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Huang J, Wu M, Lu F, Ou-Yang L, Zhu Z. Predicting synthetic lethal interactions in human cancers using graph regularized self-representative matrix factorization. BMC Bioinformatics 2019; 20:657. [PMID: 31870274 PMCID: PMC6929405 DOI: 10.1186/s12859-019-3197-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2019] [Accepted: 11/05/2019] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Synthetic lethality has attracted a lot of attentions in cancer therapeutics due to its utility in identifying new anticancer drug targets. Identifying synthetic lethal (SL) interactions is the key step towards the exploration of synthetic lethality in cancer treatment. However, biological experiments are faced with many challenges when identifying synthetic lethal interactions. Thus, it is necessary to develop computational methods which could serve as useful complements to biological experiments. RESULTS In this paper, we propose a novel graph regularized self-representative matrix factorization (GRSMF) algorithm for synthetic lethal interaction prediction. GRSMF first learns the self-representations from the known SL interactions and further integrates the functional similarities among genes derived from Gene Ontology (GO). It can then effectively predict potential SL interactions by leveraging the information provided by known SL interactions and functional annotations of genes. Extensive experiments on the synthetic lethal interaction data downloaded from SynLethDB database demonstrate the superiority of our GRSMF in predicting potential synthetic lethal interactions, compared with other competing methods. Moreover, case studies of novel interactions are conducted in this paper for further evaluating the effectiveness of GRSMF in synthetic lethal interaction prediction. CONCLUSIONS In this paper, we demonstrate that by adaptively exploiting the self-representation of original SL interaction data, and utilizing functional similarities among genes to enhance the learning of self-representation matrix, our GRSMF could predict potential SL interactions more accurately than other state-of-the-art SL interaction prediction methods.
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Affiliation(s)
- Jiang Huang
- College of Computer Science and Software Engineering, Shenzhen University, Nanhai Ave 3688, Shenzhen, 518060, China
| | - Min Wu
- Institute for Infocomm Research (I2R), A*STAR, 1 Fusionopolis Way, Singapore, Singapore
| | - Fan Lu
- Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security, College of Electronics and Information Engineering, Shenzhen University, Nanhai Ave 3688, Shenzhen, 518060, China
| | - Le Ou-Yang
- Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security, College of Electronics and Information Engineering, Shenzhen University, Nanhai Ave 3688, Shenzhen, 518060, China. .,Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China.
| | - Zexuan Zhu
- College of Computer Science and Software Engineering, Shenzhen University, Nanhai Ave 3688, Shenzhen, 518060, China.
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Das S, Deng X, Camphausen K, Shankavaram U. DiscoverSL: an R package for multi-omic data driven prediction of synthetic lethality in cancers. Bioinformatics 2019; 35:701-702. [PMID: 30059974 DOI: 10.1093/bioinformatics/bty673] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Revised: 07/09/2018] [Accepted: 07/26/2018] [Indexed: 01/22/2023] Open
Abstract
SUMMARY Synthetic lethality is a state when simultaneous loss of two genes is lethal to a cancer cell, while the loss of the individual genes is not. We developed an R package DiscoverSL to predict and visualize synthetic lethality in cancers using multi-omic cancer data. Mutation, copy number alteration and gene expression data from The Cancer Genome Atlas project were combined to develop a multi-parametric Random Forest classifier. The effects of selectively targeting the predicted synthetic lethal genes is tested in silico using shRNA and drug screening data from cancer cell line databases. The clinical outcome in patients with mutation in primary gene and over/under-expression in the synthetic lethal gene is evaluated using Kaplan-Meier analysis. The method helps to identify new therapeutic approaches by exploiting the concept of synthetic lethality. AVAILABILITY AND IMPLEMENTATION DiscoverSL package with user manual and sample workflow is available for download from github url: https://github.com/shaoli86/DiscoverSL/releases/tag/V1.0 under GNU GPL-3. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Shaoli Das
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Xiang Deng
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Kevin Camphausen
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Uma Shankavaram
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
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Computer-aided drug repurposing for cancer therapy: Approaches and opportunities to challenge anticancer targets. Semin Cancer Biol 2019; 68:59-74. [PMID: 31562957 DOI: 10.1016/j.semcancer.2019.09.023] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 09/24/2019] [Accepted: 09/24/2019] [Indexed: 12/14/2022]
Abstract
Despite huge efforts made in academic and pharmaceutical worldwide research, current anticancer therapies achieve effective treatment in a limited number of neoplasia cases only. Oncology terms such as big killers - to identify tumours with yet a high mortality rate - or undruggable cancer targets, and chemoresistance, represent the current therapeutic debacle of cancer treatments. In addition, metastases, tumour microenvironments, tumour heterogeneity, metabolic adaptations, and immunotherapy resistance are essential features controlling tumour response to therapies, but still, lack effective therapeutics or modulators. In this scenario, where the pharmaceutical productivity and drug efficacy in oncology seem to have reached a plateau, the so-called drug repurposing - i.e. the use of old drugs, already in clinical use, for a different therapeutic indication - is an appealing strategy to improve cancer therapy. Opportunities for drug repurposing are often based on occasional observations or on time-consuming pre-clinical drug screenings that are often not hypothesis-driven. In contrast, in-silico drug repurposing is an emerging, hypothesis-driven approach that takes advantage of the use of big-data. Indeed, the extensive use of -omics technologies, improved data storage, data meaning, machine learning algorithms, and computational modeling all offer unprecedented knowledge of the biological mechanisms of cancers and drugs' modes of action, providing extensive availability for both disease-related data and drugs-related data. This offers the opportunity to generate, with time and cost-effective approaches, computational drug networks to predict, in-silico, the efficacy of approved drugs against relevant cancer targets, as well as to select better responder patients or disease' biomarkers. Here, we will review selected disease-related data together with computational tools to be exploited for the in-silico repurposing of drugs against validated targets in cancer therapies, focusing on the oncogenic signaling pathways activation in cancer. We will discuss how in-silico drug repurposing has the promise to shortly improve our arsenal of anticancer drugs and, likely, overcome certain limitations of modern cancer therapies against old and new therapeutic targets in oncology.
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54
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Han Y, Wang C, Dong Q, Chen T, Yang F, Liu Y, Chen B, Zhao Z, Qi L, Zhao W, Liang H, Guo Z, Gu Y. Genetic Interaction-Based Biomarkers Identification for Drug Resistance and Sensitivity in Cancer Cells. MOLECULAR THERAPY. NUCLEIC ACIDS 2019; 17:688-700. [PMID: 31400611 PMCID: PMC6700431 DOI: 10.1016/j.omtn.2019.07.003] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Revised: 06/21/2019] [Accepted: 07/06/2019] [Indexed: 01/08/2023]
Abstract
Cancer cells generally harbor hundreds of alterations in the cancer genomes and act as crucial factors in the development and progression of cancer. Gene alterations in the cancer genome form genetic interactions, which affect the response of patients to drugs. We developed an algorithm that mines copy number alteration and whole-exome mutation profiles from The Cancer Genome Atlas (TCGA), as well as functional screen data generated to identify potential genetic interactions for specific cancer types. As a result, 4,529 synthetic viability (SV) interactions and 10,637 synthetic lethality (SL) interactions were detected. The pharmacogenomic datasets revealed that SV interactions induced drug resistance in cancer cells and that SL interactions mediated drug sensitivity in cancer cells. Deletions of HDAC1 and DVL1, both of which participate in the Notch signaling pathway, had an SV effect in cancer cells, and deletion of DVL1 induced resistance to HDAC1 inhibitors in cancer cells. In addition, patients with low expression of both HDAC1 and DVL1 had poor prognosis. Finally, by integrating current reported genetic interactions from other studies, the Cancer Genetic Interaction database (CGIdb) (http://www.medsysbio.org/CGIdb) was constructed, providing a convenient retrieval for genetic interactions in cancer.
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Affiliation(s)
- Yue Han
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China
| | - Chengyu Wang
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China
| | - Qi Dong
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China
| | - Tingting Chen
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China
| | - Fan Yang
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China
| | - Yaoyao Liu
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China
| | - Bo Chen
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China
| | - Zhangxiang Zhao
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China
| | - Lishuang Qi
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China
| | - Wenyuan Zhao
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China
| | - Haihai Liang
- Department of Pharmacology, College of Pharmacy, Harbin Medical University, Harbin, China
| | - Zheng Guo
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China; Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou, China; Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fuzhou, China.
| | - Yunyan Gu
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China.
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Kirzinger MWB, Vizeacoumar FS, Haave B, Gonzalez-Lopez C, Bonham K, Kusalik A, Vizeacoumar FJ. Humanized yeast genetic interaction mapping predicts synthetic lethal interactions of FBXW7 in breast cancer. BMC Med Genomics 2019; 12:112. [PMID: 31351478 PMCID: PMC6660958 DOI: 10.1186/s12920-019-0554-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Accepted: 06/27/2019] [Indexed: 02/08/2023] Open
Abstract
Background Synthetic lethal interactions (SLIs) that occur between gene pairs are exploited for cancer therapeutics. Studies in the model eukaryote yeast have identified ~ 550,000 negative genetic interactions that have been extensively studied, leading to characterization of novel pathways and gene functions. This resource can be used to predict SLIs that can be relevant to cancer therapeutics. Methods We used patient data to identify genes that are down-regulated in breast cancer. InParanoid orthology mapping was performed to identify yeast orthologs of the down-regulated genes and predict their corresponding SLIs in humans. The predicted network graphs were drawn with Cytoscape. CancerRXgene database was used to predict drug response. Results Harnessing the vast available knowledge of yeast genetics, we generated a Humanized Yeast Genetic Interaction Network (HYGIN) for 1009 human genes with 10,419 interactions. Through the addition of patient-data from The Cancer Genome Atlas (TCGA), we generated a breast cancer specific subnetwork. Specifically, by comparing 1009 genes in HYGIN to genes that were down-regulated in breast cancer, we identified 15 breast cancer genes with 130 potential SLIs. Interestingly, 32 of the 130 predicted SLIs occurred with FBXW7, a well-known tumor suppressor that functions as a substrate-recognition protein within a SKP/CUL1/F-Box ubiquitin ligase complex for proteasome degradation. Efforts to validate these SLIs using chemical genetic data predicted that patients with loss of FBXW7 may respond to treatment with drugs like Selumitinib or Cabozantinib. Conclusions This study provides a patient-data driven interpretation of yeast SLI data. HYGIN represents a novel strategy to uncover therapeutically relevant cancer drug targets and the yeast SLI data offers a major opportunity to mine these interactions. Electronic supplementary material The online version of this article (10.1186/s12920-019-0554-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Morgan W B Kirzinger
- Department of Computer Science, College of Arts and Science, University of Saskatchewan, 176 Thorvaldson Bldg, 110 Science Place, Saskatoon, Saskatchewan, S7N 5C9, Canada
| | - Frederick S Vizeacoumar
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Saskatchewan, 107 Wiggins Road, Saskatoon, Saskatchewan, S7N 5E5, Canada
| | - Bjorn Haave
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Saskatchewan, 107 Wiggins Road, Saskatoon, Saskatchewan, S7N 5E5, Canada
| | - Cristina Gonzalez-Lopez
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Saskatchewan, 107 Wiggins Road, Saskatoon, Saskatchewan, S7N 5E5, Canada
| | - Keith Bonham
- Cancer Research, Saskatchewan Cancer Agency, 107 Wiggins Road, Saskatoon, Saskatchewan, S7N 5E5, Canada.,Division of Oncology, College of Medicine, University of Saskatchewan, 107 Wiggins Road, Saskatoon, Saskatchewan, S7N 5E5, Canada
| | - Anthony Kusalik
- Department of Computer Science, College of Arts and Science, University of Saskatchewan, 176 Thorvaldson Bldg, 110 Science Place, Saskatoon, Saskatchewan, S7N 5C9, Canada.
| | - Franco J Vizeacoumar
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Saskatchewan, 107 Wiggins Road, Saskatoon, Saskatchewan, S7N 5E5, Canada. .,Cancer Research, Saskatchewan Cancer Agency, 107 Wiggins Road, Saskatoon, Saskatchewan, S7N 5E5, Canada. .,Division of Oncology, College of Medicine, University of Saskatchewan, 107 Wiggins Road, Saskatoon, Saskatchewan, S7N 5E5, Canada. .,Cancer Cluster, Rm 4D01.5 Health Science Bldg, University of Saskatchewan, 107 Wiggins Road, Saskatoon, SK, S7N 5E5, Canada.
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Toma M, Skorski T, Sliwinski T. DNA Double Strand Break Repair - Related Synthetic Lethality. Curr Med Chem 2019; 26:1446-1482. [PMID: 29421999 DOI: 10.2174/0929867325666180201114306] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2017] [Revised: 11/10/2017] [Accepted: 11/16/2017] [Indexed: 12/25/2022]
Abstract
Cancer is a heterogeneous disease with a high degree of diversity between and within tumors. Our limited knowledge of their biology results in ineffective treatment. However, personalized approach may represent a milestone in the field of anticancer therapy. It can increase specificity of treatment against tumor initiating cancer stem cells (CSCs) and cancer progenitor cells (CPCs) with minimal effect on normal cells and tissues. Cancerous cells carry multiple genetic and epigenetic aberrations which may disrupt pathways essential for cell survival. Discovery of synthetic lethality has led a new hope of creating effective and personalized antitumor treatment. Synthetic lethality occurs when simultaneous inactivation of two genes or their products causes cell death whereas individual inactivation of either gene is not lethal. The effectiveness of numerous anti-tumor therapies depends on induction of DNA damage therefore tumor cells expressing abnormalities in genes whose products are crucial for DNA repair pathways are promising targets for synthetic lethality. Here, we discuss mechanistic aspects of synthetic lethality in the context of deficiencies in DNA double strand break repair pathways. In addition, we review clinical trials utilizing synthetic lethality interactions and discuss the mechanisms of resistance.
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Affiliation(s)
- Monika Toma
- Laboratory of Medical Genetics, Faculty of Biology and Environmental Protection, University of Lodz, Pomorska 141/143, 90-236 Lodz, Poland
| | - Tomasz Skorski
- Department of Microbiology and Immunology, 3400 North Broad Street, Temple University Lewis Katz School of Medicine, Philadelphia, PA 19140, United States
| | - Tomasz Sliwinski
- Laboratory of Medical Genetics, Faculty of Biology and Environmental Protection, University of Lodz, Pomorska 141/143, 90-236 Lodz, Poland
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57
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Mulero-Sánchez A, Pogacar Z, Vecchione L. Importance of genetic screens in precision oncology. ESMO Open 2019; 4:e000505. [PMID: 31231569 PMCID: PMC6555615 DOI: 10.1136/esmoopen-2019-000505] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 04/12/2019] [Accepted: 04/13/2019] [Indexed: 01/05/2023] Open
Abstract
Precision oncology aims to distinguish which patients are eligible for a specific treatment in order to achieve the best possible outcome. In the last few years, genetic screens have shown their potential to find the new targets and drug combinations as well as predictive biomarkers for response and/or resistance to cancer treatment. In this review, we outline how precision oncology is changing over time and describe the different applications of genetic screens. Finally, we present some practical examples that describe the utility and the limitations of genetic screens in precision oncology.
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Affiliation(s)
- Antonio Mulero-Sánchez
- Division of Molecular Carcinogenesis, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Ziva Pogacar
- Division of Molecular Carcinogenesis, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Loredana Vecchione
- Charite Comprehensive Cancer Center, Charité - Universitätsmedizin Berlin, Berlin, Germany
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58
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Tommasini-Ghelfi S, Murnan K, Kouri FM, Mahajan AS, May JL, Stegh AH. Cancer-associated mutation and beyond: The emerging biology of isocitrate dehydrogenases in human disease. SCIENCE ADVANCES 2019; 5:eaaw4543. [PMID: 31131326 PMCID: PMC6530995 DOI: 10.1126/sciadv.aaw4543] [Citation(s) in RCA: 82] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Accepted: 04/16/2019] [Indexed: 05/12/2023]
Abstract
Isocitrate dehydrogenases (IDHs) are critical metabolic enzymes that catalyze the oxidative decarboxylation of isocitrate to α-ketoglutarate (αKG), NAD(P)H, and CO2. IDHs epigenetically control gene expression through effects on αKG-dependent dioxygenases, maintain redox balance and promote anaplerosis by providing cells with NADPH and precursor substrates for macromolecular synthesis, and regulate respiration and energy production through generation of NADH. Cancer-associated mutations in IDH1 and IDH2 represent one of the most comprehensively studied mechanisms of IDH pathogenic effect. Mutant enzymes produce (R)-2-hydroxyglutarate, which in turn inhibits αKG-dependent dioxygenase function, resulting in a global hypermethylation phenotype, increased tumor cell multipotency, and malignancy. Recent studies identified wild-type IDHs as critical regulators of normal organ physiology and, when transcriptionally induced or down-regulated, as contributing to cancer and neurodegeneration, respectively. We describe how mutant and wild-type enzymes contribute on molecular levels to disease pathogenesis, and discuss efforts to pharmacologically target IDH-controlled metabolic rewiring.
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Affiliation(s)
- Serena Tommasini-Ghelfi
- Ken and Ruth Davee Department of Neurology, The Northwestern Brain Tumor Institute, The Robert H. Lurie Comprehensive Cancer Center, Northwestern University, 303 East Superior, Chicago, IL 60611, USA
- International Institute for Nanotechnology, Northwestern University, 2145 Sheridan Road, Evanston, IL 60208, USA
| | - Kevin Murnan
- Ken and Ruth Davee Department of Neurology, The Northwestern Brain Tumor Institute, The Robert H. Lurie Comprehensive Cancer Center, Northwestern University, 303 East Superior, Chicago, IL 60611, USA
- International Institute for Nanotechnology, Northwestern University, 2145 Sheridan Road, Evanston, IL 60208, USA
| | - Fotini M. Kouri
- Ken and Ruth Davee Department of Neurology, The Northwestern Brain Tumor Institute, The Robert H. Lurie Comprehensive Cancer Center, Northwestern University, 303 East Superior, Chicago, IL 60611, USA
- International Institute for Nanotechnology, Northwestern University, 2145 Sheridan Road, Evanston, IL 60208, USA
| | - Akanksha S. Mahajan
- Ken and Ruth Davee Department of Neurology, The Northwestern Brain Tumor Institute, The Robert H. Lurie Comprehensive Cancer Center, Northwestern University, 303 East Superior, Chicago, IL 60611, USA
- International Institute for Nanotechnology, Northwestern University, 2145 Sheridan Road, Evanston, IL 60208, USA
| | - Jasmine L. May
- Ken and Ruth Davee Department of Neurology, The Northwestern Brain Tumor Institute, The Robert H. Lurie Comprehensive Cancer Center, Northwestern University, 303 East Superior, Chicago, IL 60611, USA
- International Institute for Nanotechnology, Northwestern University, 2145 Sheridan Road, Evanston, IL 60208, USA
| | - Alexander H. Stegh
- Ken and Ruth Davee Department of Neurology, The Northwestern Brain Tumor Institute, The Robert H. Lurie Comprehensive Cancer Center, Northwestern University, 303 East Superior, Chicago, IL 60611, USA
- International Institute for Nanotechnology, Northwestern University, 2145 Sheridan Road, Evanston, IL 60208, USA
- Corresponding author.
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59
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Data mining for mutation-specific targets in acute myeloid leukemia. Leukemia 2019; 33:826-843. [PMID: 30728456 DOI: 10.1038/s41375-019-0387-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Revised: 10/06/2018] [Accepted: 10/24/2018] [Indexed: 01/08/2023]
Abstract
Three mutation-specific targeted therapies have recently been approved by the FDA for the treatment of acute myeloid leukemia (AML): midostaurin for FLT3 mutations, enasidenib for relapsed or refractory cases with IDH2 mutations, and ivosidenib for cases with an IDH1 mutation. Together, these agents offer a mutation-directed treatment approach for up to 45% of de novo adult AML cases, a welcome deluge after a prolonged drought. At the same time, a number of computational tools have recently been developed that promise to further accelerate progress in mutation-specific therapy for AML and other cancers. Technical advances together with comprehensively annotated AML tissue banks have resulted in the availability of large and complex data sets for exploration by the end-user, including (i) microarray gene expression, (ii) exome sequencing, (iii) deep sequencing data of sub-clone heterogeneity, (iv) RNA sequencing of gene expression (bulk and single cell), (v) DNA methylation and chromatin, (vi) and germline quantitative trait loci. Yet few clinicians or experimental hematologists have the time or the training to access or analyze these repositories. This review summarizes the data sets and bioinformatic tools currently available to further the discovery of mutation-specific targets with an emphasis on web-based applications that are open, accessible, user-friendly, and do not require coding experience to navigate. We show examples of how available data can be mined to identify potential targets using synthetic lethality, drug repurposing, epigenetic sub-grouping, and proteomic networks while also highlighting strengths and limitations and the need for superior models for validation.
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60
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Parameswaran S, Kundapur D, Vizeacoumar FS, Freywald A, Uppalapati M, Vizeacoumar FJ. A Road Map to Personalizing Targeted Cancer Therapies Using Synthetic Lethality. Trends Cancer 2018; 5:11-29. [PMID: 30616753 DOI: 10.1016/j.trecan.2018.11.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Revised: 10/28/2018] [Accepted: 11/08/2018] [Indexed: 12/12/2022]
Abstract
Targeted therapies rely on the genetic and epigenetic status of the tumor cells and are seen as the most promising approach to treat cancer today. However, current targeted therapies focus on directly inhibiting those molecules that are altered in tumor cells. Unfortunately, targeting these molecules, even with specific inhibitors, is challenging as tumor cells rewire their genetic circuitry to eliminate genetic dependency on these targets. Here, we describe how synthetic lethality approaches can be used to identify genetic dependencies and develop personalized targeted therapies. We also discuss strategies to specifically target these genetic dependencies, using small molecule and biologic drugs.
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Affiliation(s)
- Sreejit Parameswaran
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Saskatchewan, Saskatoon, S7N 5E5, Canada; These authors contributed equally
| | - Deeksha Kundapur
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Saskatchewan, Saskatoon, S7N 5E5, Canada; These authors contributed equally
| | - Frederick S Vizeacoumar
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Saskatchewan, Saskatoon, S7N 5E5, Canada
| | - Andrew Freywald
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Saskatchewan, Saskatoon, S7N 5E5, Canada.
| | - Maruti Uppalapati
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Saskatchewan, Saskatoon, S7N 5E5, Canada.
| | - Franco J Vizeacoumar
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Saskatchewan, Saskatoon, S7N 5E5, Canada; Cancer Research, Saskatchewan Cancer Agency, 107 Wiggins Road, Saskatoon, S7N 5E5, Canada.
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Pyatnitskiy MA, Karpov DS, Moshkovskii SA. [Searching for essential genes in cancer genomes]. BIOMEDIT︠S︡INSKAI︠A︡ KHIMII︠A︡ 2018; 64:303-314. [PMID: 30135277 DOI: 10.18097/pbmc20186404303] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
The concept of essential genes, whose loss of functionality leads to cell death, is one of the fundamental concepts of genetics and is important for fundamental and applied research. This field is particularly promising in relation to oncology, since the search for genetic vulnerabilities of cancer cells allows us to identify new potential targets for antitumor therapy. The modern biotechnology capacities allow carrying out large-scale projects for sequencing somatic mutations in tumors, as well as directly interfering the genetic apparatus of cancer cells. They provided accumulation of a considerable body of knowledge about genetic variants and corresponding phenotypic manifestations in tumors. In the near future this knowledge will find application in clinical practice. This review describes the main experimental and computational approaches to the search for essential genes, concentrating on the application of these methods in the field of molecular oncology.
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Affiliation(s)
- M A Pyatnitskiy
- Institute of Biomedical Chemistry, Moscow, Russia; Higher School of Economics, Moscow, Russia
| | - D S Karpov
- Institute of Biomedical Chemistry, Moscow, Russia; Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia
| | - S A Moshkovskii
- Institute of Biomedical Chemistry, Moscow, Russia; Pirogov Russian National Research Medical University, Moscow, Russia
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62
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Lee JS, Das A, Jerby-Arnon L, Arafeh R, Auslander N, Davidson M, McGarry L, James D, Amzallag A, Park SG, Cheng K, Robinson W, Atias D, Stossel C, Buzhor E, Stein G, Waterfall JJ, Meltzer PS, Golan T, Hannenhalli S, Gottlieb E, Benes CH, Samuels Y, Shanks E, Ruppin E. Harnessing synthetic lethality to predict the response to cancer treatment. Nat Commun 2018; 9:2546. [PMID: 29959327 PMCID: PMC6026173 DOI: 10.1038/s41467-018-04647-1] [Citation(s) in RCA: 75] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2017] [Accepted: 05/15/2018] [Indexed: 12/21/2022] Open
Abstract
While synthetic lethality (SL) holds promise in developing effective cancer therapies, SL candidates found via experimental screens often have limited translational value. Here we present a data-driven approach, ISLE (identification of clinically relevant synthetic lethality), that mines TCGA cohort to identify the most likely clinically relevant SL interactions (cSLi) from a given candidate set of lab-screened SLi. We first validate ISLE via a benchmark of large-scale drug response screens and by predicting drug efficacy in mouse xenograft models. We then experimentally test a select set of predicted cSLi via new screening experiments, validating their predicted context-specific sensitivity in hypoxic vs normoxic conditions and demonstrating cSLi's utility in predicting synergistic drug combinations. We show that cSLi can successfully predict patients' drug treatment response and provide patient stratification signatures. ISLE thus complements existing actionable mutation-based methods for precision cancer therapy, offering an opportunity to expand its scope to the whole genome.
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Affiliation(s)
- Joo Sang Lee
- Center for Bioinformatics and Computational Biology, University of Maryland Institute of Advanced Computer Science (UMIACS) & Department of Computer Science, University of Maryland, College Park, MD, 20742, USA
- Cancer Data Science Lab, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA
| | - Avinash Das
- Center for Bioinformatics and Computational Biology, University of Maryland Institute of Advanced Computer Science (UMIACS) & Department of Computer Science, University of Maryland, College Park, MD, 20742, USA
| | - Livnat Jerby-Arnon
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Rand Arafeh
- Department of Molecular Cell Biology, Weizmann Institute, Rehovot, 7610001, Israel
| | - Noam Auslander
- Center for Bioinformatics and Computational Biology, University of Maryland Institute of Advanced Computer Science (UMIACS) & Department of Computer Science, University of Maryland, College Park, MD, 20742, USA
- Cancer Data Science Lab, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA
| | - Matthew Davidson
- Cancer Research UK, Beatson Institute, Switchback Road, Glasgow, G61 1BD, Scotland, UK
| | - Lynn McGarry
- Cancer Research UK, Beatson Institute, Switchback Road, Glasgow, G61 1BD, Scotland, UK
| | - Daniel James
- Cancer Research UK, Beatson Institute, Switchback Road, Glasgow, G61 1BD, Scotland, UK
| | - Arnaud Amzallag
- Massachusetts General Hospital Center for Cancer Research, Charlestown, MA, 02129, USA
- Harvard Medical School, Boston, MA, 02114, USA
- PatientsLikeMe, 160 Second Street, Cambridge, MA, 02142, USA
| | - Seung Gu Park
- Center for Bioinformatics and Computational Biology, University of Maryland Institute of Advanced Computer Science (UMIACS) & Department of Computer Science, University of Maryland, College Park, MD, 20742, USA
| | - Kuoyuan Cheng
- Center for Bioinformatics and Computational Biology, University of Maryland Institute of Advanced Computer Science (UMIACS) & Department of Computer Science, University of Maryland, College Park, MD, 20742, USA
- Cancer Data Science Lab, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA
| | - Welles Robinson
- Center for Bioinformatics and Computational Biology, University of Maryland Institute of Advanced Computer Science (UMIACS) & Department of Computer Science, University of Maryland, College Park, MD, 20742, USA
- Cancer Data Science Lab, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA
| | - Dikla Atias
- Division of Oncology, Sheba Medical Center Tel Hashomer, Ramat-Gan, 5262100, Israel
| | - Chani Stossel
- Division of Oncology, Sheba Medical Center Tel Hashomer, Ramat-Gan, 5262100, Israel
| | - Ella Buzhor
- Division of Oncology, Sheba Medical Center Tel Hashomer, Ramat-Gan, 5262100, Israel
| | - Gidi Stein
- The Sackler School of Medicine, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Joshua J Waterfall
- Genetics Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Paul S Meltzer
- Genetics Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Talia Golan
- Division of Oncology, Sheba Medical Center Tel Hashomer, Ramat-Gan, 5262100, Israel
- The Sackler School of Medicine, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Sridhar Hannenhalli
- Center for Bioinformatics and Computational Biology, University of Maryland Institute of Advanced Computer Science (UMIACS) & Department of Computer Science, University of Maryland, College Park, MD, 20742, USA
| | - Eyal Gottlieb
- Cancer Research UK, Beatson Institute, Switchback Road, Glasgow, G61 1BD, Scotland, UK
| | - Cyril H Benes
- Massachusetts General Hospital Center for Cancer Research, Charlestown, MA, 02129, USA
- Harvard Medical School, Boston, MA, 02114, USA
| | - Yardena Samuels
- Department of Molecular Cell Biology, Weizmann Institute, Rehovot, 7610001, Israel
| | - Emma Shanks
- Cancer Research UK, Beatson Institute, Switchback Road, Glasgow, G61 1BD, Scotland, UK
| | - Eytan Ruppin
- Center for Bioinformatics and Computational Biology, University of Maryland Institute of Advanced Computer Science (UMIACS) & Department of Computer Science, University of Maryland, College Park, MD, 20742, USA.
- Cancer Data Science Lab, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA.
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, 6997801, Israel.
- The Sackler School of Medicine, Tel Aviv University, Tel Aviv, 6997801, Israel.
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Senft D, Leiserson MDM, Ruppin E, Ronai ZA. Precision Oncology: The Road Ahead. Trends Mol Med 2017; 23:874-898. [PMID: 28887051 PMCID: PMC5718207 DOI: 10.1016/j.molmed.2017.08.003] [Citation(s) in RCA: 92] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2017] [Revised: 08/06/2017] [Accepted: 08/08/2017] [Indexed: 02/06/2023]
Abstract
Current efforts in precision oncology largely focus on the benefit of genomics-guided therapy. Yet, advances in sequencing techniques provide an unprecedented view of the complex genetic and nongenetic heterogeneity within individual tumors. Herein, we outline the benefits of integrating genomic and transcriptomic analyses for advanced precision oncology. We summarize relevant computational approaches to detect novel drivers and genetic vulnerabilities, suitable for therapeutic exploration. Clinically relevant platforms to functionally test predicted drugs/drug combinations for individual patients are reviewed. Finally, we highlight the technological advances in single cell analysis of tumor specimens. These may ultimately lead to the development of next-generation cancer drugs, capable of tackling the hurdles imposed by genetic and phenotypic heterogeneity on current anticancer therapies.
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Affiliation(s)
- Daniela Senft
- Tumor Initiation and Maintenance Program, NCI designated Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA
| | - Mark D M Leiserson
- Microsoft Research New England, Cambridge, MA 02142, USA; Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA
| | - Eytan Ruppin
- School of Computer Sciences and Sackler School of Medicine, Tel Aviv University, Tel Aviv, 69978, Israel; Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA
| | - Ze'ev A Ronai
- Tumor Initiation and Maintenance Program, NCI designated Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA; Technion Integrated Cancer Center, Faculty of Medicine, Technion, Israel Institute of Technology, Haifa, 31096, Israel.
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