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Song Y, Zhang J, Li Y, Cheng L, Song H, Zhang Y, Du G, Yu S, Zou Y, Xu Q. Exploring Bioinformatics Tools to Analyze the Role of CDC6 in the Progression of Polycystic Ovary Syndrome to Endometrial Cancer by Promoting Immune Infiltration. Int J Mol Sci 2024; 25:12974. [PMID: 39684684 DOI: 10.3390/ijms252312974] [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: 11/01/2024] [Revised: 11/20/2024] [Accepted: 11/25/2024] [Indexed: 12/18/2024] Open
Abstract
Cell division cycle 6 (CDC6) is essential for the initiation of DNA replication in eukaryotic cells and contributes to the development of various human tumors. Polycystic ovarian syndrome (PCOS) is a reproductive endocrine disease in women of childbearing age, with a significant risk of endometrial cancer (EC). However, the role of CDC6 in the progression of PCOS to EC is unclear. Therefore, we examined CDC6 expression in patients with PCOS and EC. We evaluated the relationship between CDC6 expression and its prognostic value, potential biological functions, and immune infiltrates in patients with EC. In vitro analyses were performed to investigate the effects of CDC6 knockdown on EC proliferation, migration, invasion, and apoptosis. CDC6 expression was significantly upregulated in patients with PCOS and EC. Moreover, this protein caused EC by promoting the aberrant infiltration of macrophages into the immune microenvironment in patients with PCOS. A functional enrichment analysis revealed that CDC6 exerted its pro-cancer and pro-immune cell infiltration functions via the PI3K-AKT pathway. Moreover, it promoted EC proliferation, migration, and invasion but inhibited apoptosis. This protein significantly reduced EC survival when mutated. These findings demonstrate that CDC6 regulates the progression of PCOS to EC and promotes immune infiltration.
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Affiliation(s)
- Yuhang Song
- School of Basic Medicine, Xinjiang Medical University, Urumqi 830054, China
- School of Clinical Medicine, Xinjiang Medical University, Urumqi 830054, China
| | - Jing Zhang
- Department of Immunology, School of Basic Medicine, Central South University, Changsha 410017, China
| | - Yao Li
- School of Basic Medicine, Xinjiang Medical University, Urumqi 830054, China
| | - Lufeng Cheng
- Basic Medical College, Xinjiang Medical University, Urumqi 830054, China
| | - Hua Song
- School of Clinical Medicine, Xinjiang Medical University, Urumqi 830054, China
| | - Yuhang Zhang
- School of Clinical Medicine, Xinjiang Medical University, Urumqi 830054, China
| | - Guoqing Du
- School of Basic Medicine, Xinjiang Medical University, Urumqi 830054, China
| | - Sunyue Yu
- School of Clinical Medicine, Xinjiang Medical University, Urumqi 830054, China
| | - Yizhou Zou
- Department of Immunology, School of Basic Medicine, Central South University, Changsha 410017, China
| | - Qi Xu
- School of Basic Medicine, Xinjiang Medical University, Urumqi 830054, China
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2
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Kuchinski K, King N, Driggers J, Lawson K, Vo M, Skrtic S, Slattery C, Lane R, Simone E, Mills SA, Escorcia W, Wetzel H. Catalogue of Somatic Mutations in Cancer Database and Structural Modeling Analysis of CYP2D6 Mutations in Human Cancers. J Pharmacol Exp Ther 2024; 391:441-449. [PMID: 39379142 DOI: 10.1124/jpet.124.002136] [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: 03/15/2024] [Revised: 09/12/2024] [Accepted: 09/19/2024] [Indexed: 10/10/2024] Open
Abstract
Single nucleotide polymorphisms (SNPs) in cytochrome P450 (CYP450) enzymes alter the metabolism of a variety of drugs. Numerous medications, including chemotherapies, are metabolized by CYP450 enzymes, making the expression of this suite of enzymes in tumor cells relevant to prescription regimens for patients with cancer. We analyzed the characteristics of mutations of the cytochrome P450 2D6 (CYP2D6) enzymes in cancer patients obtained from the Catalogue of Somatic Mutations in Cancer (COSMIC), including mutation type, age of the patient, tissue type, and histology. Mutations were analyzed through the Cancer-Related Analysis of Variants Toolkit (CRAVAT) software along with cancer-specific high-throughput annotation of somatic mutations (CHASMplus) and variant effect scoring tool (VEST4) algorithms to determine the likelihood of being a driver and/or pathogenic mutation. For mutations with significant CHASMplus and VEST4 scores, structural analysis of each corresponding mutant protein was performed. The effect of each mutation was evaluated for its impact on the overall protein stability and ligand binding using Foldit Standalone and SwissDock, respectively. Structural analysis revealed that several missense mutations in CYP2D6 resulted in altered stability after energy minimization. Three missense mutations of CYP2D6 significantly altered docking stability, and those located on alpha helices near the docking site had a more significant impact than those not found in secondary protein structures. In conclusion, we have identified a series of mutations to CYP2D6 enzymes with possible relevance to cancer pathologies. SIGNIFICANCE STATEMENT: CYP2D6 is responsible for the metabolism of many anticancer drugs. This study identified and characterized a series of mutations in the CYP2D6 enzyme that occurred in tumors. We found it likely that many of these mutations would alter enzyme function, leading to changes in drug metabolism in the tumor. We provide a basis for predicting the likelihood of a patient carrying these mutations to identify patients who may benefit from a precision medicine approach to drug selection and dosing.
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Affiliation(s)
- Kennedy Kuchinski
- Biology Department (K.K., K.L., M.V., S.S., E.S., W.E., H.W.) and Chemistry Department (N.K., J.D., C.S., R.L., S.A.M.), Xavier University, Cincinnati, Ohio; Lake Erie College of Osteopathic Medicine, Erie, Pennsylvania (M.V.); and Department of Biology, California State University, Northridge (W.E.)
| | - Nathaniel King
- Biology Department (K.K., K.L., M.V., S.S., E.S., W.E., H.W.) and Chemistry Department (N.K., J.D., C.S., R.L., S.A.M.), Xavier University, Cincinnati, Ohio; Lake Erie College of Osteopathic Medicine, Erie, Pennsylvania (M.V.); and Department of Biology, California State University, Northridge (W.E.)
| | - Julia Driggers
- Biology Department (K.K., K.L., M.V., S.S., E.S., W.E., H.W.) and Chemistry Department (N.K., J.D., C.S., R.L., S.A.M.), Xavier University, Cincinnati, Ohio; Lake Erie College of Osteopathic Medicine, Erie, Pennsylvania (M.V.); and Department of Biology, California State University, Northridge (W.E.)
| | - Kylie Lawson
- Biology Department (K.K., K.L., M.V., S.S., E.S., W.E., H.W.) and Chemistry Department (N.K., J.D., C.S., R.L., S.A.M.), Xavier University, Cincinnati, Ohio; Lake Erie College of Osteopathic Medicine, Erie, Pennsylvania (M.V.); and Department of Biology, California State University, Northridge (W.E.)
| | - Martin Vo
- Biology Department (K.K., K.L., M.V., S.S., E.S., W.E., H.W.) and Chemistry Department (N.K., J.D., C.S., R.L., S.A.M.), Xavier University, Cincinnati, Ohio; Lake Erie College of Osteopathic Medicine, Erie, Pennsylvania (M.V.); and Department of Biology, California State University, Northridge (W.E.)
| | - Shayne Skrtic
- Biology Department (K.K., K.L., M.V., S.S., E.S., W.E., H.W.) and Chemistry Department (N.K., J.D., C.S., R.L., S.A.M.), Xavier University, Cincinnati, Ohio; Lake Erie College of Osteopathic Medicine, Erie, Pennsylvania (M.V.); and Department of Biology, California State University, Northridge (W.E.)
| | - Connor Slattery
- Biology Department (K.K., K.L., M.V., S.S., E.S., W.E., H.W.) and Chemistry Department (N.K., J.D., C.S., R.L., S.A.M.), Xavier University, Cincinnati, Ohio; Lake Erie College of Osteopathic Medicine, Erie, Pennsylvania (M.V.); and Department of Biology, California State University, Northridge (W.E.)
| | - Rebecca Lane
- Biology Department (K.K., K.L., M.V., S.S., E.S., W.E., H.W.) and Chemistry Department (N.K., J.D., C.S., R.L., S.A.M.), Xavier University, Cincinnati, Ohio; Lake Erie College of Osteopathic Medicine, Erie, Pennsylvania (M.V.); and Department of Biology, California State University, Northridge (W.E.)
| | - Emma Simone
- Biology Department (K.K., K.L., M.V., S.S., E.S., W.E., H.W.) and Chemistry Department (N.K., J.D., C.S., R.L., S.A.M.), Xavier University, Cincinnati, Ohio; Lake Erie College of Osteopathic Medicine, Erie, Pennsylvania (M.V.); and Department of Biology, California State University, Northridge (W.E.)
| | - Stephen A Mills
- Biology Department (K.K., K.L., M.V., S.S., E.S., W.E., H.W.) and Chemistry Department (N.K., J.D., C.S., R.L., S.A.M.), Xavier University, Cincinnati, Ohio; Lake Erie College of Osteopathic Medicine, Erie, Pennsylvania (M.V.); and Department of Biology, California State University, Northridge (W.E.)
| | - Wilber Escorcia
- Biology Department (K.K., K.L., M.V., S.S., E.S., W.E., H.W.) and Chemistry Department (N.K., J.D., C.S., R.L., S.A.M.), Xavier University, Cincinnati, Ohio; Lake Erie College of Osteopathic Medicine, Erie, Pennsylvania (M.V.); and Department of Biology, California State University, Northridge (W.E.)
| | - Hanna Wetzel
- Biology Department (K.K., K.L., M.V., S.S., E.S., W.E., H.W.) and Chemistry Department (N.K., J.D., C.S., R.L., S.A.M.), Xavier University, Cincinnati, Ohio; Lake Erie College of Osteopathic Medicine, Erie, Pennsylvania (M.V.); and Department of Biology, California State University, Northridge (W.E.)
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3
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Possible A2E Mutagenic Effects on RPE Mitochondrial DNA from Innovative RNA-Seq Bioinformatics Pipeline. Antioxidants (Basel) 2020; 9:antiox9111158. [PMID: 33233726 PMCID: PMC7699917 DOI: 10.3390/antiox9111158] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 11/12/2020] [Accepted: 11/18/2020] [Indexed: 01/10/2023] Open
Abstract
Mitochondria are subject to continuous oxidative stress stimuli that, over time, can impair their genome and lead to several pathologies, like retinal degenerations. Our main purpose was the identification of mtDNA variants that might be induced by intense oxidative stress determined by N-retinylidene-N-retinylethanolamine (A2E), together with molecular pathways involving the genes carrying them, possibly linked to retinal degeneration. We performed a variant analysis comparison between transcriptome profiles of human retinal pigment epithelial (RPE) cells exposed to A2E and untreated ones, hypothesizing that it might act as a mutagenic compound towards mtDNA. To optimize analysis, we proposed an integrated approach that foresaw the complementary use of the most recent algorithms applied to mtDNA data, characterized by a mixed output coming from several tools and databases. An increased number of variants emerged following treatment. Variants mainly occurred within mtDNA coding sequences, corresponding with either the polypeptide-encoding genes or the RNA. Time-dependent impairments foresaw the involvement of all oxidative phosphorylation complexes, suggesting a serious damage to adenosine triphosphate (ATP) biosynthesis, that can result in cell death. The obtained results could be incorporated into clinical diagnostic settings, as they are hypothesized to modulate the phenotypic expression of mtDNA pathogenic variants, drastically improving the field of precision molecular medicine.
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Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers. PLoS Comput Biol 2019; 15:e1006658. [PMID: 30921324 PMCID: PMC6438456 DOI: 10.1371/journal.pcbi.1006658] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
At the root of the so-called precision medicine or precision oncology, which is our focus here, is the hypothesis that cancer treatment would be considerably better if therapies were guided by a tumor’s genomic alterations. This hypothesis has sparked major initiatives focusing on whole-genome and/or exome sequencing, creation of large databases, and developing tools for their statistical analyses—all aspiring to identify actionable alterations, and thus molecular targets, in a patient. At the center of the massive amount of collected sequence data is their interpretations that largely rest on statistical analysis and phenotypic observations. Statistics is vital, because it guides identification of cancer-driving alterations. However, statistics of mutations do not identify a change in protein conformation; therefore, it may not define sufficiently accurate actionable mutations, neglecting those that are rare. Among the many thematic overviews of precision oncology, this review innovates by further comprehensively including precision pharmacology, and within this framework, articulating its protein structural landscape and consequences to cellular signaling pathways. It provides the underlying physicochemical basis, thereby also opening the door to a broader community.
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Ng PKS, Li J, Jeong KJ, Shao S, Chen H, Tsang YH, Sengupta S, Wang Z, Bhavana VH, Tran R, Soewito S, Minussi DC, Moreno D, Kong K, Dogruluk T, Lu H, Gao J, Tokheim C, Zhou DC, Johnson AM, Zeng J, Ip CKM, Ju Z, Wester M, Yu S, Li Y, Vellano CP, Schultz N, Karchin R, Ding L, Lu Y, Cheung LWT, Chen K, Shaw KR, Meric-Bernstam F, Scott KL, Yi S, Sahni N, Liang H, Mills GB. Systematic Functional Annotation of Somatic Mutations in Cancer. Cancer Cell 2018; 33:450-462.e10. [PMID: 29533785 PMCID: PMC5926201 DOI: 10.1016/j.ccell.2018.01.021] [Citation(s) in RCA: 210] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2017] [Revised: 12/07/2017] [Accepted: 01/30/2018] [Indexed: 12/11/2022]
Abstract
The functional impact of the vast majority of cancer somatic mutations remains unknown, representing a critical knowledge gap for implementing precision oncology. Here, we report the development of a moderate-throughput functional genomic platform consisting of efficient mutant generation, sensitive viability assays using two growth factor-dependent cell models, and functional proteomic profiling of signaling effects for select aberrations. We apply the platform to annotate >1,000 genomic aberrations, including gene amplifications, point mutations, indels, and gene fusions, potentially doubling the number of driver mutations characterized in clinically actionable genes. Further, the platform is sufficiently sensitive to identify weak drivers. Our data are accessible through a user-friendly, public data portal. Our study will facilitate biomarker discovery, prediction algorithm improvement, and drug development.
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Affiliation(s)
- Patrick Kwok-Shing Ng
- Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jun Li
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Kang Jin Jeong
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Shan Shao
- Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Hu Chen
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Graduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX 77030, USA
| | - Yiu Huen Tsang
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Sohini Sengupta
- Division of Oncology, Department of Medicine, Washington University, St. Louis, MO 63108, USA
| | - Zixing Wang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | | | - Richard Tran
- Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Stephanie Soewito
- Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Darlan Conterno Minussi
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Daniela Moreno
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Kathleen Kong
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Turgut Dogruluk
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Hengyu Lu
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jianjiong Gao
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Collin Tokheim
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Daniel Cui Zhou
- Division of Oncology, Department of Medicine, Washington University, St. Louis, MO 63108, USA
| | - Amber M Johnson
- Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jia Zeng
- Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Carman Ka Man Ip
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Zhenlin Ju
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Matthew Wester
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Shuangxing Yu
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Yongsheng Li
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Christopher P Vellano
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Nikolaus Schultz
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Rachel Karchin
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA; Department of Oncology, Johns Hopkins Medicine, Baltimore, MD 21287, USA
| | - Li Ding
- Division of Oncology, Department of Medicine, Washington University, St. Louis, MO 63108, USA; Siteman Cancer Center, Washington University, St. Louis, MO 63108, USA
| | - Yiling Lu
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Lydia Wai Ting Cheung
- HKU Shenzhen Institute of Research and Innovation, Shenzhen, China; School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR
| | - Ken Chen
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Kenna R Shaw
- Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Funda Meric-Bernstam
- Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Breast Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Kenneth L Scott
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Song Yi
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
| | - Nidhi Sahni
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Graduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX 77030, USA.
| | - Han Liang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Graduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX 77030, USA.
| | - Gordon B Mills
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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Reeb J, Hecht M, Mahlich Y, Bromberg Y, Rost B. Predicted Molecular Effects of Sequence Variants Link to System Level of Disease. PLoS Comput Biol 2016; 12:e1005047. [PMID: 27536940 PMCID: PMC4990455 DOI: 10.1371/journal.pcbi.1005047] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2016] [Accepted: 07/04/2016] [Indexed: 11/19/2022] Open
Abstract
Developments in experimental and computational biology are advancing our understanding of how protein sequence variation impacts molecular protein function. However, the leap from the micro level of molecular function to the macro level of the whole organism, e.g. disease, remains barred. Here, we present new results emphasizing earlier work that suggested some links from molecular function to disease. We focused on non-synonymous single nucleotide variants, also referred to as single amino acid variants (SAVs). Building upon OMIA (Online Mendelian Inheritance in Animals), we introduced a curated set of 117 disease-causing SAVs in animals. Methods optimized to capture effects upon molecular function often correctly predict human (OMIM) and animal (OMIA) Mendelian disease-causing variants. We also predicted effects of human disease-causing variants in the mouse model, i.e. we put OMIM SAVs into mouse orthologs. Overall, fewer variants were predicted with effect in the model organism than in the original organism. Our results, along with other recent studies, demonstrate that predictions of molecular effects capture some important aspects of disease. Thus, in silico methods focusing on the micro level of molecular function can help to understand the macro system level of disease. The variations in the genetic sequence between individuals affect the gene-product, i.e. the protein differently. Some variants have no measurable effect (are neutral), while others affect protein function. Some of those effects are so severe they cause so called monogenic Mendelian diseases, i.e. diseases triggered by a single letter change. Some in silico methods predict the molecular impact of sequence variation. However, both experimental and computational analyses struggle to generalize from the effect upon molecular protein function to the effect upon the organism such as a disease. Here, we confirmed that methods predicting molecular effects correctly capture the type of effects causing Mendelian diseases in human and introduced a data set for animal diseases that was also captured by predictions methods. Predicted effects were less when in silico testing human variants in an animal model (here mouse). This is important to know because “mouse models” are common to study human diseases. Overall, we provided some evidence for a link between the molecular level and some type of disease.
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Affiliation(s)
- Jonas Reeb
- Department of Informatics, Bioinformatics & Computational Biology—i12, Technische Universität München, Garching/Munich, Germany
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Technische Universität München, Garching, Germany
- * E-mail:
| | - Maximilian Hecht
- Department of Informatics, Bioinformatics & Computational Biology—i12, Technische Universität München, Garching/Munich, Germany
| | - Yannick Mahlich
- Department of Informatics, Bioinformatics & Computational Biology—i12, Technische Universität München, Garching/Munich, Germany
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey, United States of America
- Institute for Advanced Study (TUM-IAS), Garching/Munich, Germany
| | - Yana Bromberg
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey, United States of America
- Institute for Advanced Study (TUM-IAS), Garching/Munich, Germany
| | - Burkhard Rost
- Department of Informatics, Bioinformatics & Computational Biology—i12, Technische Universität München, Garching/Munich, Germany
- Institute for Advanced Study (TUM-IAS), Garching/Munich, Germany
- Institute for Food and Plant Sciences WZW, Technische Universität München, Weihenstephan, Freising, Germany
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Nussinov R, Tsai CJ. 'Latent drivers' expand the cancer mutational landscape. Curr Opin Struct Biol 2015; 32:25-32. [PMID: 25661093 DOI: 10.1016/j.sbi.2015.01.004] [Citation(s) in RCA: 71] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2014] [Revised: 12/22/2014] [Accepted: 01/09/2015] [Indexed: 01/08/2023]
Abstract
A major challenge facing the community involves identification of mutations that drive cancer. Analyses of cancer genomes to detect, and distinguish, 'driver' from 'passenger' mutations are daunting tasks. Here we suggest that there is a third 'latent driver' category. 'Latent driver' mutations behave as passengers, and do not confer a cancer hallmark. However, coupled with other emerging mutations, they drive cancer development and drug resistance. 'Latent drivers' emerge prior to and during cancer evolution. These allosteric mutations can work through 'AND' all-or-none or incremental 'Graded' logic gate mechanisms. Current diagnostic platforms generally assume that actionable 'driver' mutations are those appearing most frequently in cancer. We propose that 'latent driver' detection may help forecast cancer progression and modify personalized drug regimes.
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Affiliation(s)
- Ruth Nussinov
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD 21702, United States; Sackler Inst. of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
| | - Chung-Jung Tsai
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD 21702, United States
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Identification and analysis of driver missense mutations using rotation forest with feature selection. BIOMED RESEARCH INTERNATIONAL 2014; 2014:905951. [PMID: 25250338 PMCID: PMC4163459 DOI: 10.1155/2014/905951] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/04/2014] [Revised: 08/18/2014] [Accepted: 08/19/2014] [Indexed: 12/15/2022]
Abstract
Identifying cancer-associated mutations (driver mutations) is critical for understanding the cellular function of cancer genome that leads to activation of oncogenes or inactivation of tumor suppressor genes. Many approaches are proposed which use supervised machine learning techniques for prediction with features obtained by some databases. However, often we do not know which features are important for driver mutations prediction. In this study, we propose a novel feature selection method (called DX) from 126 candidate features' set. In order to obtain the best performance, rotation forest algorithm was adopted to perform the experiment. On the train dataset which was collected from COSMIC and Swiss-Prot databases, we are able to obtain high prediction performance with 88.03% accuracy, 93.9% precision, and 81.35% recall when the 11 top-ranked features were used. Comparison with other various techniques in the TP53, EGFR, and Cosmic2plus datasets shows the generality of our method.
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Nussinov R, Jang H, Tsai CJ. The structural basis for cancer treatment decisions. Oncotarget 2014; 5:7285-302. [PMID: 25277176 PMCID: PMC4202123 DOI: 10.18632/oncotarget.2439] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2014] [Accepted: 09/03/2014] [Indexed: 12/31/2022] Open
Abstract
Cancer treatment decisions rely on genetics, large data screens and clinical pharmacology. Here we point out that genetic analysis and treatment decisions may overlook critical elements in cancer development, progression and drug resistance. Two critical structural elements are missing in genetics-based decision-making: the mechanisms of oncogenic mutations and the cellular network which is rewired in cancer. These lay the foundation for the structural basis for cancer treatment decisions, which is rooted in the physical principles of the molecular conformational behavior of single molecules and their interactions. Improved tumor mutational analysis platforms and knowledge of the redundant pathways which can take over in cancer, may not only supplement known actionable findings, but forecast possible cancer progression and resistance. Such forward-looking can be powerful, endowing the oncologist with mechanistic insight and cancer prognosis, and consequently more informed treatment options. Examples include redundant pathways taking over after inhibition of EGFR constitutive activation, mutations in PIK3CA p110α and p85, and the non-hotspot AKT1 mutants conferring constitutive membrane localization.
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Affiliation(s)
- Ruth Nussinov
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD 21702, U.S.A
- Sackler Inst. of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Hyunbum Jang
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD 21702, U.S.A
| | - Chung-Jung Tsai
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD 21702, U.S.A
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Zhao N, Han JG, Shyu CR, Korkin D. Determining effects of non-synonymous SNPs on protein-protein interactions using supervised and semi-supervised learning. PLoS Comput Biol 2014; 10:e1003592. [PMID: 24784581 PMCID: PMC4006705 DOI: 10.1371/journal.pcbi.1003592] [Citation(s) in RCA: 60] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2013] [Accepted: 03/13/2014] [Indexed: 12/31/2022] Open
Abstract
Single nucleotide polymorphisms (SNPs) are among the most common types of genetic variation in complex genetic disorders. A growing number of studies link the functional role of SNPs with the networks and pathways mediated by the disease-associated genes. For example, many non-synonymous missense SNPs (nsSNPs) have been found near or inside the protein-protein interaction (PPI) interfaces. Determining whether such nsSNP will disrupt or preserve a PPI is a challenging task to address, both experimentally and computationally. Here, we present this task as three related classification problems, and develop a new computational method, called the SNP-IN tool (non-synonymous SNP INteraction effect predictor). Our method predicts the effects of nsSNPs on PPIs, given the interaction's structure. It leverages supervised and semi-supervised feature-based classifiers, including our new Random Forest self-learning protocol. The classifiers are trained based on a dataset of comprehensive mutagenesis studies for 151 PPI complexes, with experimentally determined binding affinities of the mutant and wild-type interactions. Three classification problems were considered: (1) a 2-class problem (strengthening/weakening PPI mutations), (2) another 2-class problem (mutations that disrupt/preserve a PPI), and (3) a 3-class classification (detrimental/neutral/beneficial mutation effects). In total, 11 different supervised and semi-supervised classifiers were trained and assessed resulting in a promising performance, with the weighted f-measure ranging from 0.87 for Problem 1 to 0.70 for the most challenging Problem 3. By integrating prediction results of the 2-class classifiers into the 3-class classifier, we further improved its performance for Problem 3. To demonstrate the utility of SNP-IN tool, it was applied to study the nsSNP-induced rewiring of two disease-centered networks. The accurate and balanced performance of SNP-IN tool makes it readily available to study the rewiring of large-scale protein-protein interaction networks, and can be useful for functional annotation of disease-associated SNPs. SNIP-IN tool is freely accessible as a web-server at http://korkinlab.org/snpintool/.
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Affiliation(s)
- Nan Zhao
- Informatics Institute, University of Missouri, Columbia, Missouri, United States of America
| | - Jing Ginger Han
- Informatics Institute, University of Missouri, Columbia, Missouri, United States of America
| | - Chi-Ren Shyu
- Informatics Institute, University of Missouri, Columbia, Missouri, United States of America
- Department of Computer Science, University of Missouri, Columbia, Missouri, United States of America
| | - Dmitry Korkin
- Informatics Institute, University of Missouri, Columbia, Missouri, United States of America
- Department of Computer Science, University of Missouri, Columbia, Missouri, United States of America
- Bond Life Science Center, University of Missouri, Columbia, Missouri, United States of America
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