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

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

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

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

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 2487-2487
Author(s):  
Mika Kontro ◽  
Caroline Heckman ◽  
Evgeny Kulesskiy ◽  
Tea Pemovska ◽  
Maxim Bespalov ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Research Funding ◽  
Drug Response ◽  
Drug Sensitivity ◽  
Ex Vivo ◽  
Molecular Profiling ◽  
Resistance Testing ◽  
Patient Specific ◽  
Targeted Drugs ◽  
Response Profiles

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

A Profound Biological Difference of Chronic and Blast Phase Chronic Myeloid Leukemia in Ex Vivo Drug Responses

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 3139-3139
Author(s):  
Paavo Pietarinen ◽  
Tea Pemovska ◽  
Emma I Andersson ◽  
Perttu Koskenvesa ◽  
Mika Kontro ◽  
...  
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Myeloid Leukemia ◽  
Research Funding ◽  
Drug Sensitivity ◽  
Ex Vivo ◽  
Primary Cells ◽  
Newly Diagnosed ◽  
Biological Difference ◽  
Resistant Patient

Abstract BACKGROUND Most patients with chronic phase (CP) chronic myeloid leukemia (CML) are successfully treated with tyrosine kinase inhibitors (TKIs) targeting ABL1. Despite the good results, TKI treatment rarely results in cure, and some patients relapse and progress to advanced phases of CML. Accelerated phase and blast crisis (BC) have remained a therapy challenge. We set out to identify novel candidate drugs for chronic and advanced phase CML by using an unbiased high-throughput drug testing platform and utilizing both primary patient cells (CP and BC) and cell lines. METHODS CML BC cell lines used: K562 (erythroleukemic), MOLM-1 (megakaryocytic) and EM-2 (myeloid). Primary bone marrow (BM) and peripheral blood (PB) samples were derived from 3 CML patients with BC, two of which were TKI-resistant. Patient 1 had developed resistance to imatinib and nilotinib due to an E274K mutation in ABL1 kinase domain, whereas patient 2 was resistant to imatinib, nilotinib, and dasatinib due to a T315I mutation. In addition to BC patients, samples from 23 newly diagnosed CML CP patients were screened. BM cells from 4 healthy individuals were used as controls. Functional profiling of drug responses was performed with a high-throughput drug sensitivity and resistance testing (DSRT) platform comprising 306 anti-cancer agents. Cells were dispensed to pre-drugged 384-well plates and incubated for 72 h. Cell viability was measured using a luminescence cell viability assay (CellTiter-Glo, Promega). A Drug Sensitivity Score (DSS) was calculated for each drug using normalized dose response curve values. The drug sensitivities of the primary cells were further normalized against the median values from healthy controls, resulting in leukemia-specific sensitivity scores (sDSS). RESULTS Drug sensitivities of CML cell lines correlated closely (EM-2 vs. K-562, rS=0.89; EM-2 vs. MOLM-1, rS=0.82; K-562 vs. MOLM-1, rS=0.78; p<0.0001 for all correlations). Similarly, patient samples had good correlation with cell line samples (rS=0.82 based on median values; p<0.0001). The cell lines were highly sensitive to ABL1-targeted TKIs, with the exception of the MOLM-1, which showed only modest sensitivity (Figure). The clinically TKI-resistant patient samples were also resistant to BCR-ABL1 inhibitors ex vivo (e.g. T315I sensitive only to ponatinib), further validating the DSRT assay data. Other drugs that exhibited high DSS in the CML cell lines and high sDSS in the BC patient samples included mTORC1/2 inhibitors (e.g. AZD8055, AZD2014, INK128), HSP90 inhibitors (e.g. NVP-AUY922, BIIB021) and a NAMPT inhibitor daporinad. Remarkably, the DSRT results from newly diagnosed CML CP differed clearly from those derived from the cell line and CML BC samples. In the clustering analysis, CML BC and cell line samples clustered together, whereas CML CP samples formed a separate group (Figure). The leukemia-specific scores were generally much lower in CML CP samples, which made identifying novel candidate compounds challenging. Most surprisingly the responses to TKIs were practically nonexistent in CML CP samples. CP TKI insensitivity was further assessed with primary cells sorted in CD34pos and CD34neg fractions. Preliminary results from two patients suggested that CD34pos cells were more sensitive to TKIs when compared to CD34neg or whole mononuclear fraction. CONCLUSIONS DSRT is a powerful platform for identifying novel candidate molecules for CML BC patients. Our results indicate that mTORC1/2 inhibitors (such as AZD8055, or AZD2014), HSP90 (such as NVP-AUY922/luminespib) and NAMPT inhibitors in particular warrant further clinical evaluation. TKI-insensitivity of CML CP samples suggests that the survival of mature myeloid cells in vitro is not BCR-ABL1 dependent and reflects a clear biological difference between CP and BC patient cells. Figure 1 Figure 1. Disclosures Kallioniemi: Medisapiens: Consultancy, Membership on an entity's Board of Directors or advisory committees. Mustjoki:Bristol-Myers Squibb: Honoraria, Research Funding; Novartis: Honoraria, Research Funding. Porkka:Bristol-Myers Squibb: Honoraria, Research Funding; Novartis: Honoraria, Research Funding.

scholarly journals Predicting tumor response to drugs based on gene-expression biomarkers of sensitivity learned from cancer cell lines

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yuanyuan Li ◽  
David M. Umbach ◽  
Juno M. Krahn ◽  
Igor Shats ◽  
Xiaoling Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Cancer Cell ◽  
Predictive Models ◽  
Drug Sensitivity ◽  
Human Cancer ◽  
Cancer Cell Lines ◽  
The Cancer Genome Atlas ◽  
Differential Sensitivity ◽  
Tumor Type

Abstract Background Human cancer cell line profiling and drug sensitivity studies provide valuable information about the therapeutic potential of drugs and their possible mechanisms of action. The goal of those studies is to translate the findings from in vitro studies of cancer cell lines into in vivo therapeutic relevance and, eventually, patients’ care. Tremendous progress has been made. Results In this work, we built predictive models for 453 drugs using data on gene expression and drug sensitivity (IC50) from cancer cell lines. We identified many known drug-gene interactions and uncovered several potentially novel drug-gene associations. Importantly, we further applied these predictive models to ~ 17,000 bulk RNA-seq samples from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database to predict drug sensitivity for both normal and tumor tissues. We created a web site for users to visualize and download our predicted data (https://manticore.niehs.nih.gov/cancerRxTissue). Using trametinib as an example, we showed that our approach can faithfully recapitulate the known tumor specificity of the drug. Conclusions We demonstrated that our approach can predict drugs that 1) are tumor-type specific; 2) elicit higher sensitivity from tumor compared to corresponding normal tissue; 3) elicit differential sensitivity across breast cancer subtypes. If validated, our prediction could have relevance for preclinical drug testing and in phase I clinical design.

A pan-cancer analysis of progression mechanisms and drug sensitivity in cancer cell lines

2019 ◽  
Vol 15 (6) ◽  
pp. 399-405 ◽  
Author(s):  
Julia L. Fleck ◽  
Ana B. Pavel ◽  
Christos G. Cassandras
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Late Stage ◽  
Drug Sensitivity ◽  
Cancer Cell Lines ◽  
Resistance Mechanisms ◽  
General Effect ◽  
Tumor Types ◽  
Pan Cancer ◽  
Genetic Events

Sequences of genetic events were identified that may help explain common patterns of oncogenesis across 22 tumor types. The general effect of late-stage mutations on drug sensitivity and resistance mechanisms in cancer cell lines was evaluated.

Further Preclinical Studies with APR-246, a Novel Anticancer Compound Currently in Clinical Trials for Hematological Malignancies and Prostate Cancer.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 3773-3773
Author(s):  
Nina Mohell ◽  
Charlotta Liljebris ◽  
Jessica Alfredsson ◽  
Ylva Lindman ◽  
Maria Uustalu ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Cancer Cells ◽  
Cell Lines ◽  
Cancer Cell ◽  
Ex Vivo ◽  
Cancer Cell Lines ◽  
Solid Cancer ◽  
Dependent Manner

Abstract Abstract 3773 Poster Board III-709 Introduction The tumor suppressor protein p53 induces cell cycle arrest and/or apoptosis in response to various forms of cellular stress, through transcriptional regulation of a large number of down stream target genes. p53 is frequently mutated in cancer, and cancer cells carrying defects in the p53 protein are often more resistant to conventional chemotherapy. Thus, restoration of the wild type function to mutant p53 appears to be a new attractive strategy for cancer therapy. APR-246 is a novel small molecule quinuclidinone compound that has been shown to reactivate non-functional p53 and induce apoptosis. Although the exact molecular mechanism remains to be determined, recent results suggest that an active metabolite of APR-246 alkylates thiol groups in the core domain of p53, which promotes correct folding of p53 and induces apoptosis (Lambert et al., Cancer Cell 15, 2009). Currently, APR-246 is in Phase I/IIa clinical trials for hematological malignancies and prostate cancer. In the present abstract results from in vitro, ex vivo and in vivo preclinical studies with APR-246 are presented. Results The lead compound of APR-246, PRIMA-1 (p53 reactivation and induction of massive apoptosis), was originally identified by a cellular screening of the NCI library for low molecular weight compounds (Bykov et al., Nat. Med., 8, 2002). Further development and optimization of PRIMA-1 led to the discovery of the structural analog APR-246 (PRIMA-1MET), with improved drug like and preclinical characteristics. In in vitro experiments APR-246 reduced cell viability (WST-1 assay) in a large number of human cancer cell lines with various p53 status, including several leukemia (CCRF-CEM, CEM/VM-1, KBM3), lymphoma (U-937 GTP, U-937-vcr), and myeloma (RPMI 8226/S, 8226/dox40, 8226/LR5) cell lines, as well as many solid cancer cell lines, including osteosarcoma (SaOS-2, SaOS-2-His273,U-2OS), prostate (PC3, PC3-His175, 22Rv1), breast (BT474, MCF-7, MDA-MB-231), lung (H1299, H1299-His175) and colon cancer (HT-29). In human osteosarcoma cell lines APR-246 reduced cell viability and induced apoptosis (FLICA caspase assay) in a concentration dependent manner being more potent in the p53 mutant (SaOS-2-His273) than in the parental p53 null (SaOS-2) cells. The IC50 values (WST-1 assay) were 14 ± 3 and 27 ± 5 μM, respectively (n=35). In in vivo subcutaneous xenograft studies in SCID (severe combined immunodeficiency) mice APR-246 reduced growth of p53 mutant SaOS-2-His273 cells in a dose-dependent manner, when injected i.v. twice daily with 20 -100 mg/kg (64 – 76% inhibition). An in vivo anticancer effect of APR-246 was also observed in hollow-fiber test with NMRI mice using the acute myeloid leukemia (AML) cell line MV-4-11. An ex vivo cytotoxic effect of APR-246 and/or its lead compound PRIMA-1 has also been shown in primary cells from AML and CLL (chronic lymphocytic leukemia) patients, harbouring both hemizygously deleted p53 as well as normal karyotype (Nahi et al., Br. J. Haematol., 127, 2004; Nahi et al., Br. J. Haematol., 132, 2005; Jonsson-Videsater et al., abstract at this meeting). APR-246 was also tested in a FMCA (fluorometric microculture assay) test using normal healthy lymphocytes (PBMC) and cancer lymphocytes (CLL). It was 4-8 fold more potent in killing cancer cells than normal cells, indicating a favorable therapeutic index. This is in contrast to conventional cytostatics that often show negative ratio in this test. Furthermore, when tested in a well-defined panel of 10 human cancer cell lines consisting of both hematological and solid cancer cell lines, the cytotoxicity profile/activity pattern of APR-246 differed from common chemotherapeutic drugs (correlation coefficient less than 0.4), suggesting a different mechanism of action. Conclusion In relevant in vitro, in vivo and ex vivo cancer models, APR-246 showed unique pharmacological properties in comparison with conventional cytostatics, by being effective also in cancer cells with p53 mutations and by demonstrating tumor specificity. Moreover, in experimental safety/toxicology models required to start clinical trials, APR-246 was non toxic at the predicted therapeutic plasma concentrations. Thus, APR-246 appears to be a promising novel anticancer compound that may specifically target cancer cells in patients with genetic abnormality associated with poor prognosis. Disclosures: Mohell: Aprea AB: Employment. Liljebris:Aprea AB: Employment. Alfredsson:Aprea AB: Employment. Lindman:Aprea AB: Employment. Uustalu:Aprea AB: Employment. Wiman:Aprea AB: Co-founder, shareholder, and member of the board. Uhlin:Aprea AB: Employment.

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

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 288-288
Author(s):  
Caroline A Heckman ◽  
Mika Kontro ◽  
Tea Pemovska ◽  
Samuli Eldfors ◽  
Henrik Edgren ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Transcriptome Sequencing ◽  
Research Funding ◽  
Drug Response ◽  
Drug Sensitivity ◽  
Ex Vivo ◽  
Molecular Profiling ◽  
Resistance Testing ◽  
Response Patterns ◽  
Serial Samples

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

MicroRNAs related to intrinsic resistance to oxaliplatin and the irinotecan metabolite SN-38 in 10 colorectal cancer cell lines.

2012 ◽  
Vol 30 (4_suppl) ◽  
pp. 524-524 ◽  
Author(s):  
Niels Frank Jensen ◽  
Rolf Soekilde ◽  
Jan Stenvang ◽  
Birgitte Sander Nielsen ◽  
Thomas Litman ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cell Lines ◽  
Cancer Cell ◽  
Drug Sensitivity ◽  
Predictive Biomarkers ◽  
Cancer Cell Lines ◽  
Colorectal Cancer Cell ◽  
Intrinsic Resistance ◽  
Short Term ◽  
Colorectal Cancer Cell Lines

524 Background: Chemotherapy of metastatic colorectal cancer is based on 5-flourouracil combined with either oxaliplatin or irinotecan (active metabolite: SN-38). Identification of predictive biomarkers of drug response is needed to provide a better personalized treatment. In this study we aimed to identify microRNAs related to intrinsic resistance to oxaliplatin or irinotecan in a panel of ten colorectal cancer cell lines. Methods: Drug sensitivity towards oxaliplatin and SN-38 was determined for ten colorectal cancer cell lines (Colo-205, DLD-1, HCC-2998, HCT-15, HCT-116, HT-29, KM12, LoVo, LS-174T, and SW620), using the cell viability MTT assay and the cell death LDH assay. In addition, two cell lines (DLD-1 and LoVo) were exposed to the drugs for 6, 24 or 48 hours. MicroRNA expression profiles were generated using the Exiqon miRCURY LNA microarray platform (including 840 microRNAs), and four differentially expressed microRNAs were validated by independent qRT-PCR measurements. Results: The drug sensitivities of the ten colorectal cancer cell lines varied about 50 times between the least and most sensitive cell lines. Correlation of drug sensitivity data to microRNA expression data across the ten cell lines yielded about 25 microRNA biomarker candidates, for each of the drug/assay combinations. Following short-term drug treatment 10-20 microRNAs were altered for each drug/cell line combination. Validation by qRT-PCR showed a very good correlation to the microarray data. MicroRNAs identified by correlation to drug sensitivity and by short-term treatment were compared, and less than 10% were identified by both approaches, perhaps representing the most promising candidates. These candidates are for SN-38 miR-15a, miR-22, miR-24, miR-98, miR-142-3p, miR-1290, and let-7b, and for oxaliplatin miR-23b, miR-27a, miR-192, miR-200a, miR-222, miR-886-5p, and miR-1308. Conclusions: In the present study we identified a number of microRNAs that are potentially involved in intrinsic resistance and/or could be predictive biomarkers for either irinotecan or oxaliplatin.

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