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 ◽  
...  

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.

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 3006-3006
Author(s):  
Muntasir Mamun Majumder ◽  
Raija Silvennoinen ◽  
Pekka Anttila ◽  
David Tamborero ◽  
Samuli Eldfors ◽  
...  

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.


2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi221-vi222
Author(s):  
Gerhard Jungwirth ◽  
Tao Yu ◽  
Cao Junguo ◽  
Catharina Lotsch ◽  
Andreas Unterberg ◽  
...  

Abstract Tumor-organoids (TOs) are novel, complex three-dimensional ex vivo tissue cultures that under optimal conditions accurately reflect genotype and phenotype of the original tissue with preserved cellular heterogeneity and morphology. They may serve as a new and exciting model for studying cancer biology and directing personalized therapies. The aim of our study was to establish TOs from meningioma (MGM) and to test their usability for large-scale drug screenings. We were capable of forming several hundred TO equal in size by controlled reaggregation of freshly prepared single cell suspension of MGM tissue samples. In total, standardized TOs from 60 patients were formed, including eight grade II and three grade III MGMs. TOs reaggregated within 3 days resulting in a reducted diameter by 50%. Thereafter, TO size remained stable throughout a 14 days observation period. TOs consisted of largely viable cells, whereas dead cells were predominantly found outside of the organoid. H&E stainings confirmed the successful establishment of dense tissue-like structures. Next, we assessed the suitability and reliability of TOs for a robust large-scale drug testing by employing nine highly potent compounds, derived from a drug screening performed on several MGM cell lines. First, we tested if drug responses depend on TO size. Interestingly, drug responses to these drugs remained identical independent of their sizes. Based on a sufficient representation of low abundance cell types such as T-cells and macrophages an overall number of 25.000 cells/TO was selected for further experiments revealing FDA-approved HDAC inhibitors as highly effective drugs in most of the TOs with a mean z-AUC score of -1.33. Taken together, we developed a protocol to generate standardized TO from MGM containing low abundant cell types of the tumor microenvironment in a representative manner. Robust and reliable drug responses suggest patient-derived TOs as a novel drug testing model in meningioma research.


Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 2487-2487
Author(s):  
Mika Kontro ◽  
Caroline Heckman ◽  
Evgeny Kulesskiy ◽  
Tea Pemovska ◽  
Maxim Bespalov ◽  
...  

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.


Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 288-288
Author(s):  
Caroline A Heckman ◽  
Mika Kontro ◽  
Tea Pemovska ◽  
Samuli Eldfors ◽  
Henrik Edgren ◽  
...  

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.


Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 315-315
Author(s):  
Emma I Andersson ◽  
Shady Adnan ◽  
Leopold Sellner ◽  
Dorine Bellanger ◽  
Alexandra Schrader ◽  
...  

Abstract T-cell prolymphocytic leukemia (T-PLL) is a rare disease with an aggressive clinical course and a median overall survival of less than three years. Although almost 75% of T-PLL patients are reported to harbor translocations causing the activation of the proto-oncogene TCL1A, T-PLL is genetically heterogenous: most T-PLL patients also have mutations or deletions in the ATM gene and the genes involved in the JAK-STAT pathway are mutated in 76% of cases. There is an urgent need for more rational based therapies, but clinical trials are difficult to perform due to the rareness of the disease. Here, we systematically explored the diversity of drug responses in T-PLL patient samples ex vivo using a drug sensitivity and resistance testing (DSRT) system including 306 oncology drugs (approved or investigational). We also aimed to determine any associations between the genetic aberrations and drug sensitivities in T-PLL patients. Primary mononuclear cells were gathered from 30 T-PLL patients for drug testing. Cells were plated in 384-well plates and subjected to the 306 substances using a 10,000-fold concentration range. After 72 hours, cell viabilities were measured, the results were depicted as dose-response curves for each compound, and differential drug sensitivity scores (sDSS), representing leukemia-specific responses, were computed by comparing patient samples to healthy donors. Drug response profiles across patients were clustered and visualized by hierarchical clustering. The subgroups resulting from the clustering were statistically compared using a two-sample t-test to understand which drug classes were driving the grouping. To delineate heterogeneous pathway dependencies, drug sensitivities were correlated with somatic genetic variants and recurrent chromosomal aberrations. Genetic characterization was performed by targeted amplicon sequencing of tumor cells to profile known recurrent genetic variants (STAT5b, IL2RG, JAK1, JAK3, ATM). Information on chromosomal aberrations (TCL1A translocations, ATM deletions) was derived from parallel clinicopathologic databases. Amplicon sequencing revealed that 70% of T-PLL patients (21/30) harbored a mutation in genes involved in the JAK-STAT pathway (JAK1, JAK3, STAT5b or IL2RG). The most prevalent mutation led to an M511I amino acid exchange in the JAK3 protein (26% of patients). Interestingly, the STAT5b mutations (5/30) did not coexist with any of the JAK mutations in our cohort. Based on DSRT analysis, all T-PLL samples were sensitive to the CDK-inhibitor SNS-032 and the anti-cancer antibiotic actinomycin D. Next, we clustered patients using sDSS values for all 306 different substances, and this showed that patient samples could be divided into 3 main groups, based on their drug responses (Figure). According to two-sample t-test, the grouping was driven by the selective sensitivities of Groups II and III to HDAC inhibitors (belinostat, panobinostat, quisinostat, CUDC-101 and vorinostat) and the selective sensitivity of Group III to PI3K/AKT/mTOR inhibitors (AZD-8055, MK-2206, apitolisib, dactolisib, PF-04691502, ZSTK474, and omipalisib), HSP90 inhibitors (BIIB021, luminespib, alvespimycin, and tanespimycin) as well as JAK inhibitors (ruxolitinib, momelotinib, tofacitinib, gandotinib). Group I samples were on the other hand relatively resistant to these classes of drugs. Surprisingly, despite the prevalence of the signature event of activation of TCL1 (the established AKT coactivator) in nearly all cases, only a subset of cases (group III) responded to PI3K/AKT/mTOR inhibitors. Strikingly, the grouping of selective responses to HDAC, JAK, PI3K/mTOR/Akt and HSP90 inhibitors did not link to the presence of JAK/STAT mutations, TCL1A translocations, or ATM deletion status. Ex vivo drug screening of primary T-PLL samples revealed heterogenous selective drug responses in specific drug classes (such as HDAC-, JAK-, HSP90- and PI3K/Akt/mTOR-inhibitors). Surprisingly, the drug response patterns did not correlate with known recurrent genetic aberrations suggesting that sequencing for recurrent genetic biomarkers cannot easily be turned into effective therapeutic strategies in T-PLL, and that further elucidation of the biological pathways driving T-PLL is needed. Figure 1. Mutation status and clustering of HDAC-, PI3K/mTOR/Akt-, HSP90-, and JAK-inhibitor responses in PLL patients based on sDSS values Figure 1. Mutation status and clustering of HDAC-, PI3K/mTOR/Akt-, HSP90-, and JAK-inhibitor responses in PLL patients based on sDSS values Disclosures Koschmieder: Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel reimbursement for scientific conferences, Research Funding; Novartis Foundation: Research Funding; Baxalta/CTI: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Janssen Cilag: Other: Travel reimbursement for scientific conferences ; Pfizer: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel reimbursement for scientific conferences. Wennerberg:Pfizer: Honoraria, Research Funding. Ding:Merek: Research Funding. Mustjoki:Pfizer: Honoraria, Research Funding; Bristol Myers Squibb: Honoraria, Research Funding; Novartis: Honoraria, Research Funding.


Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 1668-1668
Author(s):  
Riikka Karjalainen ◽  
Tea Pemovska ◽  
Bhagwan Yadav ◽  
Muntasir Mamun Majumder ◽  
Mika Kontro ◽  
...  

Abstract Background Ex vivo drug sensitivity testing of cancer cells taken directly from patients would significantly facilitate optimization of clinical therapies. However, in the past, such testing has been performed in suboptimal conditions, where patient cells gradually stop proliferating and undergo apoptosis, with poor translation of Results. More reliable prediction of drug sensitivity is needed and recent focus has been directed towards Methods that take into account the supporting impact of the surrounding tumor microenvironment. Primary leukemia cell viability and long-term survival ex vivo can be promoted with co-culture Methods using stromal cells (McMillin et al. 2013). While high throughput (HT) drug testing enables rapid assessment of sensitivity to 100s of drugs or drug combinations, application of co-culture Methods is challenging considering the mixed readouts from multiple cell types. In this study we describe a HT platform based on stroma-conditioned medium for assessing the anti-leukemic activity of compounds against fresh and vital biobanked primary leukemia samples ex vivo. Methods Stroma-conditioned medium (CM) was collected from the HS-5 human bone marrow (BM) cell line and combined with RPMI medium for drug sensitivity testing. Mononuclear cell medium (Promocell) was used as the standard medium comparison. Sensitivity of primary leukemia or healthy cells to 306 approved and investigational drugs was measured at 5 different concentrations covering a 10,000-fold concentration range. Cell viability was measured after 72 h with the CellTiter-Glo assay and dose response curves generated for each tested drug. Drug sensitivity scores (DSS) were calculated based on the area under the dose response curve. Here, we compared comprehensive drug sensitivity ex vivo responses between stroma-conditioned medium and standard medium using mononuclear cells from 8 acute myeloid leukemia (AML) patients and 4 healthy donors. Results HS-5 CM supported fresh and biobanked primary AML cells, promoting proliferation and overall survival. Freshly isolated AML cells had a mean viability of 123% after 3 days in CM compared to 59% in the absence of CM. The viability of biobanked cells was 85% with CM vs. 20% in conventional medium. Improved ex vivo cell survival increased the therapeutic window of drug sensitivity testing and more drugs could be assessed with CM compared to conventional medium. Results from different healthy samples tested with the same type of medium were highly similar, but sensitivities differed significantly when comparing CM to standard medium Results. In contrast, drug sensitivity Results of AML cells from different patients were more diverse, reflecting the heterogeneity of the disease. However, comparison of CM and standard medium drug sensitivities of cells from individual AML patients showed modest differences that were primarily indicative of the increased proliferation of cells incubated with CM. Overall, both AML and healthy cells showed greater sensitivity to anti-mitotic drugs when incubated with CM. For example, the average DSS of vinblastine for healthy controls was 17 in CM vs. 9 in standard medium. In addition, AML cells often exhibited increased sensitivity to JAK inhibitors such as ruxolitinib when tested with CM compared to standard medium (DSS 14 vs. 9). In contrast, stress-related protein-targeting drugs (e.g. HSP90 inhibitors) and certain tyrosine kinase inhibitors (e.g. dasatinib, quizartinib) exhibited reduced efficacy when AML cells were incubated with CM compared to conventional media. This may be due to soluble factors present in CM that mimic the protection provided by the BM niche. Conclusions Our data support the concept that conditioned medium from stromal cells improves application of drug sensitivity testing to AML patient samples ex vivo. Stromal medium supports both fresh and biobanked AML cells, likely providing environmental cues present in the BM niche and necessary for AML cell growth and survival. This may lead to more reliable ex vivo assessment of the anti-leukemic activity of compounds for cells from leukemia patients. Importantly, stromal cell based conditions support the growth of vital biobanked leukemia samples and enable use of retrospective samples for a multitude of assays including HT drug testing. Disclosures: Porkka: Novartis: Consultancy, Research Funding, Speakers Bureau; BMS: Consultancy, Research Funding, Speakers Bureau. Kallioniemi:Medisapiens: Membership on an entity’s Board of Directors or advisory committees; Roche: Research Funding.


Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 2163-2163
Author(s):  
Disha Malani ◽  
Astrid Murumägi ◽  
Bhagwan Yadav ◽  
Tea Pemovska ◽  
John Patrick Mpindi ◽  
...  

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.


2021 ◽  
Author(s):  
Sara JC Gosline ◽  
Cristina Tognon ◽  
Michael Nestor ◽  
Sunil Joshi ◽  
Rucha Modak ◽  
...  

Acute Myeloid Leukemia (AML) affects 20,000 patients in the US annually with a five-year survival rate of approximately 25%. One reason for the low survival rate is the high prevalence of clonal evolution that gives rise to heterogeneous sub-populations of leukemia. This genetic heterogeneity is difficult to treat using conventional therapies that are generally based on the detection of a single driving mutation. Thus, the use of molecular signatures, consisting of multiple functionally related transcripts or proteins, in making treatment decisions may overcome this hurdle and provide a more effective way to inform drug treatment protocols. Toward this end, the Beat AML research program prospectively collected genomic and transcriptomic data from over 1000 AML patients and carried out ex vivo drug sensitivity assays to identify signatures that could predict patient-specific drug responses. The Clinical Proteomic Tumor Analysis Consortium is in the process of extending this cohort to collect proteomic and phosphoproteomic measurements from a subset of these patient samples to evaluate the hypothesis that proteomic signatures can robustly predict drug response in AML patients. We sought to examine this hypothesis on a sub-cohort of 38 patient samples from Beat AML with proteomic and drug response data and evaluate our ability to identify proteomic signatures that predict drug response with high accuracy. For this initial analysis we built predictive models of patient drug responses across 26 drugs of interest using the proteomics and phosphproteomics data. We found that proteomics-derived signatures provide an accurate and robust signature of drug response in the AML ex vivo samples, as well as related cell lines, with better performance than those signatures derived from mutations or mRNA expression. Furthermore, we found that in specific drug-resistant cell lines, the proteins in our prognostic signatures represented dysregulated signaling pathways compared to parental cell lines, confirming the role of the proteins in the signatures in drug resistance. In conclusion, this pilot study demonstrates strong promise for proteomics-based patient stratification to predict drug sensitivity in AML.


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 ◽  
...  

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.


Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 12-13
Author(s):  
Reinaldo Dal Bello Figueiras ◽  
Justine Pasanisi ◽  
Romane Joudinaud ◽  
Matthieu Duchmann ◽  
Gaetano Sodaro ◽  
...  

Context. Functional precision medicine is gaining momentum in AML, notably through ex vivo drug sensitivity screening (DSS) of primary patient (pt) cells (Pemovska Cancer Discov 2013, Tyner Nature 2018). The DSS landscape differs across genetic AML subgroups (Tyner Nature 2016), of which NPM1mut is the most frequent (Papaemmanuil NEJM 2016). DSS in AML has mostly been done in standard conditions, with overall viability as unique endpoint. Niche signals, which can be partly mimicked in vitro, convey drug resistance in vivo. Drugs can induce a variety of cell fates in AML. Induction of differentiation rather than killing of blasts, can result in false negative results in global viability assays. Persistence of leukemic stem cells (LSC) represents a major cause of treatment failure. GPR56 is a ubiquitous surface marker enriching for LSCs and stable upon short-term ex vivo culture (Pabst Blood 2016). Objectives. To develop an ex vivo niche-like multiparametric DSS platform for primary AML cells. To validate its clinical relevance in NPM1mut pts treated with conventional DNR-AraC chemotherapy. To discover new sensitizers to DNR-AraC chemotherapy in NPM1mut AML. Results. We designed an MFC panel to count viable blasts and measure their differentiation (CD11b/CD14/CD15) and stemness (GPR56) after exclusion of residual lymphocytes (Figure 1A). We validated GPR56 expression as stemness marker based on increased retention of GPR56+ cells in niche-like coculture combining hypoxia (O2 3%) and MSC compared to standard conditions (p<0.0001, Figure 1B) and limit dilution assays of residual GPR56+ cells at 72h of niche-like culture in 3 NPM1mut AMLs (Figure 1C). Using a limited panel of 14 drugs or combinations at fixed concentrations, our MFC readout after 72h of coculture with MSC+hypoxia revealed the distinct mode of action of different agents or combinations including the differentiation activity of ATO-ATRA, the LSC-sparring cytotoxicity of DNR-AraC and the anti-LSC- activity of VEN (Figure 1D). To further mimic in vivo conditions, we derived a MEMa-based plasma-like medium (PLM) based on targeted metabolomics (Figure 1E) and electro-chemoluminescent cytokine assays of 29 diagnostic AML bone marrow plasma samples compared to conditioned media of primary AML cells cultured in niche-like conditions (MSC, hypoxia). This instructed the design of our custom PLM with dialyzed FBS and defined low-dose (~1 ng/mL range) cytokines (CK) and amino-acid (AA) concentrations. We next investigated the contribution of MSCs, hypoxia, plasma-like AAs and CKs on blasts viability, differentiation, stemness and drug response in 3 NPM1mut AMLs exposed to fixed concentrations of 6 core AML therapies. This analysis uncovered significant interactions between these 4 niche components in dictating blast viability and stemness upon 72h ex vivo culture (Figure 1F) and revealed the distinct contribution of these niche components to drug sensitivity. RNA-seq of primary blasts cultured in niche-like, plasma-like conditions revealed marked enrichment of stemness pathways compared to ex vivo culture in standard conditions. Finally, we explored DNR-AraC (five-point serial dilution) alone or in combination with fixed, clinically relevant concentrations of 24 drugs in 49 primary AML samples (including 34 NPM1mut). Using AUCs of DNR-AraC on lymphocytes as internal control, we first validated our NEXT assay on NPM1 MRD levels in the 34 NPM1mut pts treated frontline with conventional DNR-AraC regimens (Figure 1G). Across all 49 pts, we uncovered 11 different optimal 'third-drugs', stressing the role of our NEXT assay to deploy precision medicine in daily practice. At the population level, we could nominate 3 top combinations, two of which are currently in clinical investigation (Venetoclax and Selinexor). The unpublished sensitizing effect of low dose (0.25µM) Ruxolitinib on DNR-AraC uncovered with our NEXT assay is currently being investigated in PDX models. Conclusion. We designed the NEXT assay, a multiparametric drug screening of AML viability, differentiation and stemness in niche-like culture combining hypoxia, stromal interactions and plasma-like medium. Components of the niche-like culture interact to govern leukemic viability and stemness. Our assay could predict MRD achievement in NPM1mut AML and identifies novel sensitizers to DNR-AraC in these pts. Disclosures Clappier: Amgen: Honoraria, Research Funding. Ades:Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees; takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; jazz: Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Research Funding; novartis: Research Funding; Celgene/BMS: Research Funding. Itzykson:Amgen: Membership on an entity's Board of Directors or advisory committees; Otsuka Pharma: Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Honoraria, Membership on an entity's Board of Directors or advisory committees; Stemline: Membership on an entity's Board of Directors or advisory committees; Oncoethix (now Merck): Research Funding; Janssen: Research Funding; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Abbvie: Honoraria; Daiichi Sankyo: Honoraria; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS (Celgene): Honoraria; Sanofi: Honoraria; Astellas: Honoraria.


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