◽

Clonogenic Potential ◽

Equity Ownership

Abstract Background: Recent 'omics'-based approaches have revealed that acute myeloid leukemia (AML) has significant genetic heterogeneity, and that clonal evolution in AML occurs through stepwise acquisition of somatic mutations. However, extrinsic factors may also influence this process. Here we provide strong evidence that inflammatory cytokines secreted in the AML microenvironment play a critical role in clonal expansion and disease progression. Specifically, we show that the proinflammatory cytokine IL-1 promotes in vitro clonal expansion in a large subset of primary AML samples and in vivo disease progression in a murine AML model. Methods: We used several functional assays to determine which extrinsic cytokine-activated pathways are essential for AML cell survival. We analyzed 90 primary AML samples ex vivo in the absence and presence of dose gradients of 98 distinct cytokines. We used stromal cells conditioned media and a recombinant cytokine cocktail (IL-3, IL-6, SCF, GCSF, FLT3L) for positive controls. The distributions for the positive and negative control allowed us to derive rigorous empirical thresholds to define cytokine dependence for individual samples. We also performed siRNA screens targeting 188 cytokine and growth factor receptors, and measured 30 different cytokines secreted in the plasma of peripheral blood and bone marrow samples using a multiplex Luminex assay. Functionally relevant pathways were validated using shRNA and genetic mouse models. Results: Several of the 98 cytokines promoted the growth of primary AML cells, including IL-1, GM-CSF, G-CSF, TPO, CXCL-1, IL-3, M-CSF, MCPs, TNF-α, and BMP-4. Many of these are known to induce an inflammatory response and cluster with the growth response to IL-1, a master mediator of innate immunity and inflammation. IL-1α and IL-1β had the most profound effect on the clonal expansion of myeloid progenitors, leading to 3- to 20-fold growth increase in 70% (63/90) of primary AML samples. Consistent with our findings from the cytokine screen, treatment of CD34+ AML cells with IL-1 led to increases in cell growth, survival, and clonogenic potential. Paradoxically, IL-1 suppressed both growth and colony formation in normal CD34+ cells. In support of these ex vivo findings, IL-1β was overexpressed in IL-1-sensitive AML bone marrow and peripheral blood samples compared to non-sensitive AML samples and normal samples. Intracellular FACS showed that 80% of the total IL-1β is secreted by monocytes, and to some extent by myeloid progenitors and stromal cells, but not by B or T cells in the AML bone marrow microenvironment. Consistent with this, most of the IL-1-sensitive AML samples exhibited monocytic and myelomonocytic features. These results suggest that IL-1 secreted in the bone marrow microenvironment regulates AML cell growth in a paracrine manner. Silencing of the IL-1 receptor, IL1R1, reduced the viability of AML primary samples by 60-80% and led to a significant ablation of clonogenic potential (80% reduction) of oncogene-induced leukemic cells (AML1-ETO9a, NRASG12D and MLL-ENL) in mouse bone marrow. In a murine bone marrow transplantation model, recipients of IL1R1-/- marrow transduced with AML1-ETO9a/NRASG12D survived significantly longer (39 days; range: 28-118) than did recipients of wild-type marrow (30 days; range: 27-61; p=0.012). Mechanistically, IL-1β regulates phosphorylation of p38MAPK, a downstream component of the IL-1 pathway, in AML progenitors and IL-1-sensitive AML samples exhibit more p38 phosphorylation as compared to normal cells. Conversely, knocking down IL1R1 or treating AML cells with p38MAPK inhibitors such as doramapimod reduced the growth of AML cells by decreasing p38MAPK phosphorylation. Conclusion: These results demonstrate a novel role for IL-1 and its receptor in promoting clonal growth and disease progression in a large subset of AML patients. Our findings suggest that AML patients may benefit from drugs targeting IL-1/IL1R1/p38MAPK signaling because of their potential to enhance normal hematopoiesis while inhibiting AML -- a significant clinical advantage over traditional chemotherapy, which kills both normal and leukemic cells.Since IL-1 and its receptors are not mutated in these patients, our data also highlight the importance of ex vivo functional screening for microenvironmental stimuli for the identification of novel therapeutic targets. Disclosures Tyner: Constellation Pharmaceuticals: Research Funding; Array Biopharma: Research Funding; Aptose Biosciences: Research Funding; Incyte: Research Funding; Janssen Pharmaceuticals: Research Funding. Druker:Cylene Pharmaceuticals: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; CTI Biosciences: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Blueprint Medicines: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Novartis Pharmaceuticals: Research Funding; Roche TCRC, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Aptose Therapeutics, Inc (formerly Lorus): Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Research Funding; Millipore: Patents & Royalties; McGraw Hill: Patents & Royalties; Henry Stewart Talks: Patents & Royalties; Fred Hutchinson Cancer Research Center: Research Funding; MolecularMD: Consultancy, Equity Ownership, 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; Sage Bionetworks: Research Funding; ARIAD: Research Funding; AstraZeneca: Consultancy; Gilead Sciences: Consultancy, Membership on an entity's Board of Directors or advisory committees; Oregon Health & Science University: Patents & Royalties; Oncotide Pharmaceuticals: Research Funding. Agarwal:CTI BioPharma: Research Funding.

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

Blood ◽

10.1182/blood.v124.21.2046.2046 ◽

2014 ◽

Vol 124 (21) ◽

pp. 2046-2046

Author(s):

Muntasir Mamun Majumder ◽

Raija Silvennoinen ◽

Pekka Antilla ◽

David Tamborero ◽

Samuli Eldfors ◽

...

Keyword(s):

Signaling Pathways ◽

Research Funding ◽

Drug Response ◽

Drug Sensitivity ◽

Ex Vivo ◽

Oncogenic Signaling ◽

Group I ◽

Signal Transduction Inhibitors ◽

Response Profiles ◽

Response Groups

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

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A Multidisciplinary Model Predicts Clinical Response in Relapsed Multiple Myeloma

Blood ◽

10.1182/blood.v126.23.501.501 ◽

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.

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A bispecific antibody enhances cytokine-induced killer-mediated cytolysis of autologous acute myeloid leukemia cells

Blood ◽

10.1182/blood.v81.5.1333.bloodjournal8151333 ◽

1993 ◽

Vol 81 (5) ◽

pp. 1333-1341 ◽

Cited By ~ 1

Author(s):

T Kaneko ◽

Y Fusauchi ◽

Y Kakui ◽

M Masuda ◽

M Akahoshi ◽

...

Keyword(s):

Bone Marrow ◽

Acute Myeloid Leukemia ◽

Myeloid Leukemia ◽

Bispecific Antibody ◽

Mononuclear Cells ◽

Ex Vivo ◽

Autologous Bone Marrow Transplantation ◽

Interleukin 2 ◽

Colony Assay ◽

Acute Myeloid

An anti-CD3 Fab' x anti-CD13 Fab' bispecific antibody (BsAb) was generated. This BsAb reacted with both CD3+ T cells and CD13+ acute myeloid leukemia (AML) cells. We investigated whether cytokine- stimulated peripheral blood mononuclear cells (PBMC) could lyse patient AML cells after addition of the BsAb. When interleukin-2 (IL-2)- stimulated PBMC were assayed for their cytotoxicity against 51Cr- labeled allogeneic and autologous CD13+ AML cells, their activity was markedly enhanced by the addition of the BsAb. PBMC stimulated with IL- 2 plus anti-CD3 monoclonal antibody (MoAb) showed higher proliferative ability and higher cytotoxicity if this was expressed as lytic units per culture. IL-7-stimulated PBMC also exhibited enhanced cytotoxicity against CD13+ AML cells after addition of the BsAb. Ultrastructurally, CD13+ AML cells incubated with IL-2 plus anti-CD3 MoAb-stimulated PBMC and the BsAb showed apoptotic morphologic changes. A colony assay for AML blast progenitors showed that the colony formation of CD13+ AML cells was inhibited by the addition of autologous IL-2 plus anti-CD3 MoAb-stimulated PBMC, and that this inhibition was further enhanced by the addition of the BsAb. A colony assay for normal bone marrow progenitor cells showed that the addition of autologous IL-2 plus anti- CD3 MoAb-stimulated PBMC and the BsAb inhibited the formation of granulocyte-macrophage colonies and mixed-cell colonies. However, the degree of inhibition was smaller than that for the AML blast colonies. Taken together, these findings suggest that this BsAb may be useful for ex vivo purging of CD13+ AML cells in autologous bone marrow transplantation.

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