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Alejandre C, Calle-Espinosa J, Iranzo J. Synergistic epistasis among cancer drivers can rescue early tumors from the accumulation of deleterious passengers. PLoS Comput Biol 2024; 20:e1012081. [PMID: 38687804 PMCID: PMC11087069 DOI: 10.1371/journal.pcbi.1012081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 05/10/2024] [Accepted: 04/16/2024] [Indexed: 05/02/2024] Open
Abstract
Epistasis among driver mutations is pervasive and explains relevant features of cancer, such as differential therapy response and convergence towards well-characterized molecular subtypes. Furthermore, a growing body of evidence suggests that tumor development could be hampered by the accumulation of slightly deleterious passenger mutations. In this work, we combined empirical epistasis networks, computer simulations, and mathematical models to explore how synergistic interactions among driver mutations affect cancer progression under the burden of slightly deleterious passengers. We found that epistasis plays a crucial role in tumor development by promoting the transformation of precancerous clones into rapidly growing tumors through a process that is analogous to evolutionary rescue. The triggering of epistasis-driven rescue is strongly dependent on the intensity of epistasis and could be a key rate-limiting step in many tumors, contributing to their unpredictability. As a result, central genes in cancer epistasis networks appear as key intervention targets for cancer therapy.
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Affiliation(s)
- Carla Alejandre
- Centro de Astrobiología (CAB) CSIC-INTA, Torrejón de Ardoz, Madrid, Spain
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)—Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
| | - Jorge Calle-Espinosa
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)—Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
| | - Jaime Iranzo
- Centro de Astrobiología (CAB) CSIC-INTA, Torrejón de Ardoz, Madrid, Spain
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)—Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
- Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, Zaragoza, Spain
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2
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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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Liu X, Hu J, Zheng J. SL-Miner: a web server for mining evidence and prioritization of cancer-specific synthetic lethality. Bioinformatics 2024; 40:btae016. [PMID: 38244572 PMCID: PMC10868331 DOI: 10.1093/bioinformatics/btae016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Revised: 12/10/2023] [Accepted: 01/16/2024] [Indexed: 01/22/2024] Open
Abstract
SUMMARY Synthetic lethality (SL) refers to a type of genetic interaction in which the simultaneous inactivation of two genes leads to cell death, while the inactivation of a single gene does not affect cell viability. It significantly expands the range of potential therapeutic targets for anti-cancer treatments. SL interactions are primarily identified through experimental screening and computational prediction. Although various computational methods have been proposed, they tend to ignore providing evidence to support their predictions of SL. Besides, they are rarely user-friendly for biologists who likely have limited programming skills. Moreover, the genetic context specificity of SL interactions is often not taken into consideration. Here, we introduce a web server called SL-Miner, which is designed to mine the evidence of SL relationships between a primary gene and a few candidate SL partner genes in a specific type of cancer, and to prioritize these candidate genes by integrating various types of evidence. For intuitive data visualization, SL-Miner provides a range of charts (e.g. volcano plot and box plot) to help users get insights from the data. AVAILABILITY AND IMPLEMENTATION SL-Miner is available at https://slminer.sist.shanghaitech.edu.cn.
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Affiliation(s)
- Xin Liu
- School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Jieni Hu
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Jie Zheng
- School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China
- Shanghai Engineering Research Center of Intelligent Vision and Imaging, Shanghai 201210, China
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4
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Geng H, Qian R, Zhong Y, Tang X, Zhang X, Zhang L, Yang C, Li T, Dong Z, Wang C, Zhang Z, Zhu C. Leveraging synthetic lethality to uncover potential therapeutic target in gastric cancer. Cancer Gene Ther 2024; 31:334-348. [PMID: 38040871 DOI: 10.1038/s41417-023-00706-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 11/10/2023] [Accepted: 11/16/2023] [Indexed: 12/03/2023]
Abstract
Since trastuzumab was approved in 2012 for the first-line treatment of gastric cancer (GC), no significant advancement in GC targeted therapies has occurred. Synthetic lethality refers to the concept that simultaneous dysfunction of a pair of genes results in a lethal effect on cells, while the loss of an individual gene does not cause this effect. Through exploiting synthetic lethality, novel targeted therapies can be developed for the individualized treatment of GC. In this study, we proposed a computational strategy named Gastric cancer Specific Synthetic Lethality inference (GSSL) to identify synthetic lethal interactions in GC. GSSL analysis was used to infer probable synthetic lethality in GC using four accessible clinical datasets. In addition, prediction results were confirmed by experiments. GSSL analysis identified a total of 34 candidate synthetic lethal pairs, which included 33 unique targets. Among the synthetic lethal gene pairs, TP53-CHEK1 was selected for further experimental validation. Both computational and experimental results indicated that inhibiting CHEK1 could be a potential therapeutic strategy for GC patients with TP53 mutation. Meanwhile, in vitro experimental validation of two novel synthetic lethal pairs TP53-AURKB and ARID1A-EP300 further proved the universality and reliability of GSSL. Collectively, GSSL has been shown to be a reliable and feasible method for comprehensive analysis of inferring synthetic lethal interactions of GC, which may offer novel insight into the precision medicine and individualized treatment of GC.
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Affiliation(s)
- Haigang Geng
- Department of Gastrointestinal Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Ruolan Qian
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yiqing Zhong
- Department of Gastrointestinal Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xiangyu Tang
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaojun Zhang
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Linmeng Zhang
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chen Yang
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tingting Li
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China
| | - Zhongyi Dong
- Department of Gastrointestinal Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Cun Wang
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zizhen Zhang
- Department of Gastrointestinal Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
| | - Chunchao Zhu
- Department of Gastrointestinal Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
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5
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Gökbağ B, Tang S, Fan K, Cheng L, Yu L, Zhao Y, Li L. SLKB: synthetic lethality knowledge base. Nucleic Acids Res 2024; 52:D1418-D1428. [PMID: 37889037 PMCID: PMC10767912 DOI: 10.1093/nar/gkad806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 08/16/2023] [Accepted: 09/27/2023] [Indexed: 10/28/2023] Open
Abstract
Emerging CRISPR-Cas9 technology permits synthetic lethality (SL) screening of large number of gene pairs from gene combination double knockout (CDKO) experiments. However, the poor integration and annotation of CDKO SL data in current SL databases limit their utility, and diverse methods of calculating SL scores prohibit their comparison. To overcome these shortcomings, we have developed SL knowledge base (SLKB) that incorporates data of 11 CDKO experiments in 22 cell lines, 16,059 SL gene pairs and 264,424 non-SL gene pairs. Additionally, within SLKB, we have implemented five SL calculation methods: median score with and without background control normalization (Median-B/NB), sgRNA-derived score (sgRNA-B/NB), Horlbeck score, GEMINI score and MAGeCK score. The five scores have demonstrated a mere 1.21% overlap among their top 10% SL gene pairs, reflecting high diversity. Users can browse SL networks and assess the impact of scoring methods using Venn diagrams. The SL network generated from all data in SLKB shows a greater likelihood of SL gene pair connectivity with other SL gene pairs than non-SL pairs. Comparison of SL networks between two cell lines demonstrated greater likelihood to share SL hub genes than SL gene pairs. SLKB website and pipeline can be freely accessed at https://slkb.osubmi.org and https://slkb.docs.osubmi.org/, respectively.
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Affiliation(s)
- Birkan Gökbağ
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Shan Tang
- College of Pharmacy, The Ohio State University, Columbus, OH 43210, USA
| | - Kunjie Fan
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Lijun Cheng
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Lianbo Yu
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Yue Zhao
- Department of Computational Medicine and Bioinformatics, College of Medicine, University of Michigan, Ann Arbor, MI 48104, USA
| | - Lang Li
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
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6
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Tepeli YI, Seale C, Gonçalves JP. ELISL: early-late integrated synthetic lethality prediction in cancer. BIOINFORMATICS (OXFORD, ENGLAND) 2024; 40:btad764. [PMID: 38113447 DOI: 10.1093/bioinformatics/btad764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 11/06/2023] [Accepted: 12/18/2023] [Indexed: 12/21/2023]
Abstract
MOTIVATION Anti-cancer therapies based on synthetic lethality (SL) exploit tumour vulnerabilities for treatment with reduced side effects, by targeting a gene that is jointly essential with another whose function is lost. Computational prediction is key to expedite SL screening, yet existing methods are vulnerable to prevalent selection bias in SL data and reliant on cancer or tissue type-specific omics, which can be scarce. Notably, sequence similarity remains underexplored as a proxy for related gene function and joint essentiality. RESULTS We propose ELISL, Early-Late Integrated SL prediction with forest ensembles, using context-free protein sequence embeddings and context-specific omics from cell lines and tissue. Across eight cancer types, ELISL showed superior robustness to selection bias and recovery of known SL genes, as well as promising cross-cancer predictions. Co-occurring mutations in a BRCA gene and ELISL-predicted pairs from the HH, FGF, WNT, or NEIL gene families were associated with longer patient survival times, revealing therapeutic potential. AVAILABILITY AND IMPLEMENTATION Data: 10.6084/m9.figshare.23607558 & Code: github.com/joanagoncalveslab/ELISL.
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Affiliation(s)
- Yasin I Tepeli
- Pattern Recognition & Bioinformatics, Department of Intelligent Systems, Faculty EEMCS, Delft University of Technology, Delft, The Netherlands
| | - Colm Seale
- Pattern Recognition & Bioinformatics, Department of Intelligent Systems, Faculty EEMCS, Delft University of Technology, Delft, The Netherlands
- Holland Proton Therapy Center (HollandPTC), Delft, The Netherlands
| | - Joana P Gonçalves
- Pattern Recognition & Bioinformatics, Department of Intelligent Systems, Faculty EEMCS, Delft University of Technology, Delft, The Netherlands
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7
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Karimpour M, Totonchi M, Behmanesh M, Montazeri H. Pathway-driven analysis of synthetic lethal interactions in cancer using perturbation screens. Life Sci Alliance 2024; 7:e202302268. [PMID: 37863651 PMCID: PMC10589366 DOI: 10.26508/lsa.202302268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 10/10/2023] [Accepted: 10/10/2023] [Indexed: 10/22/2023] Open
Abstract
Synthetic lethality offers a promising approach for developing effective therapeutic interventions in cancer when direct targeting of driver genes is impractical. In this study, we comprehensively analyzed large-scale CRISPR, shRNA, and PRISM screens to identify potential synthetic lethal (SL) interactions in pan-cancer and 12 individual cancer types, using a new computational framework that leverages the biological function and signaling pathway information of key driver genes to mitigate the confounding effects of background genetic alterations in different cancer cell lines. This approach has successfully identified several putative SL interactions, including KRAS-MAP3K2 and APC-TCF7L2 in pan cancer, and CCND1-METTL1, TP53-FRS3, SMO-MDM2, and CCNE1-MTOR in liver, blood, skin, and gastric cancers, respectively. In addition, we proposed several FDA-approved cancer-targeted drugs for various cancer types through PRISM drug screens, such as cabazitaxel for VHL-mutated kidney cancer and alectinib for lung cancer with NRAS or KRAS mutations. Leveraging pathway information can enhance the concordance of shRNA and CRISPR screens and provide clinically relevant findings such as the potential efficacy of dasatinib, an inhibitor of SRC, for colorectal cancer patients with mutations in the WNT signaling pathway. These analyses revealed that taking signaling pathway information into account results in the identification of more promising SL interactions.
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Affiliation(s)
- Mina Karimpour
- Department of Genetics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| | - Mehdi Totonchi
- Department of Genetics, Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mehrdad Behmanesh
- Department of Genetics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| | - Hesam Montazeri
- https://ror.org/05vf56z40 Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
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Cai C, Radhakrishnan A, Uhler C. Synthetic Lethality Screening with Recursive Feature Machines. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.03.569803. [PMID: 38106093 PMCID: PMC10723282 DOI: 10.1101/2023.12.03.569803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Synthetic lethality refers to a genetic interaction where the simultaneous perturbation of gene pairs leads to cell death. Synthetically lethal gene pairs (SL pairs) provide a potential avenue for selectively targeting cancer cells based on genetic vulnerabilities. The rise of large-scale gene perturbation screens such as the Cancer Dependency Map (DepMap) offers the opportunity to identify SL pairs automatically using machine learning. We build on a recently developed class of feature learning kernel machines known as Recursive Feature Machines (RFMs) to develop a pipeline for identifying SL pairs based on CRISPR viability data from DepMap. In particular, we first train RFMs to predict viability scores for a given CRISPR gene knockout from cell line embeddings consisting of gene expression and mutation features. After training, RFMs use a statistical operator known as average gradient outer product to provide weights for each feature indicating the importance of each feature in predicting cellular viability. We subsequently apply correlation-based filters to re-weight RFM feature importances and identify those features that are most indicative of low cellular viability. Our resulting pipeline is computationally efficient, taking under 3 minutes for analyzing all 17, 453 knockouts from DepMap for candidate SL pairs. We show that our pipeline more accurately recovers experimentally verified SL pairs than prior approaches. Moreover, our pipeline finds new candidate SL pairs, thereby opening novel avenues for identifying genetic vulnerabilities in cancer.
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Affiliation(s)
- Cathy Cai
- Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard
- Laboratory of Information and Decision Systems, Massachusetts Institute of Technology
| | - Adityanarayanan Radhakrishnan
- Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard
- School of Engineering and Applied Sciences, Harvard University
| | - Caroline Uhler
- Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard
- Laboratory of Information and Decision Systems, Massachusetts Institute of Technology
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9
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Pu M, Cheng K, Li X, Xin Y, Wei L, Jin S, Zheng W, Peng G, Tang Q, Zhou J, Zhang Y. Using graph-based model to identify cell specific synthetic lethal effects. Comput Struct Biotechnol J 2023; 21:5099-5110. [PMID: 37920819 PMCID: PMC10618116 DOI: 10.1016/j.csbj.2023.10.011] [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: 07/05/2023] [Revised: 10/05/2023] [Accepted: 10/06/2023] [Indexed: 11/04/2023] Open
Abstract
Synthetic lethal (SL) pairs are pairs of genes whose simultaneous loss-of-function results in cell death, while a damaging mutation of either gene alone does not affect the cell's survival. This makes SL pairs attractive targets for precision cancer therapies, as targeting the unimpaired gene of the SL pair can selectively kill cancer cells that already harbor the impaired gene. Limited by the difficulty of finding true SL pairs, especially on specific cell types, current computational approaches provide only limited insights because of overlooking the crucial aspects of cellular context dependency and mechanistic understanding of SL pairs. As a result, the identification of SL targets still relies on expensive, time-consuming experimental approaches. In this work, we applied cell-line specific multi-omics data to a specially designed deep learning model to predict cell-line specific SL pairs. Through incorporating multiple types of cell-specific omics data with a self-attention module, we represent gene relationships as graphs. Our approach achieves the prediction of SL pairs in a cell-specific manner and demonstrates the potential to facilitate the discovery of cell-specific SL targets for cancer therapeutics, providing a tool to unearth mechanisms underlying the origin of SL in cancer biology. The code and data of our approach can be found at https://github.com/promethiume/SLwise.
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Affiliation(s)
| | - Kaiyang Cheng
- StoneWise, AI, Ltd., Beijing, China
- Nanjing University of Chinese Medicine, Shanghai, China
| | - Xiaorong Li
- StoneWise, AI, Ltd., Beijing, China
- Minzu University of China, Beijing, China
| | | | | | - Sutong Jin
- StoneWise, AI, Ltd., Beijing, China
- Harbin Institute of Technology, Weihai, China
| | | | | | - Qihong Tang
- StoneWise, AI, Ltd., Beijing, China
- Guilin University of Electronic Science and Technology, Guangxi, China
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Cunha E, Silva M, Chaves I, Demirci H, Lagoa DR, Lima D, Rocha M, Rocha I, Dias O. The first multi-tissue genome-scale metabolic model of a woody plant highlights suberin biosynthesis pathways in Quercus suber. PLoS Comput Biol 2023; 19:e1011499. [PMID: 37729340 PMCID: PMC10545120 DOI: 10.1371/journal.pcbi.1011499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 10/02/2023] [Accepted: 09/07/2023] [Indexed: 09/22/2023] Open
Abstract
Over the last decade, genome-scale metabolic models have been increasingly used to study plant metabolic behaviour at the tissue and multi-tissue level under different environmental conditions. Quercus suber, also known as the cork oak tree, is one of the most important forest communities of the Mediterranean/Iberian region. In this work, we present the genome-scale metabolic model of the Q. suber (iEC7871). The metabolic model comprises 7871 genes, 6231 reactions, and 6481 metabolites across eight compartments. Transcriptomics data was integrated into the model to obtain tissue-specific models for the leaf, inner bark, and phellogen, with specific biomass compositions. The tissue-specific models were merged into a diel multi-tissue metabolic model to predict interactions among the three tissues at the light and dark phases. The metabolic models were also used to analyse the pathways associated with the synthesis of suberin monomers, namely the acyl-lipids, phenylpropanoids, isoprenoids, and flavonoids production. The models developed in this work provide a systematic overview of the metabolism of Q. suber, including its secondary metabolism pathways and cork formation.
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Affiliation(s)
- Emanuel Cunha
- Centre of Biological Engineering, Universidade do Minho, Braga, Portugal
| | - Miguel Silva
- Centre of Biological Engineering, Universidade do Minho, Braga, Portugal
| | - Inês Chaves
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Avenida da República, Quinta do Marquês, Oeiras, Portugal
- iBET, Instituto de Biologia Experimental e Tecnológica, Oeiras, Portugal
| | - Huseyin Demirci
- Centre of Biological Engineering, Universidade do Minho, Braga, Portugal
- SnT/University of Luxembourg, Luxembourg
| | | | - Diogo Lima
- Centre of Biological Engineering, Universidade do Minho, Braga, Portugal
| | - Miguel Rocha
- Centre of Biological Engineering, Universidade do Minho, Braga, Portugal
- LABBELS–Associate Laboratory, Braga, Guimarães, Portugal
| | - Isabel Rocha
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Avenida da República, Quinta do Marquês, Oeiras, Portugal
| | - Oscar Dias
- Centre of Biological Engineering, Universidade do Minho, Braga, Portugal
- LABBELS–Associate Laboratory, Braga, Guimarães, Portugal
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Lu X, Chen G, Li J, Hu X, Sun F. MAGCN: A Multiple Attention Graph Convolution Networks for Predicting Synthetic Lethality. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:2681-2689. [PMID: 36374879 DOI: 10.1109/tcbb.2022.3221736] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Synthetic lethality (SL) is a potential cancer therapeutic strategy and drug discovery. Computational approaches to identify synthetic lethality genes have become an effective complement to wet experiments which are time consuming and costly. Graph convolutional networks (GCN) has been utilized to such prediction task as be good at capturing the neighborhood dependency in a graph. However, it is still a lack of the mechanism of aggregating the complementary neighboring information from various heterogeneous graphs. Here, we propose the Multiple Attention Graph Convolution Networks for predicting synthetic lethality (MAGCN). First, we obtain the functional similarity features and topological structure features of genes from different data sources respectively, such as Gene Ontology data and Protein-Protein Interaction. Then, graph convolutional network is utilized to accumulate the knowledge from neighbor nodes according to synthetic lethal associations. Meanwhile, we propose a multiple graphs attention model and construct a multiple graphs attention network to learn the contribution factors of different graphs to generate embedded representation by aggregating these graphs. Finally, the generated feature matrix is decoded to predict potential synthetic lethal interaction. Experimental results show that MAGCN is superior to other baseline methods. Case study demonstrates the ability of MAGCN to predict human SL gene pairs.
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12
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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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13
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Barrena N, Valcárcel LV, Olaverri-Mendizabal D, Apaolaza I, Planes FJ. Synthetic lethality in large-scale integrated metabolic and regulatory network models of human cells. NPJ Syst Biol Appl 2023; 9:32. [PMID: 37454223 DOI: 10.1038/s41540-023-00296-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 07/04/2023] [Indexed: 07/18/2023] Open
Abstract
Synthetic lethality (SL) is a promising concept in cancer research. A wide array of computational tools has been developed to predict and exploit synthetic lethality for the identification of tumour-specific vulnerabilities. Previously, we introduced the concept of genetic Minimal Cut Sets (gMCSs), a theoretical approach to SL developed for genome-scale metabolic networks. The major challenge in our gMCS framework is to go beyond metabolic networks and extend existing algorithms to more complex protein-protein interactions. In this article, we take a step further and incorporate linear regulatory pathways into our gMCS approach. Extensive algorithmic modifications to compute gMCSs in integrated metabolic and regulatory models are presented in detail. Our extended approach is applied to calculate gMCSs in integrated models of human cells. In particular, we integrate the most recent genome-scale metabolic network, Human1, with 3 different regulatory network databases: Omnipath, Dorothea and TRRUST. Based on the computed gMCSs and transcriptomic data, we discovered new essential genes and their associated synthetic lethal for different cancer cell lines. The performance of the different integrated models is assessed with available large-scale in-vitro gene silencing data. Finally, we discuss the most relevant gene essentiality predictions based on published literature in cancer research.
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Affiliation(s)
- Naroa Barrena
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain
| | - Luis V Valcárcel
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain
- University of Navarra, Biomedical Engineering Center, Campus Universitario, 31009, Pamplona, Navarra, Spain
- University of Navarra, Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), Campus Universitario, 31080, Pamplona, Spain
| | - Danel Olaverri-Mendizabal
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain
| | - Iñigo Apaolaza
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain
- University of Navarra, Biomedical Engineering Center, Campus Universitario, 31009, Pamplona, Navarra, Spain
- University of Navarra, Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), Campus Universitario, 31080, Pamplona, Spain
| | - Francisco J Planes
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain.
- University of Navarra, Biomedical Engineering Center, Campus Universitario, 31009, Pamplona, Navarra, Spain.
- University of Navarra, Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), Campus Universitario, 31080, Pamplona, Spain.
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14
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Bod L, Kye YC, Shi J, Torlai Triglia E, Schnell A, Fessler J, Ostrowski SM, Von-Franque MY, Kuchroo JR, Barilla RM, Zaghouani S, Christian E, Delorey TM, Mohib K, Xiao S, Slingerland N, Giuliano CJ, Ashenberg O, Li Z, Rothstein DM, Fisher DE, Rozenblatt-Rosen O, Sharpe AH, Quintana FJ, Apetoh L, Regev A, Kuchroo VK. B-cell-specific checkpoint molecules that regulate anti-tumour immunity. Nature 2023; 619:348-356. [PMID: 37344597 PMCID: PMC10795478 DOI: 10.1038/s41586-023-06231-0] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 05/17/2023] [Indexed: 06/23/2023]
Abstract
The role of B cells in anti-tumour immunity is still debated and, accordingly, immunotherapies have focused on targeting T and natural killer cells to inhibit tumour growth1,2. Here, using high-throughput flow cytometry as well as bulk and single-cell RNA-sequencing and B-cell-receptor-sequencing analysis of B cells temporally during B16F10 melanoma growth, we identified a subset of B cells that expands specifically in the draining lymph node over time in tumour-bearing mice. The expanding B cell subset expresses the cell surface molecule T cell immunoglobulin and mucin domain 1 (TIM-1, encoded by Havcr1) and a unique transcriptional signature, including multiple co-inhibitory molecules such as PD-1, TIM-3, TIGIT and LAG-3. Although conditional deletion of these co-inhibitory molecules on B cells had little or no effect on tumour burden, selective deletion of Havcr1 in B cells both substantially inhibited tumour growth and enhanced effector T cell responses. Loss of TIM-1 enhanced the type 1 interferon response in B cells, which augmented B cell activation and increased antigen presentation and co-stimulation, resulting in increased expansion of tumour-specific effector T cells. Our results demonstrate that manipulation of TIM-1-expressing B cells enables engagement of the second arm of adaptive immunity to promote anti-tumour immunity and inhibit tumour growth.
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Affiliation(s)
- Lloyd Bod
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Massachusetts General Hospital Cancer Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Yoon-Chul Kye
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Jingwen Shi
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- BeiGene, Beijing, China
| | - Elena Torlai Triglia
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Alexandra Schnell
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Johannes Fessler
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
- Division of Immunology and Pathophysiology, Medical University of Graz, Graz, Austria
| | | | - Max Y Von-Franque
- Department of Dermatology, Massachusetts General Hospital, Boston, MA, USA
| | - Juhi R Kuchroo
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
- Department of Microbiology and Immunobiology, Harvard Medical School, Boston, MA, USA
| | - Rocky M Barilla
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Sarah Zaghouani
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
| | - Elena Christian
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Toni Marie Delorey
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kanishka Mohib
- Thomas E. Starzl Transplantation Institute, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Sheng Xiao
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
| | - Nadine Slingerland
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Orr Ashenberg
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Zhaorong Li
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - David M Rothstein
- Thomas E. Starzl Transplantation Institute, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - David E Fisher
- Department of Dermatology, Massachusetts General Hospital, Boston, MA, USA
| | - Orit Rozenblatt-Rosen
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Howard Hughes Medical Institute, Department of Biology and Koch Institute of Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Arlene H Sharpe
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
- Department of Microbiology and Immunobiology, Harvard Medical School, Boston, MA, USA
| | - Francisco J Quintana
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Howard Hughes Medical Institute, Department of Biology and Koch Institute of Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Lionel Apetoh
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
- INSERM, Tours, France
- Faculté de Médecine, Université de Tours, Tours, France
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Howard Hughes Medical Institute, Department of Biology and Koch Institute of Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Genentech, San Francisco, CA, USA.
| | - Vijay K Kuchroo
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA.
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
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15
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Zhang K, Wu M, Liu Y, Feng Y, Zheng J. KR4SL: knowledge graph reasoning for explainable prediction of synthetic lethality. Bioinformatics 2023; 39:i158-i167. [PMID: 37387166 PMCID: PMC10311291 DOI: 10.1093/bioinformatics/btad261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023] Open
Abstract
MOTIVATION Synthetic lethality (SL) is a promising strategy for anticancer therapy, as inhibiting SL partners of genes with cancer-specific mutations can selectively kill the cancer cells without harming the normal cells. Wet-lab techniques for SL screening have issues like high cost and off-target effects. Computational methods can help address these issues. Previous machine learning methods leverage known SL pairs, and the use of knowledge graphs (KGs) can significantly enhance the prediction performance. However, the subgraph structures of KG have not been fully explored. Besides, most machine learning methods lack interpretability, which is an obstacle for wide applications of machine learning to SL identification. RESULTS We present a model named KR4SL to predict SL partners for a given primary gene. It captures the structural semantics of a KG by efficiently constructing and learning from relational digraphs in the KG. To encode the semantic information of the relational digraphs, we fuse textual semantics of entities into propagated messages and enhance the sequential semantics of paths using a recurrent neural network. Moreover, we design an attentive aggregator to identify critical subgraph structures that contribute the most to the SL prediction as explanations. Extensive experiments under different settings show that KR4SL significantly outperforms all the baselines. The explanatory subgraphs for the predicted gene pairs can unveil prediction process and mechanisms underlying synthetic lethality. The improved predictive power and interpretability indicate that deep learning is practically useful for SL-based cancer drug target discovery. AVAILABILITY AND IMPLEMENTATION The source code is freely available at https://github.com/JieZheng-ShanghaiTech/KR4SL.
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Affiliation(s)
- Ke Zhang
- School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China
- Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Min Wu
- Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), Singapore 138632, Singapore
| | - Yong Liu
- Nanyang Technological University, Singapore 639798, Singapore
| | - Yimiao Feng
- School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China
- Lingang Laboratory, Shanghai 201602, China
| | - Jie Zheng
- School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China
- Shanghai Engineering Research Center of Intelligent Vision and Imaging, ShanghaiTech University, Shanghai 201210, China
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16
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Hanover G, Vizeacoumar FS, Banerjee SL, Nair R, Dahiya R, Osornio-Hernandez AI, Morales AM, Freywald T, Himanen JP, Toosi BM, Bisson N, Vizeacoumar FJ, Freywald A. Integration of cancer-related genetic landscape of Eph receptors and ephrins with proteomics identifies a crosstalk between EPHB6 and EGFR. Cell Rep 2023; 42:112670. [PMID: 37392382 DOI: 10.1016/j.celrep.2023.112670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 05/05/2023] [Accepted: 06/05/2023] [Indexed: 07/03/2023] Open
Abstract
Eph receptors and their ephrin ligands are viewed as promising targets for cancer treatment; however, targeting them is hindered by their context-dependent functionalities. To circumvent this, we explore molecular landscapes underlying their pro- and anti-malignant activities. Using unbiased bioinformatics approaches, we construct a cancer-related network of genetic interactions (GIs) of all Ephs and ephrins to assist in their therapeutic manipulation. We also apply genetic screening and BioID proteomics and integrate them with machine learning approaches to select the most relevant GIs of one Eph receptor, EPHB6. This identifies a crosstalk between EPHB6 and EGFR, and further experiments confirm the ability of EPHB6 to modulate EGFR signaling, enhancing the proliferation of cancer cells and tumor development. Taken together, our observations show EPHB6 involvement in EGFR action, suggesting its targeting might be beneficial in EGFR-dependent tumors, and confirm that the Eph family genetic interactome presented here can be effectively exploited in developing cancer treatment approaches.
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Affiliation(s)
- Glinton Hanover
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Saskatchewan, Royal University Hospital, Room 2841, 103 Hospital Drive, Saskatoon, SK S7N 0W8, Canada; Department of Biochemistry, Microbiology and Immunology, University of Saskatchewan, GA20 Health Sciences, 107 Wiggins Road, Saskatoon, SK S7N 5E5, Canada
| | - Frederick S Vizeacoumar
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Saskatchewan, Royal University Hospital, Room 2841, 103 Hospital Drive, Saskatoon, SK S7N 0W8, Canada
| | - Sara L Banerjee
- Department of Molecular Biology, Medical Biochemistry and Pathology, PROTEO and Centre de Recherche du Centre Hospitalier Universitaire (CHU) de Quebec-Université Laval, Division Oncologie, 9 Rue McMahon, Québec, QC G1R 3S3, Canada
| | - Raveena Nair
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Saskatchewan, Royal University Hospital, Room 2841, 103 Hospital Drive, Saskatoon, SK S7N 0W8, Canada; Department of Biochemistry, Microbiology and Immunology, University of Saskatchewan, GA20 Health Sciences, 107 Wiggins Road, Saskatoon, SK S7N 5E5, Canada
| | - Renuka Dahiya
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Saskatchewan, Royal University Hospital, Room 2841, 103 Hospital Drive, Saskatoon, SK S7N 0W8, Canada
| | - Ana I Osornio-Hernandez
- Department of Molecular Biology, Medical Biochemistry and Pathology, PROTEO and Centre de Recherche du Centre Hospitalier Universitaire (CHU) de Quebec-Université Laval, Division Oncologie, 9 Rue McMahon, Québec, QC G1R 3S3, Canada
| | - Alain Morejon Morales
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Saskatchewan, Royal University Hospital, Room 2841, 103 Hospital Drive, Saskatoon, SK S7N 0W8, Canada; Department of Biochemistry, Microbiology and Immunology, University of Saskatchewan, GA20 Health Sciences, 107 Wiggins Road, Saskatoon, SK S7N 5E5, Canada
| | - Tanya Freywald
- Cancer Research, Saskatchewan Cancer Agency and Division of Oncology, University of Saskatchewan, 4D30.2 Health Sciences Building, 107 Wiggins Road, Saskatoon, SK S7N 5E5, Canada
| | - Juha P Himanen
- Department of Biochemistry, University of Turku, 20500 Turku, Finland
| | - Behzad M Toosi
- Department of Small Animal Clinical Sciences, Western College of Veterinary Medicine, University of Saskatchewan, 52 Campus Drive, Saskatoon, SK S7N 5B4, Canada
| | - Nicolas Bisson
- Department of Molecular Biology, Medical Biochemistry and Pathology, PROTEO and Centre de Recherche du Centre Hospitalier Universitaire (CHU) de Quebec-Université Laval, Division Oncologie, 9 Rue McMahon, Québec, QC G1R 3S3, Canada.
| | - Franco J Vizeacoumar
- Cancer Research, Saskatchewan Cancer Agency and Division of Oncology, University of Saskatchewan, 4D30.2 Health Sciences Building, 107 Wiggins Road, Saskatoon, SK S7N 5E5, Canada.
| | - Andrew Freywald
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Saskatchewan, Royal University Hospital, Room 2841, 103 Hospital Drive, Saskatoon, SK S7N 0W8, Canada.
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17
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Markowska M, Budzinska MA, Coenen-Stass A, Kang S, Kizling E, Kolmus K, Koras K, Staub E, Szczurek E. Synthetic lethality prediction in DNA damage repair, chromatin remodeling and the cell cycle using multi-omics data from cell lines and patients. Sci Rep 2023; 13:7049. [PMID: 37120674 PMCID: PMC10148866 DOI: 10.1038/s41598-023-34161-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 04/25/2023] [Indexed: 05/01/2023] Open
Abstract
Discovering synthetic lethal (SL) gene partners of cancer genes is an important step in developing cancer therapies. However, identification of SL interactions is challenging, due to a large number of possible gene pairs, inherent noise and confounding factors in the observed signal. To discover robust SL interactions, we devised SLIDE-VIP, a novel framework combining eight statistical tests, including a new patient data-based test iSurvLRT. SLIDE-VIP leverages multi-omics data from four different sources: gene inactivation cell line screens, cancer patient data, drug screens and gene pathways. We applied SLIDE-VIP to discover SL interactions between genes involved in DNA damage repair, chromatin remodeling and cell cycle, and their potentially druggable partners. The top 883 ranking SL candidates had strong evidence in cell line and patient data, 250-fold reducing the initial space of 200K pairs. Drug screen and pathway tests provided additional corroboration and insights into these interactions. We rediscovered well-known SL pairs such as RB1 and E2F3 or PRKDC and ATM, and in addition, proposed strong novel SL candidates such as PTEN and PIK3CB. In summary, SLIDE-VIP opens the door to the discovery of SL interactions with clinical potential. All analysis and visualizations are available via the online SLIDE-VIP WebApp.
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Affiliation(s)
- Magda Markowska
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Stefana Banacha 2, 02-097, Warsaw, Poland
- Postgraduate School of Molecular Medicine, Medical University of Warsaw, Zwirki i Wigury 61, 02-091, Warsaw, Poland
| | - Magdalena A Budzinska
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Stefana Banacha 2, 02-097, Warsaw, Poland
- Ardigen S.A., Podole 76, 30-394, Cracow, Poland
| | - Anna Coenen-Stass
- Translational Medicine, Oncology Bioinformatics, Merck Healthcare KGaA, Frankfurt Strasse 250, 64293, Darmstadt, Germany
| | - Senbai Kang
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Stefana Banacha 2, 02-097, Warsaw, Poland
| | - Ewa Kizling
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Stefana Banacha 2, 02-097, Warsaw, Poland
| | | | - Krzysztof Koras
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Stefana Banacha 2, 02-097, Warsaw, Poland
| | - Eike Staub
- Translational Medicine, Oncology Bioinformatics, Merck Healthcare KGaA, Frankfurt Strasse 250, 64293, Darmstadt, Germany
| | - Ewa Szczurek
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Stefana Banacha 2, 02-097, Warsaw, Poland.
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18
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Gomari D, Achkar IW, Benedetti E, Tabling J, Halama A, Krumsiek J. piTracer - Automatic reconstruction of molecular cascades for the identification of synergistic drug targets. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.08.535933. [PMID: 37066188 PMCID: PMC10104160 DOI: 10.1101/2023.04.08.535933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Cancer cells frequently undergo metabolic reprogramming as a mechanism of resistance against chemotherapeutic drugs. Metabolomic profiling provides a direct readout of metabolic changes and can thus be used to identify these tumor escape mechanisms. Here, we introduce piTracer, a computational tool that uses multi-scale molecular networks to identify potential combination therapies from pre- and post-treatment metabolomics data. We first demonstrate piTracer’s core ability to reconstruct cellular cascades by inspecting well-characterized molecular pathways and previously studied associations between genetic variants and metabolite levels. We then apply a new gene ranking algorithm on differential metabolomic profiles from human breast cancer cells after glutaminase inhibition. Four of the automatically identified gene targets were experimentally tested by simultaneous inhibition of the respective targets and glutaminase. Of these combination treatments, two were be confirmed to induce synthetic lethality in the cell line. In summary, piTracer integrates the molecular monitoring of escape mechanisms into comprehensive pathway networks to accelerate drug target identification. The tool is open source and can be accessed at https://github.com/krumsieklab/pitracer .
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19
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Khadirnaikar S, Shukla S, Prasanna SRM. Machine learning based combination of multi-omics data for subgroup identification in non-small cell lung cancer. Sci Rep 2023; 13:4636. [PMID: 36944673 PMCID: PMC10030850 DOI: 10.1038/s41598-023-31426-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 03/11/2023] [Indexed: 03/23/2023] Open
Abstract
Non-small Cell Lung Cancer (NSCLC) is a heterogeneous disease with a poor prognosis. Identifying novel subtypes in cancer can help classify patients with similar molecular and clinical phenotypes. This work proposes an end-to-end pipeline for subgroup identification in NSCLC. Here, we used a machine learning (ML) based approach to compress the multi-omics NSCLC data to a lower dimensional space. This data is subjected to consensus K-means clustering to identify the five novel clusters (C1-C5). Survival analysis of the resulting clusters revealed a significant difference in the overall survival of clusters (p-value: 0.019). Each cluster was then molecularly characterized to identify specific molecular characteristics. We found that cluster C3 showed minimal genetic aberration with a high prognosis. Next, classification models were developed using data from each omic level to predict the subgroup of unseen patients. Decision‑level fused classification models were then built using these classifiers, which were used to classify unseen patients into five novel clusters. We also showed that the multi-omics-based classification model outperformed single-omic-based models, and the combination of classifiers proved to be a more accurate prediction model than the individual classifiers. In summary, we have used ML models to develop a classification method and identified five novel NSCLC clusters with different genetic and clinical characteristics.
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Affiliation(s)
- Seema Khadirnaikar
- Department of Electrical Engineering, Indian Institute of Technology Dharwad, Dharwad, India
| | - Sudhanshu Shukla
- Department of Biosciences and Bioengineering, Indian Institute of Technology Dharwad, Dharwad, India.
| | - S R M Prasanna
- Department of Electrical Engineering, Indian Institute of Technology Dharwad, Dharwad, India
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20
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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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21
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Guo L, Dou Y, Xiang Y, Luo L, Xu X, Wang Q, Zhang Y, Liang T. Systematic analysis of cancer-specific synthetic lethal interactions provides insight into personalized anticancer therapy. FEBS J 2023; 290:1531-1548. [PMID: 36181326 DOI: 10.1111/febs.16643] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 08/26/2022] [Accepted: 09/30/2022] [Indexed: 12/05/2022]
Abstract
The concept of synthetic lethality has great potential for anticancer therapy as a new strategy to specifically kill cancer cells while sparing normal cells. To further understand the potential molecular interactions and gene characteristics involved in synthetic lethality, we performed a comprehensive analysis of predicted cancer-specific genetic interactions. Many genes were identified as cancer-associated genes that contributed to multiple biological processes and pathways, and the gene features were not random, indicating their potential roles in human carcinogenesis. Some relevant genes detected in multiple cancers were prone to be enriched in specific biological progresses and pathways, especially processes associated with DNA damage, chromosome-related functions and cancer pathways. These findings strongly implicated potential roles for these genes in cancer pathophysiology and functional relationships, as well as applications for future anticancer drug discovery. Further experimental validation indicated that the synthetic lethal interaction of APC and GFER may provide a potential anticancer strategy for patients with APC-mutant colon cancer. These results will contribute to further exploration of synthetic lethal interactions and broader application of the concept of synthetic lethality in anticancer therapeutics.
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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, 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, 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, China
| | - Lulu Luo
- Jiangsu Key Laboratory for Molecular and Medical Biotechnology, School of Life Science, Nanjing Normal University, China
| | - Xinru Xu
- Jiangsu Key Laboratory for Molecular and Medical Biotechnology, School of Life Science, Nanjing Normal University, China
| | - Qiushi 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, 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, China
| | - Tingming Liang
- Jiangsu Key Laboratory for Molecular and Medical Biotechnology, School of Life Science, Nanjing Normal University, China
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22
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Zhu Y, Zhou Y, Liu Y, Wang X, Li J. SLGNN: synthetic lethality prediction in human cancers based on factor-aware knowledge graph neural network. Bioinformatics 2023; 39:6988048. [PMID: 36645245 PMCID: PMC9907046 DOI: 10.1093/bioinformatics/btad015] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 11/29/2022] [Accepted: 01/13/2023] [Indexed: 01/17/2023] Open
Abstract
MOTIVATION Synthetic lethality (SL) is a form of genetic interaction that can selectively kill cancer cells without damaging normal cells. Exploiting this mechanism is gaining popularity in the field of targeted cancer therapy and anticancer drug development. Due to the limitations of identifying SL interactions from laboratory experiments, an increasing number of research groups are devising computational prediction methods to guide the discovery of potential SL pairs. Although existing methods have attempted to capture the underlying mechanisms of SL interactions, methods that have a deeper understanding of and attempt to explain SL mechanisms still need to be developed. RESULTS In this work, we propose a novel SL prediction method, SLGNN. This method is based on the following assumption: SL interactions are caused by different molecular events or biological processes, which we define as SL-related factors that lead to SL interactions. SLGNN, apart from identifying SL interaction pairs, also models the preferences of genes for different SL-related factors, making the results more interpretable for biologists and clinicians. SLGNN consists of three steps: first, we model the combinations of relationships in the gene-related knowledge graph as the SL-related factors. Next, we derive initial embeddings of genes through an explicit message aggregation process of the knowledge graph. Finally, we derive the final gene embeddings through an SL graph, constructed using known SL gene pairs, utilizing factor-based message aggregation. At this stage, a supervised end-to-end training model is used for SL interaction prediction. Based on experimental results, the proposed SLGNN model outperforms all current state-of-the-art SL prediction methods and provides better interpretability. AVAILABILITY AND IMPLEMENTATION SLGNN is freely available at https://github.com/zy972014452/SLGNN.
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Affiliation(s)
- Yan Zhu
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
| | - Yuhuan Zhou
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
| | - Yang Liu
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China.,Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
| | - Xuan Wang
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China.,Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
| | - Junyi Li
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China.,Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
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23
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Fan K, Tang S, Gökbağ B, Cheng L, Li L. Multi-view graph convolutional network for cancer cell-specific synthetic lethality prediction. Front Genet 2023; 13:1103092. [PMID: 36699450 PMCID: PMC9868610 DOI: 10.3389/fgene.2022.1103092] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Accepted: 12/22/2022] [Indexed: 01/11/2023] Open
Abstract
Synthetic lethal (SL) genetic interactions have been regarded as a promising focus for investigating potential targeted therapeutics to tackle cancer. However, the costly investment of time and labor associated with wet-lab experimental screenings to discover potential SL relationships motivates the development of computational methods. Although graph neural network (GNN) models have performed well in the prediction of SL gene pairs, existing GNN-based models are not designed for predicting cancer cell-specific SL interactions that are more relevant to experimental validation in vitro. Besides, neither have existing methods fully utilized diverse graph representations of biological features to improve prediction performance. In this work, we propose MVGCN-iSL, a novel multi-view graph convolutional network (GCN) model to predict cancer cell-specific SL gene pairs, by incorporating five biological graph features and multi-omics data. Max pooling operation is applied to integrate five graph-specific representations obtained from GCN models. Afterwards, a deep neural network (DNN) model serves as the prediction module to predict the SL interactions in individual cancer cells (iSL). Extensive experiments have validated the model's successful integration of the multiple graph features and state-of-the-art performance in the prediction of potential SL gene pairs as well as generalization ability to novel genes.
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Affiliation(s)
- Kunjie Fan
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States
| | - Shan Tang
- College of Pharmacy, The Ohio State University, Columbus, OH, United States
| | - Birkan Gökbağ
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States
| | - Lijun Cheng
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States
| | - Lang Li
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States,College of Pharmacy, The Ohio State University, Columbus, OH, United States,*Correspondence: Lang Li,
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24
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Discovery of synthetic lethal interactions from large-scale pan-cancer perturbation screens. Nat Commun 2022; 13:7748. [PMID: 36517508 PMCID: PMC9751287 DOI: 10.1038/s41467-022-35378-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 11/29/2022] [Indexed: 12/15/2022] Open
Abstract
The development of cancer therapies is limited by the availability of suitable drug targets. Potential candidate drug targets can be identified based on the concept of synthetic lethality (SL), which refers to pairs of genes for which an aberration in either gene alone is non-lethal, but co-occurrence of the aberrations is lethal to the cell. Here, we present SLIdR (Synthetic Lethal Identification in R), a statistical framework for identifying SL pairs from large-scale perturbation screens. SLIdR successfully predicts SL pairs even with small sample sizes while minimizing the number of false positive targets. We apply SLIdR to Project DRIVE data and find both established and potential pan-cancer and cancer type-specific SL pairs consistent with findings from literature and drug response screening data. We experimentally validate two predicted SL interactions (ARID1A-TEAD1 and AXIN1-URI1) in hepatocellular carcinoma, thus corroborating the ability of SLIdR to identify potential drug targets.
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25
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ToMExO: A probabilistic tree-structured model for cancer progression. PLoS Comput Biol 2022; 18:e1010732. [DOI: 10.1371/journal.pcbi.1010732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 12/15/2022] [Accepted: 11/14/2022] [Indexed: 12/12/2022] Open
Abstract
Identifying the interrelations among cancer driver genes and the patterns in which the driver genes get mutated is critical for understanding cancer. In this paper, we study cross-sectional data from cohorts of tumors to identify the cancer-type (or subtype) specific process in which the cancer driver genes accumulate critical mutations. We model this mutation accumulation process using a tree, where each node includes a driver gene or a set of driver genes. A mutation in each node enables its children to have a chance of mutating. This model simultaneously explains the mutual exclusivity patterns observed in mutations in specific cancer genes (by its nodes) and the temporal order of events (by its edges). We introduce a computationally efficient dynamic programming procedure for calculating the likelihood of our noisy datasets and use it to build our Markov Chain Monte Carlo (MCMC) inference algorithm, ToMExO. Together with a set of engineered MCMC moves, our fast likelihood calculations enable us to work with datasets with hundreds of genes and thousands of tumors, which cannot be dealt with using available cancer progression analysis methods. We demonstrate our method’s performance on several synthetic datasets covering various scenarios for cancer progression dynamics. Then, a comparison against two state-of-the-art methods on a moderate-size biological dataset shows the merits of our algorithm in identifying significant and valid patterns. Finally, we present our analyses of several large biological datasets, including colorectal cancer, glioblastoma, and pancreatic cancer. In all the analyses, we validate the results using a set of method-independent metrics testing the causality and significance of the relations identified by ToMExO or competing methods.
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26
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Tang S, Gökbağ B, Fan K, Shao S, Huo Y, Wu X, Cheng L, Li L. Synthetic lethal gene pairs: Experimental approaches and predictive models. Front Genet 2022; 13:961611. [PMID: 36531238 PMCID: PMC9751344 DOI: 10.3389/fgene.2022.961611] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Accepted: 11/07/2022] [Indexed: 03/27/2024] Open
Abstract
Synthetic lethality (SL) refers to a genetic interaction in which the simultaneous perturbation of two genes leads to cell or organism death, whereas viability is maintained when only one of the pair is altered. The experimental exploration of these pairs and predictive modeling in computational biology contribute to our understanding of cancer biology and the development of cancer therapies. We extensively reviewed experimental technologies, public data sources, and predictive models in the study of synthetic lethal gene pairs and herein detail biological assumptions, experimental data, statistical models, and computational schemes of various predictive models, speculate regarding their influence on individual sample- and population-based synthetic lethal interactions, discuss the pros and cons of existing SL data and models, and highlight potential research directions in SL discovery.
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Affiliation(s)
- Shan Tang
- College of Pharmacy, The Ohio State University, Columbus, OH, United States
| | - Birkan Gökbağ
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, United States
| | - Kunjie Fan
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, United States
| | - Shuai Shao
- College of Pharmacy, The Ohio State University, Columbus, OH, United States
| | - Yang Huo
- Indiana University, Bloomington, IN, United States
| | - Xue Wu
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, United States
| | - Lijun Cheng
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, United States
| | - Lang Li
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, United States
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27
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Mina M, Iyer A, Ciriello G. Epistasis and evolutionary dependencies in human cancers. Curr Opin Genet Dev 2022; 77:101989. [PMID: 36182742 DOI: 10.1016/j.gde.2022.101989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 08/29/2022] [Accepted: 08/31/2022] [Indexed: 01/27/2023]
Abstract
Cancer evolution is driven by the concerted action of multiple molecular alterations, which emerge and are selected during tumor progression. An alteration is selected when it provides an advantage to the tumor cell. However, the advantage provided by a specific alteration depends on the tumor lineage, cell epigenetic state, and presence of additional alterations. In this case, we say that an evolutionary dependency exists between an alteration and what influences its selection. Epistatic interactions between altered genes lead to evolutionary dependencies (EDs), by favoring or vetoing specific combinations of events. Large-scale cancer genomics studies have discovered examples of such dependencies, and showed that they influence tumor progression, disease phenotypes, and therapeutic response. In the past decade, several algorithmic approaches have been proposed to infer EDs from large-scale genomics datasets. These methods adopt diverse strategies to address common challenges and shed new light on cancer evolutionary trajectories. Here, we review these efforts starting from a simple conceptualization of the problem, presenting the tackled and still unmet needs in the field, and discussing the implications of EDs in cancer biology and precision oncology.
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Affiliation(s)
- Marco Mina
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland; Swiss Cancer Center Leman, Lausanne, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Arvind Iyer
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland; Swiss Cancer Center Leman, Lausanne, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Giovanni Ciriello
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland; Swiss Cancer Center Leman, Lausanne, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland.
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28
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Zeng Z, Zheng W, Hou P. The role of drug-metabolizing enzymes in synthetic lethality of cancer. Pharmacol Ther 2022; 240:108219. [PMID: 35636517 DOI: 10.1016/j.pharmthera.2022.108219] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 05/23/2022] [Accepted: 05/24/2022] [Indexed: 12/14/2022]
Abstract
Drug-metabolizing enzymes (DMEs) have shown increasing importance in anticancer therapy. It is not only due to their effect on activation or deactivation of anticancer drugs, but also because of their extensive connections with pathological and biochemistry changes during tumorigenesis. Meanwhile, it has become more accessible to discovery anticancer drugs that selectively targeted cancer cells with the development of synthetic lethal screen technology. Synthetic lethal strategy makes use of unique genetic markers that different cancer cells from normal tissues to discovery anticancer agents. Dysregulation of DMEs has been found in various cancers, making them promising candidates for synthetic lethal strategy. In this review, we will systematically discuss about the role of DMEs in tumor progression, the application of synthetic lethality strategy in drug discovery, and a link between DMEs and synthetic lethal of cancer.
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Affiliation(s)
- Zekun Zeng
- Department of Endocrinology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, PR China
| | - Wenfang Zheng
- Department of Endocrinology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, PR China
| | - Peng Hou
- Department of Endocrinology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, PR China; Key Laboratory for Tumor Precision Medicine of Shaanxi Province, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, PR China.
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29
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McCarthy-Leo C, Darwiche F, Tainsky MA. DNA Repair Mechanisms, Protein Interactions and Therapeutic Targeting of the MRN Complex. Cancers (Basel) 2022; 14:5278. [PMID: 36358700 PMCID: PMC9656488 DOI: 10.3390/cancers14215278] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 10/24/2022] [Accepted: 10/26/2022] [Indexed: 08/27/2023] Open
Abstract
Repair of a DNA double-strand break relies upon a pathway of proteins to identify damage, regulate cell cycle checkpoints, and repair the damage. This process is initiated by a sensor protein complex, the MRN complex, comprised of three proteins-MRE11, RAD50, and NBS1. After a double-stranded break, the MRN complex recruits and activates ATM, in-turn activating other proteins such as BRCA1/2, ATR, CHEK1/2, PALB2 and RAD51. These proteins have been the focus of many studies for their individual roles in hereditary cancer syndromes and are included on several genetic testing panels. These panels have enabled us to acquire large amounts of genetic data, much of which remains a challenge to interpret due to the presence of variants of uncertain significance (VUS). While the primary aim of clinical testing is to accurately and confidently classify variants in order to inform medical management, the presence of VUSs has led to ambiguity in genetic counseling. Pathogenic variants within MRN complex genes have been implicated in breast, ovarian, prostate, colon cancers and gliomas; however, the hundreds of VUSs within MRE11, RAD50, and NBS1 precludes the application of these data in genetic guidance of carriers. In this review, we discuss the MRN complex's role in DNA double-strand break repair, its interactions with other cancer predisposing genes, the variants that can be found within the three MRN complex genes, and the MRN complex's potential as an anti-cancer therapeutic target.
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Affiliation(s)
- Claire McCarthy-Leo
- Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, MI 48201, USA
| | - Fatima Darwiche
- Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, MI 48201, USA
| | - Michael A. Tainsky
- Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, MI 48201, USA
- Department of Oncology, Wayne State University School of Medicine, Detroit, MI 48201, USA
- Molecular Therapeutics Program, Karmanos Cancer Institute at Wayne State University School of Medicine, Detroit, MI 48201, USA
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30
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Wang S, Feng Y, Liu X, Liu Y, Wu M, Zheng J. NSF4SL: negative-sample-free contrastive learning for ranking synthetic lethal partner genes in human cancers. Bioinformatics 2022; 38:ii13-ii19. [PMID: 36124790 DOI: 10.1093/bioinformatics/btac462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
MOTIVATION Detecting synthetic lethality (SL) is a promising strategy for identifying anti-cancer drug targets. Targeting SL partners of a primary gene mutated in cancer is selectively lethal to cancer cells. Due to high cost of wet-lab experiments and availability of gold standard SL data, supervised machine learning for SL prediction has been popular. However, most of the methods are based on binary classification and thus limited by the lack of reliable negative data. Contrastive learning can train models without any negative sample and is thus promising for finding novel SLs. RESULTS We propose NSF4SL, a negative-sample-free SL prediction model based on a contrastive learning framework. It captures the characteristics of positive SL samples by using two branches of neural networks that interact with each other to learn SL-related gene representations. Moreover, a feature-wise data augmentation strategy is used to mitigate the sparsity of SL data. NSF4SL significantly outperforms all baselines which require negative samples, even in challenging experimental settings. To the best of our knowledge, this is the first time that SL prediction is formulated as a gene ranking problem, which is more practical than the current formulation as binary classification. NSF4SL is the first contrastive learning method for SL prediction and its success points to a new direction of machine-learning methods for identifying novel SLs. AVAILABILITY AND IMPLEMENTATION Our source code is available at https://github.com/JieZheng-ShanghaiTech/NSF4SL. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Shike Wang
- School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Yimiao Feng
- School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Xin Liu
- School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Yong Liu
- Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly, Nanyang Technological University, Singapore 639798, Singapore
| | - Min Wu
- Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), Singapore 138632, Singapore
| | - Jie Zheng
- School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China.,Shanghai Engineering Research Center of Intelligent Vision and Imaging, Shanghai 201210, China
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31
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Liu X, Yu J, Tao S, Yang B, Wang S, Wang L, Bai F, Zheng J. PiLSL: pairwise interaction learning-based graph neural network for synthetic lethality prediction in human cancers. Bioinformatics 2022; 38:ii106-ii112. [PMID: 36124788 DOI: 10.1093/bioinformatics/btac476] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
MOTIVATION Synthetic lethality (SL) is a type of genetic interaction in which the simultaneous inactivation of two genes leads to cell death, while the inactivation of a single gene does not affect the cell viability. It can effectively expand the range of anti-cancer therapeutic targets. SL interactions are identified mainly by experimental screening and computational prediction. Recent machine-learning methods mostly learn the representation of each gene individually, ignoring the representation of the pairwise interaction between two genes. In addition, the mechanisms of SL, the key to translating SL into cancer therapeutics, are often unclear. RESULTS To fill the gaps, we propose a pairwise interaction learning-based graph neural network (GNN) named PiLSL to learn the representation of pairwise interaction between two genes for SL prediction. First, we construct an enclosing graph for each pair of genes from a knowledge graph. Secondly, we design an attentive embedding propagation layer in a GNN to discriminate the importance among the edges in the enclosing graph and to learn the latent features of the pairwise interaction from the weighted enclosing graph. Finally, we further fuse the latent features with explicit features extracted from multi-omics data to obtain powerful gene representations for SL prediction. Extensive experimental results demonstrate that PiLSL outperforms the best baseline by a large margin and generalizes well under three realistic scenarios. Besides, PiLSL provides an explanation of SL mechanisms via the weighted paths in the enclosing graphs by attention mechanism. AVAILABILITY AND IMPLEMENTATION Our source code is available at https://github.com/JieZheng-ShanghaiTech/PiLSL.
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Affiliation(s)
- Xin Liu
- School of Information Science and Technology, Shanghai Tech University, Shanghai 201210, China
| | - Jiale Yu
- School of Information Science and Technology, Shanghai Tech University, Shanghai 201210, China
| | - Siyu Tao
- School of Information Science and Technology, Shanghai Tech University, Shanghai 201210, China
| | - Beiyuan Yang
- School of Information Science and Technology, Shanghai Tech University, Shanghai 201210, China
| | - Shike Wang
- School of Information Science and Technology, Shanghai Tech University, Shanghai 201210, China
| | - Lin Wang
- School of Life Science and Technology, Shanghai Tech University, Shanghai 201210, China.,Shanghai Institute for Advanced Immunochemical Studies, Shanghai Tech University, Shanghai 201210, China
| | - Fang Bai
- School of Information Science and Technology, Shanghai Tech University, Shanghai 201210, China.,School of Life Science and Technology, Shanghai Tech University, Shanghai 201210, China.,Shanghai Institute for Advanced Immunochemical Studies, Shanghai Tech University, Shanghai 201210, China
| | - Jie Zheng
- School of Information Science and Technology, Shanghai Tech University, Shanghai 201210, China.,Shanghai Engineering Research Center of Intelligent Vision and Imaging, Shanghai 201210, China
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32
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Seale C, Tepeli Y, Gonçalves JP. Overcoming selection bias in synthetic lethality prediction. Bioinformatics 2022; 38:4360-4368. [PMID: 35876858 PMCID: PMC9477536 DOI: 10.1093/bioinformatics/btac523] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 07/13/2022] [Accepted: 07/22/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Synthetic lethality (SL) between two genes occurs when simultaneous loss of function leads to cell death. This holds great promise for developing anti-cancer therapeutics that target synthetic lethal pairs of endogenously disrupted genes. Identifying novel SL relationships through exhaustive experimental screens is challenging, due to the vast number of candidate pairs. Computational SL prediction is therefore sought to identify promising SL gene pairs for further experimentation. However, current SL prediction methods lack consideration for generalizability in the presence of selection bias in SL data. RESULTS We show that SL data exhibit considerable gene selection bias. Our experiments designed to assess the robustness of SL prediction reveal that models driven by the topology of known SL interactions (e.g. graph, matrix factorization) are especially sensitive to selection bias. We introduce selection bias-resilient synthetic lethality (SBSL) prediction using regularized logistic regression or random forests. Each gene pair is described by 27 molecular features derived from cancer cell line, cancer patient tissue and healthy donor tissue samples. SBSL models are built and tested using approximately 8000 experimentally derived SL pairs across breast, colon, lung and ovarian cancers. Compared to other SL prediction methods, SBSL showed higher predictive performance, better generalizability and robustness to selection bias. Gene dependency, quantifying the essentiality of a gene for cell survival, contributed most to SBSL predictions. Random forests were superior to linear models in the absence of dependency features, highlighting the relevance of mutual exclusivity of somatic mutations, co-expression in healthy tissue and differential expression in tumour samples. AVAILABILITY AND IMPLEMENTATION https://github.com/joanagoncalveslab/sbsl. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Colm Seale
- Pattern Recognition & Bioinformatics, Department of Intelligent Systems, Faculty EEMCS, Delft University of Technology, Delft 2628 XE, The Netherlands
- Holland Proton Therapy Center (HollandPTC), Delft 2600 AC, The Netherlands
| | - Yasin Tepeli
- Pattern Recognition & Bioinformatics, Department of Intelligent Systems, Faculty EEMCS, Delft University of Technology, Delft 2628 XE, The Netherlands
| | - Joana P Gonçalves
- Pattern Recognition & Bioinformatics, Department of Intelligent Systems, Faculty EEMCS, Delft University of Technology, Delft 2628 XE, The Netherlands
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33
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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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Iranzo J, Gruenhagen G, Calle-Espinosa J, Koonin EV. Pervasive conditional selection of driver mutations and modular epistasis networks in cancer. Cell Rep 2022; 40:111272. [PMID: 36001960 DOI: 10.1016/j.celrep.2022.111272] [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: 01/04/2022] [Revised: 04/18/2022] [Accepted: 08/05/2022] [Indexed: 11/19/2022] Open
Abstract
Cancer driver mutations often display mutual exclusion or co-occurrence, underscoring the key role of epistasis in carcinogenesis. However, estimating the magnitude of epistasis and quantifying its effect on tumor evolution remains a challenge. We develop a method (Coselens) to quantify conditional selection on the excess of nonsynonymous substitutions in cancer genes. Coselens infers the number of drivers per gene in different partitions of a cancer genomics dataset using covariance-based mutation models and determines whether coding mutations in a gene affect selection for drivers in any other gene. Using Coselens, we identify 296 conditionally selected gene pairs across 16 cancer types in the TCGA dataset. Conditional selection affects 25%-50% of driver substitutions in tumors with >2 drivers. Conditionally co-selected genes form modular networks, whose structures challenge the traditional interpretation of within-pathway mutual exclusivity and across-pathway synergy, suggesting a more complex scenario where gene-specific across-pathway epistasis shapes differentiated cancer subtypes.
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Affiliation(s)
- Jaime Iranzo
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Madrid, Spain; Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, Zaragoza, Spain.
| | - George Gruenhagen
- Institute of Bioengineering and Biosciences, School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA
| | - Jorge Calle-Espinosa
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Madrid, Spain
| | - Eugene V Koonin
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
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35
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Liu Z, Lin D, Zhou Y, Zhang L, Yang C, Guo B, Xia F, Li Y, Chen D, Wang C, Chen Z, Leng C, Xiao Z. Exploring synthetic lethal network for the precision treatment of clear cell renal cell carcinoma. Sci Rep 2022; 12:13222. [PMID: 35918352 PMCID: PMC9345903 DOI: 10.1038/s41598-022-16657-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 07/13/2022] [Indexed: 11/29/2022] Open
Abstract
The emerging targeted therapies have revolutionized the treatment of advanced clear cell renal cell carcinoma (ccRCC) over the past 15 years. Nevertheless, lack of personalized treatment limits the development of effective clinical guidelines and improvement of patient prognosis. In this study, large-scale genomic profiles from ccRCC cohorts were explored for integrative analysis. A credible method was developed to identify synthetic lethality (SL) pairs and a list of 72 candidate pairs was determined, which might be utilized to selectively eliminate tumors with genetic aberrations using SL partners of specific mutations. Further analysis identified BRD4 and PRKDC as novel medical targets for patients with BAP1 mutations. After mapping these target genes to the comprehensive drug datasets, two agents (BI-2536 and PI-103) were found to have considerable therapeutic potentials in the BAP1 mutant tumors. Overall, our findings provided insight into the overview of ccRCC mutation patterns and offered novel opportunities for improving individualized cancer treatment.
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Affiliation(s)
- Zhicheng Liu
- Department of Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | - Dongxu Lin
- Department and Institute of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | - Yi Zhou
- Department of Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | - Linmeng Zhang
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Chen Yang
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Bin Guo
- Department of Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | - Feng Xia
- Department of Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | - Yan Li
- Department of Immunology, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, Guangdong, China
| | - Danyang Chen
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | - Cun Wang
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Zhong Chen
- Department and Institute of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
| | - Chao Leng
- Department of Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
| | - Zhenyu Xiao
- Department of Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
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36
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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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37
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Wang J, Wu M, Huang X, Wang L, Zhang S, Liu H, Zheng J. SynLethDB 2.0: a web-based knowledge graph database on synthetic lethality for novel anticancer drug discovery. Database (Oxford) 2022; 2022:6585691. [PMID: 35562840 PMCID: PMC9216587 DOI: 10.1093/database/baac030] [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: 01/18/2022] [Revised: 04/04/2022] [Accepted: 04/24/2022] [Indexed: 11/30/2022]
Abstract
Two genes are synthetic lethal if mutations in both genes result in impaired cell viability, while mutation of either gene does not affect the cell survival. The potential usage of synthetic lethality (SL) in anticancer therapeutics has attracted many researchers to identify synthetic lethal gene pairs. To include newly identified SLs and more related knowledge, we present a new version of the SynLethDB database to facilitate the discovery of clinically relevant SLs. We extended the first version of SynLethDB database significantly by including new SLs identified through Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) screening, a knowledge graph about human SLs, a new web interface, etc. Over 16 000 new SLs and 26 types of other relationships have been added, encompassing relationships among 14 100 genes, 53 cancers, 1898 drugs, etc. Moreover, a brand-new web interface has been developed to include modules such as SL query by disease or compound, SL partner gene set enrichment analysis and knowledge graph browsing through a dynamic graph viewer. The data can be downloaded directly from the website or through the RESTful Application Programming Interfaces (APIs). Database URL: https://synlethdb.sist.shanghaitech.edu.cn/v2.
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Affiliation(s)
- Jie Wang
- School of Information Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China
| | - Min Wu
- Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, Singapore 138632, Singapore
| | - Xuhui Huang
- School of Computing, National University of Singapore, Computing 1, 13 Computing Drive, Singapore 117417, Singapore
| | - Li Wang
- School of Information Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China
| | - Sophia Zhang
- College of Agriculture and Life Sciences, Cornell University, 260 Roberts Hall, Ithaca, NY 14853, USA
| | - Hui Liu
- School of Computer Science and Technology, Nanjing Tech University, 30 Puzhu Road, Nanjing 211816, China
| | - Jie Zheng
- School of Information Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China.,Shanghai Engineering Research Center of Intelligent Vision and Imaging, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China
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38
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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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39
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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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40
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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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41
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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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42
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Liany H, Lin Y, Jeyasekharan A, Rajan V. An Algorithm to Mine Therapeutic Motifs for Cancer from Networks of Genetic Interactions. IEEE J Biomed Health Inform 2022; 26:2830-2838. [PMID: 34990373 DOI: 10.1109/jbhi.2022.3141076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Study of pairwise genetic interactions, such as mutually exclusive mutations, has led to understanding of underlying mechanisms in cancer. Investigation of various combinatorial motifs within networks of such interactions can lead to deeper insights into its mutational landscape and inform therapy development. One such motif called the Between-Pathway Model (BPM) represents redundant or compensatory pathways that can be therapeutically exploited. Finding such BPM motifs is challenging since most formulations require solving variants of the NP-complete maximum weight bipartite subgraph problem. In this paper we design an algorithm based on Integer Linear Programming (ILP) to solve this problem. In our experiments, our approach outperforms the best previous method to mine BPM motifs. Further, our ILP-based approach allows us to easily model additional application-specific constraints. We illustrate this advantage through a new application of BPM motifs that can potentially aid in finding combination therapies to combat cancer.
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43
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Yang HT, Chien MY, Chiang JH, Lin PC. Literature-based translation from synthetic lethality screening into therapeutics targets: CD82 is a novel target for KRAS mutation in colon cancer. Comput Struct Biotechnol J 2022; 20:5287-5295. [PMID: 36212540 PMCID: PMC9519430 DOI: 10.1016/j.csbj.2022.09.025] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 09/19/2022] [Accepted: 09/19/2022] [Indexed: 12/04/2022] Open
Abstract
Synthetic lethality (SL) is an emerging therapeutic paradigm in cancer. We introduced a different approach to prioritize SL gene pairs through literature mining and RAS-mutant high-throughput screening (HTS) data. We matched essential genes from text-mining and mutant genes from the COSMIC and CCLE HTS datasets to build a prediction model of SL gene pairs. CCLE gene expression data were used to enrich the essential-mutant SL gene pairs using Spearman’s correlation coefficient and literature mining. In total, 223 essential trigger terms were extracted and ranked. The threshold of the essential gene score (Sg) was set to 10. We identified 586 genes essential for the SL prediction model of colon cancer. Seven essential RAS-mutant SL gene pairs were identified in our model, including CD82-KRAS/NRAS, PEBP1-NRAS, MT-CO2-HRAS, IFI27-NRAS/KRAS, and SUMO1-HRAS gene pairs. Using RAS-mutant HTS data validation, we identified two potential SL gene pairs, including the CD82 (essential gene)–KRAS (mutant gene) pair and CD82–NRAS pair in the DLD-1 colon cancer cell line (Spearman’s correlation p-values = 0.004786 and 0.00249, respectively). Based on further annotations by PubChem, we observed that digitonin targeted the complex comprising CD82, especially in KRAS-mutated HCT116 cancer cells. Moreover, we experimentally demonstrated that CD82 exhibited selective vulnerability in KRAS-mutant colorectal cancer. We used literature mining and HTS data to identify candidates for SL targets for RAS-mutant colon cancer.
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Kuipers J, Moore AL, Jahn K, Schraml P, Wang F, Morita K, Futreal PA, Takahashi K, Beisel C, Moch H, Beerenwinkel N. Statistical tests for intra-tumour clonal co-occurrence and exclusivity. PLoS Comput Biol 2021; 17:e1009036. [PMID: 34910733 PMCID: PMC8716063 DOI: 10.1371/journal.pcbi.1009036] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 12/29/2021] [Accepted: 11/19/2021] [Indexed: 12/31/2022] Open
Abstract
Tumour progression is an evolutionary process in which different clones evolve over time, leading to intra-tumour heterogeneity. Interactions between clones can affect tumour evolution and hence disease progression and treatment outcome. Intra-tumoural pairs of mutations that are overrepresented in a co-occurring or clonally exclusive fashion over a cohort of patient samples may be suggestive of a synergistic effect between the different clones carrying these mutations. We therefore developed a novel statistical testing framework, called GeneAccord, to identify such gene pairs that are altered in distinct subclones of the same tumour. We analysed our framework for calibration and power. By comparing its performance to baseline methods, we demonstrate that to control type I errors, it is essential to account for the evolutionary dependencies among clones. In applying GeneAccord to the single-cell sequencing of a cohort of 123 acute myeloid leukaemia patients, we find 1 clonally co-occurring and 8 clonally exclusive gene pairs. The clonally exclusive pairs mostly involve genes of the key signalling pathways. Tumours typically display high levels of heterogeneity, not only between different tumours but also within a single one. Intra-tumour heterogeneity results from an evolutionary process, giving rise to different populations of cancer cells known as clones. How clones interact may affect tumour evolution, which in turn determines disease progression and treatment outcome. In practice, we may observe pairs of mutations that co-occur in clones or exclude each other more often than we would expect for a given cohort of patient samples. Exclusive pairs are suggestive that clones carrying one or the other mutation may cooperate in the evolutionary process. Targeting only one of them may then suffice to alter the tumour evolution. Therefore it is critical to have statistical methods which allow us to identify such pairs. GeneAccord is a novel statistical testing framework we developed especially to identify pairs of genes altered in distinct clones of the same tumour. Accounting for the evolutionary dependencies among clones emerged as critical to adequately control testing errors. In a cohort of 123 acute myeloid leukaemia patients, GeneAccord identified one clonally co-occurring and eight clonally exclusive gene pairs. The latter predominantly involved genes of key signalling pathways.
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Affiliation(s)
- Jack Kuipers
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Ariane L. Moore
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Katharina Jahn
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Peter Schraml
- Department of Pathology and Molecular Pathology, University and University Hospital Zurich, Zurich, Switzerland
| | - Feng Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Kiyomi Morita
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - P. Andrew Futreal
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Koichi Takahashi
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Christian Beisel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Holger Moch
- Department of Pathology and Molecular Pathology, University and University Hospital Zurich, Zurich, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
- * E-mail:
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45
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Precise Characterization of Genetic Interactions in Cancer via Molecular Network Refining Processes. Int J Mol Sci 2021; 22:ijms222011114. [PMID: 34681774 PMCID: PMC8540220 DOI: 10.3390/ijms222011114] [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: 09/17/2021] [Revised: 10/05/2021] [Accepted: 10/13/2021] [Indexed: 12/13/2022] Open
Abstract
Genetic interactions (GIs), such as the synthetic lethal interaction, are promising therapeutic targets in precision medicine. However, despite extensive efforts to characterize GIs by large-scale perturbation screening, considerable false positives have been reported in multiple studies. We propose a new computational approach for improved precision in GI identification by applying constraints that consider actual biological phenomena. In this study, GIs were characterized by assessing mutation, loss of function, and expression profiles in the DEPMAP database. The expression profiles were used to exclude loss-of-function data for nonexpressed genes in GI characterization. More importantly, the characterized GIs were refined based on Kyoto Encyclopedia of Genes and Genomes (KEGG) or protein–protein interaction (PPI) networks, under the assumption that genes genetically interacting with a certain mutated gene are adjacent in the networks. As a result, the initial GIs characterized with CRISPR and RNAi screenings were refined to 65 and 23 GIs based on KEGG networks and to 183 and 142 GIs based on PPI networks. The evaluation of refined GIs showed improved precision with respect to known synthetic lethal interactions. The refining process also yielded a synthetic partner network (SPN) for each mutated gene, which provides insight into therapeutic strategies for the mutated genes; specifically, exploring the SPN of mutated BRAF revealed ELAVL1 as a potential target for treating BRAF-mutated cancer, as validated by previous research. We expect that this work will advance cancer therapeutic research.
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Johnson KL, Qi Z, Yan Z, Wen X, Nguyen TC, Zaleta-Rivera K, Chen CJ, Fan X, Sriram K, Wan X, Chen ZB, Zhong S. Revealing protein-protein interactions at the transcriptome scale by sequencing. Mol Cell 2021; 81:4091-4103.e9. [PMID: 34348091 PMCID: PMC8500946 DOI: 10.1016/j.molcel.2021.07.006] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 06/12/2021] [Accepted: 07/08/2021] [Indexed: 02/07/2023]
Abstract
We describe PROPER-seq (protein-protein interaction sequencing) to map protein-protein interactions (PPIs) en masse. PROPER-seq first converts transcriptomes of input cells into RNA-barcoded protein libraries, in which all interacting protein pairs are captured through nucleotide barcode ligation, recorded as chimeric DNA sequences, and decoded at once by sequencing and mapping. We applied PROPER-seq to human embryonic kidney cells, T lymphocytes, and endothelial cells and identified 210,518 human PPIs (collected in the PROPER v.1.0 database). Among these, 1,365 and 2,480 PPIs are supported by published co-immunoprecipitation (coIP) and affinity purification-mass spectrometry (AP-MS) data, 17,638 PPIs are predicted by the prePPI algorithm without previous experimental validation, and 100 PPIs overlap human synthetic lethal gene pairs. In addition, four previously uncharacterized interaction partners with poly(ADP-ribose) polymerase 1 (PARP1) (a critical protein in DNA repair) known as XPO1, MATR3, IPO5, and LEO1 are validated in vivo. PROPER-seq presents a time-effective technology to map PPIs at the transcriptome scale, and PROPER v.1.0 provides a rich resource for studying PPIs.
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Affiliation(s)
- Kara L Johnson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Zhijie Qi
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Zhangming Yan
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Xingzhao Wen
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA
| | - Tri C Nguyen
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Kathia Zaleta-Rivera
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Chien-Ju Chen
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA
| | - Xiaochen Fan
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Kiran Sriram
- Department of Diabetes Complications and Metabolism, Beckman Research Institute, City of Hope, Duarte, CA 91010, USA
| | - Xueyi Wan
- Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA
| | - Zhen Bouman Chen
- Department of Diabetes Complications and Metabolism, Beckman Research Institute, City of Hope, Duarte, CA 91010, USA
| | - Sheng Zhong
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA.
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Shan Y, Wang F, Wei Z, Lu Y. Synthetic lethality theory approaches to effective substance discovery and functional mechanisms elucidation of anti-cancer phytomedicine. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2021; 91:153718. [PMID: 34531099 DOI: 10.1016/j.phymed.2021.153718] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 08/04/2021] [Accepted: 08/15/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Longstanding, successful use of combinations of phytopharmaceuticals in traditional Chinese medicine (TCM) has caught the attention of several pharmacologists to natural medicines. However, the development and popularisation of TCM is mainly limited because of the unavailability of reports clarifying the mechanisms of action and pharmacologically active ingredients in such formulations. Previous studies on natural medicines have mostly focused on their dominant components using forward pharmacology which often neglects trace components. It is necessary to assess the pharmacological and therapeutic superiority of many such trace components in comparison with single constituents. PURPOSE In this study, we aimed to propose a new pharmacological research strategy for TCM. In particular, we presented the possibility that the effective mechanism of action of trace components of TCM is based on synthetic lethality. We sincerely hope to explore this theory further. METHOD We obtained retrieve published research information related to synthetic lethality, phytochemicals and Chinese medicine from PubMed and Google scholar. Based on the inclusion criteria, 71 studies were selected and discussed in this review. RESULTS As an interaction among genes, synthetic lethality can amplify co-regulatory biological effects exponentially. Synthetic strategies have been successfully applied for research and development of anti-tumour agents, including poly ADP-ribose polymerase inhibitors and clinical combination of chemotherapeutic agents for efficacy enhancement and toxicity reduction. TCM drugs contain several secondary metabolites to combat environmental stresses, providing a multi-component basis for corresponding synergistic targets. Therefore, we aimed to study whether this method could be used to identify active components present in trace amounts in TCM drugs. Based on a reverse concept of target-component-effect and identified synergistic targets, we explored the mechanisms of action of weakly active components present in trace amounts in TCM drugs to assess combinations of potential synergistic components. CONCLUSION This pattern of synthetic lethality not only elucidated the mechanisms of action of TCM drugs from a new perspective but also inspired future studies on discovering naturally occurring active components.
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Affiliation(s)
- Yunlong Shan
- Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China
| | - Fang Wang
- College of Food Science and Engineering/Collaborative Innovation Center for Modern Grain Circulation and Safety/Key Laboratory of Grains and Oils Quality Control and Processing, Nanjing University of Finance and Economics, Nanjing 210023, China
| | - Zhonghong Wei
- School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, Jiangsu Province, China.
| | - Yin Lu
- School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, Jiangsu Province, China.
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Montazeri H, Coto-Llerena M, Bianco G, Zangene E, Taha-Mehlitz S, Paradiso V, Srivatsa S, de Weck A, Roma G, Lanzafame M, Bolli M, Beerenwinkel N, von Flüe M, Terracciano L, Piscuoglio S, Ng CKY. Systematic identification of novel cancer genes through analysis of deep shRNA perturbation screens. Nucleic Acids Res 2021; 49:8488-8504. [PMID: 34313788 PMCID: PMC8421231 DOI: 10.1093/nar/gkab627] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 07/07/2021] [Accepted: 07/13/2021] [Indexed: 11/30/2022] Open
Abstract
Systematic perturbation screens provide comprehensive resources for the elucidation of cancer driver genes. The perturbation of many genes in relatively few cell lines in such functional screens necessitates the development of specialized computational tools with sufficient statistical power. Here we developed APSiC (Analysis of Perturbation Screens for identifying novel Cancer genes) to identify genetic drivers and effectors in perturbation screens even with few samples. Applying APSiC to the shRNA screen Project DRIVE, APSiC identified well-known and novel putative mutational and amplified cancer genes across all cancer types and in specific cancer types. Additionally, APSiC discovered tumor-promoting and tumor-suppressive effectors, respectively, for individual cancer types, including genes involved in cell cycle control, Wnt/β-catenin and hippo signalling pathways. We functionally demonstrated that LRRC4B, a putative novel tumor-suppressive effector, suppresses proliferation by delaying cell cycle and modulates apoptosis in breast cancer. We demonstrate APSiC is a robust statistical framework for discovery of novel cancer genes through analysis of large-scale perturbation screens. The analysis of DRIVE using APSiC is provided as a web portal and represents a valuable resource for the discovery of novel cancer genes.
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Affiliation(s)
- Hesam Montazeri
- Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
| | - Mairene Coto-Llerena
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
- Visceral Surgery and Precision Medicine Research laboratory, Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Gaia Bianco
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
- Visceral Surgery and Precision Medicine Research laboratory, Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Ehsan Zangene
- Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Stephanie Taha-Mehlitz
- Visceral Surgery and Precision Medicine Research laboratory, Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Viola Paradiso
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
| | - Sumana Srivatsa
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Antoine de Weck
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Basel, Switzerland
| | - Guglielmo Roma
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Basel, Switzerland
| | - Manuela Lanzafame
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
| | - Martin Bolli
- Clarunis, Department of Visceral Surgery, University Centre for Gastrointestinal and Liver Diseases, St. Clara Hospital and University Hospital Basel, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Markus von Flüe
- Clarunis, Department of Visceral Surgery, University Centre for Gastrointestinal and Liver Diseases, St. Clara Hospital and University Hospital Basel, Switzerland
| | - Luigi M Terracciano
- Department of Pathology, Humanitas Clinical and Research Center, IRCCS, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
| | - Salvatore Piscuoglio
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
- Visceral Surgery and Precision Medicine Research laboratory, Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Charlotte K Y Ng
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
- Department for BioMedical Research, University of Bern, Bern, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
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Benfatto S, Serçin Ö, Dejure FR, Abdollahi A, Zenke FT, Mardin BR. Uncovering cancer vulnerabilities by machine learning prediction of synthetic lethality. Mol Cancer 2021; 20:111. [PMID: 34454516 PMCID: PMC8401190 DOI: 10.1186/s12943-021-01405-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 08/10/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Synthetic lethality describes a genetic interaction between two perturbations, leading to cell death, whereas neither event alone has a significant effect on cell viability. This concept can be exploited to specifically target tumor cells. CRISPR viability screens have been widely employed to identify cancer vulnerabilities. However, an approach to systematically infer genetic interactions from viability screens is missing. METHODS Here we describe PAn-canceR Inferred Synthetic lethalities (PARIS), a machine learning approach to identify cancer vulnerabilities. PARIS predicts synthetic lethal (SL) interactions by combining CRISPR viability screens with genomics and transcriptomics data across hundreds of cancer cell lines profiled within the Cancer Dependency Map. RESULTS Using PARIS, we predicted 15 high confidence SL interactions within 549 DNA damage repair (DDR) genes. We show experimental validation of an SL interaction between the tumor suppressor CDKN2A, thymidine phosphorylase (TYMP) and the thymidylate synthase (TYMS), which may allow stratifying patients for treatment with TYMS inhibitors. Using genome-wide mapping of SL interactions for DDR genes, we unraveled a dependency between the aldehyde dehydrogenase ALDH2 and the BRCA-interacting protein BRIP1. Our results suggest BRIP1 as a potential therapeutic target in ~ 30% of all tumors, which express low levels of ALDH2. CONCLUSIONS PARIS is an unbiased, scalable and easy to adapt platform to identify SL interactions that should aid in improving cancer therapy with increased availability of cancer genomics data.
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Affiliation(s)
- Salvatore Benfatto
- BioMed X Institute (GmbH), Im Neuenheimer Feld 583, 69120, Heidelberg, Germany
| | - Özdemirhan Serçin
- BioMed X Institute (GmbH), Im Neuenheimer Feld 583, 69120, Heidelberg, Germany
| | - Francesca R Dejure
- BioMed X Institute (GmbH), Im Neuenheimer Feld 583, 69120, Heidelberg, Germany
| | - Amir Abdollahi
- Division of Molecular and Translational Radiation Oncology, National Centre for Tumour Diseases (NCT), Heidelberg University Hospital, 69120, Heidelberg, Germany
| | - Frank T Zenke
- Translational Innovation Platform Oncology & Immuno-Oncology, Merck KGaA, Frankfurter Str. 250, 64293, Darmstadt, Germany
| | - Balca R Mardin
- BioMed X Institute (GmbH), Im Neuenheimer Feld 583, 69120, Heidelberg, Germany.
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50
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Yang C, Guo Y, Qian R, Huang Y, Zhang L, Wang J, Huang X, Liu Z, Qin W, Wang C, Chen H, Ma X, Zhang D. Mapping the landscape of synthetic lethal interactions in liver cancer. Theranostics 2021; 11:9038-9053. [PMID: 34522226 PMCID: PMC8419043 DOI: 10.7150/thno.63416] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 08/14/2021] [Indexed: 12/11/2022] Open
Abstract
Almost all the current therapies against liver cancer are based on the "one size fits all" principle and offer only limited survival benefit. Fortunately, synthetic lethality (SL) may provide an alternate route towards individualized therapy in liver cancer. The concept that simultaneous losses of two genes are lethal to a cell while a single loss is non-lethal can be utilized to selectively eliminate tumors with genetic aberrations. Methods: To infer liver cancer-specific SL interactions, we propose a computational pipeline termed SiLi (statistical inference-based synthetic lethality identification) that incorporates five inference procedures. Based on large-scale sequencing datasets, SiLi analysis was performed to identify SL interactions in liver cancer. Results: By SiLi analysis, a total of 272 SL pairs were discerned, which included 209 unique target candidates. Among these, polo-like kinase 1 (PLK1) was considered to have considerable therapeutic potential. Further computational and experimental validation of the SL pair TP53-PLK1 demonstrated that inhibition of PLK1 could be a novel therapeutic strategy specifically targeting those patients with TP53-mutant liver tumors. Conclusions: In this study, we report a comprehensive analysis of synthetic lethal interactions of liver cancer. Our findings may open new possibilities for patient-tailored therapeutic interventions in liver cancer.
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Affiliation(s)
- Chen Yang
- Department of Clinical Medicine, School of Medicine, Zhejiang University City College, Hangzhou, China
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuchen Guo
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ruolan Qian
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yiwen Huang
- Department of Clinical Medicine, Fujian Medical University, Fuzhou, China
| | - Linmeng Zhang
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jun Wang
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaowen Huang
- Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Zhicheng Liu
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wenxin Qin
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Cun Wang
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Huimin Chen
- Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xuhui Ma
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Dayong Zhang
- Department of Clinical Medicine, School of Medicine, Zhejiang University City College, Hangzhou, China
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