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Shirvanizadeh N, Vihinen M. VariBench, new variation benchmark categories and data sets. FRONTIERS IN BIOINFORMATICS 2023; 3:1248732. [PMID: 37795169 PMCID: PMC10546188 DOI: 10.3389/fbinf.2023.1248732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 09/08/2023] [Indexed: 10/06/2023] Open
Affiliation(s)
| | - Mauno Vihinen
- Department of Experimental Medical Science, Lund University, Lund, Sweden
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2
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Gabryelska MM, Conn SJ. The RNA interactome in the Hallmarks of Cancer. WILEY INTERDISCIPLINARY REVIEWS. RNA 2023; 14:e1786. [PMID: 37042179 PMCID: PMC10909452 DOI: 10.1002/wrna.1786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 03/12/2023] [Accepted: 03/20/2023] [Indexed: 04/13/2023]
Abstract
Ribonucleic acid (RNA) molecules are indispensable for cellular homeostasis in healthy and malignant cells. However, the functions of RNA extend well beyond that of a protein-coding template. Rather, both coding and non-coding RNA molecules function through critical interactions with a plethora of cellular molecules, including other RNAs, DNA, and proteins. Deconvoluting this RNA interactome, including the interacting partners, the nature of the interaction, and dynamic changes of these interactions in malignancies has yielded fundamental advances in knowledge and are emerging as a novel therapeutic strategy in cancer. Here, we present an RNA-centric review of recent advances in the field of RNA-RNA, RNA-protein, and RNA-DNA interactomic network analysis and their impact across the Hallmarks of Cancer. This article is categorized under: RNA in Disease and Development > RNA in Disease RNA Interactions with Proteins and Other Molecules > RNA-Protein Complexes.
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Affiliation(s)
- Marta M Gabryelska
- Flinders Health and Medical Research Institute (FHMRI), College of Medicine and Public Health, Flinders University, Bedford Park, South Australia, Australia
| | - Simon J Conn
- Flinders Health and Medical Research Institute (FHMRI), College of Medicine and Public Health, Flinders University, Bedford Park, South Australia, Australia
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3
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Johnson A, Ng PKS, Kahle M, Castillo J, Amador B, Wang Y, Zeng J, Holla V, Vu T, Su F, Kim SH, Conway T, Jiang X, Chen K, Shaw KRM, Yap TA, Rodon J, Mills GB, Meric-Bernstam F. Actionability classification of variants of unknown significance correlates with functional effect. NPJ Precis Oncol 2023; 7:67. [PMID: 37454202 DOI: 10.1038/s41698-023-00420-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 07/03/2023] [Indexed: 07/18/2023] Open
Abstract
Genomically-informed therapy requires consideration of the functional impact of genomic alterations on protein expression and/or function. However, a substantial number of variants are of unknown significance (VUS). The MD Anderson Precision Oncology Decision Support (PODS) team developed an actionability classification scheme that categorizes VUS as either "Unknown" or "Potentially" actionable based on their location within functional domains and/or proximity to known oncogenic variants. We then compared PODS VUS actionability classification with results from a functional genomics platform consisting of mutant generation and cell viability assays. 106 (24%) of 438 VUS in 20 actionable genes were classified as oncogenic in functional assays. Variants categorized by PODS as Potentially actionable (N = 204) were more likely to be oncogenic than those categorized as Unknown (N = 230) (37% vs 13%, p = 4.08e-09). Our results demonstrate that rule-based actionability classification of VUS can identify patients more likely to have actionable variants for consideration with genomically-matched therapy.
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Affiliation(s)
- Amber Johnson
- Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Patrick Kwok-Shing Ng
- Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Michael Kahle
- Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Julia Castillo
- Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Bianca Amador
- Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yujia Wang
- Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jia Zeng
- Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Vijaykumar Holla
- Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Thuy Vu
- Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Fei Su
- Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sun-Hee Kim
- Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Tara Conway
- Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xianli Jiang
- Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ken Chen
- Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Kenna R Mills Shaw
- Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Timothy A Yap
- Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Investigational Cancer Therapeutics (Phase I Program), The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jordi Rodon
- Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Investigational Cancer Therapeutics (Phase I Program), The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Gordon B Mills
- Division of Oncological Sciences, Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA
| | - Funda Meric-Bernstam
- Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- Investigational Cancer Therapeutics (Phase I Program), The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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Xu J, Jiang T, Guo J, Pan T, Li Y. Landscape and perturbation of enhancer-driven core transcription regulatory circuits in cancer. MOLECULAR THERAPY. NUCLEIC ACIDS 2023; 32:872-874. [PMID: 37273782 PMCID: PMC10238570 DOI: 10.1016/j.omtn.2023.05.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Affiliation(s)
- Juan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Tiantongfei Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Jiyu Guo
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin 150081, China
| | - Tao Pan
- College of Biomedical Information and Engineering, Hainan Medical University, Haikou 571199, China
| | - Yongsheng Li
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin 150081, China
- College of Biomedical Information and Engineering, Hainan Medical University, Haikou 571199, China
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5
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Pan X, Coban Akdemir ZH, Gao R, Jiang X, Sheynkman GM, Wu E, Huang JH, Sahni N, Yi SS. AD-Syn-Net: systematic identification of Alzheimer's disease-associated mutation and co-mutation vulnerabilities via deep learning. Brief Bioinform 2023; 24:bbad030. [PMID: 36752347 PMCID: PMC10025433 DOI: 10.1093/bib/bbad030] [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: 07/09/2022] [Revised: 12/19/2022] [Accepted: 01/13/2023] [Indexed: 02/09/2023] Open
Abstract
Alzheimer's disease (AD) is one of the most challenging neurodegenerative diseases because of its complicated and progressive mechanisms, and multiple risk factors. Increasing research evidence demonstrates that genetics may be a key factor responsible for the occurrence of the disease. Although previous reports identified quite a few AD-associated genes, they were mostly limited owing to patient sample size and selection bias. There is a lack of comprehensive research aimed to identify AD-associated risk mutations systematically. To address this challenge, we hereby construct a large-scale AD mutation and co-mutation framework ('AD-Syn-Net'), and propose deep learning models named Deep-SMCI and Deep-CMCI configured with fully connected layers that are capable of predicting cognitive impairment of subjects effectively based on genetic mutation and co-mutation profiles. Next, we apply the customized frameworks to data sets to evaluate the importance scores of the mutations and identified mutation effectors and co-mutation combination vulnerabilities contributing to cognitive impairment. Furthermore, we evaluate the influence of mutation pairs on the network architecture to dissect the genetic organization of AD and identify novel co-mutations that could be responsible for dementia, laying a solid foundation for proposing future targeted therapy for AD precision medicine. Our deep learning model codes are available open access here: https://github.com/Pan-Bio/AD-mutation-effectors.
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Affiliation(s)
- Xingxin Pan
- Livestrong Cancer Institutes, and Department of Oncology, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA
| | - Zeynep H Coban Akdemir
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Ruixuan Gao
- Departments of Chemistry and Biological Sciences, University of Illinois Chicago, Chicago, IL 60607, USA
| | - Xiaoqian Jiang
- School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX 77030, USA
| | - Gloria M Sheynkman
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA 22903, USA
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA
- Center for Public Health Genomics, and UVA Comprehensive Cancer Center, University of Virginia, Charlottesville, VA 22903, USA
| | - Erxi Wu
- Livestrong Cancer Institutes, and Department of Oncology, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA
- Neuroscience Institute and Department of Neurosurgery, Baylor Scott & White Health, Temple, TX 76502, USA
- Department of Surgery, Texas A & M University Health Science Center, College of Medicine, Temple, TX 76508, USA
- Department of Pharmaceutical Sciences, Texas A & M University Health Science Center, College of Pharmacy, College Station, TX 77843, USA
| | - Jason H Huang
- Neuroscience Institute and Department of Neurosurgery, Baylor Scott & White Health, Temple, TX 76502, USA
- Department of Surgery, Texas A & M University Health Science Center, College of Medicine, Temple, TX 76508, USA
| | - Nidhi Sahni
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Quantitative and Computational Biosciences Program, Baylor College of Medicine, Houston, TX 77030, USA
| | - S Stephen Yi
- Livestrong Cancer Institutes, and Department of Oncology, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA
- Oden Institute for Computational Engineering and Sciences (ICES), The University of Texas at Austin, Austin, TX 78712, USA
- Interdisciplinary Life Sciences Graduate Programs (ILSGP), College of Natural Sciences, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Biomedical Engineering, Cockrell School of Engineering, The University of Texas at Austin, Austin, TX 78712, USA
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Zhou W, Jie Q, Pan T, Shi J, Jiang T, Zhang Y, Ding N, Xu J, Ma Y, Li Y. Single-cell RNA binding protein regulatory network analyses reveal oncogenic HNRNPK-MYC signalling pathway in cancer. Commun Biol 2023; 6:82. [PMID: 36681772 PMCID: PMC9867709 DOI: 10.1038/s42003-023-04457-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 01/10/2023] [Indexed: 01/22/2023] Open
Abstract
RNA-binding proteins (RBPs) are key players of gene expression and perturbations of RBP-RNA regulatory network have been observed in various cancer types. Here, we propose a computational method, RBPreg, to identify the RBP regulators by integration of single cell RNA-Seq (N = 233,591) and RBP binding data. Pan-cancer analyses suggest that RBP regulators exhibit cancer and cell specificity and perturbations of RBP regulatory network are involved in cancer hallmark-related functions. We prioritize an oncogenic RBP-HNRNPK, which is highly expressed in tumors and associated with poor prognosis of patients. Functional assays performed in cancer cells reveal that HNRNPK promotes cancer cell proliferation, migration, and invasion in vitro and in vivo. Mechanistic investigations further demonstrate that HNRNPK promotes tumorigenesis and progression by directly binding to MYC and perturbed the MYC targets pathway in lung cancer. Our results provide a valuable resource for characterizing RBP regulatory networks in cancer, yielding potential biomarkers for precision medicine.
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Affiliation(s)
- Weiwei Zhou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, China
| | - Qiuling Jie
- Hainan Provincial Key Laboratory for Human Reproductive Medicine and Genetic Research, Hainan Clinical Research Center for Thalassemia, Reproductive Medical Center, National Center for International Research "China-Myanmar Joint Research Center for Prevention and Treatment of Regional Major Disease", The First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199, China
| | - Tao Pan
- College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, 571199, China
| | - Jingyi Shi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, China
| | - Tiantongfei Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, China
| | - Ya Zhang
- College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, 571199, China
| | - Na Ding
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, China
| | - Juan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, China.
| | - Yanlin Ma
- Hainan Provincial Key Laboratory for Human Reproductive Medicine and Genetic Research, Hainan Clinical Research Center for Thalassemia, Reproductive Medical Center, National Center for International Research "China-Myanmar Joint Research Center for Prevention and Treatment of Regional Major Disease", The First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199, China.
| | - Yongsheng Li
- Hainan Provincial Key Laboratory for Human Reproductive Medicine and Genetic Research, Hainan Clinical Research Center for Thalassemia, Reproductive Medical Center, National Center for International Research "China-Myanmar Joint Research Center for Prevention and Treatment of Regional Major Disease", The First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199, China.
- College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, 571199, China.
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7
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Chen Y, Li H, Sun X. Construction and analysis of sample-specific driver modules for breast cancer. BMC Genomics 2022; 23:717. [PMID: 36266635 PMCID: PMC9583575 DOI: 10.1186/s12864-022-08928-4] [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: 03/29/2022] [Accepted: 10/07/2022] [Indexed: 11/12/2022] Open
Abstract
Background It is important to understand the functional impact of somatic mutation and methylation aberration at an individual level to implement precision medicine. Recent studies have demonstrated that the perturbation of gene interaction networks can provide a fundamental link between genotype (or epigenotype) and phenotype. However, it is unclear how individual mutations affect the function of biological networks, especially for individual methylation aberration. To solve this, we provided a sample-specific driver module construction method using the 2-order network theory and hub-gene theory to identify individual perturbation networks driven by mutations or methylation aberrations. Results Our method integrated multi-omics of breast cancer, including genomics, transcriptomics, epigenomics and interactomics, and provided new insight into the synergistic collaboration between methylation and mutation at an individual level. A common driver pattern of breast cancer was identified from a novel perspective of a driver module, which is correlated to the occurrence and development of breast cancer. The constructed driver module reflects the survival prognosis and degree of malignancy among different subtypes of breast cancer. Additionally, subtype-specific driver modules were identified. Conclusions This study explores the driver module of individual cancer, and contributes to a better understanding of the mechanism of breast cancer driven by the mutations and methylation variations from the point of view of the driver network. This work will help identify new therapeutic combinations of gene mutations and drugs in humans. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-022-08928-4.
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Affiliation(s)
- Yuanyuan Chen
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, P. R. China.,College of Science, Nanjing Agricultural University, Nanjing, 210095, P. R. China
| | - Haitao Li
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, P. R. China
| | - Xiao Sun
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, P. R. China.
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8
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Li Q, Ren Z, Cao K, Li MM, Wang K, Zhou Y. CancerVar: An artificial intelligence-empowered platform for clinical interpretation of somatic mutations in cancer. SCIENCE ADVANCES 2022; 8:eabj1624. [PMID: 35544644 PMCID: PMC9075800 DOI: 10.1126/sciadv.abj1624] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 03/21/2022] [Indexed: 05/12/2023]
Abstract
Several knowledgebases are manually curated to support clinical interpretations of thousands of hotspot somatic mutations in cancer. However, discrepancies or even conflicting interpretations are observed among these databases. Furthermore, many previously undocumented mutations may have clinical or functional impacts on cancer but are not systematically interpreted by existing knowledgebases. To address these challenges, we developed CancerVar to facilitate automated and standardized interpretations for 13 million somatic mutations based on the AMP/ASCO/CAP 2017 guidelines. We further introduced a deep learning framework to predict oncogenicity for these variants using both functional and clinical features. CancerVar achieved satisfactory performance when compared to several independent knowledgebases and, using clinically curated datasets, demonstrated practical utility in classifying somatic variants. In summary, by integrating clinical guidelines with a deep learning framework, CancerVar facilitates clinical interpretation of somatic variants, reduces manual work, improves consistency in variant classification, and promotes implementation of the guidelines.
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Affiliation(s)
- Quan Li
- Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G2C1, Canada
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Zilin Ren
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Kajia Cao
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Marilyn M. Li
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kai Wang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yunyun Zhou
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
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