1
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Wooller SK, Pearl LH, Pearl FMG. Identifying actionable synthetically lethal cancer gene pairs using mutual exclusivity. FEBS Lett 2024; 598:2028-2039. [PMID: 38977941 DOI: 10.1002/1873-3468.14950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 04/25/2024] [Accepted: 05/09/2024] [Indexed: 07/10/2024]
Abstract
Mutually exclusive loss-of-function alterations in gene pairs are those that occur together less frequently than may be expected and may denote a synthetically lethal relationship (SSL) between the genes. SSLs can be exploited therapeutically to selectively kill cancer cells. Here, we analysed mutation, copy number variation, and methylation levels in samples from The Cancer Genome Atlas, using the hypergeometric and the Poisson binomial tests to identify mutually exclusive inactivated genes. We focused on gene pairs where one is an inactivated tumour suppressor and the other a gene whose protein product can be inhibited by known drugs. This provided an abundance of potential targeted therapeutics and repositioning opportunities for several cancers. These data are available on the MexDrugs website, https://bioinformaticslab.sussex.ac.uk/mexdrugs.
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Affiliation(s)
- Sarah K Wooller
- Bioinformatics Lab, School of Life Sciences, University of Sussex, Brighton, UK
| | - Laurence H Pearl
- Genome Damage Stability Centre, School of Life Sciences, University of Sussex, Brighton, UK
| | - Frances M G Pearl
- Bioinformatics Lab, School of Life Sciences, University of Sussex, Brighton, UK
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2
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Zhao Y, Chen J, Zheng H, Luo Y, An M, Lin Y, Pang M, Li Y, Kong Y, He W, Lin T, Chen C. SUMOylation-Driven mRNA Circularization Enhances Translation and Promotes Lymphatic Metastasis of Bladder Cancer. Cancer Res 2024; 84:434-448. [PMID: 37991737 PMCID: PMC10831341 DOI: 10.1158/0008-5472.can-23-2278] [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: 07/31/2023] [Revised: 10/10/2023] [Accepted: 11/17/2023] [Indexed: 11/23/2023]
Abstract
Aberrant gene expression is a prominent feature of metastatic cancer. Translational initiation is a vital step in fine-tuning gene expression. Thus, exploring translation initiation regulators may identify therapeutic targets for preventing and treating metastasis. Herein, we identified that DHCR24 was overexpressed in lymph node (LN) metastatic bladder cancer and correlated with poor prognosis of patients. DHCR24 promoted lymphangiogenesis and LN metastasis of bladder cancer in vitro and in vivo. Mechanistically, DHCR24 mediated and recognized the SUMO2 modification at lysine 108 of hnRNPA2B1 to foster TBK1 mRNA circularization and eIF4F initiation complex assembly by enhancing hnRNPA2B1-eIF4G1 interaction. Moreover, DHCR24 directly anchored to TBK1 mRNA 3'-untranslated region to increase its stability, thus forming a feed forward loop to elevate TBK1 expression. TBK1 activated PI3K/Akt signaling to promote VEGFC secretion, resulting in lymphangiogenesis and LN metastasis. DHCR24 silencing significantly impeded bladder cancer lymphangiogenesis and lymphatic metastasis in a patient-derived xenograft model. Collectively, these findings elucidate DHCR24-mediated translation machinery that promotes lymphatic metastasis of bladder cancer and supports the potential application of DHCR24-targeted therapy for LN-metastatic bladder cancer. SIGNIFICANCE DHCR24 is a SUMOylation regulator that controls translation initiation complex assembly and orchestrates TBK1 mRNA circularization to activate Akt/VEGFC signaling, which stimulates lymphangiogenesis and promotes lymph node metastasis in bladder cancer.
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Affiliation(s)
- Yue Zhao
- Department of Interventional Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
| | - Jiancheng Chen
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, State Key Laboratory of Oncology in South China, Guangzhou, Guangdong, P. R. China
| | - Hanhao Zheng
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, State Key Laboratory of Oncology in South China, Guangzhou, Guangdong, P. R. China
| | - Yuming Luo
- Department of General Surgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, P. R. China
| | - Mingjie An
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, State Key Laboratory of Oncology in South China, Guangzhou, Guangdong, P. R. China
| | - Yan Lin
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, State Key Laboratory of Oncology in South China, Guangzhou, Guangdong, P. R. China
| | - Mingrui Pang
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, State Key Laboratory of Oncology in South China, Guangzhou, Guangdong, P. R. China
| | - Yuanlong Li
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, State Key Laboratory of Oncology in South China, Guangzhou, Guangdong, P. R. China
| | - Yao Kong
- Department of General Surgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, P. R. China
| | - Wang He
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, State Key Laboratory of Oncology in South China, Guangzhou, Guangdong, P. R. China
| | - Tianxin Lin
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, State Key Laboratory of Oncology in South China, Guangzhou, Guangdong, P. R. China
| | - Changhao Chen
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, State Key Laboratory of Oncology in South China, Guangzhou, Guangdong, P. R. China
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3
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Guo L, Dou Y, Xia D, Yin Z, Xiang Y, Luo L, Zhang Y, Wang J, Liang T. SLOAD: a comprehensive database of cancer-specific synthetic lethal interactions for precision cancer therapy via multi-omics analysis. Database (Oxford) 2022; 2022:6677988. [PMID: 36029479 PMCID: PMC9419874 DOI: 10.1093/database/baac075] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 07/27/2022] [Accepted: 08/20/2022] [Indexed: 11/14/2022]
Abstract
Abstract
Synthetic lethality has been widely concerned because of its potential role in cancer treatment, which can be harnessed to selectively kill cancer cells via identifying inactive genes in a specific cancer type and further targeting the corresponding synthetic lethal partners. Herein, to obtain cancer-specific synthetic lethal interactions, we aimed to predict genetic interactions via a pan-cancer analysis from multiple molecular levels using random forest and then develop a user-friendly database. First, based on collected public gene pairs with synthetic lethal interactions, candidate gene pairs were analyzed via integrating multi-omics data, mainly including DNA mutation, copy number variation, methylation and mRNA expression data. Then, integrated features were used to predict cancer-specific synthetic lethal interactions using random forest. Finally, SLOAD (http://www.tmliang.cn/SLOAD) was constructed via integrating these findings, which was a user-friendly database for data searching, browsing, downloading and analyzing. These results can provide candidate cancer-specific synthetic lethal interactions, which will contribute to drug designing in cancer treatment that can promote therapy strategies based on the principle of synthetic lethality.
Database URL http://www.tmliang.cn/SLOAD/
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Affiliation(s)
- Li Guo
- Department of Bioinformatics, Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications , No. 9, Wenyuan Road, Qixia District, Nanjing, Jiangsu 210023, China
| | - Yuyang Dou
- Department of Bioinformatics, Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications , No. 9, Wenyuan Road, Qixia District, Nanjing, Jiangsu 210023, China
| | - Daoliang Xia
- Department of Bioinformatics, Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications , No. 9, Wenyuan Road, Qixia District, Nanjing, Jiangsu 210023, China
| | - Zibo Yin
- Department of Bioinformatics, Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications , No. 9, Wenyuan Road, Qixia District, Nanjing, Jiangsu 210023, China
| | - Yangyang Xiang
- Department of Bioinformatics, Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications , No. 9, Wenyuan Road, Qixia District, Nanjing, Jiangsu 210023, China
| | - Lulu Luo
- Jiangsu Key Laboratory for Molecular and Medical Biotechnology, School of Life Science, Nanjing Normal University , No. 1, Wenyuan Road, Qixia District, Nanjing, Jiangsu 210023, China
| | - Yuting Zhang
- Department of Bioinformatics, Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications , No. 9, Wenyuan Road, Qixia District, Nanjing, Jiangsu 210023, China
| | - Jun Wang
- Department of Bioinformatics, Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications , No. 9, Wenyuan Road, Qixia District, Nanjing, Jiangsu 210023, China
| | - Tingming Liang
- Jiangsu Key Laboratory for Molecular and Medical Biotechnology, School of Life Science, Nanjing Normal University , No. 1, Wenyuan Road, Qixia District, Nanjing, Jiangsu 210023, China
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4
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Tsyvina V, Zelikovsky A, Snir S, Skums P. Inference of mutability landscapes of tumors from single cell sequencing data. PLoS Comput Biol 2020; 16:e1008454. [PMID: 33253159 PMCID: PMC7728263 DOI: 10.1371/journal.pcbi.1008454] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 12/10/2020] [Accepted: 10/20/2020] [Indexed: 11/18/2022] Open
Abstract
One of the hallmarks of cancer is the extremely high mutability and genetic instability of tumor cells. Inherent heterogeneity of intra-tumor populations manifests itself in high variability of clone instability rates. Analogously to fitness landscapes, the instability rates of clonal populations form their mutability landscapes. Here, we present MULAN (MUtability LANdscape inference), a maximum-likelihood computational framework for inference of mutation rates of individual cancer subclones using single-cell sequencing data. It utilizes the partial information about the orders of mutation events provided by cancer mutation trees and extends it by inferring full evolutionary history and mutability landscape of a tumor. Evaluation of mutation rates on the level of subclones rather than individual genes allows to capture the effects of genomic interactions and epistasis. We estimate the accuracy of our approach and demonstrate that it can be used to study the evolution of genetic instability and infer tumor evolutionary history from experimental data. MULAN is available at https://github.com/compbel/MULAN.
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Affiliation(s)
- Viachaslau Tsyvina
- Department of Computer Science, Georgia State University, Atlanta, Georgia, United States of America
| | - Alex Zelikovsky
- Department of Computer Science, Georgia State University, Atlanta, Georgia, United States of America
| | - Sagi Snir
- Department of Evolutionary and Environmental Biology, University of Haifa, Haifa, Israel
| | - Pavel Skums
- Department of Computer Science, Georgia State University, Atlanta, Georgia, United States of America
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5
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Beyond Synthetic Lethality: Charting the Landscape of Pairwise Gene Expression States Associated with Survival in Cancer. Cell Rep 2020; 28:938-948.e6. [PMID: 31340155 DOI: 10.1016/j.celrep.2019.06.067] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Revised: 04/01/2019] [Accepted: 06/18/2019] [Indexed: 12/14/2022] Open
Abstract
The phenotypic effect of perturbing a gene's activity depends on the activity level of other genes, reflecting the notion that phenotypes are emergent properties of a network of functionally interacting genes. In the context of cancer, contemporary investigations have primarily focused on just one type of functional relationship between two genes-synthetic lethality (SL). Here, we define the more general concept of "survival-associated pairwise gene expression states" (SPAGEs) as gene pairs whose joint expression levels are associated with survival. We describe a data-driven approach called SPAGE-finder that when applied to The Cancer Genome Atlas (TCGA) data identified 71,946 SPAGEs spanning 12 distinct types, only a minority of which are SLs. The detected SPAGEs explain cancer driver genes' tissue specificity and differences in patients' response to drugs and stratify breast cancer tumors into refined subtypes. These results expand the scope of cancer SPAGEs and lay a conceptual basis for future studies of SPAGEs and their translational applications.
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6
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Wang R, Han Y, Zhao Z, Yang F, Chen T, Zhou W, Wang X, Qi L, Zhao W, Guo Z, Gu Y. Link synthetic lethality to drug sensitivity of cancer cells. Brief Bioinform 2020; 20:1295-1307. [PMID: 29300844 DOI: 10.1093/bib/bbx172] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2017] [Revised: 11/22/2017] [Indexed: 12/16/2022] Open
Abstract
Synthetic lethal (SL) interactions occur when alterations in two genes lead to cell death but alteration in only one of them is not lethal. SL interactions provide a new strategy for molecular-targeted cancer therapy. Currently, there are few drugs targeting SL interactions that entered into clinical trials. Therefore, it is necessary to investigate the link between SL interactions and drug sensitivity of cancer cells systematically for drug development purpose. We identified SL interactions by integrating the high-throughput data from The Cancer Genome Atlas, small hairpin RNA data and genetic interactions of yeast. By integrating SL interactions from other studies, we tested whether the SL pairs that consist of drug target genes and the genes with genomic alterations are related with drug sensitivity of cancer cells. We found that only 6.26%∼34.61% of SL interactions showed the expected significant drug sensitivity using the pooled cancer cell line data from different tissues, but the proportion increased significantly to approximately 90% using the cancer cell line data for each specific tissue. From an independent pharmacogenomics data of 41 breast cancer cell lines, we found three SL interactions (ABL1-IFI16, ABL1-SLC50A1 and ABL1-SYT11) showed significantly better prognosis for the patients with both genes being altered than the patients with only one gene being altered, which partially supports the SL effect between the gene pairs. Our study not only provides a new way for unraveling the complex mechanisms of drug sensitivity but also suggests numerous potentially important drug targets for cancer therapy.
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7
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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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8
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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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9
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Fernández E, Collins MO, Frank RAW, Zhu F, Kopanitsa MV, Nithianantharajah J, Lemprière SA, Fricker D, Elsegood KA, McLaughlin CL, Croning MDR, Mclean C, Armstrong JD, Hill WD, Deary IJ, Cencelli G, Bagni C, Fromer M, Purcell SM, Pocklington AJ, Choudhary JS, Komiyama NH, Grant SGN. Arc Requires PSD95 for Assembly into Postsynaptic Complexes Involved with Neural Dysfunction and Intelligence. Cell Rep 2018; 21:679-691. [PMID: 29045836 PMCID: PMC5656750 DOI: 10.1016/j.celrep.2017.09.045] [Citation(s) in RCA: 67] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2015] [Revised: 08/03/2017] [Accepted: 09/13/2017] [Indexed: 12/12/2022] Open
Abstract
Arc is an activity-regulated neuronal protein, but little is known about its interactions, assembly into multiprotein complexes, and role in human disease and cognition. We applied an integrated proteomic and genetic strategy by targeting a tandem affinity purification (TAP) tag and Venus fluorescent protein into the endogenous Arc gene in mice. This allowed biochemical and proteomic characterization of native complexes in wild-type and knockout mice. We identified many Arc-interacting proteins, of which PSD95 was the most abundant. PSD95 was essential for Arc assembly into 1.5-MDa complexes and activity-dependent recruitment to excitatory synapses. Integrating human genetic data with proteomic data showed that Arc-PSD95 complexes are enriched in schizophrenia, intellectual disability, autism, and epilepsy mutations and normal variants in intelligence. We propose that Arc-PSD95 postsynaptic complexes potentially affect human cognitive function. TAP tag and purification of endogenous Arc protein complexes from the mouse brain PSD95 is the major Arc binding protein, and both assemble into 1.5-MDa supercomplexes PSD95 is essential for recruitment of Arc to synapses Mutations and genetic variants in Arc-PSD95 are linked to cognition
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Affiliation(s)
- Esperanza Fernández
- Genes to Cognition Programme, The Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK; KU Leuven, Center for Human Genetics and Leuven Institute for Neurodegenerative Diseases (LIND), and VIB Center for the Biology of Disease, Leuven, Belgium
| | - Mark O Collins
- Proteomic Mass Spectrometry, The Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK
| | - René A W Frank
- Genes to Cognition Programme, The Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK; Medical Research Council Laboratory of Molecular Biology, Cambridge, UK
| | - Fei Zhu
- Genes to Cognition Programme, The Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK; Genes to Cognition Programme, Centre for Clinical Brain Science, University of Edinburgh, Edinburgh, UK
| | - Maksym V Kopanitsa
- Genes to Cognition Programme, The Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK; Synome Ltd., Moneta Building, Babraham Research Campus, Cambridge, UK
| | - Jess Nithianantharajah
- Genes to Cognition Programme, The Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK; Genes to Cognition Programme, Centre for Clinical Brain Science, University of Edinburgh, Edinburgh, UK
| | - Sarah A Lemprière
- Genes to Cognition Programme, Centre for Clinical Brain Science, University of Edinburgh, Edinburgh, UK
| | - David Fricker
- Genes to Cognition Programme, The Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK; Synome Ltd., Moneta Building, Babraham Research Campus, Cambridge, UK
| | - Kathryn A Elsegood
- Genes to Cognition Programme, The Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK; Genes to Cognition Programme, Centre for Clinical Brain Science, University of Edinburgh, Edinburgh, UK
| | - Catherine L McLaughlin
- Genes to Cognition Programme, Centre for Clinical Brain Science, University of Edinburgh, Edinburgh, UK
| | - Mike D R Croning
- Genes to Cognition Programme, Centre for Clinical Brain Science, University of Edinburgh, Edinburgh, UK
| | - Colin Mclean
- School of Informatics, Institute for Adaptive and Neural Computation, University of Edinburgh, UK
| | - J Douglas Armstrong
- School of Informatics, Institute for Adaptive and Neural Computation, University of Edinburgh, UK
| | - W David Hill
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, UK
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, UK
| | - Giulia Cencelli
- KU Leuven, Center for Human Genetics and Leuven Institute for Neurodegenerative Diseases (LIND), and VIB Center for the Biology of Disease, Leuven, Belgium; Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy
| | - Claudia Bagni
- KU Leuven, Center for Human Genetics and Leuven Institute for Neurodegenerative Diseases (LIND), and VIB Center for the Biology of Disease, Leuven, Belgium; Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy
| | - Menachem Fromer
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Shaun M Purcell
- Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Andrew J Pocklington
- Institute of Psychological Medicine & Clinical Neurosciences, University of Cardiff, Cardiff, Wales, UK
| | - Jyoti S Choudhary
- Proteomic Mass Spectrometry, The Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK
| | - Noboru H Komiyama
- Genes to Cognition Programme, The Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK; Genes to Cognition Programme, Centre for Clinical Brain Science, University of Edinburgh, Edinburgh, UK
| | - Seth G N Grant
- Genes to Cognition Programme, The Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK; Genes to Cognition Programme, Centre for Clinical Brain Science, University of Edinburgh, Edinburgh, UK.
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10
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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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11
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Benstead-Hume G, Wooller SK, Pearl FM. Computational Approaches to Identify Genetic Interactions for Cancer Therapeutics. J Integr Bioinform 2017; 14:/j/jib.2017.14.issue-3/jib-2017-0027/jib-2017-0027.xml. [PMID: 28941356 PMCID: PMC6042820 DOI: 10.1515/jib-2017-0027] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2017] [Revised: 07/28/2017] [Accepted: 08/10/2017] [Indexed: 12/17/2022] Open
Abstract
The development of improved cancer therapies is frequently cited as an urgent unmet medical need. Here we describe how genetic interactions are being therapeutically exploited to identify novel targeted treatments for cancer. We discuss the current methodologies that use 'omics data to identify genetic interactions, in particular focusing on synthetic sickness lethality (SSL) and synthetic dosage lethality (SDL). We describe the experimental and computational approaches undertaken both in humans and model organisms to identify these interactions. Finally we discuss some of the identified targets with licensed drugs, inhibitors in clinical trials or with compounds under development.
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12
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Matlak D, Szczurek E. Epistasis in genomic and survival data of cancer patients. PLoS Comput Biol 2017; 13:e1005626. [PMID: 28678836 PMCID: PMC5517071 DOI: 10.1371/journal.pcbi.1005626] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2017] [Revised: 07/19/2017] [Accepted: 06/14/2017] [Indexed: 12/19/2022] Open
Abstract
Cancer aggressiveness and its effect on patient survival depends on mutations in the tumor genome. Epistatic interactions between the mutated genes may guide the choice of anticancer therapy and set predictive factors of its success. Inhibitors targeting synthetic lethal partners of genes mutated in tumors are already utilized for efficient and specific treatment in the clinic. The space of possible epistatic interactions, however, is overwhelming, and computational methods are needed to limit the experimental effort of validating the interactions for therapy and characterizing their biomarkers. Here, we introduce SurvLRT, a statistical likelihood ratio test for identifying epistatic gene pairs and triplets from cancer patient genomic and survival data. Compared to established approaches, SurvLRT performed favorable in predicting known, experimentally verified synthetic lethal partners of PARP1 from TCGA data. Our approach is the first to test for epistasis between triplets of genes to identify biomarkers of synthetic lethality-based therapy. SurvLRT proved successful in identifying the known gene TP53BP1 as the biomarker of success of the therapy targeting PARP in BRCA1 deficient tumors. Search for other biomarkers for the same interaction revealed a region whose deletion was a more significant biomarker than deletion of TP53BP1. With the ability to detect not only pairwise but twelve different types of triple epistasis, applicability of SurvLRT goes beyond cancer therapy, to the level of characterization of shapes of fitness landscapes. Genomic alterations in tumors affect the fitness of tumor cells, controlling how well they replicate and survive compared to other cells. The landscape of tumor fitness is shaped by epistasis. Epistasis occurs when the contribution of gene alterations to the total fitness is non-linear. The type of epistatic genetic interactions with great potential for cancer therapy is synthetic lethality. Inhibitors targeting synthetic lethal partners of genes mutated in tumors can selectively kill tumor and not normal cells. Therapy based on synthetic lethality is, however, context dependent, and it is crucial to identify its biomarkers. Unfortunately, the space of possible interactions and their biomarkers is overwhelming for experimental validation. Computational pre-selection methods are required to limit the experimental effort. Here, we introduce a statistical approach called SurvLRT, for the identification of epistatic gene pairs and triplets based on patient genomic and survival data. First, we show that using SurvLRT, we can deliver synthetic lethal interactions of pairs of genes that are specific to cancer. Second, we demonstrate the applicability of SurvLRT to identify biomarkers for synthetic lethality, such as mutational status of other genes that can alleviate the synthetic effect.
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Affiliation(s)
- Dariusz Matlak
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland
| | - Ewa Szczurek
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland
- * E-mail:
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13
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Szappanos B, Fritzemeier J, Csörgő B, Lázár V, Lu X, Fekete G, Bálint B, Herczeg R, Nagy I, Notebaart RA, Lercher MJ, Pál C, Papp B. Adaptive evolution of complex innovations through stepwise metabolic niche expansion. Nat Commun 2016; 7:11607. [PMID: 27197754 PMCID: PMC5411730 DOI: 10.1038/ncomms11607] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2015] [Accepted: 04/12/2016] [Indexed: 11/09/2022] Open
Abstract
A central challenge in evolutionary biology concerns the mechanisms by which complex metabolic innovations requiring multiple mutations arise. Here, we propose that metabolic innovations accessible through the addition of a single reaction serve as stepping stones towards the later establishment of complex metabolic features in another environment. We demonstrate the feasibility of this hypothesis through three complementary analyses. First, using genome-scale metabolic modelling, we show that complex metabolic innovations in Escherichia coli can arise via changing nutrient conditions. Second, using phylogenetic approaches, we demonstrate that the acquisition patterns of complex metabolic pathways during the evolutionary history of bacterial genomes support the hypothesis. Third, we show how adaptation of laboratory populations of E. coli to one carbon source facilitates the later adaptation to another carbon source. Our work demonstrates how complex innovations can evolve through series of adaptive steps without the need to invoke non-adaptive processes.
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Affiliation(s)
- Balázs Szappanos
- Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre of the Hungarian Academy of Sciences, Temesvári krt. 62, Szeged H-6726, Hungary
| | - Jonathan Fritzemeier
- Department for Computer Science, Heinrich Heine University, Universitätsstraße 1, Düsseldorf D-40221, Germany
| | - Bálint Csörgő
- Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre of the Hungarian Academy of Sciences, Temesvári krt. 62, Szeged H-6726, Hungary
| | - Viktória Lázár
- Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre of the Hungarian Academy of Sciences, Temesvári krt. 62, Szeged H-6726, Hungary
| | - Xiaowen Lu
- Department of Bioinformatics (CMBI), Radboud University Medical Centre, Geert Grooteplein Zuid 26–28, Nijmegen 6525 GA, The Netherlands
| | - Gergely Fekete
- Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre of the Hungarian Academy of Sciences, Temesvári krt. 62, Szeged H-6726, Hungary
| | - Balázs Bálint
- SeqOmics Biotechnology Ltd, Vállalkozók útja 7, Mórahalom H-6782, Hungary
| | - Róbert Herczeg
- SeqOmics Biotechnology Ltd, Vállalkozók útja 7, Mórahalom H-6782, Hungary
| | - István Nagy
- SeqOmics Biotechnology Ltd, Vállalkozók útja 7, Mórahalom H-6782, Hungary
- Sequencing Platform, Institute of Biochemistry, Biological Research Centre of the Hungarian Academy of Sciences, Temesvári krt. 62, Szeged H-6726, Hungary
| | - Richard A. Notebaart
- Department of Bioinformatics (CMBI), Radboud University Medical Centre, Geert Grooteplein Zuid 26–28, Nijmegen 6525 GA, The Netherlands
- Department of Internal Medicine, Radboud University Medical Center, Geert Grooteplein Zuid 8, Nijmegen 6525 GA, The Netherlands
| | - Martin J. Lercher
- Department for Computer Science, Heinrich Heine University, Universitätsstraße 1, Düsseldorf D-40221, Germany
| | - Csaba Pál
- Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre of the Hungarian Academy of Sciences, Temesvári krt. 62, Szeged H-6726, Hungary
| | - Balázs Papp
- Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre of the Hungarian Academy of Sciences, Temesvári krt. 62, Szeged H-6726, Hungary
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14
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Guo J, Liu H, Zheng J. SynLethDB: synthetic lethality database toward discovery of selective and sensitive anticancer drug targets. Nucleic Acids Res 2015; 44:D1011-7. [PMID: 26516187 PMCID: PMC4702809 DOI: 10.1093/nar/gkv1108] [Citation(s) in RCA: 90] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2015] [Accepted: 10/09/2015] [Indexed: 02/03/2023] Open
Abstract
Synthetic lethality (SL) is a type of genetic interaction between two genes such that simultaneous perturbations of the two genes result in cell death or a dramatic decrease of cell viability, while a perturbation of either gene alone is not lethal. SL reflects the biologically endogenous difference between cancer cells and normal cells, and thus the inhibition of SL partners of genes with cancer-specific mutations could selectively kill cancer cells but spare normal cells. Therefore, SL is emerging as a promising anticancer strategy that could potentially overcome the drawbacks of traditional chemotherapies by reducing severe side effects. Researchers have developed experimental technologies and computational prediction methods to identify SL gene pairs on human and a few model species. However, there has not been a comprehensive database dedicated to collecting SL pairs and related knowledge. In this paper, we propose a comprehensive database, SynLethDB (http://histone.sce.ntu.edu.sg/SynLethDB/), which contains SL pairs collected from biochemical assays, other related databases, computational predictions and text mining results on human and four model species, i.e. mouse, fruit fly, worm and yeast. For each SL pair, a confidence score was calculated by integrating individual scores derived from different evidence sources. We also developed a statistical analysis module to estimate the druggability and sensitivity of cancer cells upon drug treatments targeting human SL partners, based on large-scale genomic data, gene expression profiles and drug sensitivity profiles on more than 1000 cancer cell lines. To help users access and mine the wealth of the data, we developed other practical functionalities, such as search and filtering, orthology search, gene set enrichment analysis. Furthermore, a user-friendly web interface has been implemented to facilitate data analysis and interpretation. With the integrated data sets and analytics functionalities, SynLethDB would be a useful resource for biomedical research community and pharmaceutical industry.
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Affiliation(s)
- Jing Guo
- School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore
| | - Hui Liu
- School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore Lab of Information Management, Changzhou University, Jiangsu 213164, China
| | - Jie Zheng
- School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore Genome Institute of Singapore (GIS), Biopolis, Singapore 138672, Singapore
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15
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Madhukar NS, Elemento O, Pandey G. Prediction of Genetic Interactions Using Machine Learning and Network Properties. Front Bioeng Biotechnol 2015; 3:172. [PMID: 26579514 PMCID: PMC4620407 DOI: 10.3389/fbioe.2015.00172] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2015] [Accepted: 10/12/2015] [Indexed: 12/04/2022] Open
Abstract
A genetic interaction (GI) is a type of interaction where the effect of one gene is modified by the effect of one or several other genes. These interactions are important for delineating functional relationships among genes and their corresponding proteins, as well as elucidating complex biological processes and diseases. An important type of GI - synthetic sickness or synthetic lethality - involves two or more genes, where the loss of either gene alone has little impact on cell viability, but the combined loss of all genes leads to a severe decrease in fitness (sickness) or cell death (lethality). The identification of GIs is an important problem for it can help delineate pathways, protein complexes, and regulatory dependencies. Synthetic lethal interactions have important clinical and biological significance, such as providing therapeutically exploitable weaknesses in tumors. While near systematic high-content screening for GIs is possible in single cell organisms such as yeast, the systematic discovery of GIs is extremely difficult in mammalian cells. Therefore, there is a great need for computational approaches to reliably predict GIs, including synthetic lethal interactions, in these organisms. Here, we review the state-of-the-art approaches, strategies, and rigorous evaluation methods for learning and predicting GIs, both under general (healthy/standard laboratory) conditions and under specific contexts, such as diseases.
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Affiliation(s)
- Neel S Madhukar
- Department of Physiology and Biophysics, Meyer Cancer Center, Institute for Precision Medicine and Institute for Computational Biomedicine, Weill Cornell Medical College , New York, NY , USA ; Tri-Institutional Training Program in Computational Biology and Medicine , New York, NY , USA
| | - Olivier Elemento
- Department of Physiology and Biophysics, Meyer Cancer Center, Institute for Precision Medicine and Institute for Computational Biomedicine, Weill Cornell Medical College , New York, NY , USA ; Tri-Institutional Training Program in Computational Biology and Medicine , New York, NY , USA
| | - Gaurav Pandey
- Department of Genetics and Genomic Sciences and Graduate School of Biomedical Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai , New York, NY , USA
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16
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Synthetic dosage lethality in the human metabolic network is highly predictive of tumor growth and cancer patient survival. Proc Natl Acad Sci U S A 2015; 112:12217-22. [PMID: 26371301 DOI: 10.1073/pnas.1508573112] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Synthetic dosage lethality (SDL) denotes a genetic interaction between two genes whereby the underexpression of gene A combined with the overexpression of gene B is lethal. SDLs offer a promising way to kill cancer cells by inhibiting the activity of SDL partners of activated oncogenes in tumors, which are often difficult to target directly. As experimental genome-wide SDL screens are still scarce, here we introduce a network-level computational modeling framework that quantitatively predicts human SDLs in metabolism. For each enzyme pair (A, B) we systematically knock out the flux through A combined with a stepwise flux increase through B and search for pairs that reduce cellular growth more than when either enzyme is perturbed individually. The predictive signal of the emerging network of 12,000 SDLs is demonstrated in five different ways. (i) It can be successfully used to predict gene essentiality in shRNA cancer cell line screens. Moving to clinical tumors, we show that (ii) SDLs are significantly underrepresented in tumors. Furthermore, breast cancer tumors with SDLs active (iii) have smaller sizes and (iv) result in increased patient survival, indicating that activation of SDLs increases cancer vulnerability. Finally, (v) patient survival improves when multiple SDLs are present, pointing to a cumulative effect. This study lays the basis for quantitative identification of cancer SDLs in a model-based mechanistic manner. The approach presented can be used to identify SDLs in species and cell types in which "omics" data necessary for data-driven identification are missing.
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17
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Sveen A, Kilpinen S, Ruusulehto A, Lothe RA, Skotheim RI. Aberrant RNA splicing in cancer; expression changes and driver mutations of splicing factor genes. Oncogene 2015; 35:2413-27. [PMID: 26300000 DOI: 10.1038/onc.2015.318] [Citation(s) in RCA: 333] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Revised: 07/22/2015] [Accepted: 07/22/2015] [Indexed: 02/07/2023]
Abstract
Alternative splicing is a widespread process contributing to structural transcript variation and proteome diversity. In cancer, the splicing process is commonly disrupted, resulting in both functional and non-functional end-products. Cancer-specific splicing events are known to contribute to disease progression; however, the dysregulated splicing patterns found on a genome-wide scale have until recently been less well-studied. In this review, we provide an overview of aberrant RNA splicing and its regulation in cancer. We then focus on the executors of the splicing process. Based on a comprehensive catalog of splicing factor encoding genes and analyses of available gene expression and somatic mutation data, we identify cancer-associated patterns of dysregulation. Splicing factor genes are shown to be significantly differentially expressed between cancer and corresponding normal samples, and to have reduced inter-individual expression variation in cancer. Furthermore, we identify enrichment of predicted cancer-critical genes among the splicing factors. In addition to previously described oncogenic splicing factor genes, we propose 24 novel cancer-critical splicing factors predicted from somatic mutations.
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Affiliation(s)
- A Sveen
- Department of Molecular Oncology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway.,K.G. Jebsen Colorectal Cancer Research Centre, Oslo University Hospital, Oslo, Norway.,Centre for Cancer Biomedicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | | | | | - R A Lothe
- Department of Molecular Oncology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway.,K.G. Jebsen Colorectal Cancer Research Centre, Oslo University Hospital, Oslo, Norway.,Centre for Cancer Biomedicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - R I Skotheim
- Department of Molecular Oncology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway.,K.G. Jebsen Colorectal Cancer Research Centre, Oslo University Hospital, Oslo, Norway.,Centre for Cancer Biomedicine, Faculty of Medicine, University of Oslo, Oslo, Norway
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18
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Calzone L, Barillot E, Zinovyev A. Predicting genetic interactions from Boolean models of biological networks. Integr Biol (Camb) 2015; 7:921-9. [PMID: 25958956 DOI: 10.1039/c5ib00029g] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Genetic interaction can be defined as a deviation of the phenotypic quantitative effect of a double gene mutation from the effect predicted from single mutations using a simple (e.g., multiplicative or linear additive) statistical model. Experimentally characterized genetic interaction networks in model organisms provide important insights into relationships between different biological functions. We describe a computational methodology allowing us to systematically and quantitatively characterize a Boolean mathematical model of a biological network in terms of genetic interactions between all loss of function and gain of function mutations with respect to all model phenotypes or outputs. We use the probabilistic framework defined in MaBoSS software, based on continuous time Markov chains and stochastic simulations. In addition, we suggest several computational tools for studying the distribution of double mutants in the space of model phenotype probabilities. We demonstrate this methodology on three published models for each of which we derive the genetic interaction networks and analyze their properties. We classify the obtained interactions according to their class of epistasis, dependence on the chosen initial conditions and the phenotype. The use of this methodology for validating mathematical models from experimental data and designing new experiments is discussed.
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19
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Lu X, Megchelenbrink W, Notebaart RA, Huynen MA. Predicting human genetic interactions from cancer genome evolution. PLoS One 2015; 10:e0125795. [PMID: 25933428 PMCID: PMC4416779 DOI: 10.1371/journal.pone.0125795] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2014] [Accepted: 03/25/2015] [Indexed: 11/18/2022] Open
Abstract
Synthetic Lethal (SL) genetic interactions play a key role in various types of biological research, ranging from understanding genotype-phenotype relationships to identifying drug-targets against cancer. Despite recent advances in empirical measuring SL interactions in human cells, the human genetic interaction map is far from complete. Here, we present a novel approach to predict this map by exploiting patterns in cancer genome evolution. First, we show that empirically determined SL interactions are reflected in various gene presence, absence, and duplication patterns in hundreds of cancer genomes. The most evident pattern that we discovered is that when one member of an SL interaction gene pair is lost, the other gene tends not to be lost, i.e. the absence of co-loss. This observation is in line with expectation, because the loss of an SL interacting pair will be lethal to the cancer cell. SL interactions are also reflected in gene expression profiles, such as an under representation of cases where the genes in an SL pair are both under expressed, and an over representation of cases where one gene of an SL pair is under expressed, while the other one is over expressed. We integrated the various previously unknown cancer genome patterns and the gene expression patterns into a computational model to identify SL pairs. This simple, genome-wide model achieves a high prediction power (AUC = 0.75) for known genetic interactions. It allows us to present for the first time a comprehensive genome-wide list of SL interactions with a high estimated prediction precision, covering up to 591,000 gene pairs. This unique list can potentially be used in various application areas ranging from biotechnology to medical genetics.
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Affiliation(s)
- Xiaowen Lu
- Department of Bioinformatics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Wout Megchelenbrink
- Department of Bioinformatics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Centre, Nijmegen, The Netherlands
- Institute for Computing and Information Sciences, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Richard A. Notebaart
- Department of Bioinformatics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Centre, Nijmegen, The Netherlands
- Centre for Systems Biology and Bioenergetics, Radboud University Medical Centre, Nijmegen, The Netherlands
- * E-mail: (RAN); (MAH)
| | - Martijn A. Huynen
- Department of Bioinformatics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Centre, Nijmegen, The Netherlands
- Centre for Systems Biology and Bioenergetics, Radboud University Medical Centre, Nijmegen, The Netherlands
- * E-mail: (RAN); (MAH)
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20
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Marashi SA, Hosseini Z. On correlated reaction sets and coupled reaction sets in metabolic networks. J Bioinform Comput Biol 2015; 13:1571003. [PMID: 25747383 DOI: 10.1142/s0219720015710031] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Two reactions are in the same "correlated reaction set" (or "Co-Set") if their fluxes are linearly correlated. On the other hand, two reactions are "coupled" if nonzero flux through one reaction implies nonzero flux through the other reaction. Flux correlation analysis has been previously used in the analysis of enzyme dysregulation and enzymopathy, while flux coupling analysis has been used to predict co-expression of genes and to model network evolution. The goal of this paper is to emphasize, through a few examples, that these two concepts are inherently different. In other words, except for the case of full coupling, which implies perfect correlation between two fluxes (R(2) = 1), there are no constraints on Pearson correlation coefficients (CC) in case of any other type of (un)coupling relations. In other words, Pearson CC can take any value between 0 and 1 in other cases. Furthermore, by analyzing genome-scale metabolic networks, we confirm that there are some examples in real networks of bacteria, yeast and human, which approve that flux coupling and flux correlation cannot be used interchangeably.
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Affiliation(s)
- Sayed-Amir Marashi
- Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran.,Department of Bioinformatics, School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | - Zhaleh Hosseini
- Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran
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21
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Jerby-Arnon L, Pfetzer N, Waldman YY, McGarry L, James D, Shanks E, Seashore-Ludlow B, Weinstock A, Geiger T, Clemons PA, Gottlieb E, Ruppin E. Predicting cancer-specific vulnerability via data-driven detection of synthetic lethality. Cell 2014; 158:1199-1209. [PMID: 25171417 DOI: 10.1016/j.cell.2014.07.027] [Citation(s) in RCA: 184] [Impact Index Per Article: 18.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2014] [Revised: 03/25/2014] [Accepted: 07/18/2014] [Indexed: 12/29/2022]
Abstract
Synthetic lethality occurs when the inhibition of two genes is lethal while the inhibition of each single gene is not. It can be harnessed to selectively treat cancer by identifying inactive genes in a given cancer and targeting their synthetic lethal (SL) partners. We present a data-driven computational pipeline for the genome-wide identification of SL interactions in cancer by analyzing large volumes of cancer genomic data. First, we show that the approach successfully captures known SL partners of tumor suppressors and oncogenes. We then validate SL predictions obtained for the tumor suppressor VHL. Next, we construct a genome-wide network of SL interactions in cancer and demonstrate its value in predicting gene essentiality and clinical prognosis. Finally, we identify synthetic lethality arising from gene overactivation and use it to predict drug efficacy. These results form a computational basis for exploiting synthetic lethality to uncover cancer-specific susceptibilities.
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Affiliation(s)
- Livnat Jerby-Arnon
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 6997801, Israel.
| | - Nadja Pfetzer
- Cancer Research UK, The Beatson Institute for Cancer Research, Switchback Road, Glasgow G61 1BD, Scotland, UK
| | - Yedael Y Waldman
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Lynn McGarry
- Cancer Research UK, The Beatson Institute for Cancer Research, Switchback Road, Glasgow G61 1BD, Scotland, UK
| | - Daniel James
- Cancer Research UK, The Beatson Institute for Cancer Research, Switchback Road, Glasgow G61 1BD, Scotland, UK
| | - Emma Shanks
- Cancer Research UK, The Beatson Institute for Cancer Research, Switchback Road, Glasgow G61 1BD, Scotland, UK
| | - Brinton Seashore-Ludlow
- Center for the Science of Therapeutics, Broad Institute of Harvard and MIT, 7 Cambridge Center, Cambridge, MA 02142, USA
| | - Adam Weinstock
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Tamar Geiger
- The Sackler School of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Paul A Clemons
- Center for the Science of Therapeutics, Broad Institute of Harvard and MIT, 7 Cambridge Center, Cambridge, MA 02142, USA
| | - Eyal Gottlieb
- Cancer Research UK, The Beatson Institute for Cancer Research, Switchback Road, Glasgow G61 1BD, Scotland, UK
| | - Eytan Ruppin
- 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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22
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Jeon J, Nim S, Teyra J, Datti A, Wrana JL, Sidhu SS, Moffat J, Kim PM. A systematic approach to identify novel cancer drug targets using machine learning, inhibitor design and high-throughput screening. Genome Med 2014; 6:57. [PMID: 25165489 PMCID: PMC4143549 DOI: 10.1186/s13073-014-0057-7] [Citation(s) in RCA: 69] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2014] [Accepted: 07/18/2014] [Indexed: 12/14/2022] Open
Abstract
We present an integrated approach that predicts and validates novel anti-cancer drug targets. We first built a classifier that integrates a variety of genomic and systematic datasets to prioritize drug targets specific for breast, pancreatic and ovarian cancer. We then devised strategies to inhibit these anti-cancer drug targets and selected a set of targets that are amenable to inhibition by small molecules, antibodies and synthetic peptides. We validated the predicted drug targets by showing strong anti-proliferative effects of both synthetic peptide and small molecule inhibitors against our predicted targets.
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Affiliation(s)
- Jouhyun Jeon
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1 Canada
| | - Satra Nim
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1 Canada
| | - Joan Teyra
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1 Canada
| | - Alessandro Datti
- Center for Systems Biology, Samuel Lunenfeld Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, ON M5S 3E1 Canada
- Department of Agricultural, Food and Environmental Sciences, University of Perugia, Perugia, 06100 Italy
| | - Jeffrey L Wrana
- Center for Systems Biology, Samuel Lunenfeld Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, ON M5S 3E1 Canada
| | - Sachdev S Sidhu
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1 Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1 Canada
| | - Jason Moffat
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1 Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1 Canada
| | - Philip M Kim
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1 Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1 Canada
- Department of Computer Science, University of Toronto, Toronto, ON M5S 3E1 Canada
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23
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Abstract
Proteins are not monolithic entities; rather, they can contain multiple domains that mediate distinct interactions, and their functionality can be regulated through post-translational modifications at multiple distinct sites. Traditionally, network biology has ignored such properties of proteins and has instead examined either the physical interactions of whole proteins or the consequences of removing entire genes. In this Review, we discuss experimental and computational methods to increase the resolution of protein-protein, genetic and drug-gene interaction studies to the domain and residue levels. Such work will be crucial for using interaction networks to connect sequence and structural information, and to understand the biological consequences of disease-associated mutations, which will hopefully lead to more effective therapeutic strategies.
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