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Murumägi A, Ungureanu D, Khan S, Arjama M, Välimäki K, Ianevski A, Ianevski P, Bergström R, Dini A, Kanerva A, Koivisto-Korander R, Tapper J, Lassus H, Loukovaara M, Mägi A, Hirasawa A, Aoki D, Pietiäinen V, Pellinen T, Bützow R, Aittokallio T, Kallioniemi O. Drug response profiles in patient-derived cancer cells across histological subtypes of ovarian cancer: real-time therapy tailoring for a patient with low-grade serous carcinoma. Br J Cancer 2023; 128:678-690. [PMID: 36476658 PMCID: PMC9938120 DOI: 10.1038/s41416-022-02067-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 11/09/2022] [Accepted: 11/11/2022] [Indexed: 12/12/2022] Open
Abstract
Many efforts are underway to develop novel therapies against the aggressive high-grade serous ovarian cancers (HGSOCs), while our understanding of treatment options for low-grade (LGSOC) or mucinous (MUCOC) of ovarian malignancies is not developing as well. We describe here a functional precision oncology (fPO) strategy in epithelial ovarian cancers (EOC), which involves high-throughput drug testing of patient-derived ovarian cancer cells (PDCs) with a library of 526 oncology drugs, combined with genomic and transcriptomic profiling. HGSOC, LGSOC and MUCOC PDCs had statistically different overall drug response profiles, with LGSOCs responding better to targeted inhibitors than HGSOCs. We identified several subtype-specific drug responses, such as LGSOC PDCs showing high sensitivity to MDM2, ERBB2/EGFR inhibitors, MUCOC PDCs to MEK inhibitors, whereas HGSOCs showed strongest effects with CHK1 inhibitors and SMAC mimetics. We also explored several drug combinations and found that the dual inhibition of MEK and SHP2 was synergistic in MAPK-driven EOCs. We describe a clinical case study, where real-time fPO analysis of samples from a patient with metastatic, chemorefractory LGSOC with a CLU-NRG1 fusion guided clinical therapy selection. fPO-tailored therapy with afatinib, followed by trastuzumab and pertuzumab, successfully reduced tumour burden and blocked disease progression over a five-year period. In summary, fPO is a powerful approach for the identification of systematic drug response differences across EOC subtypes, as well as to highlight patient-specific drug regimens that could help to optimise therapies to individual patients in the future.
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Affiliation(s)
- Astrid Murumägi
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland.
| | - Daniela Ungureanu
- Applied Tumor Genomics Research Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland
| | - Suleiman Khan
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
- Helsinki Institute for Information Technology (HIIT), Department of Computer Science, Aalto University, Espoo, Finland
| | - Mariliina Arjama
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Katja Välimäki
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Aleksandr Ianevski
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
- Helsinki Institute for Information Technology (HIIT), Department of Computer Science, Aalto University, Espoo, Finland
| | - Philipp Ianevski
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Rebecka Bergström
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden
| | - Alice Dini
- Applied Tumor Genomics Research Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland
| | - Anna Kanerva
- Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Riitta Koivisto-Korander
- Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Johanna Tapper
- Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Heini Lassus
- Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Mikko Loukovaara
- Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | | | - Akira Hirasawa
- Department of Clinical Genomic Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan
| | - Daisuke Aoki
- Department of Obstetrics and Gynecology, Keio University School of Medicine, Tokyo, Japan
| | - Vilja Pietiäinen
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
- iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, Helsinki, Finland
| | - Teijo Pellinen
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Ralf Bützow
- Department of Pathology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Tero Aittokallio
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
- Helsinki Institute for Information Technology (HIIT), Department of Computer Science, Aalto University, Espoo, Finland
- Institute for Cancer Research, Department of Cancer Genetics, Oslo University Hospital, Oslo, Norway
- Centre for Biostatistics and Epidemiology (OCBE), Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Olli Kallioniemi
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland.
- iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, Helsinki, Finland.
- Science for Life Laboratory (SciLifeLab), Department of Oncology and Pathology, Karolinska Institutet, Solna, Sweden.
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2
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Targeting Apoptosis Pathways With BCL2 and MDM2 Inhibitors in Adult B-cell Acute Lymphoblastic Leukemia. Hemasphere 2022; 6:e701. [PMID: 35233509 PMCID: PMC8878725 DOI: 10.1097/hs9.0000000000000701] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 02/07/2022] [Indexed: 11/26/2022] Open
Abstract
In adult patients, the treatment outcome of acute lymphoblastic leukemia (ALL) remains suboptimal. Here, we used an ex vivo drug testing platform and comprehensive molecular profiling to discover new drug candidates for B-ALL. We analyzed sensitivity of 18 primary B-ALL adult patient samples to 64 drugs in a physiological concentration range. Whole-transcriptome sequencing and publicly available expression data were used to examine gene expression biomarkers for observed drug responses. Apoptotic modulators targeting BCL2 and MDM2 were highly effective. Philadelphia chromosome–negative (Ph–) samples were sensitive to both BCL2/BCL-W/BCL-XL-targeting agent navitoclax and BCL2-selective venetoclax, whereas Ph-positive (Ph+) samples were more sensitive to navitoclax. Expression of BCL2 was downregulated and BCL-W and BCL-XL upregulated in Ph+ ALL compared with Ph– samples, providing elucidation for the observed difference in drug responses. A majority of the samples were sensitive to MDM2 inhibitor idasanutlin. The regulatory protein MDM2 suppresses the function of tumor suppressor p53, leading to impaired apoptosis. In B-ALL, the expression of MDM2 was increased compared with other hematological malignancies. In B-ALL cell lines, a combination of BCL2 and MDM2 inhibitor was synergistic. In summary, antiapoptotic proteins including BCL2 and MDM2 comprise promising targets for future drug studies in B-ALL.
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3
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Wang T, Gautam P, Rousu J, Aittokallio T. Systematic mapping of cancer cell target dependencies using high-throughput drug screening in triple-negative breast cancer. Comput Struct Biotechnol J 2020; 18:3819-3832. [PMID: 33335681 PMCID: PMC7720026 DOI: 10.1016/j.csbj.2020.11.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 10/23/2020] [Accepted: 11/01/2020] [Indexed: 12/31/2022] Open
Abstract
While high-throughput drug screening offers possibilities to profile phenotypic responses of hundreds of compounds, elucidation of the cell context-specific mechanisms of drug action requires additional analyses. To that end, we developed a computational target deconvolution pipeline that identifies the key target dependencies based on collective drug response patterns in each cell line separately. The pipeline combines quantitative drug-cell line responses with drug-target interaction networks among both intended on- and potent off-targets to identify pharmaceutically actionable and selective therapeutic targets. To demonstrate its performance, the target deconvolution pipeline was applied to 310 small molecules tested on 20 genetically and phenotypically heterogeneous triple-negative breast cancer (TNBC) cell lines to identify cell line-specific target mechanisms in terms of cytotoxic and cytostatic drug target vulnerabilities. The functional essentiality of each protein target was quantified with a target addiction score (TAS), as a measure of dependency of the cell line on the therapeutic target. The target dependency profiling was shown to capture inhibitory information that is complementary to that obtained from the structure or sensitivity of the drugs. Comparison of the TAS profiles and gene essentiality scores from CRISPR-Cas9 knockout screens revealed that certain proteins with low gene essentiality showed high target addictions, suggesting that they might be functioning as protein groups, and therefore be resistant to single gene knock-out. The comparative analysis discovered protein groups of potential multi-target synthetic lethal interactions, for instance, among histone deacetylases (HDACs). Our integrated approach also recovered a number of well-established TNBC cell line-specific drivers and known TNBC therapeutic targets, such as HDACs and cyclin-dependent kinases (CDKs). The present work provides novel insights into druggable vulnerabilities for TNBC, and opportunities to identify multi-target synthetic lethal interactions for further studies.
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Affiliation(s)
- Tianduanyi Wang
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.,Helsinki Institute for Information Technology (HIIT), Department of Computer Science, Aalto University, Espoo, Finland
| | - Prson Gautam
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Juho Rousu
- Helsinki Institute for Information Technology (HIIT), Department of Computer Science, Aalto University, Espoo, Finland
| | - Tero Aittokallio
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.,Helsinki Institute for Information Technology (HIIT), Department of Computer Science, Aalto University, Espoo, Finland.,Institute for Cancer Research, Department of Cancer Genetics, Oslo University Hospital, Oslo, Norway.,Centre for Biostatistics and Epidemiology (OCBE), Faculty of Medicine, University of Oslo, Oslo, Norway
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4
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More P, Goedtel-Armbrust U, Shah V, Mathaes M, Kindler T, Andrade-Navarro MA, Wojnowski L. Drivers of topoisomerase II poisoning mimic and complement cytotoxicity in AML cells. Oncotarget 2019; 10:5298-5312. [PMID: 31523390 PMCID: PMC6731103 DOI: 10.18632/oncotarget.27112] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Accepted: 06/19/2019] [Indexed: 11/25/2022] Open
Abstract
Recently approved cancer drugs remain out-of-reach to most patients due to prohibitive costs and only few produce clinically meaningful benefits. An untapped alternative is to enhance the efficacy and safety of existing cancer drugs. We hypothesized that the response to topoisomerase II poisons, a very successful group of cancer drugs, can be improved by considering treatment-associated transcript levels. To this end, we analyzed transcriptomes from Acute Myeloid Leukemia (AML) cell lines treated with the topoisomerase II poison etoposide. Using complementary criteria of co-regulation within networks and of essentiality for cell survival, we identified and functionally confirmed 11 druggable drivers of etoposide cytotoxicity. Drivers with pre-treatment expression predicting etoposide response (e.g., PARP9) generally synergized with etoposide. Drivers repressed by etoposide (e.g., PLK1) displayed standalone cytotoxicity. Drivers, whose modulation evoked etoposide-like gene expression changes (e.g., mTOR), were cytotoxic both alone and in combination with etoposide. In summary, both pre-treatment gene expression and treatment-driven changes contribute to the cell killing effect of etoposide. Such targets can be tweaked to enhance the efficacy of etoposide. This strategy can be used to identify combination partners or even replacements for other classical anticancer drugs, especially those interfering with DNA integrity and transcription.
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Affiliation(s)
- Piyush More
- Department of Pharmacology, University Medical Center, Johannes Gutenberg University Mainz, Mainz, Germany
| | - Ute Goedtel-Armbrust
- Department of Pharmacology, University Medical Center, Johannes Gutenberg University Mainz, Mainz, Germany
| | - Viral Shah
- Department of Hematology, Medical Oncology and Pneumology, University Medical Center, Johannes Gutenberg University Mainz, Mainz, Germany.,University Cancer Center of Mainz, Mainz, Germany
| | - Marianne Mathaes
- Department of Pharmacology, University Medical Center, Johannes Gutenberg University Mainz, Mainz, Germany
| | - Thomas Kindler
- Department of Hematology, Medical Oncology and Pneumology, University Medical Center, Johannes Gutenberg University Mainz, Mainz, Germany.,University Cancer Center of Mainz, Mainz, Germany
| | - Miguel A Andrade-Navarro
- Computational Biology and Data Mining, Faculty of Biology, Johannes Gutenberg University Mainz, Mainz, Germany
| | - Leszek Wojnowski
- Department of Pharmacology, University Medical Center, Johannes Gutenberg University Mainz, Mainz, Germany
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5
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Jaiswal A, Yadav B, Wennerberg K, Aittokallio T. Integrated Analysis of Drug Sensitivity and Selectivity to Predict Synergistic Drug Combinations and Target Coaddictions in Cancer. Methods Mol Biol 2019; 1888:205-217. [PMID: 30519949 DOI: 10.1007/978-1-4939-8891-4_12] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
High-throughput drug sensitivity testing provides a powerful phenotypic profiling approach to identify effective drug candidates for individual cell lines or patient-derived samples. Here, we describe an experimental-computational pipeline, named target addiction scoring (TAS), which mathematically transforms the drug response profiles into target addiction signatures, and thereby provides a ranking of potential therapeutic targets according to their functional importance in a particular cancer sample. The TAS pipeline makes use of drug polypharmacology to integrate the drug sensitivity and selectivity profiles through systems-wide interconnection networks between drugs and their targets, including both primary protein targets as well as secondary off-targets. We show how the TAS pipeline enables one to identify not only single-target addictions but also combinatorial coaddictions among targets that often underlie synergistic drug combinations.
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Affiliation(s)
- Alok Jaiswal
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Bhagwan Yadav
- Hematology Research Unit Helsinki (HRUH), University of Helsinki, Helsinki, Finland
| | - Krister Wennerberg
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Tero Aittokallio
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.
- Department of Mathematics and Statistics, University of Turku, Turku, Finland.
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6
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Thakuri PS, Liu C, Luker GD, Tavana H. Biomaterials-Based Approaches to Tumor Spheroid and Organoid Modeling. Adv Healthc Mater 2018; 7:e1700980. [PMID: 29205942 PMCID: PMC5867257 DOI: 10.1002/adhm.201700980] [Citation(s) in RCA: 78] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Revised: 09/21/2017] [Indexed: 12/22/2022]
Abstract
Evolving understanding of structural and biological complexity of tumors has stimulated development of physiologically relevant tumor models for cancer research and drug discovery. A major motivation for developing new tumor models is to recreate the 3D environment of tumors and context-mediated functional regulation of cancer cells. Such models overcome many limitations of standard monolayer cancer cell cultures. Under defined culture conditions, cancer cells self-assemble into 3D constructs known as spheroids. Additionally, cancer cells may recapitulate steps in embryonic development to self-organize into 3D cultures known as organoids. Importantly, spheroids and organoids reproduce morphology and biologic properties of tumors, providing valuable new tools for research, drug discovery, and precision medicine in cancer. This Progress Report discusses uses of both natural and synthetic biomaterials to culture cancer cells as spheroids or organoids, specifically highlighting studies that demonstrate how these models recapitulate key properties of native tumors. The report concludes with the perspectives on the utility of these models and areas of need for future developments to more closely mimic pathologic events in tumors.
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Affiliation(s)
- Pradip Shahi Thakuri
- Department of Biomedical Engineering, The University of Akron, Akron, OH, 44325, USA
| | - Chun Liu
- Departments of Radiology, Biomedical Engineering and Microbiology and Immunology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Gary D Luker
- Departments of Radiology, Biomedical Engineering and Microbiology and Immunology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Hossein Tavana
- Department of Biomedical Engineering, The University of Akron, Akron, OH, 44325, USA
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7
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Abstract
BACKGROUND Predicting the response to a drug for cancer disease patients based on genomic information is an important problem in modern clinical oncology. This problem occurs in part because many available drug sensitivity prediction algorithms do not consider better quality cancer cell lines and the adoption of new feature representations; both lead to the accurate prediction of drug responses. By predicting accurate drug responses to cancer, oncologists gain a more complete understanding of the effective treatments for each patient, which is a core goal in precision medicine. RESULTS In this paper, we model cancer drug sensitivity as a link prediction, which is shown to be an effective technique. We evaluate our proposed link prediction algorithms and compare them with an existing drug sensitivity prediction approach based on clinical trial data. The experimental results based on the clinical trial data show the stability of our link prediction algorithms, which yield the highest area under the ROC curve (AUC) and are statistically significant. CONCLUSIONS We propose a link prediction approach to obtain new feature representation. Compared with an existing approach, the results show that incorporating the new feature representation to the link prediction algorithms has significantly improved the performance.
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Affiliation(s)
- Turki Turki
- Department of Computer Science, King Abdulaziz University, P.O. Box 80221, Jeddah, 21589, Saudi Arabia. .,Bioinformatics Program and Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, 07102, USA.
| | - Zhi Wei
- Bioinformatics Program and Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, 07102, USA.
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8
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Turki T. Learning approaches to improve prediction of drug sensitivity in breast cancer patients. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2016:3314-3320. [PMID: 28269014 DOI: 10.1109/embc.2016.7591437] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Predicting drug response to cancer disease is an important problem in modern clinical oncology that attracted increasing recent attention from various domains such as computational biology, machine learning, and data mining. Cancer patients respond differently to each cancer therapy owing to disease diversity, genetic factors, and environmental causes. Thus, oncologists aim to identify the effective therapies for cancer patients and avoid adverse drug reactions in patients. By predicting the drug response to cancer, oncologists gain full understanding of the effective treatments on each patient, which leads to better personalized treatment. In this paper, we present three learning approaches to improve the prediction of breast cancer patients' response to chemotherapy drug: the instance selection approach, the oversampling approach, and the hybrid approach. We evaluate the performance of our approaches and compare them against the baseline approach using the Area Under the ROC Curve (AUC) on clinical trial data, in addition to testing the stability of the approaches. Our experimental results show the stability of our approaches giving the highest AUC with statistical significance.
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9
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Malani D, Murumägi A, Yadav B, Kontro M, Eldfors S, Kumar A, Karjalainen R, Majumder MM, Ojamies P, Pemovska T, Wennerberg K, Heckman C, Porkka K, Wolf M, Aittokallio T, Kallioniemi O. Enhanced sensitivity to glucocorticoids in cytarabine-resistant AML. Leukemia 2016; 31:1187-1195. [PMID: 27833094 PMCID: PMC5420795 DOI: 10.1038/leu.2016.314] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2016] [Revised: 09/22/2016] [Accepted: 09/26/2016] [Indexed: 12/20/2022]
Abstract
We sought to identify drugs that could counteract cytarabine resistance in acute myeloid leukemia (AML) by generating eight resistant variants from MOLM-13 and SHI-1 AML cell lines by long-term drug treatment. These cells were compared with 66 ex vivo chemorefractory samples from cytarabine-treated AML patients. The models and patient cells were subjected to genomic and transcriptomic profiling and high-throughput testing with 250 emerging and clinical oncology compounds. Genomic profiling uncovered deletion of the deoxycytidine kinase (DCK) gene in both MOLM-13- and SHI-1-derived cytarabine-resistant variants and in an AML patient sample. Cytarabine-resistant SHI-1 variants and a subset of chemorefractory AML patient samples showed increased sensitivity to glucocorticoids that are often used in treatment of lymphoid leukemia but not AML. Paired samples taken from AML patients before treatment and at relapse also showed acquisition of glucocorticoid sensitivity. Enhanced glucocorticoid sensitivity was only seen in AML patient samples that were negative for the FLT3 mutation (P=0.0006). Our study shows that development of cytarabine resistance is associated with increased sensitivity to glucocorticoids in a subset of AML, suggesting a new therapeutic strategy that should be explored in a clinical trial of chemorefractory AML patients carrying wild-type FLT3.
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Affiliation(s)
- D Malani
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
| | - A Murumägi
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
| | - B Yadav
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
| | - M Kontro
- Hematology Research Unit Helsinki, Department of Hematology, University of Helsinki and Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland
| | - S Eldfors
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
| | - A Kumar
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
| | - R Karjalainen
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
| | - M M Majumder
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
| | - P Ojamies
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
| | - T Pemovska
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
| | - K Wennerberg
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
| | - C Heckman
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
| | - K Porkka
- Hematology Research Unit Helsinki, Department of Hematology, University of Helsinki and Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland
| | - M Wolf
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
| | - T Aittokallio
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland.,Department of Mathematics and Statistics, University of Turku, Turku, Finland
| | - O Kallioniemi
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland.,Science for Life Laboratory, Department of Oncology and Pathology, Karolinska Institutet, Solna, Sweden
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Breyer MD, Look AT, Cifra A. From bench to patient: model systems in drug discovery. Dis Model Mech 2016; 8:1171-4. [PMID: 26438689 PMCID: PMC4610244 DOI: 10.1242/dmm.023036] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Model systems, including laboratory animals, microorganisms, and cell- and tissue-based systems, are central to the discovery and development of new and better drugs for the treatment of human disease. In this issue, Disease Models & Mechanisms launches a Special Collection that illustrates the contribution of model systems to drug discovery and optimisation across multiple disease areas. This collection includes reviews, Editorials, interviews with leading scientists with a foot in both academia and industry, and original research articles reporting new and important insights into disease therapeutics. This Editorial provides a summary of the collection's current contents, highlighting the impact of multiple model systems in moving new discoveries from the laboratory bench to the patients' bedsides. Drug Discovery Collection: This Editorial introduces the new DMM Special Collection entitled ‘From bench to patient: model systems in drug discovery’, providing a summary of its contents and highlighting the impact of multiple model systems in moving new discoveries from bench to patient.
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Affiliation(s)
| | - A Thomas Look
- Department of Pediatric Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA 01864, USA
| | - Alessandra Cifra
- Disease Models & Mechanisms, The Company of Biologists, Bidder Building, Station Road, Histon, Cambridgeshire, CB24 9LF, UK
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