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Zhao H, Meng L, Du P, Liao X, Mo X, Gong M, Chen J, Liao Y. IDH1 mutation produces R-2-hydroxyglutarate (R-2HG) and induces mir-182-5p expression to regulate cell cycle and tumor formation in glioma. Biol Res 2024; 57:30. [PMID: 38760850 PMCID: PMC11100189 DOI: 10.1186/s40659-024-00512-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 05/02/2024] [Indexed: 05/19/2024] Open
Abstract
BACKGROUND Mutations in isocitrate dehydrogenase 1 and 2 (IDH1 and IDH2), are present in most gliomas. IDH1 mutation is an important prognostic marker in glioma. However, its regulatory mechanism in glioma remains incompletely understood. RESULTS miR-182-5p expression was increased within IDH1-mutant glioma specimens according to TCGA, CGGA, and online dataset GSE119740, as well as collected clinical samples. (R)-2-hydroxyglutarate ((R)-2HG) treatment up-regulated the expression of miR-182-5p, enhanced glioma cell proliferation, and suppressed apoptosis; miR-182-5p inhibition partially eliminated the oncogenic effects of R-2HG upon glioma cells. By direct binding to Cyclin Dependent Kinase Inhibitor 2 C (CDKN2C) 3'UTR, miR-182-5p inhibited CDKN2C expression. Regarding cellular functions, CDKN2C knockdown promoted R-2HG-treated glioma cell viability, suppressed apoptosis, and relieved cell cycle arrest. Furthermore, CDKN2C knockdown partially attenuated the effects of miR-182-5p inhibition on cell phenotypes. Moreover, CDKN2C knockdown exerted opposite effects on cell cycle check point and apoptosis markers to those of miR-182-5p inhibition; also, CDKN2C knockdown partially attenuated the functions of miR-182-5p inhibition in cell cycle check point and apoptosis markers. The engineered CS-NPs (antagomir-182-5p) effectively encapsulated and delivered antagomir-182-5p, enhancing anti-tumor efficacy in vivo, indicating the therapeutic potential of CS-NPs(antagomir-182-5p) in targeting the miR-182-5p/CDKN2C axis against R-2HG-driven oncogenesis in mice models. CONCLUSIONS These insights highlight the potential of CS-NPs(antagomir-182-5p) to target the miR-182-5p/CDKN2C axis, offering a promising therapeutic avenue against R-2HG's oncogenic influence to glioma.
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Affiliation(s)
- Haiting Zhao
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, P.R. China
- Department of Neurology, Xiangya Hospital, The Central South University (CSU), Changsha, 410008, P.R. China
| | - Li Meng
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, P.R. China
- Department of Radiology, Xiangya Hospital, Central South University (CSU), Changsha, 410008, P.R. China
| | - Peng Du
- Department of Neurosurgery, The Second Affiliated Hospital, Xinjiang Medical University, Urumqi, 830063, PR China
| | - Xinbin Liao
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, P.R. China
- Department of Neurosurgery, Xiangya Hospital, Central South University (CSU), Changsha, 410008, P.R. China
| | - Xin Mo
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, P.R. China
- Department of Neurosurgery, Xiangya Hospital, Central South University (CSU), Changsha, 410008, P.R. China
| | - Mengqi Gong
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, P.R. China
- Department of Neurosurgery, Xiangya Hospital, Central South University (CSU), Changsha, 410008, P.R. China
| | - Jiaxin Chen
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, P.R. China
- Department of Neurology, Xiangya Hospital, The Central South University (CSU), Changsha, 410008, P.R. China
| | - Yiwei Liao
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, P.R. China.
- Department of Neurosurgery, Xiangya Hospital, Central South University (CSU), Changsha, 410008, P.R. China.
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2
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Bashi AC, Coker EA, Bulusu KC, Jaaks P, Crafter C, Lightfoot H, Milo M, McCarten K, Jenkins DF, van der Meer D, Lynch JT, Barthorpe S, Andersen CL, Barry ST, Beck A, Cidado J, Gordon JA, Hall C, Hall J, Mali I, Mironenko T, Mongeon K, Morris J, Richardson L, Smith PD, Tavana O, Tolley C, Thomas F, Willis BS, Yang W, O'Connor MJ, McDermott U, Critchlow SE, Drew L, Fawell SE, Mettetal JT, Garnett MJ. Large-scale Pan-cancer Cell Line Screening Identifies Actionable and Effective Drug Combinations. Cancer Discov 2024; 14:846-865. [PMID: 38456804 PMCID: PMC11061612 DOI: 10.1158/2159-8290.cd-23-0388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 11/01/2023] [Accepted: 02/02/2024] [Indexed: 03/09/2024]
Abstract
Oncology drug combinations can improve therapeutic responses and increase treatment options for patients. The number of possible combinations is vast and responses can be context-specific. Systematic screens can identify clinically relevant, actionable combinations in defined patient subtypes. We present data for 109 anticancer drug combinations from AstraZeneca's oncology small molecule portfolio screened in 755 pan-cancer cell lines. Combinations were screened in a 7 × 7 concentration matrix, with more than 4 million measurements of sensitivity, producing an exceptionally data-rich resource. We implement a new approach using combination Emax (viability effect) and highest single agent (HSA) to assess combination benefit. We designed a clinical translatability workflow to identify combinations with clearly defined patient populations, rationale for tolerability based on tumor type and combination-specific "emergent" biomarkers, and exposures relevant to clinical doses. We describe three actionable combinations in defined cancer types, confirmed in vitro and in vivo, with a focus on hematologic cancers and apoptotic targets. SIGNIFICANCE We present the largest cancer drug combination screen published to date with 7 × 7 concentration response matrices for 109 combinations in more than 750 cell lines, complemented by multi-omics predictors of response and identification of "emergent" combination biomarkers. We prioritize hits to optimize clinical translatability, and experimentally validate novel combination hypotheses. This article is featured in Selected Articles from This Issue, p. 695.
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Affiliation(s)
| | | | | | | | | | | | - Marta Milo
- Oncology R&D, AstraZeneca, Cambridge, United Kingdom
| | | | | | | | | | - Syd Barthorpe
- Wellcome Sanger Institute, Cambridge, United Kingdom
| | | | | | | | | | | | - Caitlin Hall
- Wellcome Sanger Institute, Cambridge, United Kingdom
| | - James Hall
- Wellcome Sanger Institute, Cambridge, United Kingdom
| | - Iman Mali
- Wellcome Sanger Institute, Cambridge, United Kingdom
| | | | | | - James Morris
- Wellcome Sanger Institute, Cambridge, United Kingdom
| | | | - Paul D. Smith
- Oncology R&D, AstraZeneca, Cambridge, United Kingdom
| | - Omid Tavana
- Oncology R&D, AstraZeneca, Waltham, Massachusetts
| | | | | | | | - Wanjuan Yang
- Wellcome Sanger Institute, Cambridge, United Kingdom
| | | | | | | | - Lisa Drew
- Oncology R&D, AstraZeneca, Waltham, Massachusetts
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3
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Huang X, Chen Z, Xiang X, Liu Y, Long X, Li K, Qin M, Long C, Mo X, Tang W, Liu J. Comprehensive multi-omics analysis of the m7G in pan-cancer from the perspective of predictive, preventive, and personalized medicine. EPMA J 2022; 13:671-697. [PMID: 36505892 PMCID: PMC9727047 DOI: 10.1007/s13167-022-00305-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Accepted: 11/01/2022] [Indexed: 11/24/2022]
Abstract
Background The N7-methylguanosine modification (m7G) of the 5' cap structure in the mRNA plays a crucial role in gene expression. However, the relation between m7G and tumor immune remains unclear. Hence, we intended to perform a pan-cancer analysis of m7G which can help explore the underlying mechanism and contribute to predictive, preventive, and personalized medicine (PPPM / 3PM). Methods The gene expression, genetic variation, clinical information, methylation, and digital pathological section from 33 cancer types were downloaded from the TCGA database. Immunohistochemistry (IHC) was used to validate the expression of the m7G regulator genes (m7RGs) hub-gene. The m7G score was calculated by single-sample gene-set enrichment analysis. The association of m7RGs with copy number variation, clinical features, immune-related genes, TMB, MSI, and tumor immune dysfunction and exclusion (TIDE) was comprehensively assessed. CellProfiler was used to extract pathological section characteristics. XGBoost and random forest were used to construct the m7G score prediction model. Single-cell transcriptome sequencing (scRNA-seq) was used to assess the activation state of the m7G in the tumor microenvironment. Results The m7RGs were highly expressed in tumors and most of the m7RGs are risk factors for prognosis. Moreover, the cellular pathway enrichment analysis suggested that m7G score was closely associated with invasion, cell cycle, DNA damage, and repair. In several cancers, m7G score was significantly negatively correlated with MSI and TMB and positively correlated with TIDE, suggesting an ICB marker potential. XGBoost-based pathomics model accurately predicts m7G scores with an area under the ROC curve (AUC) of 0.97. Analysis of scRNA-seq suggests that m7G differs significantly among cells of the tumor microenvironment. IHC confirmed high expression of EIF4E in breast cancer. The m7G prognostic model can accurately assess the prognosis of tumor patients with an AUC of 0.81, which was publicly hosted at https://pan-cancer-m7g.shinyapps.io/Panca-m7g/. Conclusion The current study explored for the first time the m7G in pan-cancer and identified m7G as an innovative marker in predicting clinical outcomes and immunotherapeutic efficacy, with the potential for deeper integration with PPPM. Combining m7G within the framework of PPPM will provide a unique opportunity for clinical intelligence and new approaches. Supplementary Information The online version contains supplementary material available at 10.1007/s13167-022-00305-1.
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Affiliation(s)
- Xiaoliang Huang
- Division of Colorectal & Anal Surgery, Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, The People’s Republic of China
| | - Zuyuan Chen
- Division of Colorectal & Anal Surgery, Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, The People’s Republic of China
| | - Xiaoyun Xiang
- Division of Colorectal & Anal Surgery, Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, The People’s Republic of China
| | - Yanling Liu
- Division of Colorectal & Anal Surgery, Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, The People’s Republic of China
| | - Xingqing Long
- Division of Colorectal & Anal Surgery, Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, The People’s Republic of China
| | - Kezhen Li
- Division of Colorectal & Anal Surgery, Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, The People’s Republic of China
| | - Mingjian Qin
- Division of Colorectal & Anal Surgery, Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, The People’s Republic of China
| | - Chenyan Long
- Division of Colorectal & Anal Surgery, Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, The People’s Republic of China
| | - Xianwei Mo
- Division of Colorectal & Anal Surgery, Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, The People’s Republic of China
| | - Weizhong Tang
- Division of Colorectal & Anal Surgery, Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, The People’s Republic of China
| | - Jungang Liu
- Division of Colorectal & Anal Surgery, Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, The People’s Republic of China
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4
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Trastulla L, Noorbakhsh J, Vazquez F, McFarland J, Iorio F. Computational estimation of quality and clinical relevance of cancer cell lines. Mol Syst Biol 2022; 18:e11017. [PMID: 35822563 PMCID: PMC9277610 DOI: 10.15252/msb.202211017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 06/10/2022] [Accepted: 06/13/2022] [Indexed: 12/12/2022] Open
Abstract
Immortal cancer cell lines (CCLs) are the most widely used system for investigating cancer biology and for the preclinical development of oncology therapies. Pharmacogenomic and genome‐wide editing screenings have facilitated the discovery of clinically relevant gene–drug interactions and novel therapeutic targets via large panels of extensively characterised CCLs. However, tailoring pharmacological strategies in a precision medicine context requires bridging the existing gaps between tumours and in vitro models. Indeed, intrinsic limitations of CCLs such as misidentification, the absence of tumour microenvironment and genetic drift have highlighted the need to identify the most faithful CCLs for each primary tumour while addressing their heterogeneity, with the development of new models where necessary. Here, we discuss the most significant limitations of CCLs in representing patient features, and we review computational methods aiming at systematically evaluating the suitability of CCLs as tumour proxies and identifying the best patient representative in vitro models. Additionally, we provide an overview of the applications of these methods to more complex models and discuss future machine‐learning‐based directions that could resolve some of the arising discrepancies.
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Affiliation(s)
- Lucia Trastulla
- Human Technopole, Milano, Italy.,Open Targets, Cambridge, UK
| | | | - Francisca Vazquez
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Francesco Iorio
- Human Technopole, Milano, Italy.,Open Targets, Cambridge, UK
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5
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Najgebauer H, Yang M, Francies HE, Pacini C, Stronach EA, Garnett MJ, Saez-Rodriguez J, Iorio F. CELLector: Genomics-Guided Selection of Cancer In Vitro Models. Cell Syst 2021; 10:424-432.e6. [PMID: 32437684 DOI: 10.1016/j.cels.2020.04.007] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Revised: 03/05/2020] [Accepted: 04/21/2020] [Indexed: 12/20/2022]
Abstract
Selecting appropriate cancer models is a key prerequisite for maximizing translational potential and clinical relevance of in vitro oncology studies. We developed CELLector: an R package and R Shiny application allowing researchers to select the most relevant cancer cell lines in a patient-genomic-guided fashion. CELLector leverages tumor genomics to identify recurrent subtypes with associated genomic signatures. It then evaluates these signatures in cancer cell lines to prioritize their selection. This enables users to choose appropriate in vitro models for inclusion or exclusion in retrospective analyses and future studies. Moreover, this allows bridging outcomes from cancer cell line screens to precisely defined sub-cohorts of primary tumors. Here, we demonstrate the usefulness and applicability of CELLector, showing how it can aid prioritization of in vitro models for future development and unveil patient-derived multivariate prognostic and therapeutic markers. CELLector is freely available at https://ot-cellector.shinyapps.io/CELLector_App/ (code at https://github.com/francescojm/CELLector and https://github.com/francescojm/CELLector_App).
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Affiliation(s)
- Hanna Najgebauer
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK; European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK; Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
| | - Mi Yang
- Faculty of Medicine, Joint Research Centre for Computational Biomedicine, RWTH Aachen University, Aachen 52057, Germany
| | - Hayley E Francies
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
| | - Clare Pacini
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK; Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
| | - Euan A Stronach
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK; Functional Genomics GlaxoSmithKline, Stevenage, UK
| | - Mathew J Garnett
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK; Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
| | - Julio Saez-Rodriguez
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK; Faculty of Medicine, Joint Research Centre for Computational Biomedicine, RWTH Aachen University, Aachen 52057, Germany; Institute for Computational Biomedicine, Faculty of Medicine, BIOQUANT-Center, Heidelberg University, Heidelberg, Germany
| | - Francesco Iorio
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK; Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK; Human Technopole, 20157, Milano, Italy.
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6
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Esteves L, Caramelo F, Ribeiro IP, Carreira IM, de Melo JB. Probability distribution of copy number alterations along the genome: an algorithm to distinguish different tumour profiles. Sci Rep 2020; 10:14868. [PMID: 32913269 PMCID: PMC7483770 DOI: 10.1038/s41598-020-71859-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Accepted: 08/13/2020] [Indexed: 11/11/2022] Open
Abstract
Copy number alterations (CNAs) comprise deletions or amplifications of fragments of genomic material that are particularly common in cancer and play a major contribution in its development and progression. High resolution microarray-based genome-wide technologies have been widely used to detect CNAs, generating complex datasets that require further steps to allow for the determination of meaningful results. In this work, we propose a methodology to determine common regions of CNAs from these datasets, that in turn are used to infer the probability distribution of disease profiles in the population. This methodology was validated using simulated data and assessed using real data from Head and Neck Squamous Cell Carcinoma and Lung Adenocarcinoma, from the TCGA platform. Probability distribution profiles were produced allowing for the distinction between different phenotypic groups established within that cohort. This method may be used to distinguish between groups in the diseased population, within well-established degrees of confidence. The application of such methods may be of greater value in the clinical context both as a diagnostic or prognostic tool and, even as a useful way for helping to establish the most adequate treatment and care plans.
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Affiliation(s)
- Luísa Esteves
- Cytogenetics and Genomics Laboratory, Faculty of Medicine, University of Coimbra, Polo Ciências da Saúde, 3000-354, Coimbra, Portugal
| | - Francisco Caramelo
- Laboratory of Biostatistics and Medical Informatics, IBILI-Faculty of Medicine, University of Coimbra, 3000-354, Coimbra, Portugal
| | - Ilda Patrícia Ribeiro
- Cytogenetics and Genomics Laboratory, Faculty of Medicine, University of Coimbra, Polo Ciências da Saúde, 3000-354, Coimbra, Portugal.,iCBR-CIMAGO-Center of Investigation on Environment, Genetics and Oncobiology-Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - Isabel M Carreira
- Cytogenetics and Genomics Laboratory, Faculty of Medicine, University of Coimbra, Polo Ciências da Saúde, 3000-354, Coimbra, Portugal.,iCBR-CIMAGO-Center of Investigation on Environment, Genetics and Oncobiology-Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - Joana Barbosa de Melo
- Cytogenetics and Genomics Laboratory, Faculty of Medicine, University of Coimbra, Polo Ciências da Saúde, 3000-354, Coimbra, Portugal. .,iCBR-CIMAGO-Center of Investigation on Environment, Genetics and Oncobiology-Faculty of Medicine, University of Coimbra, Coimbra, Portugal.
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7
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Ayestaran I, Galhoz A, Spiegel E, Sidders B, Dry JR, Dondelinger F, Bender A, McDermott U, Iorio F, Menden MP. Identification of Intrinsic Drug Resistance and Its Biomarkers in High-Throughput Pharmacogenomic and CRISPR Screens. PATTERNS (NEW YORK, N.Y.) 2020; 1:100065. [PMID: 33205120 PMCID: PMC7660407 DOI: 10.1016/j.patter.2020.100065] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 05/14/2020] [Accepted: 06/08/2020] [Indexed: 12/15/2022]
Abstract
High-throughput drug screens in cancer cell lines test compounds at low concentrations, thereby enabling the identification of drug-sensitivity biomarkers, while resistance biomarkers remain underexplored. Dissecting meaningful drug responses at high concentrations is challenging due to cytotoxicity, i.e., off-target effects, thus limiting resistance biomarker discovery to frequently mutated cancer genes. To address this, we interrogate subpopulations carrying sensitivity biomarkers and consecutively investigate unexpectedly resistant (UNRES) cell lines for unique genetic alterations that may drive resistance. By analyzing the GDSC and CTRP datasets, we find 53 and 35 UNRES cases, respectively. For 24 and 28 of them, we highlight putative resistance biomarkers. We find clinically relevant cases such as EGFRT790M mutation in NCI-H1975 or PTEN loss in NCI-H1650 cells, in lung adenocarcinoma treated with EGFR inhibitors. Interrogating the underpinnings of drug resistance with publicly available CRISPR phenotypic assays assists in prioritizing resistance drivers, offering hypotheses for drug combinations.
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Affiliation(s)
- Iñigo Ayestaran
- Institute of Computational Biology, Helmholtz Zentrum München GmbH—German Research Center for Environmental Health, Neuherberg 85764, Germany
- CRUK Cambridge Centre Early Detection Programme, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Ana Galhoz
- Institute of Computational Biology, Helmholtz Zentrum München GmbH—German Research Center for Environmental Health, Neuherberg 85764, Germany
- Department of Biology, Ludwig-Maximilians University Munich, Martinsried 82152, Germany
| | - Elmar Spiegel
- Institute of Computational Biology, Helmholtz Zentrum München GmbH—German Research Center for Environmental Health, Neuherberg 85764, Germany
| | - Ben Sidders
- Research and Early Development, Oncology, AstraZeneca, Cambridge CB4 0WG, UK
| | - Jonathan R. Dry
- Research and Early Development, Oncology, AstraZeneca, Boston, MA 02451, USA
- Tempus Labs, Boston, MA, USA
| | - Frank Dondelinger
- Centre for Health Informatics, Computation and Statistics, Lancaster Medical School, Lancaster University, Lancaster LA1 4YW, UK
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK
| | - Ultan McDermott
- Research and Early Development, Oncology, AstraZeneca, Cambridge CB4 0WG, UK
| | - Francesco Iorio
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1RQ, UK
- Human Technopole, Palazzo Italia Via Cristina Belgioioso 171, Milano 20157, Italy
| | - Michael P. Menden
- Institute of Computational Biology, Helmholtz Zentrum München GmbH—German Research Center for Environmental Health, Neuherberg 85764, Germany
- Department of Biology, Ludwig-Maximilians University Munich, Martinsried 82152, Germany
- German Centre for Diabetes Research (DZD e.V.), Neuherberg 85764, Germany
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8
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Integrative analysis of genomic amplification-dependent expression and loss-of-function screen identifies ASAP1 as a driver gene in triple-negative breast cancer progression. Oncogene 2020; 39:4118-4131. [PMID: 32235890 PMCID: PMC7220851 DOI: 10.1038/s41388-020-1279-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 03/14/2020] [Accepted: 03/17/2020] [Indexed: 02/07/2023]
Abstract
The genetically heterogeneous triple-negative breast cancer (TNBC) continues to be an intractable disease, due to lack of effective targeted therapies. Gene amplification is a major event in tumorigenesis. Genes with amplification-dependent expression are being explored as therapeutic targets for cancer treatment. In this study, we have applied Analytical Multi-scale Identification of Recurring Events analysis and transcript quantification in the TNBC genome across 222 TNBC tumors and identified 138 candidate genes with positive correlation in copy number gain (CNG) and gene expression. siRNA-based loss-of-function screen of the candidate genes has validated EGFR, MYC, ASAP1, IRF2BP2, and CCT5 genes as drivers promoting proliferation in different TNBC cells. MYC, ASAP1, IRF2BP2, and CCT5 display frequent CNG and concurrent expression over 2173 breast cancer tumors (cBioPortal dataset). More frequently are MYC and ASAP1 amplified in TNBC tumors (>30%, n = 320). In particular, high expression of ASAP1, the ADP-ribosylation factor GTPase-activating protein, is significantly related to poor metastatic relapse-free survival of TNBC patients (n = 257, bc-GenExMiner). Furthermore, we have revealed that silencing of ASAP1 modulates numerous cytokine and apoptosis signaling components, such as IL1B, TRAF1, AIFM2, and MAP3K11 that are clinically relevant to survival outcomes of TNBC patients. ASAP1 has been reported to promote invasion and metastasis in various cancer cells. Our findings that ASAP1 is an amplification-dependent TNBC driver gene promoting TNBC cell proliferation, functioning upstream apoptosis components, and correlating to clinical outcomes of TNBC patients, support ASAP1 as a potential actionable target for TNBC treatment.
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9
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Xi J, Li A, Wang M. HetRCNA: A Novel Method to Identify Recurrent Copy Number Alternations from Heterogeneous Tumor Samples Based on Matrix Decomposition Framework. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:422-434. [PMID: 29994262 DOI: 10.1109/tcbb.2018.2846599] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
A common strategy to discovering cancer associated copy number aberrations (CNAs) from a cohort of cancer samples is to detect recurrent CNAs (RCNAs). Although the previous methods can successfully identify communal RCNAs shared by nearly all tumor samples, detecting subgroup-specific RCNAs and their related subgroup samples from cancer samples with heterogeneity is still invalid for these existing approaches. In this paper, we introduce a novel integrated method called HetRCNA, which can identify statistically significant subgroup-specific RCNAs and their related subgroup samples. Based on matrix decomposition framework with weight constraint, HetRCNA can successfully measure the subgroup samples by coefficients of left vectors with weight constraint and subgroup-specific RCNAs by coefficients of the right vectors and significance test. When we evaluate HetRCNA on simulated dataset, the results show that HetRCNA gives the best performances among the competing methods and is robust to the noise factors of the simulated data. When HetRCNA is applied on a real breast cancer dataset, our approach successfully identifies a bunch of RCNA regions and the result is highly correlated with the results of the other two investigated approaches. Notably, the genomic regions identified by HetRCNA harbor many breast cancer related genes reported by previous researches.
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10
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Schubert M, Colomé-Tatché M, Foijer F. Gene networks in cancer are biased by aneuploidies and sample impurities. BIOCHIMICA ET BIOPHYSICA ACTA-GENE REGULATORY MECHANISMS 2019; 1863:194444. [PMID: 31654805 DOI: 10.1016/j.bbagrm.2019.194444] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Revised: 09/05/2019] [Accepted: 10/14/2019] [Indexed: 12/14/2022]
Abstract
Gene regulatory network inference is a standard technique for obtaining structured regulatory information from, for instance, gene expression measurements. Methods performing this task have been extensively evaluated on synthetic, and to a lesser extent real data sets. In contrast to these test evaluations, applications to gene expression data of human cancers are often limited by fewer samples and more potential regulatory links, and are biased by copy number aberrations as well as cell mixtures and sample impurities. Here, we take networks inferred from TCGA cohorts as an example to show that (1) transcription factor annotations are essential to obtain reliable networks, and (2) even for state of the art methods, we expect that between 20 and 80% of edges are caused by copy number changes and cell mixtures rather than transcription factor regulation.
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Affiliation(s)
- Michael Schubert
- European Research Institute for the Biology of Ageing, University of Groningen, University Medical Center Groningen, 9713 AV, Groningen, the Netherlands; Institute of Computational Biology, Helmholtz Zentrum München, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany.
| | - Maria Colomé-Tatché
- European Research Institute for the Biology of Ageing, University of Groningen, University Medical Center Groningen, 9713 AV, Groningen, the Netherlands; Institute of Computational Biology, Helmholtz Zentrum München, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany; TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Floris Foijer
- European Research Institute for the Biology of Ageing, University of Groningen, University Medical Center Groningen, 9713 AV, Groningen, the Netherlands.
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11
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Annunziato S, de Ruiter JR, Henneman L, Brambillasca CS, Lutz C, Vaillant F, Ferrante F, Drenth AP, van der Burg E, Siteur B, van Gerwen B, de Bruijn R, van Miltenburg MH, Huijbers IJ, van de Ven M, Visvader JE, Lindeman GJ, Wessels LFA, Jonkers J. Comparative oncogenomics identifies combinations of driver genes and drug targets in BRCA1-mutated breast cancer. Nat Commun 2019; 10:397. [PMID: 30674894 PMCID: PMC6344487 DOI: 10.1038/s41467-019-08301-2] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Accepted: 12/21/2018] [Indexed: 01/22/2023] Open
Abstract
BRCA1-mutated breast cancer is primarily driven by DNA copy-number alterations (CNAs) containing large numbers of candidate driver genes. Validation of these candidates requires novel approaches for high-throughput in vivo perturbation of gene function. Here we develop genetically engineered mouse models (GEMMs) of BRCA1-deficient breast cancer that permit rapid introduction of putative drivers by either retargeting of GEMM-derived embryonic stem cells, lentivirus-mediated somatic overexpression or in situ CRISPR/Cas9-mediated gene disruption. We use these approaches to validate Myc, Met, Pten and Rb1 as bona fide drivers in BRCA1-associated mammary tumorigenesis. Iterative mouse modeling and comparative oncogenomics analysis show that MYC-overexpression strongly reshapes the CNA landscape of BRCA1-deficient mammary tumors and identify MCL1 as a collaborating driver in these tumors. Moreover, MCL1 inhibition potentiates the in vivo efficacy of PARP inhibition (PARPi), underscoring the therapeutic potential of this combination for treatment of BRCA1-mutated cancer patients with poor response to PARPi monotherapy.
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Affiliation(s)
- Stefano Annunziato
- Division of Molecular Pathology, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.,Cancer Genomics Netherlands, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.,Oncode Institute, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands
| | - Julian R de Ruiter
- Division of Molecular Pathology, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.,Cancer Genomics Netherlands, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.,Oncode Institute, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.,Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands
| | - Linda Henneman
- Division of Molecular Pathology, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.,Transgenic Core Facility, Mouse Clinic for Cancer and Aging (MCCA), The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands
| | - Chiara S Brambillasca
- Division of Molecular Pathology, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.,Cancer Genomics Netherlands, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.,Oncode Institute, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands
| | - Catrin Lutz
- Division of Molecular Pathology, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.,Cancer Genomics Netherlands, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.,Oncode Institute, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands
| | - François Vaillant
- ACRF Stem Cells and Cancer Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.,Department of Medical Biology, University of Melbourne, Parkville, VIC, 3010, Australia
| | - Federica Ferrante
- Division of Molecular Pathology, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.,Cancer Genomics Netherlands, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.,Oncode Institute, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands
| | - Anne Paulien Drenth
- Division of Molecular Pathology, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.,Cancer Genomics Netherlands, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.,Oncode Institute, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands
| | - Eline van der Burg
- Division of Molecular Pathology, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.,Cancer Genomics Netherlands, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.,Oncode Institute, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands
| | - Bjørn Siteur
- Preclinical Intervention Unit, Mouse Clinic for Cancer and Aging (MCCA), The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands
| | - Bas van Gerwen
- Preclinical Intervention Unit, Mouse Clinic for Cancer and Aging (MCCA), The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands
| | - Roebi de Bruijn
- Division of Molecular Pathology, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.,Cancer Genomics Netherlands, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.,Oncode Institute, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.,Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands
| | - Martine H van Miltenburg
- Division of Molecular Pathology, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.,Cancer Genomics Netherlands, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.,Oncode Institute, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands
| | - Ivo J Huijbers
- Transgenic Core Facility, Mouse Clinic for Cancer and Aging (MCCA), The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands
| | - Marieke van de Ven
- Preclinical Intervention Unit, Mouse Clinic for Cancer and Aging (MCCA), The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands
| | - Jane E Visvader
- ACRF Stem Cells and Cancer Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.,Department of Medical Biology, University of Melbourne, Parkville, VIC, 3010, Australia
| | - Geoffrey J Lindeman
- ACRF Stem Cells and Cancer Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.,Department of Medicine, University of Medicine, Parkville, VIC, 3010, Australia.,Parkville Familial Cancer Centre, Royal Melbourne Hospital and Peter MacCallum Cancer Centre, Parkville, VIC, 3050, Australia
| | - Lodewyk F A Wessels
- Cancer Genomics Netherlands, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands. .,Oncode Institute, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands. .,Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.
| | - Jos Jonkers
- Division of Molecular Pathology, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands. .,Cancer Genomics Netherlands, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands. .,Oncode Institute, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.
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12
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Abstract
High-throughput biological technologies are routinely used to generate gene expression profiling or cytogenetics data. To achieve high performance, methods available in the literature become more specialized and often require high computational resources. Here, we propose a new versatile method based on the data-ordering rank values. We use linear algebra, the Perron-Frobenius theorem and also extend a method presented earlier for searching differentially expressed genes for the detection of recurrent copy number aberration. A result derived from the proposed method is a one-sample Student's t-test based on rank values. The proposed method is to our knowledge the only that applies to gene expression profiling and to cytogenetics data sets. This new method is fast, deterministic, and requires a low computational load. Probabilities are associated with genes to allow a statistically significant subset selection in the data set. Stability scores are also introduced as quality parameters. The performance and comparative analyses were carried out using real data sets. The proposed method can be accessed through an R package available from the CRAN (Comprehensive R Archive Network) website: https://cran.r-project.org/web/packages/fcros .
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Affiliation(s)
- Doulaye Dembélé
- Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), CNRS UMR 7104, INSERM U 1258, Université de Strasbourg, Illkirch-Graffenstaden, France
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13
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RUBIC identifies driver genes by detecting recurrent DNA copy number breaks. Nat Commun 2016; 7:12159. [PMID: 27396759 PMCID: PMC4942583 DOI: 10.1038/ncomms12159] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2016] [Accepted: 06/07/2016] [Indexed: 01/12/2023] Open
Abstract
The frequent recurrence of copy number aberrations across tumour samples is a reliable hallmark of certain cancer driver genes. However, state-of-the-art algorithms for detecting recurrent aberrations fail to detect several known drivers. In this study, we propose RUBIC, an approach that detects recurrent copy number breaks, rather than recurrently amplified or deleted regions. This change of perspective allows for a simplified approach as recursive peak splitting procedures and repeated re-estimation of the background model are avoided. Furthermore, we control the false discovery rate on the level of called regions, rather than at the probe level, as in competing algorithms. We benchmark RUBIC against GISTIC2 (a state-of-the-art approach) and RAIG (a recently proposed approach) on simulated copy number data and on three SNP6 and NGS copy number data sets from TCGA. We show that RUBIC calls more focal recurrent regions and identifies a much larger fraction of known cancer genes. Analysis of cancer genome sequencing data has been used to predict genes associated with the pathogenesis of cancer. Here, the authors propose a new algorithm entitled RUBIC that predicts breaks in DNA as opposed to previously published methods that predict amplifications and deletions of DNA.
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14
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Iorio F, Knijnenburg TA, Vis DJ, Bignell GR, Menden MP, Schubert M, Aben N, Gonçalves E, Barthorpe S, Lightfoot H, Cokelaer T, Greninger P, van Dyk E, Chang H, de Silva H, Heyn H, Deng X, Egan RK, Liu Q, Mironenko T, Mitropoulos X, Richardson L, Wang J, Zhang T, Moran S, Sayols S, Soleimani M, Tamborero D, Lopez-Bigas N, Ross-Macdonald P, Esteller M, Gray NS, Haber DA, Stratton MR, Benes CH, Wessels LFA, Saez-Rodriguez J, McDermott U, Garnett MJ. A Landscape of Pharmacogenomic Interactions in Cancer. Cell 2016; 166:740-754. [PMID: 27397505 PMCID: PMC4967469 DOI: 10.1016/j.cell.2016.06.017] [Citation(s) in RCA: 1166] [Impact Index Per Article: 145.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2015] [Revised: 12/23/2015] [Accepted: 06/03/2016] [Indexed: 12/31/2022]
Abstract
Systematic studies of cancer genomes have provided unprecedented insights into the molecular nature of cancer. Using this information to guide the development and application of therapies in the clinic is challenging. Here, we report how cancer-driven alterations identified in 11,289 tumors from 29 tissues (integrating somatic mutations, copy number alterations, DNA methylation, and gene expression) can be mapped onto 1,001 molecularly annotated human cancer cell lines and correlated with sensitivity to 265 drugs. We find that cell lines faithfully recapitulate oncogenic alterations identified in tumors, find that many of these associate with drug sensitivity/resistance, and highlight the importance of tissue lineage in mediating drug response. Logic-based modeling uncovers combinations of alterations that sensitize to drugs, while machine learning demonstrates the relative importance of different data types in predicting drug response. Our analysis and datasets are rich resources to link genotypes with cellular phenotypes and to identify therapeutic options for selected cancer sub-populations. We integrate heterogeneous molecular data of 11,289 tumors and 1,001 cell lines We measure the response of 1,001 cancer cell lines to 265 anti-cancer drugs We uncover numerous oncogenic aberrations that sensitize to an anti-cancer drug Our study forms a resource to identify therapeutic options for cancer sub-populations
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Affiliation(s)
- Francesco Iorio
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK; Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
| | - Theo A Knijnenburg
- Institute for Systems Biology, Seattle, WA 98109, USA; Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Amsterdam 1066 CX, The Netherlands
| | - Daniel J Vis
- Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Amsterdam 1066 CX, The Netherlands
| | - Graham R Bignell
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
| | - Michael P Menden
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK; Faculty of Medicine, Joint Research Centre for Computational Biomedicine, RWTH Aachen University, Aachen 52057, Germany
| | - Michael Schubert
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
| | - Nanne Aben
- Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Amsterdam 1066 CX, The Netherlands; Department of EEMCS, Delft University of Technology, Delft 2628 CD, the Netherlands
| | - Emanuel Gonçalves
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
| | - Syd Barthorpe
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
| | - Howard Lightfoot
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
| | - Thomas Cokelaer
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK; Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
| | - Patricia Greninger
- Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
| | - Ewald van Dyk
- Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Amsterdam 1066 CX, The Netherlands
| | - Han Chang
- Genetically Defined Diseases and Genomics, Bristol-Myers Squibb Research and Development, Hopewell, NJ 08534, USA
| | - Heshani de Silva
- Genetically Defined Diseases and Genomics, Bristol-Myers Squibb Research and Development, Hopewell, NJ 08534, USA
| | - Holger Heyn
- Cancer Epigenetics and Biology Program (PEBC), Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet 08908, Barcelona, Catalonia, Spain
| | - Xianming Deng
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Biological Chemistry & Molecular Pharmacology, Harvard Medical School, Boston, MA 02215, USA
| | - Regina K Egan
- Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
| | - Qingsong Liu
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Biological Chemistry & Molecular Pharmacology, Harvard Medical School, Boston, MA 02215, USA
| | - Tatiana Mironenko
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
| | - Xeni Mitropoulos
- Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
| | - Laura Richardson
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
| | - Jinhua Wang
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Biological Chemistry & Molecular Pharmacology, Harvard Medical School, Boston, MA 02215, USA
| | - Tinghu Zhang
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Biological Chemistry & Molecular Pharmacology, Harvard Medical School, Boston, MA 02215, USA
| | - Sebastian Moran
- Cancer Epigenetics and Biology Program (PEBC), Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet 08908, Barcelona, Catalonia, Spain
| | - Sergi Sayols
- Cancer Epigenetics and Biology Program (PEBC), Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet 08908, Barcelona, Catalonia, Spain
| | - Maryam Soleimani
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
| | - David Tamborero
- Research Program on Biomedical Informatics, IMIM Hospital del Mar Medical Research Institute and Universitat Pompeu Fabra, Barcelona 08003, Spain
| | - Nuria Lopez-Bigas
- Research Program on Biomedical Informatics, IMIM Hospital del Mar Medical Research Institute and Universitat Pompeu Fabra, Barcelona 08003, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Catalonia, Spain
| | - Petra Ross-Macdonald
- Genetically Defined Diseases and Genomics, Bristol-Myers Squibb Research and Development, Hopewell, NJ 08534, USA
| | - Manel Esteller
- Cancer Epigenetics and Biology Program (PEBC), Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet 08908, Barcelona, Catalonia, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Catalonia, Spain; Department of Physiological Sciences II of the School of Medicine, University of Barcelona, L'Hospitalet 08908, Barcelona, Catalonia, Spain
| | - Nathanael S Gray
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Biological Chemistry & Molecular Pharmacology, Harvard Medical School, Boston, MA 02215, USA
| | - Daniel A Haber
- Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
| | - Michael R Stratton
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
| | - Cyril H Benes
- Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
| | - Lodewyk F A Wessels
- Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Amsterdam 1066 CX, The Netherlands; Department of EEMCS, Delft University of Technology, Delft 2628 CD, the Netherlands; Cancer Genomics Netherlands, Uppsalalaan 8, Utrecht 3584CT, the Netherlands
| | - Julio Saez-Rodriguez
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK; Faculty of Medicine, Joint Research Centre for Computational Biomedicine, RWTH Aachen University, Aachen 52057, Germany
| | - Ultan McDermott
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK.
| | - Mathew J Garnett
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK.
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15
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Xi J, Li A. Discovering Recurrent Copy Number Aberrations in Complex Patterns via Non-Negative Sparse Singular Value Decomposition. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2016; 13:656-668. [PMID: 26372614 DOI: 10.1109/tcbb.2015.2474404] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Recurrent copy number aberrations (RCNAs) in multiple cancer samples are strongly associated with tumorigenesis, and RCNA discovery is helpful to cancer research and treatment. Despite the emergence of numerous RCNA discovering methods, most of them are unable to detect RCNAs in complex patterns that are influenced by complicating factors including aberration in partial samples, co-existing of gains and losses and normal-like tumor samples. Here, we propose a novel computational method, called non-negative sparse singular value decomposition (NN-SSVD), to address the RCNA discovering problem in complex patterns. In NN-SSVD, the measurement of RCNA is based on the aberration frequency in a part of samples rather than all samples, which can circumvent the complexity of different RCNA patterns. We evaluate NN-SSVD on synthetic dataset by comparison on detection scores and Receiver Operating Characteristics curves, and the results show that NN-SSVD outperforms existing methods in RCNA discovery and demonstrate more robustness to RCNA complicating factors. Applying our approach on a breast cancer dataset, we successfully identify a number of genomic regions that are strongly correlated with previous studies, which harbor a bunch of known breast cancer associated genes.
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16
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Jang H, Hur Y, Lee H. Identification of cancer-driver genes in focal genomic alterations from whole genome sequencing data. Sci Rep 2016; 6:25582. [PMID: 27156852 PMCID: PMC4860638 DOI: 10.1038/srep25582] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2016] [Accepted: 04/19/2016] [Indexed: 11/18/2022] Open
Abstract
DNA copy number alterations (CNAs) are the main genomic events that occur during the initiation and development of cancer. Distinguishing driver aberrant regions from passenger regions, which might contain candidate target genes for cancer therapies, is an important issue. Several methods for identifying cancer-driver genes from multiple cancer patients have been developed for single nucleotide polymorphism (SNP) arrays. However, for NGS data, methods for the SNP array cannot be directly applied because of different characteristics of NGS such as higher resolutions of data without predefined probes and incorrectly mapped reads to reference genomes. In this study, we developed a wavelet-based method for identification of focal genomic alterations for sequencing data (WIFA-Seq). We applied WIFA-Seq to whole genome sequencing data from glioblastoma multiforme, ovarian serous cystadenocarcinoma and lung adenocarcinoma, and identified focal genomic alterations, which contain candidate cancer-related genes as well as previously known cancer-driver genes.
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Affiliation(s)
- Ho Jang
- Gwangju Institute of Science and Technology, School of Electrical Engineering and Computer Science, Gwangju, 500-712, South Korea
| | - Youngmi Hur
- Yonsei University, Department of Mathematics, Seoul, 120-749, South Korea
| | - Hyunju Lee
- Gwangju Institute of Science and Technology, School of Electrical Engineering and Computer Science, Gwangju, 500-712, South Korea
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17
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Michaut M, Chin SF, Majewski I, Severson TM, Bismeijer T, de Koning L, Peeters JK, Schouten PC, Rueda OM, Bosma AJ, Tarrant F, Fan Y, He B, Xue Z, Mittempergher L, Kluin RJ, Heijmans J, Snel M, Pereira B, Schlicker A, Provenzano E, Ali HR, Gaber A, O’Hurley G, Lehn S, Muris JJ, Wesseling J, Kay E, Sammut SJ, Bardwell HA, Barbet AS, Bard F, Lecerf C, O’Connor DP, Vis DJ, Benes CH, McDermott U, Garnett MJ, Simon IM, Jirström K, Dubois T, Linn SC, Gallagher WM, Wessels LF, Caldas C, Bernards R. Integration of genomic, transcriptomic and proteomic data identifies two biologically distinct subtypes of invasive lobular breast cancer. Sci Rep 2016; 6:18517. [PMID: 26729235 PMCID: PMC4700448 DOI: 10.1038/srep18517] [Citation(s) in RCA: 118] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2015] [Accepted: 11/19/2015] [Indexed: 12/23/2022] Open
Abstract
Invasive lobular carcinoma (ILC) is the second most frequently occurring histological breast cancer subtype after invasive ductal carcinoma (IDC), accounting for around 10% of all breast cancers. The molecular processes that drive the development of ILC are still largely unknown. We have performed a comprehensive genomic, transcriptomic and proteomic analysis of a large ILC patient cohort and present here an integrated molecular portrait of ILC. Mutations in CDH1 and in the PI3K pathway are the most frequent molecular alterations in ILC. We identified two main subtypes of ILCs: (i) an immune related subtype with mRNA up-regulation of PD-L1, PD-1 and CTLA-4 and greater sensitivity to DNA-damaging agents in representative cell line models; (ii) a hormone related subtype, associated with Epithelial to Mesenchymal Transition (EMT), and gain of chromosomes 1q and 8q and loss of chromosome 11q. Using the somatic mutation rate and eIF4B protein level, we identified three groups with different clinical outcomes, including a group with extremely good prognosis. We provide a comprehensive overview of the molecular alterations driving ILC and have explored links with therapy response. This molecular characterization may help to tailor treatment of ILC through the application of specific targeted, chemo- and/or immune-therapies.
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Affiliation(s)
- Magali Michaut
- Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Suet-Feung Chin
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK
| | - Ian Majewski
- Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Tesa M. Severson
- Division of Molecular Pathology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Tycho Bismeijer
- Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Leanne de Koning
- Translational Research Department, Institut Curie, 26 rue d’Ulm, 75248 Paris cedex 05, France
| | | | - Philip C. Schouten
- Division of Molecular Pathology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Oscar M. Rueda
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK
| | - Astrid J. Bosma
- Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Finbarr Tarrant
- Cancer Biology and Therapeutics Laboratory, UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Belfield, Dublin 4, Ireland
- OncoMark Limited, NovaUCD, Belfield Innovation Park, Dublin 4, Ireland
| | - Yue Fan
- Cancer Biology and Therapeutics Laboratory, UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Belfield, Dublin 4, Ireland
| | - Beilei He
- Translational Research Department, Institut Curie, 26 rue d’Ulm, 75248 Paris cedex 05, France
| | - Zheng Xue
- Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Lorenza Mittempergher
- Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Roelof J.C. Kluin
- Genomic Core Facility, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Jeroen Heijmans
- Agendia NV, Science Park 406, 1098 XH Amsterdam, The Netherlands
| | - Mireille Snel
- Agendia NV, Science Park 406, 1098 XH Amsterdam, The Netherlands
| | - Bernard Pereira
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK
| | - Andreas Schlicker
- Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Elena Provenzano
- Cambridge Experimental Cancer Medicine Centre (ECMR) and NIHR Cambridge Biomedical Research Centre, Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK
- Cambridge Breast Unit and Cambridge University Hospitals, NHS Foundation Trust, Hills Road, Cambridge CB2 0QQ, UK
| | - Hamid Raza Ali
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK
- Department of Pathology, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QP, UK
| | - Alexander Gaber
- Department of Clinical Sciences Lund, Division of Oncology and Pathology, Lund University, SE-221 85 Lund, Sweden
| | - Gillian O’Hurley
- OncoMark Limited, NovaUCD, Belfield Innovation Park, Dublin 4, Ireland
| | - Sophie Lehn
- Department of Clinical Sciences Lund, Division of Oncology and Pathology, Lund University, SE-221 85 Lund, Sweden
| | - Jettie J.F. Muris
- Division of Molecular Pathology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Jelle Wesseling
- Division of Molecular Pathology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Elaine Kay
- Department of Pathology, RCSI ERC, Beaumont Hospital, Dublin 9, Ireland
| | - Stephen John Sammut
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK
| | - Helen A. Bardwell
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK
| | - Aurélie S. Barbet
- Translational Research Department, Institut Curie, 26 rue d’Ulm, 75248 Paris cedex 05, France
| | - Floriane Bard
- Translational Research Department, Institut Curie, 26 rue d’Ulm, 75248 Paris cedex 05, France
| | - Caroline Lecerf
- Translational Research Department, Institut Curie, 26 rue d’Ulm, 75248 Paris cedex 05, France
| | - Darran P. O’Connor
- Cancer Biology and Therapeutics Laboratory, UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Belfield, Dublin 4, Ireland
| | - Daniël J. Vis
- Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Cyril H. Benes
- Massachusetts General Hospital Cancer Center, Harvard Medical School, Charlestown, Massachusetts 02129, USA
| | - Ultan McDermott
- Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton CB10 1SA, UK
| | - Mathew J. Garnett
- Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton CB10 1SA, UK
| | - Iris M. Simon
- Agendia NV, Science Park 406, 1098 XH Amsterdam, The Netherlands
| | - Karin Jirström
- Department of Clinical Sciences Lund, Division of Oncology and Pathology, Lund University, SE-221 85 Lund, Sweden
| | - Thierry Dubois
- Translational Research Department, Institut Curie, 26 rue d’Ulm, 75248 Paris cedex 05, France
| | - Sabine C. Linn
- Division of Molecular Pathology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Division of Medical Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Department of Pathology, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - William M. Gallagher
- Cancer Biology and Therapeutics Laboratory, UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Belfield, Dublin 4, Ireland
- OncoMark Limited, NovaUCD, Belfield Innovation Park, Dublin 4, Ireland
| | - Lodewyk F.A. Wessels
- Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Department of EEMCS, Delft University of Technology, Delft, The Netherlands
| | - Carlos Caldas
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK
- Cambridge Experimental Cancer Medicine Centre (ECMR) and NIHR Cambridge Biomedical Research Centre, Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK
- Cambridge Breast Unit and Cambridge University Hospitals, NHS Foundation Trust, Hills Road, Cambridge CB2 0QQ, UK
- Department of Oncology, University of Cambridge, Addenbrooke’s Hospital, Hills Road, Cambridge, CB2 0QQ, UK
| | - Rene Bernards
- Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Agendia NV, Science Park 406, 1098 XH Amsterdam, The Netherlands
- Cancer Genomics Netherlands, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
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18
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Biesma HD, Schouten PC, Lacle MM, Sanders J, Brugman W, Kerkhoven R, Mandjes I, van der Groep P, van Diest PJ, Linn SC. Copy number profiling by array comparative genomic hybridization identifies frequently occurring BRCA2-like male breast cancer. Genes Chromosomes Cancer 2015; 54:734-44. [DOI: 10.1002/gcc.22284] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2015] [Accepted: 06/25/2015] [Indexed: 11/08/2022] Open
Affiliation(s)
- Hedde D. Biesma
- Department of Molecular Pathology; Netherlands Cancer Institute; Amsterdam The Netherlands
| | - Philip C. Schouten
- Department of Molecular Pathology; Netherlands Cancer Institute; Amsterdam The Netherlands
| | - Miangela M. Lacle
- Department of Pathology; University Medical Center Utrecht; The Netherlands
| | - Joyce Sanders
- Department of Pathology; Netherlands Cancer Institute; Amsterdam The Netherlands
| | - Wim Brugman
- Genomics Core Facility, Netherlands Cancer Institute; Amsterdam The Netherlands
| | - Ron Kerkhoven
- Genomics Core Facility, Netherlands Cancer Institute; Amsterdam The Netherlands
| | - Ingrid Mandjes
- Data Center, Netherlands Cancer Institute; Amsterdam The Netherlands
| | | | - Paul J. van Diest
- Department of Pathology; University Medical Center Utrecht; The Netherlands
| | - Sabine C. Linn
- Department of Molecular Pathology; Netherlands Cancer Institute; Amsterdam The Netherlands
- Department of Pathology; University Medical Center Utrecht; The Netherlands
- Department of Medical Oncology; Netherlands Cancer Institute; Amsterdam The Netherlands
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19
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Oleksiewicz U, Tomczak K, Woropaj J, Markowska M, Stępniak P, Shah PK. Computational characterisation of cancer molecular profiles derived using next generation sequencing. Contemp Oncol (Pozn) 2015; 19:A78-91. [PMID: 25691827 PMCID: PMC4322529 DOI: 10.5114/wo.2014.47137] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023] Open
Abstract
Our current understanding of cancer genetics is grounded on the principle that cancer arises from a clone that has accumulated the requisite somatically acquired genetic aberrations, leading to the malignant transformation. It also results in aberrent of gene and protein expression. Next generation sequencing (NGS) or deep sequencing platforms are being used to create large catalogues of changes in copy numbers, mutations, structural variations, gene fusions, gene expression, and other types of information for cancer patients. However, inferring different types of biological changes from raw reads generated using the sequencing experiments is algorithmically and computationally challenging. In this article, we outline common steps for the quality control and processing of NGS data. We highlight the importance of accurate and application-specific alignment of these reads and the methodological steps and challenges in obtaining different types of information. We comment on the importance of integrating these data and building infrastructure to analyse it. We also provide exhaustive lists of available software to obtain information and point the readers to articles comparing software for deeper insight in specialised areas. We hope that the article will guide readers in choosing the right tools for analysing oncogenomic datasets.
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Affiliation(s)
- Urszula Oleksiewicz
- Laboratory of Gene Therapy, Department of Cancer Immunology, The Greater Poland Cancer Centre, Poznan, Poland ; Department of Cancer Immunology and Diagnostics, Chair of Medical Biotechnology, Poznan University of Medical Sciences, Poznan, Poland ; These authors contributed equally to this paper
| | - Katarzyna Tomczak
- Laboratory of Gene Therapy, Department of Cancer Immunology, The Greater Poland Cancer Centre, Poznan, Poland ; Department of Cancer Immunology and Diagnostics, Chair of Medical Biotechnology, Poznan University of Medical Sciences, Poznan, Poland ; Postgraduate School of Molecular Medicine, Medical University of Warsaw, Warsaw ; These authors contributed equally to this paper
| | - Jakub Woropaj
- Poznan University of Economics, Poznań, Poland ; These authors contributed equally to this paper
| | | | | | - Parantu K Shah
- Institute for Applied Cancer Science, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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20
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Raphael BJ, Dobson JR, Oesper L, Vandin F. Identifying driver mutations in sequenced cancer genomes: computational approaches to enable precision medicine. Genome Med 2014; 6:5. [PMID: 24479672 PMCID: PMC3978567 DOI: 10.1186/gm524] [Citation(s) in RCA: 131] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
High-throughput DNA sequencing is revolutionizing the study of cancer and enabling the measurement of the somatic mutations that drive cancer development. However, the resulting sequencing datasets are large and complex, obscuring the clinically important mutations in a background of errors, noise, and random mutations. Here, we review computational approaches to identify somatic mutations in cancer genome sequences and to distinguish the driver mutations that are responsible for cancer from random, passenger mutations. First, we describe approaches to detect somatic mutations from high-throughput DNA sequencing data, particularly for tumor samples that comprise heterogeneous populations of cells. Next, we review computational approaches that aim to predict driver mutations according to their frequency of occurrence in a cohort of samples, or according to their predicted functional impact on protein sequence or structure. Finally, we review techniques to identify recurrent combinations of somatic mutations, including approaches that examine mutations in known pathways or protein-interaction networks, as well as de novo approaches that identify combinations of mutations according to statistical patterns of mutual exclusivity. These techniques, coupled with advances in high-throughput DNA sequencing, are enabling precision medicine approaches to the diagnosis and treatment of cancer.
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Affiliation(s)
- Benjamin J Raphael
- Department of Computer Science, Brown University, 115 Waterman Street, Providence, RI 02912, USA
- Center for Computational Molecular Biology, Brown University, 115 Waterman Street, Providence, RI 02912, USA
| | - Jason R Dobson
- Department of Computer Science, Brown University, 115 Waterman Street, Providence, RI 02912, USA
- Center for Computational Molecular Biology, Brown University, 115 Waterman Street, Providence, RI 02912, USA
- Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, 185 Meeting Street, Providence, RI 02912, USA
| | - Layla Oesper
- Department of Computer Science, Brown University, 115 Waterman Street, Providence, RI 02912, USA
| | - Fabio Vandin
- Department of Computer Science, Brown University, 115 Waterman Street, Providence, RI 02912, USA
- Center for Computational Molecular Biology, Brown University, 115 Waterman Street, Providence, RI 02912, USA
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