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Dou Y, Ren Y, Zhao X, Jin J, Xiong S, Luo L, Xu X, Yang X, Yu J, Guo L, Liang T. CSSLdb: Discovery of cancer-specific synthetic lethal interactions based on machine learning and statistic inference. Comput Biol Med 2024; 170:108066. [PMID: 38310806 DOI: 10.1016/j.compbiomed.2024.108066] [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: 11/04/2023] [Revised: 12/22/2023] [Accepted: 01/27/2024] [Indexed: 02/06/2024]
Abstract
Synthetic lethality (SL) occurs when the inactivation of two genes results in cell death while the inactivation of either gene alone is non-lethal. SL-based therapy has become a promising anti-cancer treatment option with the increasing researches and applications in clinical practice, while the specific therapeutic opportunities for various cancers have not yet been comprehensively investigated. Herein, we described a computational approach based on machine learning and statistical inference to discover the cancer-specific synthetic lethal interactions. First, Random Forest and One-Class SVM were used to perform cancer unbiased prediction of synthetic lethality. Then, two strategies, including mutual exclusivity and differential expression, were used to screen cancer-specific synthetic lethal interactions, resulting in 14,582 SL gene pairs in 33 cancer types. Finally, we developed a freely available database of CSSLdb (Cancer Specific Synthetic Lethality Database, http://www.tmliang.cn/CSSL/) to present cancer-specific synthetic lethal genetic interactions, which would enrich the relevant research and contribute to underlying therapy strategies based on synthetic lethality.
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Affiliation(s)
- Yuyang Dou
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Yujie Ren
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Xinmiao Zhao
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Jiaming Jin
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Shizheng Xiong
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Lulu Luo
- Jiangsu Key Laboratory for Molecular and Medical Biotechnology, School of Life Science, Nanjing Normal University, Nanjing, 210023, China
| | - Xinru Xu
- Jiangsu Key Laboratory for Molecular and Medical Biotechnology, School of Life Science, Nanjing Normal University, Nanjing, 210023, China
| | - Xueni Yang
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Jiafeng Yu
- Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou, 253023, China
| | - Li Guo
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications, Nanjing, 210023, China.
| | - Tingming Liang
- Jiangsu Key Laboratory for Molecular and Medical Biotechnology, School of Life Science, Nanjing Normal University, Nanjing, 210023, China.
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2
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Du Y, Luo L, Xu X, Yang X, Yang X, Xiong S, Yu J, Liang T, Guo L. Unleashing the Power of Synthetic Lethality: Augmenting Treatment Efficacy through Synergistic Integration with Chemotherapy Drugs. Pharmaceutics 2023; 15:2433. [PMID: 37896193 PMCID: PMC10610204 DOI: 10.3390/pharmaceutics15102433] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 09/28/2023] [Accepted: 10/06/2023] [Indexed: 10/29/2023] Open
Abstract
Cancer is the second leading cause of death in the world, and chemotherapy is one of the main methods of cancer treatment. However, the resistance of cancer cells to chemotherapeutic drugs has always been the main reason affecting the therapeutic effect. Synthetic lethality has emerged as a promising approach to augment the sensitivity of cancer cells to chemotherapy agents. Synthetic lethality (SL) refers to the specific cell death resulting from the simultaneous mutation of two non-lethal genes, which individually allow cell survival. This comprehensive review explores the classification of SL, screening methods, and research advancements in SL inhibitors, including Poly (ADP-ribose) polymerase (PARP) inhibitors, Ataxia telangiectasia and Rad3-related (ATR) inhibitors, WEE1 G2 checkpoint kinase (WEE1) inhibitors, and protein arginine methyltransferase 5 (PRMT5) inhibitors. Emphasizing their combined use with chemotherapy drugs, we aim to unveil more effective treatment strategies for cancer patients.
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Affiliation(s)
- Yajing Du
- Jiangsu Key Laboratory for Molecular and Medical Biotechnology, School of Life Science, Nanjing Normal University, Nanjing 210023, China; (Y.D.); (L.L.); (X.X.); (X.Y.)
| | - Lulu Luo
- Jiangsu Key Laboratory for Molecular and Medical Biotechnology, School of Life Science, Nanjing Normal University, Nanjing 210023, China; (Y.D.); (L.L.); (X.X.); (X.Y.)
| | - Xinru Xu
- Jiangsu Key Laboratory for Molecular and Medical Biotechnology, School of Life Science, Nanjing Normal University, Nanjing 210023, China; (Y.D.); (L.L.); (X.X.); (X.Y.)
| | - Xinbing Yang
- Jiangsu Key Laboratory for Molecular and Medical Biotechnology, School of Life Science, Nanjing Normal University, Nanjing 210023, China; (Y.D.); (L.L.); (X.X.); (X.Y.)
| | - Xueni Yang
- 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, Nanjing 210023, China; (X.Y.); (S.X.)
| | - Shizheng Xiong
- 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, Nanjing 210023, China; (X.Y.); (S.X.)
| | - Jiafeng Yu
- Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou 253023, China;
| | - Tingming Liang
- Jiangsu Key Laboratory for Molecular and Medical Biotechnology, School of Life Science, Nanjing Normal University, Nanjing 210023, China; (Y.D.); (L.L.); (X.X.); (X.Y.)
| | - 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, Nanjing 210023, China; (X.Y.); (S.X.)
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3
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Staheli JP, Neal ML, Navare A, Mast FD, Aitchison JD. Predicting host-based, synthetic lethal antiviral targets from omics data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.15.553430. [PMID: 37645861 PMCID: PMC10462099 DOI: 10.1101/2023.08.15.553430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Traditional antiviral therapies often have limited effectiveness due to toxicity and development of drug resistance. Host-based antivirals, while an alternative, may lead to non-specific effects. Recent evidence shows that virus-infected cells can be selectively eliminated by targeting synthetic lethal (SL) partners of proteins disrupted by viral infection. Thus, we hypothesized that genes depleted in CRISPR KO screens of virus-infected cells may be enriched in SL partners of proteins altered by infection. To investigate this, we established a computational pipeline predicting SL drug targets of viral infections. First, we identified SARS-CoV-2-induced changes in gene products via a large compendium of omics data. Second, we identified SL partners for each altered gene product. Last, we screened CRISPR KO data for SL partners required for cell viability in infected cells. Despite differences in virus-induced alterations detected by various omics data, they share many predicted SL targets, with significant enrichment in CRISPR KO-depleted datasets. Comparing data from SARS-CoV-2 and influenza infections, we found possible broad-spectrum, host-based antiviral SL targets. This suggests that CRISPR KO data are replete with common antiviral targets due to their SL relationship with virus-altered states and that such targets can be revealed from analysis of omics datasets and SL predictions.
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Affiliation(s)
- Jeannette P. Staheli
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute, Seattle, Washington, 98101, USA
| | - Maxwell L. Neal
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute, Seattle, Washington, 98101, USA
| | - Arti Navare
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute, Seattle, Washington, 98101, USA
| | - Fred D. Mast
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute, Seattle, Washington, 98101, USA
| | - John D. Aitchison
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute, Seattle, Washington, 98101, USA
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4
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Nair NU, Greninger P, Zhang X, Friedman AA, Amzallag A, Cortez E, Sahu AD, Lee JS, Dastur A, Egan RK, Murchie E, Ceribelli M, Crowther GS, Beck E, McClanaghan J, Klump-Thomas C, Boisvert JL, Damon LJ, Wilson KM, Ho J, Tam A, McKnight C, Michael S, Itkin Z, Garnett MJ, Engelman JA, Haber DA, Thomas CJ, Ruppin E, Benes CH. A landscape of response to drug combinations in non-small cell lung cancer. Nat Commun 2023; 14:3830. [PMID: 37380628 PMCID: PMC10307832 DOI: 10.1038/s41467-023-39528-9] [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: 01/31/2023] [Accepted: 06/14/2023] [Indexed: 06/30/2023] Open
Abstract
Combination of anti-cancer drugs is broadly seen as way to overcome the often-limited efficacy of single agents. The design and testing of combinations are however very challenging. Here we present a uniquely large dataset screening over 5000 targeted agent combinations across 81 non-small cell lung cancer cell lines. Our analysis reveals a profound heterogeneity of response across the tumor models. Notably, combinations very rarely result in a strong gain in efficacy over the range of response observable with single agents. Importantly, gain of activity over single agents is more often seen when co-targeting functionally proximal genes, offering a strategy for designing more efficient combinations. Because combinatorial effect is strongly context specific, tumor specificity should be achievable. The resource provided, together with an additional validation screen sheds light on major challenges and opportunities in building efficacious combinations against cancer and provides an opportunity for training computational models for synergy prediction.
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Affiliation(s)
- Nishanth Ulhas Nair
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Xiaohu Zhang
- Howard Hughes Medical Institute, Bethesda, MD, USA
| | - Adam A Friedman
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Arnaud Amzallag
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Eliane Cortez
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Avinash Das Sahu
- University of New Mexico, Comprehensive Cancer Center, Albuquerque, NM, USA
| | - Joo Sang Lee
- Samsung Medical Center, Sungkyunkwan University School of Medicine, Suwon, 16419, Republic of Korea
| | - Anahita Dastur
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Regina K Egan
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ellen Murchie
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | | | - Erin Beck
- Howard Hughes Medical Institute, Bethesda, MD, USA
| | | | | | | | - Leah J Damon
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Jeffrey Ho
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Angela Tam
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Sam Michael
- Howard Hughes Medical Institute, Bethesda, MD, USA
| | - Zina Itkin
- Howard Hughes Medical Institute, Bethesda, MD, USA
| | - Mathew J Garnett
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SA, UK
| | | | - Daniel A Haber
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Howard Hughes Medical Institute, Bethesda, MD, USA
| | - Craig J Thomas
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institute of Health, Rockville, MD, 20850, USA
- Lymphoid Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Eytan Ruppin
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
| | - Cyril H Benes
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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5
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Wong MRE, Lim KH, Hee EXY, Chen H, Kuick CH, Jet AS, Chang KTE, Sulaiman NS, Low SY, Hartono S, Tran ANT, Ahamed SH, Lam CMJ, Soh SY, Hannan KM, Hannan RD, Coupland LA, Loh AHP. Targeting Mutant Dicer Tumorigenesis in Pleuropulmonary Blastoma via Inhibition of RNA Polymerase I. Transl Res 2023:S1931-5244(23)00041-5. [PMID: 36921796 DOI: 10.1016/j.trsl.2023.03.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 02/23/2023] [Accepted: 03/07/2023] [Indexed: 03/14/2023]
Abstract
DICER1 mutations predispose to increased risk for various cancers, particularly pleuropulmonary blastoma (PPB), the commonest lung malignancy of childhood. There is a paucity of directly actionable molecular targets as these tumors are driven by loss-of-function mutations of DICER1. Therapeutic development for PPB is further limited by a lack of biologically and physiologically-representative disease models. Given recent evidence of Dicer's role as a haploinsufficient tumor suppressor regulating RNA polymerase I (Pol I), Pol I inhibition could abrogate mutant Dicer-mediated accumulation of stalled polymerases to trigger apoptosis. Hence, we developed a novel sub-pleural orthotopic PPB patient-derived xenograft (PDX) model that retained both RNase IIIa and IIIb hotspot mutations and recapitulated the cardiorespiratory physiology of intra-thoracic disease, and with it evaluated the tolerability and efficacy of first-in-class Pol I inhibitor CX-5461. In PDX tumors, CX-5461 significantly reduced H3K9 di-methylation and increased nuclear p53 expression, within 24 hours' exposure. Following treatment at the maximum tolerated dosing regimen (12 doses, 30mg/kg), tumors were smaller and less hemorrhagic than controls, with significantly decreased cellular proliferation, and increased apoptosis. As demonstrated in a novel intra-thoracic tumor model of PPB, Pol I inhibition with CX-5461 could be a tolerable and clinically-feasible therapeutic strategy for mutant Dicer tumors, inducing anti-tumor effects by decreasing H3K9 methylation and enhancing p53-mediated apoptosis.
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Affiliation(s)
- Megan Rui En Wong
- VIVA-KKH Paediatric Brain and Solid Tumour Programme, Children's Blood and Cancer Centre, KK Women's and Children's Hospital, Singapore 229899
| | - Kia Hui Lim
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597
| | - Esther Xuan Yi Hee
- VIVA-KKH Paediatric Brain and Solid Tumour Programme, Children's Blood and Cancer Centre, KK Women's and Children's Hospital, Singapore 229899
| | - Huiyi Chen
- Department of Pathology and Laboratory Medicine, KK Women's and Children's Hospital, Singapore 229899
| | - Chik Hong Kuick
- Department of Pathology and Laboratory Medicine, KK Women's and Children's Hospital, Singapore 229899
| | - Aw Sze Jet
- Department of Pathology and Laboratory Medicine, KK Women's and Children's Hospital, Singapore 229899
| | - Kenneth Tou En Chang
- VIVA-KKH Paediatric Brain and Solid Tumour Programme, Children's Blood and Cancer Centre, KK Women's and Children's Hospital, Singapore 229899; Department of Pathology and Laboratory Medicine, KK Women's and Children's Hospital, Singapore 229899; Duke-NUS School of Medicine, Singapore 169857
| | - Nurfarhanah Syed Sulaiman
- VIVA-KKH Paediatric Brain and Solid Tumour Programme, Children's Blood and Cancer Centre, KK Women's and Children's Hospital, Singapore 229899; Department of Neurology, National Neuroscience Institute, Singapore 308433
| | - Sharon Yy Low
- VIVA-KKH Paediatric Brain and Solid Tumour Programme, Children's Blood and Cancer Centre, KK Women's and Children's Hospital, Singapore 229899; Department of Neurology, National Neuroscience Institute, Singapore 308433; Duke-NUS School of Medicine, Singapore 169857
| | - Septian Hartono
- Department of Oncologic Imaging, National Cancer Centre Singapore, Singapore 169610
| | - Anh Nguyen Tuan Tran
- Department of Oncologic Imaging, National Cancer Centre Singapore, Singapore 169610
| | - Summaiyya Hanum Ahamed
- Duke-NUS School of Medicine, Singapore 169857; Department of Diagnostic and Interventional Imaging, KK Women's and Children's Hospital, Singapore 229899
| | - Ching Mei Joyce Lam
- Duke-NUS School of Medicine, Singapore 169857; Department of Paediatric Subspecialties Haematology/Oncology Service, KK Women's and Children's Hospital, Singapore 229899
| | - Shui Yen Soh
- VIVA-KKH Paediatric Brain and Solid Tumour Programme, Children's Blood and Cancer Centre, KK Women's and Children's Hospital, Singapore 229899; Duke-NUS School of Medicine, Singapore 169857; Department of Paediatric Subspecialties Haematology/Oncology Service, KK Women's and Children's Hospital, Singapore 229899
| | - Katherine M Hannan
- Department of Cancer Biology and Therapeutics, The John Curtin School of Medical Research, the Australian National University, Canberra, Australia; Department of Biochemistry and Molecular Biology, The University of Melbourne, Parkville, VIC, Australia
| | - Ross D Hannan
- Department of Cancer Biology and Therapeutics, The John Curtin School of Medical Research, the Australian National University, Canberra, Australia; Department of Biochemistry and Molecular Biology, The University of Melbourne, Parkville, VIC, Australia; Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Lucy A Coupland
- Department of Cancer Biology and Therapeutics, The John Curtin School of Medical Research, the Australian National University, Canberra, Australia
| | - Amos Hong Pheng Loh
- VIVA-KKH Paediatric Brain and Solid Tumour Programme, Children's Blood and Cancer Centre, KK Women's and Children's Hospital, Singapore 229899; Duke-NUS School of Medicine, Singapore 169857; Department of Paediatric Surgery, KK Women's and Children's Hospital, Singapore 229899.
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6
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Weng S, Ruan H. Multi-omics characterization of synthetic lethality-related molecular features: implications for SL-based therapeutic target screening. FEBS J 2023; 290:1477-1480. [PMID: 36461713 DOI: 10.1111/febs.16692] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Accepted: 11/25/2022] [Indexed: 12/05/2022]
Abstract
Synthetic lethality (SL) represents the co-occurrence of two or more non-lethal disordered genes that could lead to cell death. SL-based anticancer therapeutics could specifically kill the cancer cells carrying the targeted mutated gene while leaving normal cells alive. Recent large-scale computational and experimental screenings provide rich resources of SL information while lacking systematic research on molecular features of SL genes. Combined with comprehensive multi-omics data analysis and experimental validation of one SL gene pair, Guo et al. portrayed a systematic layout of cancer-specific SL interactions that could improve understanding of carcinogenesis and potentially assist the subsequent screening of anticancer therapeutic targets.
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Affiliation(s)
- Shenghui Weng
- Institutes of Biology and Medical Sciences, Soochow University, China
| | - Hang Ruan
- Institutes of Biology and Medical Sciences, Soochow University, China
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7
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Dinstag G, Shulman ED, Elis E, Ben-Zvi DS, Tirosh O, Maimon E, Meilijson I, Elalouf E, Temkin B, Vitkovsky P, Schiff E, Hoang DT, Sinha S, Nair NU, Lee JS, Schäffer AA, Ronai Z, Juric D, Apolo AB, Dahut WL, Lipkowitz S, Berger R, Kurzrock R, Papanicolau-Sengos A, Karzai F, Gilbert MR, Aldape K, Rajagopal PS, Beker T, Ruppin E, Aharonov R. Clinically oriented prediction of patient response to targeted and immunotherapies from the tumor transcriptome. MED 2023; 4:15-30.e8. [PMID: 36513065 PMCID: PMC10029756 DOI: 10.1016/j.medj.2022.11.001] [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: 04/11/2022] [Revised: 08/30/2022] [Accepted: 10/31/2022] [Indexed: 12/14/2022]
Abstract
BACKGROUND Precision oncology is gradually advancing into mainstream clinical practice, demonstrating significant survival benefits. However, eligibility and response rates remain limited in many cases, calling for better predictive biomarkers. METHODS We present ENLIGHT, a transcriptomics-based computational approach that identifies clinically relevant genetic interactions and uses them to predict a patient's response to a variety of therapies in multiple cancer types without training on previous treatment response data. We study ENLIGHT in two translationally oriented scenarios: personalized oncology (PO), aimed at prioritizing treatments for a single patient, and clinical trial design (CTD), selecting the most likely responders in a patient cohort. FINDINGS Evaluating ENLIGHT's performance on 21 blinded clinical trial datasets in the PO setting, we show that it can effectively predict a patient's treatment response across multiple therapies and cancer types. Its prediction accuracy is better than previously published transcriptomics-based signatures and is comparable with that of supervised predictors developed for specific indications and drugs. In combination with the interferon-γ signature, ENLIGHT achieves an odds ratio larger than 4 in predicting response to immune checkpoint therapy. In the CTD scenario, ENLIGHT can potentially enhance clinical trial success for immunotherapies and other monoclonal antibodies by excluding non-responders while overall achieving more than 90% of the response rate attainable under an optimal exclusion strategy. CONCLUSIONS ENLIGHT demonstrably enhances the ability to predict therapeutic response across multiple cancer types from the bulk tumor transcriptome. FUNDING This research was supported in part by the Intramural Research Program, NIH and by the Israeli Innovation Authority.
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Affiliation(s)
| | | | | | | | | | | | - Isaac Meilijson
- Pangea Biomed Ltd., Tel Aviv, Israel; Tel Aviv University, Tel Aviv, Israel
| | | | | | | | | | - Danh-Tai Hoang
- Biological Data Science Institute, College of Science, The Australian National University, Canberra, ACT, Australia
| | - Sanju Sinha
- Cancer Data Science Laboratory (CDSL), National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Nishanth Ulhas Nair
- Cancer Data Science Laboratory (CDSL), National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Joo Sang Lee
- Department of Precision Medicine, School of Medicine & Department of Artificial Intelligence, Sungkyunkwan University, Suwon, Republic of Korea
| | - Alejandro A Schäffer
- Cancer Data Science Laboratory (CDSL), National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Ze'ev Ronai
- Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Dejan Juric
- Department of Medicine, Massachusetts General Hospital Cancer Center, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Andrea B Apolo
- Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - William L Dahut
- Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Stanley Lipkowitz
- Women's Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Raanan Berger
- Cancer Center, Chaim Sheba Medical Center, Tel Hashomer, Israel
| | - Razelle Kurzrock
- Worldwide Innovative Network (WIN) for Personalized Cancer Therapy, Chevilly-Larue, France
| | | | - Fatima Karzai
- Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Mark R Gilbert
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Kenneth Aldape
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Padma S Rajagopal
- Cancer Data Science Laboratory (CDSL), National Cancer Institute, National Institutes of Health, Bethesda, MD, USA; Women's Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Eytan Ruppin
- Cancer Data Science Laboratory (CDSL), National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
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8
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WRN suppresses p53/PUMA-induced apoptosis in colorectal cancer with microsatellite instability/mismatch repair deficiency. Proc Natl Acad Sci U S A 2023; 120:e2219963120. [PMID: 36598947 PMCID: PMC9926267 DOI: 10.1073/pnas.2219963120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
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9
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Schubert OT, Bloom JS, Sadhu MJ, Kruglyak L. Genome-wide base editor screen identifies regulators of protein abundance in yeast. eLife 2022; 11:e79525. [PMID: 36326816 PMCID: PMC9633064 DOI: 10.7554/elife.79525] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 09/23/2022] [Indexed: 11/07/2022] Open
Abstract
Proteins are key molecular players in a cell, and their abundance is extensively regulated not just at the level of gene expression but also post-transcriptionally. Here, we describe a genetic screen in yeast that enables systematic characterization of how protein abundance regulation is encoded in the genome. The screen combines a CRISPR/Cas9 base editor to introduce point mutations with fluorescent tagging of endogenous proteins to facilitate a flow-cytometric readout. We first benchmarked base editor performance in yeast with individual gRNAs as well as in positive and negative selection screens. We then examined the effects of 16,452 genetic perturbations on the abundance of eleven proteins representing a variety of cellular functions. We uncovered hundreds of regulatory relationships, including a novel link between the GAPDH isoenzymes Tdh1/2/3 and the Ras/PKA pathway. Many of the identified regulators are specific to one of the eleven proteins, but we also found genes that, upon perturbation, affected the abundance of most of the tested proteins. While the more specific regulators usually act transcriptionally, broad regulators often have roles in protein translation. Overall, our novel screening approach provides unprecedented insights into the components, scale and connectedness of the protein regulatory network.
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Affiliation(s)
- Olga T Schubert
- Department of Human Genetics, University of California, Los AngelesLos AngelesUnited States
- Department of Biological Chemistry, University of California, Los AngelesLos AngelesUnited States
- Howard Hughes Medical Institute, University of California, Los AngelesLos AngelesUnited States
- Institute for Quantitative and Computational Biology, University of California, Los AngelesLos AngelesUnited States
- Department of Environmental Systems Science, Swiss Federal Institute of Technology (ETH)ZürichSwitzerland
- Department of Environmental Microbiology, Swiss Federal Institute of Aquatic Science and Technology (Eawag)DübendorfSwitzerland
| | - Joshua S Bloom
- Department of Human Genetics, University of California, Los AngelesLos AngelesUnited States
- Department of Biological Chemistry, University of California, Los AngelesLos AngelesUnited States
- Howard Hughes Medical Institute, University of California, Los AngelesLos AngelesUnited States
- Institute for Quantitative and Computational Biology, University of California, Los AngelesLos AngelesUnited States
| | - Meru J Sadhu
- Department of Human Genetics, University of California, Los AngelesLos AngelesUnited States
- Department of Biological Chemistry, University of California, Los AngelesLos AngelesUnited States
- Howard Hughes Medical Institute, University of California, Los AngelesLos AngelesUnited States
- Institute for Quantitative and Computational Biology, University of California, Los AngelesLos AngelesUnited States
| | - Leonid Kruglyak
- Department of Human Genetics, University of California, Los AngelesLos AngelesUnited States
- Department of Biological Chemistry, University of California, Los AngelesLos AngelesUnited States
- Howard Hughes Medical Institute, University of California, Los AngelesLos AngelesUnited States
- Institute for Quantitative and Computational Biology, University of California, Los AngelesLos AngelesUnited States
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10
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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: 0] [Impact Index Per Article: 0] [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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11
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A comprehensive analysis of ncRNA-mediated interactions reveals potential prognostic biomarkers in prostate adenocarcinoma. Comput Struct Biotechnol J 2022; 20:3839-3850. [PMID: 35891787 PMCID: PMC9307580 DOI: 10.1016/j.csbj.2022.07.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 07/10/2022] [Accepted: 07/11/2022] [Indexed: 11/20/2022] Open
Abstract
As one of common malignancies, prostate adenocarcinoma (PRAD) has been a growing health problem and a leading cause of cancer-related death. To obtain expression and functional relevant RNAs, we firstly screened candidate hub mRNAs and characterized their associations with cancer. Eight deregulated genes were identified and used to build a risk model (AUC was 0.972 at 10 years) that may be a specific biomarker for cancer prognosis. Then, relevant miRNAs and lncRNAs were screened, and the constructed primarily interaction networks showed the potential cross-talks among diverse RNAs. IsomiR landscapes were surveyed to understand the detailed isomiRs in relevant homologous miRNA loci, which largely enriched RNA interaction network due to diversities of sequence and expression. We finally characterized TK1, miR-222-3p and SNHG3 as crucial RNAs, and the abnormal expression patterns of them were correlated with poor survival outcomes. TK1 was found synthetic lethal interactions with other genes, implicating potential therapeutic target in precision medicine. LncRNA SNHG3 can sponge miR-222-3p to perturb RNA regulatory network and TK1 expression. These results demonstrate that TK1:miR-222-3p:SNHG3 axis may be a potential prognostic biomarker, which will contribute to further understanding cancer pathophysiology and providing potential therapeutic targets in precision medicine.
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12
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Al-Anzi BF, Khajah M, Fakhraldeen SA. Predicting and explaining the impact of genetic disruptions and interactions on organismal viability. Bioinformatics 2022; 38:4088-4099. [PMID: 35861390 PMCID: PMC9438956 DOI: 10.1093/bioinformatics/btac519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 06/30/2022] [Accepted: 07/20/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Existing computational models can predict single- and double-mutant fitness but they do have limitations. First, they are often tested via evaluation metrics that are inappropriate for imbalanced datasets. Second, all of them only predict a binary outcome (viable or not, and negatively interacting or not). Third, most are uninterpretable black box machine learning models. RESULTS Budding yeast datasets were used to develop high-performance Multinomial Regression (MN) models capable of predicting the impact of single, double and triple genetic disruptions on viability. These models are interpretable and give realistic non-binary predictions and can predict negative genetic interactions (GIs) in triple-gene knockouts. They are based on a limited set of gene features and their predictions are influenced by the probability of target gene participating in molecular complexes or pathways. Furthermore, the MN models have utility in other organisms such as fission yeast, fruit flies and humans, with the single gene fitness MN model being able to distinguish essential genes necessary for cell-autonomous viability from those required for multicellular survival. Finally, our models exceed the performance of previous models, without sacrificing interpretability. AVAILABILITY AND IMPLEMENTATION All code and processed datasets used to generate results and figures in this manuscript are available at our Github repository at https://github.com/KISRDevelopment/cell_viability_paper. The repository also contains a link to the GI prediction website that lets users search for GIs using the MN models. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | - Saja A Fakhraldeen
- Ecosystem-based Management of Marine Resources Program, Kuwait Institute for Scientific Research, Safat, 13109, Kuwait
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13
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Bagnoli M, Nicoletti R, Valitutti M, Rizzo A, Napoli A, Montalvão De Azevedo R, Tomassetti A, Mezzanzanica D. Impairment of RAD17 Functions by miR-506-3p as a Novel Synthetic Lethal Approach Targeting DNA Repair Pathways in Ovarian Cancer. Front Oncol 2022; 12:923508. [PMID: 35924161 PMCID: PMC9340372 DOI: 10.3389/fonc.2022.923508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 06/23/2022] [Indexed: 11/13/2022] Open
Abstract
Epithelial ovarian cancer (EOC) remains the most lethal gynecological cancer and development of chemo-resistance is a major factor in disease relapse. Homologous recombination (HR) is a critical pathway for DNA double strand break repair and its deficiency is associated to a better response to DNA damage-inducing agents. Strategies to inhibit HR-mediated DNA repair is a clinical need to improve patients’ outcome. MicroRNA (miRNAs) affect most of cellular processes including response to cancer treatment. We previously showed that miR-506-3p targets RAD51, an essential HR component. In this study we demonstrated that: i) another HR component, RAD17, is also a direct target of miR-506-3p and that it is involved in mediating miR-506-3p phenotypic effects; ii) the impairment of miR-506-3p binding to RAD17 3’ UTR reverted the miR-506-3p induced platinum sensitization; iii) miR-506-3p/RAD17 axis reduces the ability of EOC cell to sense DNA damage, abrogates the G2/M cell cycle checkpoint thus delaying the G2/M cell cycle arrest likely allowing the entry into mitosis of heavily DNA-damaged cells with a consequent mitotic catastrophe; iv) RAD17 expression, regulated by miR-506-3p, is synthetically lethal with inhibitors of cell cycle checkpoint kinases Chk1 and Wee1 in platinum resistant cell line. Overall miR-506-3p expression may recapitulate a BRCAness phenotype sensitizing EOC cells to chemotherapy and helping in selecting patients susceptible to DNA damaging drugs in combination with new small molecules targeting DNA-damage repair pathway.
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Alfatah M, Eisenhaber F. The PICLS high-throughput screening method for agents extending cellular longevity identifies 2,5-anhydro-D-mannitol as novel anti-aging compound. GeroScience 2022; 45:141-158. [PMID: 35705837 PMCID: PMC9886722 DOI: 10.1007/s11357-022-00598-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 05/25/2022] [Indexed: 02/03/2023] Open
Abstract
Although aging is the biggest risk factor for human chronic (cancer, diabetic, cardiovascular, and neurodegenerative) diseases, few interventions are known besides caloric restriction and a small number of drugs (with substantial side effects) that directly address aging. Thus, there is an urgent need for new options that can generally delay aging processes and prevent age-related diseases. Cellular aging is at the basis of aging processes. Chronological lifespan (CLS) of yeast Saccharomyces cerevisiae is the well-established model system for investigating the interventions of human post-mitotic cellular aging. CLS is defined as the number of days cells remain viable in a stationary phase. We developed a new, cheap, and fast quantitative method for measuring CLS in cell cultures incubated together with various chemical agents and controls on 96-well plates. Our PICLS protocol with (1) the use of propidium iodide for fluorescent-based cell survival reading in a microplate reader and (2) total cell count measurement via OD600nm absorption from the same plate provides real high-throughput capacity. Depending on logistics, large numbers of plates can be processed in parallel so that the screening of thousands of compounds becomes feasible in a short time. The method was validated by measuring the effect of rapamycin and calorie restriction on yeast CLS. We utilized this approach for chemical agent screening. We discovered the anti-aging/geroprotective potential of 2,5-anhydro-D-mannitol (2,5-AM) and suggest its usage individually or in combination with other anti-aging interventions.
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Affiliation(s)
- Mohammad Alfatah
- Bioinformatics Institute (BII), Singapore, 138671, A*STAR, Singapore.
| | - Frank Eisenhaber
- Bioinformatics Institute (BII), Singapore, 138671, A*STAR, Singapore. .,Genome Institute of Singapore (GIS), Singapore, 138672, A*STAR, Singapore. .,School of Biological Sciences (SBS), Nanyang Technological University (NTU), Singapore, 637551, Singapore.
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15
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Tercan B, Qin G, Kim TK, Aguilar B, Phan J, Longabaugh W, Pot D, Kemp CJ, Chambwe N, Shmulevich I. SL-Cloud: A Cloud-based resource to support synthetic lethal interaction discovery. F1000Res 2022; 11:493. [PMID: 36761837 PMCID: PMC9880341 DOI: 10.12688/f1000research.110903.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/13/2022] [Indexed: 12/24/2022] Open
Abstract
Synthetic lethal interactions (SLIs), genetic interactions in which the simultaneous inactivation of two genes leads to a lethal phenotype, are promising targets for therapeutic intervention in cancer, as exemplified by the recent success of PARP inhibitors in treating BRCA1/2-deficient tumors. We present SL-Cloud, a new component of the Institute for Systems Biology Cancer Gateway in the Cloud (ISB-CGC), that provides an integrated framework of cloud-hosted data resources and curated workflows to enable facile prediction of SLIs. This resource addresses two main challenges related to SLI inference: the need to wrangle and preprocess large multi-omic datasets and the availability of multiple comparable prediction approaches. SL-Cloud enables customizable computational inference of SLIs and testing of prediction approaches across multiple datasets. We anticipate that cancer researchers will find utility in this tool for discovery of SLIs to support further investigation into potential drug targets for anticancer therapies.
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Affiliation(s)
- Bahar Tercan
- Institute for Systems Biology, Seattle, WA, 98109, USA
| | - Guangrong Qin
- Institute for Systems Biology, Seattle, WA, 98109, USA
| | - Taek-Kyun Kim
- Institute for Systems Biology, Seattle, WA, 98109, USA
| | - Boris Aguilar
- Institute for Systems Biology, Seattle, WA, 98109, USA
| | - John Phan
- General Dynamics Information Technology, Rockville, MD, 20852, USA
| | | | - David Pot
- General Dynamics Information Technology, Rockville, MD, 20852, USA
| | - Christopher J. Kemp
- Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
| | - Nyasha Chambwe
- Institute for Systems Biology, Seattle, WA, 98109, USA
- Institute of Molecular Medicine, Feinstein Institutes for Medical Research, Manhasset, NY, 11030, USA
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16
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Tercan B, Qin G, Kim TK, Aguilar B, Phan J, Longabaugh W, Pot D, Kemp CJ, Chambwe N, Shmulevich I. SL-Cloud: A Cloud-based resource to support synthetic lethal interaction discovery. F1000Res 2022; 11:493. [PMID: 36761837 PMCID: PMC9880341 DOI: 10.12688/f1000research.110903.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/04/2022] [Indexed: 12/22/2023] Open
Abstract
Synthetic lethal interactions (SLIs), genetic interactions in which the simultaneous inactivation of two genes leads to a lethal phenotype, are promising targets for therapeutic intervention in cancer, as exemplified by the recent success of PARP inhibitors in treating BRCA1/2-deficient tumors. We present SL-Cloud, a new component of the Institute for Systems Biology Cancer Gateway in the Cloud (ISB-CGC), that provides an integrated framework of cloud-hosted data resources and curated workflows to enable facile prediction of SLIs. This resource addresses two main challenges related to SLI inference: the need to wrangle and preprocess large multi-omic datasets and the availability of multiple comparable prediction approaches. SL-Cloud enables customizable computational inference of SLIs and testing of prediction approaches across multiple datasets. We anticipate that cancer researchers will find utility in this tool for discovery of SLIs to support further investigation into potential drug targets for anticancer therapies.
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Affiliation(s)
- Bahar Tercan
- Institute for Systems Biology, Seattle, WA, 98109, USA
| | - Guangrong Qin
- Institute for Systems Biology, Seattle, WA, 98109, USA
| | - Taek-Kyun Kim
- Institute for Systems Biology, Seattle, WA, 98109, USA
| | - Boris Aguilar
- Institute for Systems Biology, Seattle, WA, 98109, USA
| | - John Phan
- General Dynamics Information Technology, Rockville, MD, 20852, USA
| | | | - David Pot
- General Dynamics Information Technology, Rockville, MD, 20852, USA
| | - Christopher J. Kemp
- Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
| | - Nyasha Chambwe
- Institute for Systems Biology, Seattle, WA, 98109, USA
- Institute of Molecular Medicine, Feinstein Institutes for Medical Research, Manhasset, NY, 11030, USA
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17
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Wang J, Zhang Q, Han J, Zhao Y, Zhao C, Yan B, Dai C, Wu L, Wen Y, Zhang Y, Leng D, Wang Z, Yang X, He S, Bo X. Computational methods, databases and tools for synthetic lethality prediction. Brief Bioinform 2022; 23:6555403. [PMID: 35352098 PMCID: PMC9116379 DOI: 10.1093/bib/bbac106] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 02/15/2022] [Accepted: 03/02/2022] [Indexed: 12/17/2022] Open
Abstract
Synthetic lethality (SL) occurs between two genes when the inactivation of either gene alone has no effect on cell survival but the inactivation of both genes results in cell death. SL-based therapy has become one of the most promising targeted cancer therapies in the last decade as PARP inhibitors achieve great success in the clinic. The key point to exploiting SL-based cancer therapy is the identification of robust SL pairs. Although many wet-lab-based methods have been developed to screen SL pairs, known SL pairs are less than 0.1% of all potential pairs due to large number of human gene combinations. Computational prediction methods complement wet-lab-based methods to effectively reduce the search space of SL pairs. In this paper, we review the recent applications of computational methods and commonly used databases for SL prediction. First, we introduce the concept of SL and its screening methods. Second, various SL-related data resources are summarized. Then, computational methods including statistical-based methods, network-based methods, classical machine learning methods and deep learning methods for SL prediction are summarized. In particular, we elaborate on the negative sampling methods applied in these models. Next, representative tools for SL prediction are introduced. Finally, the challenges and future work for SL prediction are discussed.
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Affiliation(s)
- Jing Wang
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Qinglong Zhang
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Junshan Han
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Yanpeng Zhao
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Caiyun Zhao
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Bowei Yan
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Chong Dai
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Lianlian Wu
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Yuqi Wen
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Yixin Zhang
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Dongjin Leng
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Zhongming Wang
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Xiaoxi Yang
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Song He
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Xiaochen Bo
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
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18
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Chen HQ, Xing Q, Cheng C, Zhang MM, Liu CG, Champreda V, Zhao XQ. Identification of Kic1p and Cdc42p as Novel Targets to Engineer Yeast Acetic Acid Stress Tolerance. Front Bioeng Biotechnol 2022; 10:837813. [PMID: 35402407 PMCID: PMC8992792 DOI: 10.3389/fbioe.2022.837813] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 02/09/2022] [Indexed: 11/13/2022] Open
Abstract
Robust yeast strains that are tolerant to multiple stress environments are desired for an efficient biorefinery. Our previous studies revealed that zinc sulfate serves as an important nutrient for stress tolerance of budding yeast Saccharomyces cerevisiae. Acetic acid is a common inhibitor in cellulosic hydrolysate, and the development of acetic acid-tolerant strains is beneficial for lignocellulosic biorefineries. In this study, comparative proteomic studies were performed using S. cerevisiae cultured under acetic acid stress with or without zinc sulfate addition, and novel zinc-responsive proteins were identified. Among the differentially expressed proteins, the protein kinase Kic1p and the small rho-like GTPase Cdc42p, which is required for cell integrity and regulation of cell polarity, respectively, were selected for further studies. Overexpression of KIC1 and CDC42 endowed S. cerevisiae with faster growth and ethanol fermentation under the stresses of acetic acid and mixed inhibitors, as well as in corncob hydrolysate. Notably, the engineered yeast strains showed a 12 h shorter lag phase under the three tested conditions, leading to up to 52.99% higher ethanol productivity than that of the control strain. Further studies showed that the transcription of genes related to stress response was significantly upregulated in the engineered strains under the stress condition. Our results in this study provide novel insights in exploring zinc-responsive proteins for applications of synthetic biology in developing a robust industrial yeast.
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Affiliation(s)
- Hong-Qi Chen
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Qi Xing
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Cheng Cheng
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Ming-Ming Zhang
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Chen-Guang Liu
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Verawat Champreda
- National Center for Genetic Engineering and Biotechnology, Pathumthani, Thailand
| | - Xin-Qing Zhao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
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19
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Inhibition of ALDH2 by disulfiram leads to synthetic lethality via ROS strikes twice in ARID1A-deficient cholangiocarcinoma. Genes Dis 2022; 10:69-71. [PMID: 37013042 PMCID: PMC10066240 DOI: 10.1016/j.gendis.2022.02.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Accepted: 02/22/2022] [Indexed: 11/23/2022] Open
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20
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Functional buffering via cell-specific gene expression promotes tissue homeostasis and cancer robustness. Sci Rep 2022; 12:2974. [PMID: 35194081 PMCID: PMC8863889 DOI: 10.1038/s41598-022-06813-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 02/03/2022] [Indexed: 11/08/2022] Open
Abstract
Functional buffering that ensures biological robustness is critical for maintaining tissue homeostasis, organismal survival, and evolution of novelty. However, the mechanism underlying functional buffering, particularly in multicellular organisms, remains largely elusive. Here, we proposed that functional buffering can be mediated via expression of buffering genes in specific cells and tissues, by which we named Cell-specific Expression-BUffering (CEBU). We developed an inference index (C-score) for CEBU by computing C-scores across 684 human cell lines using genome-wide CRISPR screens and transcriptomic RNA-seq. We report that C-score-identified putative buffering gene pairs are enriched for members of the same duplicated gene family, pathway, and protein complex. Furthermore, CEBU is especially prevalent in tissues of low regenerative capacity (e.g., bone and neuronal tissues) and is weakest in highly regenerative blood cells, linking functional buffering to tissue regeneration. Clinically, the buffering capacity enabled by CEBU can help predict patient survival for multiple cancers. Our results suggest CEBU as a potential buffering mechanism contributing to tissue homeostasis and cancer robustness in humans.
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21
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Harnessing Synthetic Lethal Interactions for Personalized Medicine. J Pers Med 2022; 12:jpm12010098. [PMID: 35055413 PMCID: PMC8779047 DOI: 10.3390/jpm12010098] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 01/07/2022] [Accepted: 01/09/2022] [Indexed: 02/01/2023] Open
Abstract
Two genes are said to have synthetic lethal (SL) interactions if the simultaneous mutations in a cell lead to lethality, but each individual mutation does not. Targeting SL partners of mutated cancer genes can kill cancer cells but leave normal cells intact. The applicability of translating this concept into clinics has been demonstrated by three drugs that have been approved by the FDA to target PARP for tumors bearing mutations in BRCA1/2. This article reviews applications of the SL concept to translational cancer medicine over the past five years. Topics are (1) exploiting the SL concept for drug combinations to circumvent tumor resistance, (2) using synthetic lethality to identify prognostic and predictive biomarkers, (3) applying SL interactions to stratify patients for targeted and immunotherapy, and (4) discussions on challenges and future directions.
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22
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Mukherjee S, Cogan JD, Newman JH, Phillips JA, Hamid R, Meiler J, Capra JA. Identifying digenic disease genes via machine learning in the Undiagnosed Diseases Network. Am J Hum Genet 2021; 108:1946-1963. [PMID: 34529933 PMCID: PMC8546038 DOI: 10.1016/j.ajhg.2021.08.010] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 08/25/2021] [Indexed: 12/20/2022] Open
Abstract
Rare diseases affect millions of people worldwide, and discovering their genetic causes is challenging. More than half of the individuals analyzed by the Undiagnosed Diseases Network (UDN) remain undiagnosed. The central hypothesis of this work is that many of these rare genetic disorders are caused by multiple variants in more than one gene. However, given the large number of variants in each individual genome, experimentally evaluating combinations of variants for potential to cause disease is currently infeasible. To address this challenge, we developed the digenic predictor (DiGePred), a random forest classifier for identifying candidate digenic disease gene pairs by features derived from biological networks, genomics, evolutionary history, and functional annotations. We trained the DiGePred classifier by using DIDA, the largest available database of known digenic-disease-causing gene pairs, and several sets of non-digenic gene pairs, including variant pairs derived from unaffected relatives of UDN individuals. DiGePred achieved high precision and recall in cross-validation and on a held-out test set (PR area under the curve > 77%), and we further demonstrate its utility by using digenic pairs from the recent literature. In contrast to other approaches, DiGePred also appropriately controls the number of false positives when applied in realistic clinical settings. Finally, to enable the rapid screening of variant gene pairs for digenic disease potential, we freely provide the predictions of DiGePred on all human gene pairs. Our work enables the discovery of genetic causes for rare non-monogenic diseases by providing a means to rapidly evaluate variant gene pairs for the potential to cause digenic disease.
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Affiliation(s)
- Souhrid Mukherjee
- Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235, USA
| | - Joy D Cogan
- Department of Pediatrics, Division of Medical Genetics and Genomic Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - John H Newman
- Pulmonary Hypertension Center, Division of Allergy, Pulmonary, and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - John A Phillips
- Department of Pediatrics, Division of Medical Genetics and Genomic Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Rizwan Hamid
- Department of Pediatrics, Division of Medical Genetics and Genomic Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN 37235, USA; Department of Pharmacology, Vanderbilt University, Nashville, TN 37235, USA; Center for Structural Biology, Vanderbilt University, Nashville, TN 37235, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Institute for Drug Discovery, Leipzig University Medical School, Leipzig 04103, Germany; Department of Chemistry, Leipzig University, Leipzig 04109, Germany; Department of Computer Science, Leipzig University, Leipzig 04109, Germany.
| | - John A Capra
- Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235, USA; Center for Structural Biology, Vanderbilt University, Nashville, TN 37235, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Bakar Computational Health Sciences Institute and Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94143, USA.
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23
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A systematic analysis of genetic interactions and their underlying biology in childhood cancer. Commun Biol 2021; 4:1139. [PMID: 34615983 PMCID: PMC8494736 DOI: 10.1038/s42003-021-02647-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 09/08/2021] [Indexed: 02/08/2023] Open
Abstract
Childhood cancer is a major cause of child death in developed countries. Genetic interactions between mutated genes play an important role in cancer development. They can be detected by searching for pairs of mutated genes that co-occur more (or less) often than expected. Co-occurrence suggests a cooperative role in cancer development, while mutual exclusivity points to synthetic lethality, a phenomenon of interest in cancer treatment research. Little is known about genetic interactions in childhood cancer. We apply a statistical pipeline to detect genetic interactions in a combined dataset comprising over 2,500 tumors from 23 cancer types. The resulting genetic interaction map of childhood cancers comprises 15 co-occurring and 27 mutually exclusive candidates. The biological explanation of most candidates points to either tumor subtype, pathway epistasis or cooperation while synthetic lethality plays a much smaller role. Thus, other explanations beyond synthetic lethality should be considered when interpreting genetic interaction test results.
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24
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Ramirez Reyes JMJ, Cuesta R, Pause A. Folliculin: A Regulator of Transcription Through AMPK and mTOR Signaling Pathways. Front Cell Dev Biol 2021; 9:667311. [PMID: 33981707 PMCID: PMC8107286 DOI: 10.3389/fcell.2021.667311] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Accepted: 03/29/2021] [Indexed: 12/15/2022] Open
Abstract
Folliculin (FLCN) is a tumor suppressor gene responsible for the inherited Birt-Hogg-Dubé (BHD) syndrome, which affects kidneys, skin and lungs. FLCN is a highly conserved protein that forms a complex with folliculin interacting proteins 1 and 2 (FNIP1/2). Although its sequence does not show homology to known functional domains, structural studies have determined a role of FLCN as a GTPase activating protein (GAP) for small GTPases such as Rag GTPases. FLCN GAP activity on the Rags is required for the recruitment of mTORC1 and the transcriptional factors TFEB and TFE3 on the lysosome, where mTORC1 phosphorylates and inactivates these factors. TFEB/TFE3 are master regulators of lysosomal biogenesis and function, and autophagy. By this mechanism, FLCN/FNIP complex participates in the control of metabolic processes. AMPK, a key regulator of catabolism, interacts with FLCN/FNIP complex. FLCN loss results in constitutive activation of AMPK, which suggests an additional mechanism by which FLCN/FNIP may control metabolism. AMPK regulates the expression and activity of the transcriptional cofactors PGC1α/β, implicated in the control of mitochondrial biogenesis and oxidative metabolism. In this review, we summarize our current knowledge of the interplay between mTORC1, FLCN/FNIP, and AMPK and their implications in the control of cellular homeostasis through the transcriptional activity of TFEB/TFE3 and PGC1α/β. Other pathways and cellular processes regulated by FLCN will be briefly discussed.
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Affiliation(s)
- Josué M. J. Ramirez Reyes
- Goodman Cancer Research Center, McGill University, Montréal, QC, Canada
- Department of Biochemistry, McGill University, Montréal, QC, Canada
| | - Rafael Cuesta
- Goodman Cancer Research Center, McGill University, Montréal, QC, Canada
- Department of Biochemistry, McGill University, Montréal, QC, Canada
| | - Arnim Pause
- Goodman Cancer Research Center, McGill University, Montréal, QC, Canada
- Department of Biochemistry, McGill University, Montréal, QC, Canada
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25
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David F, Davis AM, Gossing M, Hayes MA, Romero E, Scott LH, Wigglesworth MJ. A Perspective on Synthetic Biology in Drug Discovery and Development-Current Impact and Future Opportunities. SLAS DISCOVERY 2021; 26:581-603. [PMID: 33834873 DOI: 10.1177/24725552211000669] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The global impact of synthetic biology has been accelerating, because of the plummeting cost of DNA synthesis, advances in genetic engineering, growing understanding of genome organization, and explosion in data science. However, much of the discipline's application in the pharmaceutical industry remains enigmatic. In this review, we highlight recent examples of the impact of synthetic biology on target validation, assay development, hit finding, lead optimization, and chemical synthesis, through to the development of cellular therapeutics. We also highlight the availability of tools and technologies driving the discipline. Synthetic biology is certainly impacting all stages of drug discovery and development, and the recognition of the discipline's contribution can further enhance the opportunities for the drug discovery and development value chain.
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Affiliation(s)
- Florian David
- Department of Biology and Biological Engineering, Division of Systems and Synthetic Biology, Chalmers University of Technology, Gothenburg, Sweden
| | - Andrew M Davis
- Discovery Sciences, Biopharmaceutical R&D, AstraZeneca, Cambridge, UK
| | - Michael Gossing
- Discovery Sciences, Biopharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Martin A Hayes
- Discovery Sciences, Biopharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Elvira Romero
- Discovery Sciences, Biopharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Louis H Scott
- Discovery Sciences, Biopharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
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26
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Bury M, Le Calvé B, Ferbeyre G, Blank V, Lessard F. New Insights into CDK Regulators: Novel Opportunities for Cancer Therapy. Trends Cell Biol 2021; 31:331-344. [PMID: 33676803 DOI: 10.1016/j.tcb.2021.01.010] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 01/28/2021] [Accepted: 01/29/2021] [Indexed: 12/13/2022]
Abstract
Cyclins and their catalytic partners, the cyclin-dependent kinases (CDKs), control the transition between different phases of the cell cycle. CDK/cyclin activity is regulated by CDK inhibitors (CKIs), currently comprising the CDK-interacting protein/kinase inhibitory protein (CIP/KIP) family and the inhibitor of kinase (INK) family. Recent studies have identified a third group of CKIs, called ribosomal protein-inhibiting CDKs (RPICs). RPICs were discovered in the context of cellular senescence, a stable cell cycle arrest with tumor-suppressing abilities. RPICs accumulate in the nonribosomal fraction of senescent cells due to a decrease in rRNA biogenesis. Accordingly, RPICs are often downregulated in human cancers together with other ribosomal proteins, the tumor-suppressor functions of which are still under study. In this review, we discuss unique therapies that have been developed to target CDK activity in the context of cancer treatment or senescence-associated pathologies, providing novel tools for precision medicine.
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Affiliation(s)
- Marina Bury
- De Duve Institute, UCLouvain, 1200 Brussels, Belgium
| | | | - Gerardo Ferbeyre
- Department of Biochemistry and Molecular Medicine, University of Montreal, Montreal, QC, H3C 3J7, Canada.
| | - Volker Blank
- Lady Davis Institute for Medical Research, Departments of Medicine and Physiology, McGill University, Montreal, QC, H3T 1E2, Canada.
| | - Frédéric Lessard
- Department of Biochemistry and Molecular Medicine, University of Montreal, Montreal, QC, H3C 3J7, Canada.
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27
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Abstract
Glioblastoma remains incurable despite advances in surgery, radiation, and chemotherapy, underscoring the need for new therapies. The genetic heterogenicity, presence of redundant molecular pathways, and the blood-brain barrier have limited the applicability of molecularly targeted agents. The therapeutic benefit seen with a small subset of patients suggests, however, that patient selection is critical. Recent investigations show that molecularly targeted synthetic lethality is a promising complementary approach. The article provides an overview of the challenges of molecularly targeted therapy in adults with glioblastoma, including current trials and future therapeutic directions.
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Affiliation(s)
- Matthew A Smith-Cohn
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, 37 Convent Drive, Building 37, Room 1016, Bethesda, MD 20892, USA; Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Orieta Celiku
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, 37 Convent Drive, Building 37, Room 1142, Bethesda, MD 20892, USA
| | - Mark R Gilbert
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
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28
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τ-SGA: synthetic genetic array analysis for systematically screening and quantifying trigenic interactions in yeast. Nat Protoc 2021; 16:1219-1250. [PMID: 33462440 PMCID: PMC9127509 DOI: 10.1038/s41596-020-00456-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 10/28/2020] [Indexed: 01/29/2023]
Abstract
Systematic complex genetic interaction studies have provided insight into high-order functional redundancies and genetic network wiring of the cell. Here, we describe a method for screening and quantifying trigenic interactions from ordered arrays of yeast strains grown on agar plates as individual colonies. The protocol instructs users on the trigenic synthetic genetic array analysis technique, τ-SGA, for high-throughput screens. The steps describe construction of the double-mutant query strains and the corresponding single-mutant control query strains, which are screened in parallel in two replicates. The screening experimental set-up consists of sequential replica-pinning steps that enable automated mating, meiotic recombination and successive haploid selection steps for the generation of triple mutants, which are scored for colony size as a proxy for fitness, which enables the calculation of trigenic interactions. The procedure described here was used to conduct 422 trigenic interaction screens, which generated ~460,000 yeast triple mutants for trigenic interaction analysis. Users should be familiar with robotic equipment required for high-throughput genetic interaction screens and be proficient at the command line to execute the scoring pipeline. Large-scale screen computational analysis is achieved by using MATLAB pipelines that score raw colony size data to produce τ-SGA interaction scores. Additional recommendations are included for optimizing experimental design and analysis of smaller-scale trigenic interaction screens by using a web-based analysis system, SGAtools. This protocol provides a resource for those who would like to gain a deeper, more practical understanding of trigenic interaction screening and quantification methodology.
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29
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Savino A, Provero P, Poli V. Differential Co-Expression Analyses Allow the Identification of Critical Signalling Pathways Altered during Tumour Transformation and Progression. Int J Mol Sci 2020; 21:ijms21249461. [PMID: 33322692 PMCID: PMC7764314 DOI: 10.3390/ijms21249461] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 12/02/2020] [Accepted: 12/09/2020] [Indexed: 02/02/2023] Open
Abstract
Biological systems respond to perturbations through the rewiring of molecular interactions, organised in gene regulatory networks (GRNs). Among these, the increasingly high availability of transcriptomic data makes gene co-expression networks the most exploited ones. Differential co-expression networks are useful tools to identify changes in response to an external perturbation, such as mutations predisposing to cancer development, and leading to changes in the activity of gene expression regulators or signalling. They can help explain the robustness of cancer cells to perturbations and identify promising candidates for targeted therapy, moreover providing higher specificity with respect to standard co-expression methods. Here, we comprehensively review the literature about the methods developed to assess differential co-expression and their applications to cancer biology. Via the comparison of normal and diseased conditions and of different tumour stages, studies based on these methods led to the definition of pathways involved in gene network reorganisation upon oncogenes’ mutations and tumour progression, often converging on immune system signalling. A relevant implementation still lagging behind is the integration of different data types, which would greatly improve network interpretability. Most importantly, performance and predictivity evaluation of the large variety of mathematical models proposed would urgently require experimental validations and systematic comparisons. We believe that future work on differential gene co-expression networks, complemented with additional omics data and experimentally tested, will considerably improve our insights into the biology of tumours.
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Affiliation(s)
- Aurora Savino
- Molecular Biotechnology Center, Department of Molecular Biotechnology and Health Sciences, University of Turin, Via Nizza 52, 10126 Turin, Italy
- Correspondence: (A.S.); (V.P.)
| | - Paolo Provero
- Department of Neurosciences “Rita Levi Montalcini”, University of Turin, Corso Massimo D’Ázeglio 52, 10126 Turin, Italy;
- Center for Omics Sciences, Ospedale San Raffaele IRCCS, Via Olgettina 60, 20132 Milan, Italy
| | - Valeria Poli
- Molecular Biotechnology Center, Department of Molecular Biotechnology and Health Sciences, University of Turin, Via Nizza 52, 10126 Turin, Italy
- Correspondence: (A.S.); (V.P.)
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30
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García-Martínez J, Pérez-Martínez ME, Pérez-Ortín JE, Alepuz P. Recruitment of Xrn1 to stress-induced genes allows efficient transcription by controlling RNA polymerase II backtracking. RNA Biol 2020; 18:1458-1474. [PMID: 33258404 DOI: 10.1080/15476286.2020.1857521] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
A new paradigm has emerged proposing that the crosstalk between nuclear transcription and cytoplasmic mRNA stability keeps robust mRNA levels in cells under steady-state conditions. A key piece in this crosstalk is the highly conserved 5'-3' RNA exonuclease Xrn1, which degrades most cytoplasmic mRNAs but also associates with nuclear chromatin to activate transcription by not well-understood mechanisms. Here, we investigated the role of Xrn1 in the transcriptional response of Saccharomyces cerevisiae cells to osmotic stress. We show that a lack of Xrn1 results in much lower transcriptional induction of the upregulated genes but in similar high levels of their transcripts because of parallel mRNA stabilization. Unexpectedly, lower transcription in xrn1 occurs with a higher accumulation of RNA polymerase II (RNAPII) at stress-inducible genes, suggesting that this polymerase remains inactive backtracked. Xrn1 seems to be directly implicated in the formation of a competent elongation complex because Xrn1 is recruited to the osmotic stress-upregulated genes in parallel with the RNAPII complex, and both are dependent on the mitogen-activated protein kinase Hog1. Our findings extend the role of Xrn1 in preventing the accumulation of inactive RNAPII at highly induced genes to other situations of rapid and strong transcriptional upregulation.
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Affiliation(s)
- José García-Martínez
- ERI Biotecmed, Facultad De Ciencias Biológicas, Universitat De València, Burjassot, Spain.,Departamento De Genética, Facultad De Ciencias Biológicas, Universitat De València, Burjassot, Spain
| | - María E Pérez-Martínez
- ERI Biotecmed, Facultad De Ciencias Biológicas, Universitat De València, Burjassot, Spain.,Departamento De Bioquímica Y Biología Molecular, Facultad De Ciencias Biológicas, Universitat De València, Burjassot, Spain
| | - José E Pérez-Ortín
- ERI Biotecmed, Facultad De Ciencias Biológicas, Universitat De València, Burjassot, Spain.,Departamento De Bioquímica Y Biología Molecular, Facultad De Ciencias Biológicas, Universitat De València, Burjassot, Spain
| | - Paula Alepuz
- ERI Biotecmed, Facultad De Ciencias Biológicas, Universitat De València, Burjassot, Spain.,Departamento De Bioquímica Y Biología Molecular, Facultad De Ciencias Biológicas, Universitat De València, Burjassot, Spain
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31
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Brockway S, Wang G, Jackson JM, Amici DR, Takagishi SR, Clutter MR, Bartom ET, Mendillo ML. Quantitative and multiplexed chemical-genetic phenotyping in mammalian cells with QMAP-Seq. Nat Commun 2020; 11:5722. [PMID: 33184288 PMCID: PMC7661543 DOI: 10.1038/s41467-020-19553-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 10/14/2020] [Indexed: 12/26/2022] Open
Abstract
Chemical-genetic interaction profiling in model organisms has proven powerful in providing insights into compound mechanism of action and gene function. However, identifying chemical-genetic interactions in mammalian systems has been limited to low-throughput or computational methods. Here, we develop Quantitative and Multiplexed Analysis of Phenotype by Sequencing (QMAP-Seq), which leverages next-generation sequencing for pooled high-throughput chemical-genetic profiling. We apply QMAP-Seq to investigate how cellular stress response factors affect therapeutic response in cancer. Using minimal automation, we treat pools of 60 cell types—comprising 12 genetic perturbations in five cell lines—with 1440 compound-dose combinations, generating 86,400 chemical-genetic measurements. QMAP-Seq produces precise and accurate quantitative measures of acute drug response comparable to gold standard assays, but with increased throughput at lower cost. Moreover, QMAP-Seq reveals clinically actionable drug vulnerabilities and functional relationships involving these stress response factors, many of which are activated in cancer. Thus, QMAP-Seq provides a broadly accessible and scalable strategy for chemical-genetic profiling in mammalian cells. Identifying chemical-genetic interactions in mammalian cells is limited to low-throughput or computational methods. Here, the authors present QMAP-Seq, a broadly accessible and scalable approach that uses NGS for pooled high-throughput chemical-genetic profiling in mammalian cells.
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Affiliation(s)
- Sonia Brockway
- Department of Biochemistry and Molecular Genetics, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.,Simpson Querrey Institute for Epigenetics, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.,Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.,Driskill Graduate Program in Life Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Geng Wang
- Department of Biochemistry and Molecular Genetics, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.,Simpson Querrey Institute for Epigenetics, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.,Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Jasen M Jackson
- Department of Biochemistry and Molecular Genetics, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.,Simpson Querrey Institute for Epigenetics, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.,Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - David R Amici
- Department of Biochemistry and Molecular Genetics, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.,Simpson Querrey Institute for Epigenetics, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.,Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.,Medical Scientist Training Program, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Seesha R Takagishi
- Department of Biochemistry and Molecular Genetics, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.,Simpson Querrey Institute for Epigenetics, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.,Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Matthew R Clutter
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.,Chemistry of Life Processes Institute, Northwestern University, Evanston, IL, 60208, USA.,Department of Molecular Biosciences, Northwestern University, Evanston, IL, 60208, USA
| | - Elizabeth T Bartom
- Department of Biochemistry and Molecular Genetics, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.,Simpson Querrey Institute for Epigenetics, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Marc L Mendillo
- Department of Biochemistry and Molecular Genetics, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA. .,Simpson Querrey Institute for Epigenetics, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA. .,Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.
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32
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Integrative omics analysis reveals relationships of genes with synthetic lethal interactions through a pan-cancer analysis. Comput Struct Biotechnol J 2020; 18:3243-3254. [PMID: 33240468 PMCID: PMC7658657 DOI: 10.1016/j.csbj.2020.10.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Revised: 10/10/2020] [Accepted: 10/12/2020] [Indexed: 02/07/2023] Open
Abstract
Synthetic lethality is thought to play an important role in anticancer therapies. Herein, to understand the potential distributions and relationships between synthetic lethal interactions between genes, especially for pairs deriving from different sources, we performed an integrative analysis of genes at multiple molecular levels. Based on inter-species phylogenetic conservation of synthetic lethal interactions, gene pairs from yeast and humans were analyzed; a total of 37,588 candidate gene pairs containing 7,816 genes were collected. Of these, 49.74% of genes had 2–10 interactions, 22.93% were involved in hallmarks of cancer, and 21.61% were identified as core essential genes. Many genes were shown to have important biological roles via functional enrichment analysis, and 65 were identified as potentially crucial in the pathophysiology of cancer. Gene pairs with dysregulated expression patterns had higher prognostic values. Further screening based on mutation and expression levels showed that remaining gene pairs were mainly derived from human predicted or validated pairs, while most predicted pairs from yeast were filtered from analysis. Genes with synthetic lethality were further analyzed with their interactive microRNAs (miRNAs) at the isomiR level which have been widely studied as negatively regulatory molecules. The miRNA–mRNA interaction network revealed that many synthetic lethal genes contributed to the cell cycle (seven of 12 genes), cancer pathways (five of 12 genes), oocyte meiosis, the p53 signaling pathway, and hallmarks of cancer. Our study contributes to the understanding of synthetic lethal interactions and promotes the application of genetic interactions in further cancer precision medicine.
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Key Words
- ACC, adrenocortical carcinoma
- BLCA, bladder urothelial carcinoma
- BRCA, breast invasive carcinoma
- CESC, cervical squamous cell carcinoma and endocervical adenocarcinoma
- CHOL, cholangiocarcinoma
- COAD, colon adenocarcinoma
- Cancer therapy
- DLBC, lymphoid neoplasm diffuse large B-cell lymphoma
- ESCA, esophageal carcinoma
- GBM, glioblastoma multiforme
- HNSC, head and neck squamous cell carcinoma
- KICH, kidney chromophobe
- KIRC, kidney renal clear cell carcinoma
- KIRP, kidney renal papillary cell carcinoma
- LAML, acute myeloid leukemia
- LGG, brain lower grade glioma
- LIHC, liver hepatocellular carcinoma
- LUAD, lung adenocarcinoma
- LUSC, lung squamous cell carcinoma
- MESO, mesothelioma
- OV, ovarian serous cystadenocarcinoma
- PAAD, pancreatic adenocarcinoma
- PCPG, pheochromocytoma and paraganglioma
- PRAD, prostate adenocarcinoma
- Pan-cancer analysis
- READ, rectum adenocarcinoma
- RNA interaction
- SARC, sarcoma
- SKCM, skin cutaneous melanoma
- STAD, stomach adenocarcinoma
- Synthetic lethality
- TGCT, testicular germ cell tumors
- THCA, thyroid carcinoma
- THYM, thymoma
- TSG, tumor suppressor gene
- UCEC, uterine corpus endometrial carcinoma
- UCS, uterine carcinosarcoma
- UVM, uveal melanoma
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33
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Liany H, Jeyasekharan A, Rajan V. Predicting synthetic lethal interactions using heterogeneous data sources. Bioinformatics 2020; 36:2209-2216. [PMID: 31782759 DOI: 10.1093/bioinformatics/btz893] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Revised: 10/31/2019] [Accepted: 11/27/2019] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION A synthetic lethal (SL) interaction is a relationship between two functional entities where the loss of either one of the entities is viable but the loss of both entities is lethal to the cell. Such pairs can be used as drug targets in targeted anticancer therapies, and so, many methods have been developed to identify potential candidate SL pairs. However, these methods use only a subset of available data from multiple platforms, at genomic, epigenomic and transcriptomic levels; and hence are limited in their ability to learn from complex associations in heterogeneous data sources. RESULTS In this article, we develop techniques that can seamlessly integrate multiple heterogeneous data sources to predict SL interactions. Our approach obtains latent representations by collective matrix factorization-based techniques, which in turn are used for prediction through matrix completion. Our experiments, on a variety of biological datasets, illustrate the efficacy and versatility of our approach, that outperforms state-of-the-art methods for predicting SL interactions and can be used with heterogeneous data sources with minimal feature engineering. AVAILABILITY AND IMPLEMENTATION Software available at https://github.com/lianyh. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Herty Liany
- Department of Computer Science, School of Computing, National University of Singapore, Singapore, Singapore
| | - Anand Jeyasekharan
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Vaibhav Rajan
- Department of Information Systems and Analytics, School of Computing, National University of Singapore, Singapore, Singapore
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34
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A cancer drug atlas enables synergistic targeting of independent drug vulnerabilities. Nat Commun 2020; 11:2935. [PMID: 32523045 PMCID: PMC7287046 DOI: 10.1038/s41467-020-16735-2] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Accepted: 05/06/2020] [Indexed: 12/13/2022] Open
Abstract
Personalized cancer treatments using combinations of drugs with a synergistic effect is attractive but proves to be highly challenging. Here we present an approach to uncover the efficacy of drug combinations based on the analysis of mono-drug effects. For this we used dose-response data from pharmacogenomic encyclopedias and represent these as a drug atlas. The drug atlas represents the relations between drug effects and allows to identify independent processes for which the tumor might be particularly vulnerable when attacked by two drugs. Our approach enables the prediction of combination-therapy which can be linked to tumor-driving mutations. By using this strategy, we can uncover potential effective drug combinations on a pan-cancer scale. Predicted synergies are provided and have been validated in glioblastoma, breast cancer, melanoma and leukemia mouse-models, resulting in therapeutic synergy in 75% of the tested models. This indicates that we can accurately predict effective drug combinations with translational value.
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35
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Fischer J, Song YS, Yosef N, di Iulio J, Churchman LS, Choder M. The yeast exoribonuclease Xrn1 and associated factors modulate RNA polymerase II processivity in 5' and 3' gene regions. J Biol Chem 2020; 295:11435-11454. [PMID: 32518159 DOI: 10.1074/jbc.ra120.013426] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Revised: 06/05/2020] [Indexed: 11/06/2022] Open
Abstract
mRNA levels are determined by the balance between mRNA synthesis and decay. Protein factors that mediate both processes, including the 5'-3' exonuclease Xrn1, are responsible for a cross-talk between the two processes that buffers steady-state mRNA levels. However, the roles of these proteins in transcription remain elusive and controversial. Applying native elongating transcript sequencing (NET-seq) to yeast cells, we show that Xrn1 functions mainly as a transcriptional activator and that its disruption manifests as a reduction of RNA polymerase II (Pol II) occupancy downstream of transcription start sites. By combining our sequencing data and mathematical modeling of transcription, we found that Xrn1 modulates transcription initiation and elongation of its target genes. Furthermore, Pol II occupancy markedly increased near cleavage and polyadenylation sites in xrn1Δ cells, whereas its activity decreased, a characteristic feature of backtracked Pol II. We also provide indirect evidence that Xrn1 is involved in transcription termination downstream of polyadenylation sites. We noted that two additional decay factors, Dhh1 and Lsm1, seem to function similarly to Xrn1 in transcription, perhaps as a complex, and that the decay factors Ccr4 and Rpb4 also perturb transcription in other ways. Interestingly, the decay factors could differentiate between SAGA- and TFIID-dominated promoters. These two classes of genes responded differently to XRN1 deletion in mRNA synthesis and were differentially regulated by mRNA decay pathways, raising the possibility that one distinction between these two gene classes lies in the mechanisms that balance mRNA synthesis with mRNA decay.
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Affiliation(s)
- Jonathan Fischer
- Computer Science Division, University of California, Berkeley, California, USA.,Department of Statistics, University of California, Berkeley, California, USA
| | - Yun S Song
- Computer Science Division, University of California, Berkeley, California, USA.,Department of Statistics, University of California, Berkeley, California, USA.,Chan Zuckerberg BioHub, San Francisco, California, USA
| | - Nir Yosef
- Chan Zuckerberg BioHub, San Francisco, California, USA.,Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California, USA.,Ragon Institute of MGH, MIT, and Harvard, Cambridge, Massachusetts, USA
| | - Julia di Iulio
- Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Mordechai Choder
- Department of Molecular Microbiology, Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
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Lord CJ, Quinn N, Ryan CJ. Integrative analysis of large-scale loss-of-function screens identifies robust cancer-associated genetic interactions. eLife 2020; 9:e58925. [PMID: 32463358 PMCID: PMC7289598 DOI: 10.7554/elife.58925] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Accepted: 05/18/2020] [Indexed: 12/13/2022] Open
Abstract
Genetic interactions, including synthetic lethal effects, can now be systematically identified in cancer cell lines using high-throughput genetic perturbation screens. Despite this advance, few genetic interactions have been reproduced across multiple studies and many appear highly context-specific. Here, by developing a new computational approach, we identified 220 robust driver-gene associated genetic interactions that can be reproduced across independent experiments and across non-overlapping cell line panels. Analysis of these interactions demonstrated that: (i) oncogene addiction effects are more robust than oncogene-related synthetic lethal effects; and (ii) robust genetic interactions are enriched among gene pairs whose protein products physically interact. Exploiting the latter observation, we used a protein-protein interaction network to identify robust synthetic lethal effects associated with passenger gene alterations and validated two new synthetic lethal effects. Our results suggest that protein-protein interaction networks can be used to prioritise therapeutic targets that will be more robust to tumour heterogeneity.
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Affiliation(s)
- Christopher J Lord
- Breast Cancer Now Toby Robins Research Centre and Cancer Research UK Gene Function Laboratory, Institute of Cancer ResearchLondonUnited Kingdom
| | - Niall Quinn
- School of Computer Science and Systems Biology Ireland, University College DublinDublinIreland
| | - Colm J Ryan
- School of Computer Science and Systems Biology Ireland, University College DublinDublinIreland
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37
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G2G: A web-server for the prediction of human synthetic lethal interactions. Comput Struct Biotechnol J 2020; 18:1028-1031. [PMID: 32419903 PMCID: PMC7215103 DOI: 10.1016/j.csbj.2020.04.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 04/18/2020] [Accepted: 04/19/2020] [Indexed: 12/04/2022] Open
Abstract
Genetic interactions (GIs) are fundamental to our understanding of biological processes in the cell. While GIs have been systematically mapped in yeast, there is scarce information about them in humans. Recently, we have suggested a state-of-the-art hierarchical method that leverages gene ontology information for predicting GIs in yeast. Here, we adapt this method and apply it for the first time to predict GIs in human. We introduce a web service called G2G for this task that is available at http://bnet.cs.tau.ac.il/g2g/.
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A Multimodal Genotoxic Anticancer Drug Characterized by Pharmacogenetic Analysis in Caenorhabditis elegans. Genetics 2020; 215:609-621. [PMID: 32414869 PMCID: PMC7337070 DOI: 10.1534/genetics.120.303169] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 05/08/2020] [Indexed: 01/05/2023] Open
Abstract
New anticancer therapeutics require extensive in vivo characterization to identify endogenous and exogenous factors affecting efficacy, to measure toxicity and mutagenicity, and to determine genotypes that result in therapeutic sensitivity or resistance. We used Caenorhabditis elegans as a platform with which to characterize properties of the anticancer therapeutic CX-5461. To understand the processes that respond to CX-5461-induced damage, we generated pharmacogenetic profiles for a panel of C. elegans DNA replication and repair mutants with common DNA-damaging agents for comparison with the profile of CX-5461. We found that multiple repair pathways, including homology-directed repair, microhomology-mediated end joining, nucleotide excision repair, and translesion synthesis, were needed for CX-5461 tolerance. To determine the frequency and spectrum of CX-5461-induced mutations, we used a genetic balancer to capture CX-5461-induced mutations. We found that CX-5461 is mutagenic, resulting in both large copy number variations and a high frequency of single-nucleotide variations (SNVs), which are consistent with the pharmacogenetic profile for CX-5461. Whole-genome sequencing of CX-5461-exposed animals found that CX-5461-induced SNVs exhibited a distinct mutational signature. We also phenocopied the CX-5461 photoreactivity observed in clinical trials and demonstrated that CX-5461 generates reactive oxygen species when exposed to UVA radiation. Together, the data from C. elegans demonstrate that CX-5461 is a multimodal DNA-damaging anticancer agent.
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39
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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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40
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Chang JW, Ding Y, Tahir Ul Qamar M, Shen Y, Gao J, Chen LL. A deep learning model based on sparse auto-encoder for prioritizing cancer-related genes and drug target combinations. Carcinogenesis 2020; 40:624-632. [PMID: 30944926 DOI: 10.1093/carcin/bgz044] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Revised: 01/06/2019] [Accepted: 03/10/2019] [Indexed: 12/21/2022] Open
Abstract
Prioritization of cancer-related genes from gene expression profiles and proteomic data is vital to improve the targeted therapies research. Although computational approaches have been complementing high-throughput biological experiments on the understanding of human diseases, it still remains a big challenge to accurately discover cancer-related proteins/genes via automatic learning from large-scale protein/gene expression data and protein-protein interaction data. Most of the existing methods are based on network construction combined with gene expression profiles, which ignore the diversity between normal samples and disease cell lines. In this study, we introduced a deep learning model based on a sparse auto-encoder to learn the specific characteristics of protein interactions in cancer cell lines integrated with protein expression data. The model showed learning ability to identify cancer-related proteins/genes from the input of different protein expression profiles by extracting the characteristics of protein interaction information, which could also predict cancer-related protein combinations. Comparing with other reported methods including differential expression and network-based methods, our model got the highest area under the curve value (>0.8) in predicting cancer-related genes. Our study prioritized ~500 high-confidence cancer-related genes; among these genes, 211 already known cancer drug targets were found, which supported the accuracy of our method. The above results indicated that the proposed auto-encoder model could computationally prioritize candidate proteins/genes involved in cancer and improve the targeted therapies research.
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Affiliation(s)
- Ji-Wei Chang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, P. R. China
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, P. R. China
| | - Yuduan Ding
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, P. R. China
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, P. R. China
| | - Muhammad Tahir Ul Qamar
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, P. R. China
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, P. R. China
| | - Yin Shen
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, P. R. China
| | - Junxiang Gao
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, P. R. China
| | - Ling-Ling Chen
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, P. R. China
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, P. R. China
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Wan F, Li S, Tian T, Lei Y, Zhao D, Zeng J. EXP2SL: A Machine Learning Framework for Cell-Line-Specific Synthetic Lethality Prediction. Front Pharmacol 2020; 11:112. [PMID: 32184722 PMCID: PMC7058988 DOI: 10.3389/fphar.2020.00112] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2019] [Accepted: 01/28/2020] [Indexed: 12/13/2022] Open
Abstract
Synthetic lethality (SL), an important type of genetic interaction, can provide useful insight into the target identification process for the development of anticancer therapeutics. Although several well-established SL gene pairs have been verified to be conserved in humans, most SL interactions remain cell-line specific. Here, we demonstrated that the cell-line-specific gene expression profiles derived from the shRNA perturbation experiments performed in the LINCS L1000 project can provide useful features for predicting SL interactions in human. In this paper, we developed a semi-supervised neural network-based method called EXP2SL to accurately identify SL interactions from the L1000 gene expression profiles. Through a systematic evaluation on the SL datasets of three different cell lines, we demonstrated that our model achieved better performance than the baseline methods and verified the effectiveness of using the L1000 gene expression features and the semi-supervise training technique in SL prediction.
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Affiliation(s)
- Fangping Wan
- Institute of Interdisciplinary Information Science, Tsinghua University, Beijing, China
| | - Shuya Li
- Institute of Interdisciplinary Information Science, Tsinghua University, Beijing, China
| | - Tingzhong Tian
- Institute of Interdisciplinary Information Science, Tsinghua University, Beijing, China
| | - Yipin Lei
- Machine Learning Department, Silexon AI Technology Co. Ltd., Nanjing, China
| | - Dan Zhao
- Institute of Interdisciplinary Information Science, Tsinghua University, Beijing, China
| | - Jianyang Zeng
- Institute of Interdisciplinary Information Science, Tsinghua University, Beijing, China
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Liu C, Zhao J, Lu W, Dai Y, Hockings J, Zhou Y, Nussinov R, Eng C, Cheng F. Individualized genetic network analysis reveals new therapeutic vulnerabilities in 6,700 cancer genomes. PLoS Comput Biol 2020; 16:e1007701. [PMID: 32101536 PMCID: PMC7062285 DOI: 10.1371/journal.pcbi.1007701] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Revised: 03/09/2020] [Accepted: 01/30/2020] [Indexed: 02/06/2023] Open
Abstract
Tumor-specific genomic alterations allow systematic identification of genetic interactions that promote tumorigenesis and tumor vulnerabilities, offering novel strategies for development of targeted therapies for individual patients. We develop an Individualized Network-based Co-Mutation (INCM) methodology by inspecting over 2.5 million nonsynonymous somatic mutations derived from 6,789 tumor exomes across 14 cancer types from The Cancer Genome Atlas. Our INCM analysis reveals a higher genetic interaction burden on the significantly mutated genes, experimentally validated cancer genes, chromosome regulatory factors, and DNA damage repair genes, as compared to human pan-cancer essential genes identified by CRISPR-Cas9 screenings on 324 cancer cell lines. We find that genes involved in the cancer type-specific genetic subnetworks identified by INCM are significantly enriched in established cancer pathways, and the INCM-inferred putative genetic interactions are correlated with patient survival. By analyzing drug pharmacogenomics profiles from the Genomics of Drug Sensitivity in Cancer database, we show that the network-predicted putative genetic interactions (e.g., BRCA2-TP53) are significantly correlated with sensitivity/resistance of multiple therapeutic agents. We experimentally validated that afatinib has the strongest cytotoxic activity on BT474 (IC50 = 55.5 nM, BRCA2 and TP53 co-mutant) compared to MCF7 (IC50 = 7.7 μM, both BRCA2 and TP53 wild type) and MDA-MB-231 (IC50 = 7.9 μM, BRCA2 wild type but TP53 mutant). Finally, drug-target network analysis reveals several potential druggable genetic interactions by targeting tumor vulnerabilities. This study offers a powerful network-based methodology for identification of candidate therapeutic pathways that target tumor vulnerabilities and prioritization of potential pharmacogenomics biomarkers for development of personalized cancer medicine. Recent efforts to map genetic interactions in tumor cells have suggested that tumor vulnerabilities can be exploited for development of novel targeted therapies. Tumor-specific genomic alterations derived from multi-center cancer genome projects allow identification of genetic interactions that promote tumor vulnerabilities, offering novel strategies for development of targeted cancer therapies. This study develops a novel Individualized Network-based Co-Mutation (termed INCM) methodology for quantifying the putative genetic interactions in cancer. Trained on over 2.5 million nonsynonymous somatic mutations derived from 6,789 tumor exomes across 14 cancer type, we found that genes identified in the cancer type-specific genetic subnetworks were significantly enriched in established cancer pathways. The network-predicted putative genetic interactions are correlated with patient survival. By analyzing drug pharmacogenomics profiles, we showed that the network-predicted putative genetic interactions (e.g., BRCA2-TP53) were significantly correlated with sensitivity/resistance of anticancer drugs (e.g., afatinib) and we experimentally validated it in breast cancer cell lines. Finally, drug-target network analysis reveals several potential druggable genetic interactions (e.g., PIK3CA-PTEN) by targeting tumor vulnerabilities. This study offers a generalizable network-based approach for comprehensive identification of candidate therapeutic pathways that target tumor vulnerabilities and prioritization of potential prognostic and pharmacogenomics biomarkers for development of personalized cancer medicine.
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Affiliation(s)
- Chuang Liu
- Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Junfei Zhao
- Department of Systems Biology, Columbia University, New York, New York, United States of America
- Department of Biomedical Informatics, Columbia University, New York, New York, United States of America
| | - Weiqiang Lu
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, China
| | - Yao Dai
- Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Jennifer Hockings
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Yadi Zhou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Ruth Nussinov
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, Maryland, United States of America
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Charis Eng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
- Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
- Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
- * E-mail:
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Tang L, Chen R, Xu X. Synthetic lethality: A promising therapeutic strategy for hepatocellular carcinoma. Cancer Lett 2020; 476:120-128. [PMID: 32070778 DOI: 10.1016/j.canlet.2020.02.016] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 02/11/2020] [Accepted: 02/12/2020] [Indexed: 12/24/2022]
Abstract
Hepatocellular carcinoma (HCC), the main cause of liver cancer-related death, is one of the main cancers in terms of incidence and mortality. However, HCC is difficult to target and develops strong drug resistance. Therefore, a new treatment strategy is urgently needed. The clinical application of the concept of synthetic lethality in recent years provides a new therapeutic direction for the accurate treatment of HCC. Here, we introduce the concept of synthetic lethality, the screening used to study synthetic lethality, and the identified and potential genetic interactions that induce synthetic lethality in HCC. In addition, we propose opportunities and challenges for translating synthetic lethal interactions to the clinical treatment of HCC.
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Affiliation(s)
- Linsong Tang
- Department of Hepatobiliary and Pancreatic Surgery, Department of Surgery, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China; NHFPC Key Lab of Combined Multi-Organ Transplantation, Ministry of Public Health, Hangzhou, 310003, China.
| | - Ronggao Chen
- Department of Hepatobiliary and Pancreatic Surgery, Department of Surgery, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China; NHFPC Key Lab of Combined Multi-Organ Transplantation, Ministry of Public Health, Hangzhou, 310003, China.
| | - Xiao Xu
- Department of Hepatobiliary and Pancreatic Surgery, Department of Surgery, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China; NHFPC Key Lab of Combined Multi-Organ Transplantation, Ministry of Public Health, Hangzhou, 310003, China.
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Jariyal H, Weinberg F, Achreja A, Nagarath D, Srivastava A. Synthetic lethality: a step forward for personalized medicine in cancer. Drug Discov Today 2020; 25:305-320. [DOI: 10.1016/j.drudis.2019.11.014] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2019] [Revised: 11/06/2019] [Accepted: 11/27/2019] [Indexed: 12/15/2022]
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Begley V, Corzo D, Jordán-Pla A, Cuevas-Bermúdez A, Miguel-Jiménez LD, Pérez-Aguado D, Machuca-Ostos M, Navarro F, Chávez MJ, Pérez-Ortín JE, Chávez S. The mRNA degradation factor Xrn1 regulates transcription elongation in parallel to Ccr4. Nucleic Acids Res 2019; 47:9524-9541. [PMID: 31392315 PMCID: PMC6765136 DOI: 10.1093/nar/gkz660] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Revised: 06/26/2019] [Accepted: 07/17/2019] [Indexed: 01/05/2023] Open
Abstract
Co-transcriptional imprinting of mRNA by Rpb4 and Rpb7 subunits of RNA polymerase II (RNAPII) and by the Ccr4-Not complex conditions its post-transcriptional fate. In turn, mRNA degradation factors like Xrn1 are able to influence RNAPII-dependent transcription, making a feedback loop that contributes to mRNA homeostasis. In this work, we have used repressible yeast GAL genes to perform accurate measurements of transcription and mRNA degradation in a set of mutants. This genetic analysis uncovered a link from mRNA decay to transcription elongation. We combined this experimental approach with computational multi-agent modelling and tested different possibilities of Xrn1 and Ccr4 action in gene transcription. This double strategy brought us to conclude that both Xrn1-decaysome and Ccr4-Not regulate RNAPII elongation, and that they do it in parallel. We validated this conclusion measuring TFIIS genome-wide recruitment to elongating RNAPII. We found that xrn1Δ and ccr4Δ exhibited very different patterns of TFIIS versus RNAPII occupancy, which confirmed their distinct role in controlling transcription elongation. We also found that the relative influence of Xrn1 and Ccr4 is different in the genes encoding ribosomal proteins as compared to the rest of the genome.
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Affiliation(s)
- Victoria Begley
- Instituto de Biomedicina de Sevilla, Universidad de Sevilla-CSIC-Hospital Universitario V. del Rocío, Seville 41012, Spain
| | - Daniel Corzo
- Escuela Técnica Superior de Informática, Universidad de Sevilla, Seville 41012, Spain
| | - Antonio Jordán-Pla
- E.R.I. Biotecmed, Universitat de València; Burjassot, Valencia 46100, Spain
| | - Abel Cuevas-Bermúdez
- Departamento de Biología Experimental, Facultad de Ciencias Experimentales, Universidad de Jaén, Jaén 23071, Spain
| | - Lola de Miguel-Jiménez
- Instituto de Biomedicina de Sevilla, Universidad de Sevilla-CSIC-Hospital Universitario V. del Rocío, Seville 41012, Spain
| | - David Pérez-Aguado
- Instituto de Biomedicina de Sevilla, Universidad de Sevilla-CSIC-Hospital Universitario V. del Rocío, Seville 41012, Spain
| | - Mercedes Machuca-Ostos
- Instituto de Biomedicina de Sevilla, Universidad de Sevilla-CSIC-Hospital Universitario V. del Rocío, Seville 41012, Spain
| | - Francisco Navarro
- Departamento de Biología Experimental, Facultad de Ciencias Experimentales, Universidad de Jaén, Jaén 23071, Spain
| | - María José Chávez
- Departamento de Matemática Aplicada I and Instituto de Matemáticas, Universidad de Sevilla, Seville 41012, Spain
| | - José E Pérez-Ortín
- E.R.I. Biotecmed, Universitat de València; Burjassot, Valencia 46100, Spain
| | - Sebastián Chávez
- Instituto de Biomedicina de Sevilla, Universidad de Sevilla-CSIC-Hospital Universitario V. del Rocío, Seville 41012, Spain
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Ghelli Luserna Di Rorà A, Bocconcelli M, Ferrari A, Terragna C, Bruno S, Imbrogno E, Beeharry N, Robustelli V, Ghetti M, Napolitano R, Chirumbolo G, Marconi G, Papayannidis C, Paolini S, Sartor C, Simonetti G, Yen TJ, Martinelli G. Synergism Through WEE1 and CHK1 Inhibition in Acute Lymphoblastic Leukemia. Cancers (Basel) 2019; 11:cancers11111654. [PMID: 31717700 PMCID: PMC6895917 DOI: 10.3390/cancers11111654] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Revised: 10/16/2019] [Accepted: 10/23/2019] [Indexed: 12/30/2022] Open
Abstract
Introduction: Screening for synthetic lethality markers has demonstrated that the inhibition of the cell cycle checkpoint kinases WEE1 together with CHK1 drastically affects stability of the cell cycle and induces cell death in rapidly proliferating cells. Exploiting this finding for a possible therapeutic approach has showed efficacy in various solid and hematologic tumors, though not specifically tested in acute lymphoblastic leukemia. Methods: The efficacy of the combination between WEE1 and CHK1 inhibitors in B and T cell precursor acute lymphoblastic leukemia (B/T-ALL) was evaluated in vitro and ex vivo studies. The efficacy of the therapeutic strategy was tested in terms of cytotoxicity, induction of apoptosis, and changes in cell cycle profile and protein expression using B/T-ALL cell lines. In addition, the efficacy of the drug combination was studied in primary B-ALL blasts using clonogenic assays. Results: This study reports, for the first time, the efficacy of the concomitant inhibition of CHK1/CHK2 and WEE1 in ALL cell lines and primary leukemic B-ALL cells using two selective inhibitors: PF-0047736 (CHK1/CHK2 inhibitor) and AZD-1775 (WEE1 inhibitor). We showed strong synergism in the reduction of cell viability, proliferation and induction of apoptosis. The efficacy of the combination was related to the induction of early S-phase arrest and to the induction of DNA damage, ultimately triggering cell death. We reported evidence that the efficacy of the combination treatment is independent from the activation of the p53-p21 pathway. Moreover, gene expression analysis on B-ALL primary samples showed that Chek1 and Wee1 are significantly co-expressed in samples at diagnosis (Pearson r = 0.5770, p = 0.0001) and relapse (Pearson r= 0.8919; p = 0.0001). Finally, the efficacy of the combination was confirmed by the reduction in clonogenic survival of primary leukemic B-ALL cells. Conclusion: Our findings suggest that the combination of CHK1 and WEE1 inhibitors may be a promising therapeutic strategy to be tested in clinical trials for adult ALL.
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Affiliation(s)
| | - Matteo Bocconcelli
- Department of Experimental, Diagnostic and Specialty Medicine, Institute of Hematology “L. e A. Seràgnoli”, University of Bologna, 40138 Bologna, Italy
| | - Anna Ferrari
- Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, 47014 Meldola, Italy; (A.G.L.D.R.)
| | - Carolina Terragna
- Department of Experimental, Diagnostic and Specialty Medicine, Institute of Hematology “L. e A. Seràgnoli”, University of Bologna, 40138 Bologna, Italy
| | - Samantha Bruno
- Department of Experimental, Diagnostic and Specialty Medicine, Institute of Hematology “L. e A. Seràgnoli”, University of Bologna, 40138 Bologna, Italy
| | - Enrica Imbrogno
- Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, 47014 Meldola, Italy; (A.G.L.D.R.)
| | | | - Valentina Robustelli
- Department of Experimental, Diagnostic and Specialty Medicine, Institute of Hematology “L. e A. Seràgnoli”, University of Bologna, 40138 Bologna, Italy
| | - Martina Ghetti
- Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, 47014 Meldola, Italy; (A.G.L.D.R.)
| | - Roberta Napolitano
- Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, 47014 Meldola, Italy; (A.G.L.D.R.)
| | - Gabriella Chirumbolo
- Department of Experimental, Diagnostic and Specialty Medicine, Institute of Hematology “L. e A. Seràgnoli”, University of Bologna, 40138 Bologna, Italy
| | - Giovanni Marconi
- Department of Experimental, Diagnostic and Specialty Medicine, Institute of Hematology “L. e A. Seràgnoli”, University of Bologna, 40138 Bologna, Italy
| | - Cristina Papayannidis
- Department of Experimental, Diagnostic and Specialty Medicine, Institute of Hematology “L. e A. Seràgnoli”, University of Bologna, 40138 Bologna, Italy
| | - Stefania Paolini
- Department of Experimental, Diagnostic and Specialty Medicine, Institute of Hematology “L. e A. Seràgnoli”, University of Bologna, 40138 Bologna, Italy
| | - Chiara Sartor
- Department of Experimental, Diagnostic and Specialty Medicine, Institute of Hematology “L. e A. Seràgnoli”, University of Bologna, 40138 Bologna, Italy
| | - Giorgia Simonetti
- Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, 47014 Meldola, Italy; (A.G.L.D.R.)
- Correspondence:
| | - Timothy J. Yen
- Cancer Biology Program, Fox Chase Cancer Center, Philadelphia, PA 19111-2497, USA
| | - Giovanni Martinelli
- Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, 47014 Meldola, Italy; (A.G.L.D.R.)
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Tutuncuoglu B, Krogan NJ. Mapping genetic interactions in cancer: a road to rational combination therapies. Genome Med 2019; 11:62. [PMID: 31640753 PMCID: PMC6805649 DOI: 10.1186/s13073-019-0680-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 10/16/2019] [Indexed: 01/08/2023] Open
Abstract
The discovery of synthetic lethal interactions between poly (ADP-ribose) polymerase (PARP) inhibitors and BRCA genes, which are involved in homologous recombination, led to the approval of PARP inhibition as a monotherapy for patients with BRCA1/2-mutated breast or ovarian cancer. Studies following the initial observation of synthetic lethality demonstrated that the reach of PARP inhibitors is well beyond just BRCA1/2 mutants. Insights into the mechanisms of action of anticancer drugs are fundamental for the development of targeted monotherapies or rational combination treatments that will synergize to promote cancer cell death and overcome mechanisms of resistance. The development of targeted therapeutic agents is premised on mapping the physical and functional dependencies of mutated genes in cancer. An important part of this effort is the systematic screening of genetic interactions in a variety of cancer types. Until recently, genetic-interaction screens have relied either on the pairwise perturbations of two genes or on the perturbation of genes of interest combined with inhibition by commonly used anticancer drugs. Here, we summarize recent advances in mapping genetic interactions using targeted, genome-wide, and high-throughput genetic screens, and we discuss the therapeutic insights obtained through such screens. We further focus on factors that should be considered in order to develop a robust analysis pipeline. Finally, we discuss the integration of functional interaction data with orthogonal methods and suggest that such approaches will increase the reach of genetic-interaction screens for the development of rational combination therapies.
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Affiliation(s)
- Beril Tutuncuoglu
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, 16th Street, Mission Bay Campus, San Francisco, CA, 94158-2140, USA.,The J. David Gladstone Institutes, Owens Street, San Francisco, CA, 94158, USA.,Quantitative Biosciences Institute, University of California, San Francisco, 4th Street, San Francisco, CA, 94158, USA.,Cancer Cell Map Initiative (CCMI), La Jolla and San Francisco, CA, USA
| | - Nevan J Krogan
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, 16th Street, Mission Bay Campus, San Francisco, CA, 94158-2140, USA. .,The J. David Gladstone Institutes, Owens Street, San Francisco, CA, 94158, USA. .,Quantitative Biosciences Institute, University of California, San Francisco, 4th Street, San Francisco, CA, 94158, USA. .,Cancer Cell Map Initiative (CCMI), La Jolla and San Francisco, CA, USA.
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A Humanized Yeast Phenomic Model of Deoxycytidine Kinase to Predict Genetic Buffering of Nucleoside Analog Cytotoxicity. Genes (Basel) 2019; 10:genes10100770. [PMID: 31575041 PMCID: PMC6826991 DOI: 10.3390/genes10100770] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 09/17/2019] [Accepted: 09/23/2019] [Indexed: 12/22/2022] Open
Abstract
Knowledge about synthetic lethality can be applied to enhance the efficacy of anticancer therapies in individual patients harboring genetic alterations in their cancer that specifically render it vulnerable. We investigated the potential for high-resolution phenomic analysis in yeast to predict such genetic vulnerabilities by systematic, comprehensive, and quantitative assessment of drug–gene interaction for gemcitabine and cytarabine, substrates of deoxycytidine kinase that have similar molecular structures yet distinct antitumor efficacy. Human deoxycytidine kinase (dCK) was conditionally expressed in the Saccharomyces cerevisiae genomic library of knockout and knockdown (YKO/KD) strains, to globally and quantitatively characterize differential drug–gene interaction for gemcitabine and cytarabine. Pathway enrichment analysis revealed that autophagy, histone modification, chromatin remodeling, and apoptosis-related processes influence gemcitabine specifically, while drug–gene interaction specific to cytarabine was less enriched in gene ontology. Processes having influence over both drugs were DNA repair and integrity checkpoints and vesicle transport and fusion. Non-gene ontology (GO)-enriched genes were also informative. Yeast phenomic and cancer cell line pharmacogenomics data were integrated to identify yeast–human homologs with correlated differential gene expression and drug efficacy, thus providing a unique resource to predict whether differential gene expression observed in cancer genetic profiles are causal in tumor-specific responses to cytotoxic agents.
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Hinz TK, Kleczko EK, Singleton KR, Calhoun J, Marek LA, Kim J, Tan AC, Heasley LE. Functional RNAi Screens Define Distinct Protein Kinase Vulnerabilities in EGFR-Dependent HNSCC Cell Lines. Mol Pharmacol 2019; 96:862-870. [PMID: 31554698 DOI: 10.1124/mol.119.117804] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Accepted: 09/14/2019] [Indexed: 12/15/2022] Open
Abstract
The inhibitory epidermal growth factor receptor (EGFR) antibody, cetuximab, is an approved therapy for head and neck squamous cell carcinoma (HNSCC). Despite tumor response observed in some HNSCC patients, cetuximab alone or combined with radio- or chemotherapy fails to yield long-term control or cures. We hypothesize that a flexible receptor tyrosine kinase coactivation signaling network supports HNSCC survival in the setting of EGFR blockade, and that drugs disrupting this network will provide superior tumor control when combined with EGFR inhibitors. In this work, we submitted EGFR-dependent HNSCC cell lines to RNA interference-based functional genomics screens to identify, in an unbiased fashion, essential protein kinases for growth and survival as well as synthetic lethal targets for combined inhibition with EGFR antagonists. Mechanistic target of rapamycin kinase (MTOR) and erythroblastosis oncogene B (ERBB)3 were identified as high-ranking essential kinase hits in the HNSCC cell lines. MTOR dependency was confirmed by distinct short hairpin RNAs (shRNAs) and high sensitivity of the cell lines to AZD8055, whereas ERBB3 dependency was validated by shRNA-mediated silencing. Furthermore, a synthetic lethal kinome shRNA screen with a pan-ERBB inhibitor, AZD8931, identified multiple components of the extracellular signal-regulated kinase (ERK) mitogen-activated protein kinase pathway, consistent with ERK reactivation and/or incomplete ERK pathway inhibition in response to EGFR inhibitor monotherapy. As validation, distinct mitogen-activated protein kinase kinase (MEK) inhibitors yielded synergistic growth inhibition when combined with the EGFR inhibitors, gefitinib and AZD8931. The findings identify ERBB3 and MTOR as important pharmacological vulnerabilities in HNSCC and support combining MEK and EGFR inhibitors to enhance clinical efficacy in HNSCC. SIGNIFICANCE STATEMENT: Many cancers are driven by nonmutated receptor tyrosine kinase coactivation networks that defy full inhibition with single targeted drugs. This study identifies erythroblastosis oncogene B (ERBB)3 as an essential protein kinase in epidermal growth factor receptor-dependent head and neck squamous cell cancer (HNSCC) cell lines and a synthetic lethal interaction with the extracellular signal-regulated kinase mitogen-activated protein kinase pathway that provides a rationale for combining pan-ERBB and mitogen-activated protein kinase inhibitors as a therapeutic approach in subsets of HNSCC.
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Affiliation(s)
- Trista K Hinz
- Departments of Craniofacial Biology (T.K.H., E.K.K., K.R.S., J.C., L.A.M., L.E.H.) and Medicine (J.K., A.C.T.), University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Emily K Kleczko
- Departments of Craniofacial Biology (T.K.H., E.K.K., K.R.S., J.C., L.A.M., L.E.H.) and Medicine (J.K., A.C.T.), University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Katherine R Singleton
- Departments of Craniofacial Biology (T.K.H., E.K.K., K.R.S., J.C., L.A.M., L.E.H.) and Medicine (J.K., A.C.T.), University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Jacob Calhoun
- Departments of Craniofacial Biology (T.K.H., E.K.K., K.R.S., J.C., L.A.M., L.E.H.) and Medicine (J.K., A.C.T.), University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Lindsay A Marek
- Departments of Craniofacial Biology (T.K.H., E.K.K., K.R.S., J.C., L.A.M., L.E.H.) and Medicine (J.K., A.C.T.), University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Jihye Kim
- Departments of Craniofacial Biology (T.K.H., E.K.K., K.R.S., J.C., L.A.M., L.E.H.) and Medicine (J.K., A.C.T.), University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Aik Choon Tan
- Departments of Craniofacial Biology (T.K.H., E.K.K., K.R.S., J.C., L.A.M., L.E.H.) and Medicine (J.K., A.C.T.), University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Lynn E Heasley
- Departments of Craniofacial Biology (T.K.H., E.K.K., K.R.S., J.C., L.A.M., L.E.H.) and Medicine (J.K., A.C.T.), University of Colorado Anschutz Medical Campus, Aurora, Colorado
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Kirzinger MWB, Vizeacoumar FS, Haave B, Gonzalez-Lopez C, Bonham K, Kusalik A, Vizeacoumar FJ. Humanized yeast genetic interaction mapping predicts synthetic lethal interactions of FBXW7 in breast cancer. BMC Med Genomics 2019; 12:112. [PMID: 31351478 PMCID: PMC6660958 DOI: 10.1186/s12920-019-0554-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Accepted: 06/27/2019] [Indexed: 02/08/2023] Open
Abstract
Background Synthetic lethal interactions (SLIs) that occur between gene pairs are exploited for cancer therapeutics. Studies in the model eukaryote yeast have identified ~ 550,000 negative genetic interactions that have been extensively studied, leading to characterization of novel pathways and gene functions. This resource can be used to predict SLIs that can be relevant to cancer therapeutics. Methods We used patient data to identify genes that are down-regulated in breast cancer. InParanoid orthology mapping was performed to identify yeast orthologs of the down-regulated genes and predict their corresponding SLIs in humans. The predicted network graphs were drawn with Cytoscape. CancerRXgene database was used to predict drug response. Results Harnessing the vast available knowledge of yeast genetics, we generated a Humanized Yeast Genetic Interaction Network (HYGIN) for 1009 human genes with 10,419 interactions. Through the addition of patient-data from The Cancer Genome Atlas (TCGA), we generated a breast cancer specific subnetwork. Specifically, by comparing 1009 genes in HYGIN to genes that were down-regulated in breast cancer, we identified 15 breast cancer genes with 130 potential SLIs. Interestingly, 32 of the 130 predicted SLIs occurred with FBXW7, a well-known tumor suppressor that functions as a substrate-recognition protein within a SKP/CUL1/F-Box ubiquitin ligase complex for proteasome degradation. Efforts to validate these SLIs using chemical genetic data predicted that patients with loss of FBXW7 may respond to treatment with drugs like Selumitinib or Cabozantinib. Conclusions This study provides a patient-data driven interpretation of yeast SLI data. HYGIN represents a novel strategy to uncover therapeutically relevant cancer drug targets and the yeast SLI data offers a major opportunity to mine these interactions. Electronic supplementary material The online version of this article (10.1186/s12920-019-0554-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Morgan W B Kirzinger
- Department of Computer Science, College of Arts and Science, University of Saskatchewan, 176 Thorvaldson Bldg, 110 Science Place, Saskatoon, Saskatchewan, S7N 5C9, Canada
| | - Frederick S Vizeacoumar
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Saskatchewan, 107 Wiggins Road, Saskatoon, Saskatchewan, S7N 5E5, Canada
| | - Bjorn Haave
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Saskatchewan, 107 Wiggins Road, Saskatoon, Saskatchewan, S7N 5E5, Canada
| | - Cristina Gonzalez-Lopez
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Saskatchewan, 107 Wiggins Road, Saskatoon, Saskatchewan, S7N 5E5, Canada
| | - Keith Bonham
- Cancer Research, Saskatchewan Cancer Agency, 107 Wiggins Road, Saskatoon, Saskatchewan, S7N 5E5, Canada.,Division of Oncology, College of Medicine, University of Saskatchewan, 107 Wiggins Road, Saskatoon, Saskatchewan, S7N 5E5, Canada
| | - Anthony Kusalik
- Department of Computer Science, College of Arts and Science, University of Saskatchewan, 176 Thorvaldson Bldg, 110 Science Place, Saskatoon, Saskatchewan, S7N 5C9, Canada.
| | - Franco J Vizeacoumar
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Saskatchewan, 107 Wiggins Road, Saskatoon, Saskatchewan, S7N 5E5, Canada. .,Cancer Research, Saskatchewan Cancer Agency, 107 Wiggins Road, Saskatoon, Saskatchewan, S7N 5E5, Canada. .,Division of Oncology, College of Medicine, University of Saskatchewan, 107 Wiggins Road, Saskatoon, Saskatchewan, S7N 5E5, Canada. .,Cancer Cluster, Rm 4D01.5 Health Science Bldg, University of Saskatchewan, 107 Wiggins Road, Saskatoon, SK, S7N 5E5, Canada.
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