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Pandey S, Yadav P. Liquid biopsy in cancer management: Integrating diagnostics and clinical applications. Pract Lab Med 2025; 43:e00446. [PMID: 39839814 PMCID: PMC11743551 DOI: 10.1016/j.plabm.2024.e00446] [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: 10/12/2024] [Revised: 11/26/2024] [Accepted: 12/23/2024] [Indexed: 01/23/2025] Open
Abstract
Liquid biopsy is an innovative, minimally invasive diagnostic tool revolutionizing cancer management by enabling the detection and analysis of cancer-related biomarkers from bodily fluids such as blood, urine, or cerebrospinal fluid. Unlike traditional tissue biopsies, which require invasive procedures, liquid biopsy offers a more accessible and repeatable method for tracking cancer progression, detecting early-stage cancers, and monitoring therapeutic responses. The technology primarily focuses on analyzing circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), and other cancer-derived genetic materials. These biomarkers provide critical information on tumor heterogeneity, mutation profiles, and potential drug resistance. In clinical practice, liquid biopsy has demonstrated its utility in identifying actionable mutations, guiding personalized treatment strategies, and assessing minimal residual disease (MRD). While liquid biopsy holds immense promise, challenges related to its sensitivity, specificity, and standardization remain. Efforts to optimize pre-analytical and analytical processes, along with the establishment of robust regulatory frameworks, are crucial for its widespread clinical adoption. This abstract highlights the transformative potential of liquid biopsy in cancer diagnosis, prognosis, and treatment monitoring, emphasizing its role in advancing personalized oncology. Further research, clinical trials, and regulatory harmonization will be vital in realizing its full potential in precision cancer care.
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Affiliation(s)
| | - Preeti Yadav
- Corresponding author. Department of Pharmaceutical Sciences School of Pharmaceutical Science Babasaheb Bhimrao Ambedkar University Vidya Vihar, Raibareli Road, 226 025, Lucknow, India.
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Fatima I, Rehman A, Ding Y, Wang P, Meng Y, Rehman HU, Warraich DA, Wang Z, Feng L, Liao M. Breakthroughs in AI and multi-omics for cancer drug discovery: A review. Eur J Med Chem 2024; 280:116925. [PMID: 39378826 DOI: 10.1016/j.ejmech.2024.116925] [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: 06/28/2024] [Revised: 09/19/2024] [Accepted: 09/27/2024] [Indexed: 10/10/2024]
Abstract
Cancer is one of the biggest medical challenges we face today. It is characterized by abnormal, uncontrolled growth of cells that can spread to different parts of the body. Cancer is extremely complex, with genetic variations and the ability to adapt and evolve. This means we must continuously pursue innovative approaches to developing new cancer drugs. While traditional drug discovery methods have led to important breakthroughs, they also have significant limitations that make it difficult to efficiently create new, cost-effective cancer therapies. Integrating computational tools into the cancer drug discovery process is a major step forward. By harnessing computing power, we can overcome some of the inherent barriers of traditional methods. This review examines the range of computational techniques now being used, such as molecular docking, QSAR models, virtual screening, and pharmacophore modeling. It looks at recent advances in areas like machine learning and molecular simulations. The review also discusses the current challenges with these technologies and envisions future directions, underscoring how transformative these computational tools can be for creating targeted, new cancer treatments.
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Affiliation(s)
- Israr Fatima
- Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China
| | - Abdur Rehman
- Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China.
| | - Yanheng Ding
- Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China
| | - Peng Wang
- Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China
| | - Yuxuan Meng
- Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China
| | - Hafeez Ur Rehman
- Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China
| | - Dawood Ahmad Warraich
- Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China
| | - Zhibo Wang
- Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China
| | - Lijun Feng
- Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China
| | - Mingzhi Liao
- Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China.
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Han J, Ullah M, Andoh V, Khan MN, Feng Y, Guo Z, Chen H. Engineering Bacterial Chitinases for Industrial Application: From Protein Engineering to Bacterial Strains Mutation! A Comprehensive Review of Physical, Molecular, and Computational Approaches. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024; 72:23082-23096. [PMID: 39388625 DOI: 10.1021/acs.jafc.4c06856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/12/2024]
Abstract
Bacterial chitinases are integral in breaking down chitin, the natural polymer in crustacean and insect exoskeletons. Their increasing utilization across various sectors such as agriculture, waste management, biotechnology, food processing, and pharmaceutical industries highlights their significance as biocatalysts. The current review investigates various scientific strategies to maximize the efficiency and production of bacterial chitinases for industrial use. Our goal is to optimize the heterologous production process using physical, molecular, and computational tools. Physical methods focus on isolating, purifying, and characterizing chitinases from various sources to ensure optimal conditions for maximum enzyme activity. Molecular techniques involve gene cloning, site-directed mutation, and CRISPR-Cas9 gene editing as an approach for creating chitinases with improved catalytic activity, substrate specificity, and stability. Computational approaches use molecular modeling, docking, and simulation techniques to accurately predict enzyme-substrate interactions and enhance chitinase variants' design. Integrating multidisciplinary strategies enables the development of highly efficient chitinases tailored for specific industrial applications. This review summarizes current knowledge and advances in chitinase engineering to serve as an indispensable guideline for researchers and industrialists seeking to optimize chitinase production for various uses.
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Affiliation(s)
- Jianda Han
- School of Life Sciences, Jiangsu University, Zhenjiang, Jiangsu 212000, P. R. China
| | - Mati Ullah
- School of Life Sciences, Jiangsu University, Zhenjiang, Jiangsu 212000, P. R. China
| | - Vivian Andoh
- School of Life Sciences, Jiangsu University, Zhenjiang, Jiangsu 212000, P. R. China
| | - Muhammad Nadeem Khan
- Department of Cell Biology and Genetics, Shantou University Medical College, Shantou 515041, P. R. China
| | - Yong Feng
- School of Life Sciences, Jiangsu University, Zhenjiang, Jiangsu 212000, P. R. China
| | - Zhongjian Guo
- School of Life Sciences, Jiangsu University, Zhenjiang, Jiangsu 212000, P. R. China
| | - Huayou Chen
- School of Life Sciences, Jiangsu University, Zhenjiang, Jiangsu 212000, P. R. China
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Patil SS, Roberts SA, Gebremedhin AH. Network analysis of driver genes in human cancers. FRONTIERS IN BIOINFORMATICS 2024; 4:1365200. [PMID: 39040139 PMCID: PMC11260686 DOI: 10.3389/fbinf.2024.1365200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 06/14/2024] [Indexed: 07/24/2024] Open
Abstract
Cancer is a heterogeneous disease that results from genetic alteration of cell cycle and proliferation controls. Identifying mutations that drive cancer, understanding cancer type specificities, and delineating how driver mutations interact with each other to establish disease is vital for identifying therapeutic vulnerabilities. Such cancer specific patterns and gene co-occurrences can be identified by studying tumor genome sequences, and networks have proven effective in uncovering relationships between sequences. We present two network-based approaches to identify driver gene patterns among tumor samples. The first approach relies on analysis using the Directed Weighted All Nearest Neighbors (DiWANN) model, which is a variant of sequence similarity network, and the second approach uses bipartite network analysis. A data reduction framework was implemented to extract the minimal relevant information for the sequence similarity network analysis, where a transformed reference sequence is generated for constructing the driver gene network. This data reduction process combined with the efficiency of the DiWANN network model, greatly lowered the computational cost (in terms of execution time and memory usage) of generating the networks enabling us to work at a much larger scale than previously possible. The DiWANN network helped us identify cancer types in which samples were more closely connected to each other suggesting they are less heterogeneous and potentially susceptible to a common drug. The bipartite network analysis provided insight into gene associations and co-occurrences. We identified genes that were broadly mutated in multiple cancer types and mutations exclusive to only a few. Additionally, weighted one-mode gene projections of the bipartite networks revealed a pattern of occurrence of driver genes in different cancers. Our study demonstrates that network-based approaches can be an effective tool in cancer genomics. The analysis identifies co-occurring and exclusive driver genes and mutations for specific cancer types, providing a better understanding of the driver genes that lead to tumor initiation and evolution.
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Affiliation(s)
- Shruti S. Patil
- School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, United States
| | - Steven A. Roberts
- School of Molecular Biosciences, Washington State University, Pullman, WA, United States
- Department of Microbiology and Molecular Genetics, University of Vermont, Burlington, VT, United States
- UVM’s Larner College of Medicine, University of Vermont Cancer Center, Burlington, VT, United States
| | - Assefaw H. Gebremedhin
- School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, United States
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Nam K, Shao Y, Major DT, Wolf-Watz M. Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development. ACS OMEGA 2024; 9:7393-7412. [PMID: 38405524 PMCID: PMC10883025 DOI: 10.1021/acsomega.3c09084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 01/15/2024] [Accepted: 01/19/2024] [Indexed: 02/27/2024]
Abstract
Understanding enzyme mechanisms is essential for unraveling the complex molecular machinery of life. In this review, we survey the field of computational enzymology, highlighting key principles governing enzyme mechanisms and discussing ongoing challenges and promising advances. Over the years, computer simulations have become indispensable in the study of enzyme mechanisms, with the integration of experimental and computational exploration now established as a holistic approach to gain deep insights into enzymatic catalysis. Numerous studies have demonstrated the power of computer simulations in characterizing reaction pathways, transition states, substrate selectivity, product distribution, and dynamic conformational changes for various enzymes. Nevertheless, significant challenges remain in investigating the mechanisms of complex multistep reactions, large-scale conformational changes, and allosteric regulation. Beyond mechanistic studies, computational enzyme modeling has emerged as an essential tool for computer-aided enzyme design and the rational discovery of covalent drugs for targeted therapies. Overall, enzyme design/engineering and covalent drug development can greatly benefit from our understanding of the detailed mechanisms of enzymes, such as protein dynamics, entropy contributions, and allostery, as revealed by computational studies. Such a convergence of different research approaches is expected to continue, creating synergies in enzyme research. This review, by outlining the ever-expanding field of enzyme research, aims to provide guidance for future research directions and facilitate new developments in this important and evolving field.
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Affiliation(s)
- Kwangho Nam
- Department
of Chemistry and Biochemistry, University
of Texas at Arlington, Arlington, Texas 76019, United States
| | - Yihan Shao
- Department
of Chemistry and Biochemistry, University
of Oklahoma, Norman, Oklahoma 73019-5251, United States
| | - Dan T. Major
- Department
of Chemistry and Institute for Nanotechnology & Advanced Materials, Bar-Ilan University, Ramat-Gan 52900, Israel
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Nussinov R, Jang H. Direct K-Ras Inhibitors to Treat Cancers: Progress, New Insights, and Approaches to Treat Resistance. Annu Rev Pharmacol Toxicol 2024; 64:231-253. [PMID: 37524384 DOI: 10.1146/annurev-pharmtox-022823-113946] [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] [Indexed: 08/02/2023]
Abstract
Here we discuss approaches to K-Ras inhibition and drug resistance scenarios. A breakthrough offered a covalent drug against K-RasG12C. Subsequent innovations harnessed same-allele drug combinations, as well as cotargeting K-RasG12C with a companion drug to upstream regulators or downstream kinases. However, primary, adaptive, and acquired resistance inevitably emerge. The preexisting mutation load can explain how even exceedingly rare mutations with unobservable effects can promote drug resistance, seeding growth of insensitive cell clones, and proliferation. Statistics confirm the expectation that most resistance-related mutations are in cis, pointing to the high probability of cooperative, same-allele effects. In addition to targeted Ras inhibitors and drug combinations, bifunctional molecules and innovative tri-complex inhibitors to target Ras mutants are also under development. Since the identities and potential contributions of preexisting and evolving mutations are unknown, selecting a pharmacologic combination is taxing. Collectively, our broad review outlines considerations and provides new insights into pharmacology and resistance.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, National Cancer Institute, Frederick, Maryland, USA;
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, National Cancer Institute, Frederick, Maryland, USA;
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Craig JM, Gerhard GS, Sharma S, Yankovskiy A, Miura S, Kumar S. Methods for Estimating Personal Disease Risk and Phylogenetic Diversity of Hematopoietic Stem Cells. Mol Biol Evol 2024; 41:msad279. [PMID: 38124397 PMCID: PMC10768883 DOI: 10.1093/molbev/msad279] [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: 09/01/2023] [Revised: 12/08/2023] [Accepted: 12/12/2023] [Indexed: 12/23/2023] Open
Abstract
An individual's chronological age does not always correspond to the health of different tissues in their body, especially in cases of disease. Therefore, estimating and contrasting the physiological age of tissues with an individual's chronological age may be a useful tool to diagnose disease and its progression. In this study, we present novel metrics to quantify the loss of phylogenetic diversity in hematopoietic stem cells (HSCs), which are precursors to most blood cell types and are associated with many blood-related diseases. These metrics showed an excellent correspondence with an age-related increase in blood cancer incidence, enabling a model to estimate the phylogeny-derived age (phyloAge) of HSCs present in an individual. The HSC phyloAge was generally older than the chronological age of patients suffering from myeloproliferative neoplasms (MPNs). We present a model that relates excess HSC aging with increased MPN risk. It predicted an over 200 times greater risk based on the HSC phylogenies of the youngest MPN patients analyzed. Our new metrics are designed to be robust to sampling biases and do not rely on prior knowledge of driver mutations or physiological assessments. Consequently, they complement conventional biomarker-based methods to estimate physiological age and disease risk.
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Affiliation(s)
- Jack M Craig
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, USA
- Department of Biology, Temple University, Philadelphia, PA, USA
| | - Glenn S Gerhard
- Department of Medical Genetics and Molecular Biochemistry, Lewis Katz School of Medicine, Temple University, Philadelphia, PA, USA
| | - Sudip Sharma
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, USA
- Department of Biology, Temple University, Philadelphia, PA, USA
| | - Anastasia Yankovskiy
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, USA
- Department of Biology, Temple University, Philadelphia, PA, USA
| | - Sayaka Miura
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, USA
- Department of Biology, Temple University, Philadelphia, PA, USA
| | - Sudhir Kumar
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, USA
- Department of Biology, Temple University, Philadelphia, PA, USA
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Littman R, Cheng M, Wang N, Peng C, Yang X. SCING: Inference of robust, interpretable gene regulatory networks from single cell and spatial transcriptomics. iScience 2023; 26:107124. [PMID: 37434694 PMCID: PMC10331489 DOI: 10.1016/j.isci.2023.107124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 03/31/2023] [Accepted: 06/09/2023] [Indexed: 07/13/2023] Open
Abstract
Gene regulatory network (GRN) inference is an integral part of understanding physiology and disease. Single cell/nuclei RNA-seq (scRNA-seq/snRNA-seq) data has been used to elucidate cell-type GRNs; however, the accuracy and speed of current scRNAseq-based GRN approaches are suboptimal. Here, we present Single Cell INtegrative Gene regulatory network inference (SCING), a gradient boosting and mutual information-based approach for identifying robust GRNs from scRNA-seq, snRNA-seq, and spatial transcriptomics data. Performance evaluation using Perturb-seq datasets, held-out data, and the mouse cell atlas combined with the DisGeNET database demonstrates the improved accuracy and biological interpretability of SCING compared to existing methods. We applied SCING to the entire mouse single cell atlas, human Alzheimer's disease (AD), and mouse AD spatial transcriptomics. SCING GRNs reveal unique disease subnetwork modeling capabilities, have intrinsic capacity to correct for batch effects, retrieve disease relevant genes and pathways, and are informative on spatial specificity of disease pathogenesis.
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Affiliation(s)
- Russell Littman
- Department of Integrative Biology & Physiology, UCLA, Los Angeles, CA, USA
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA
| | - Michael Cheng
- Department of Integrative Biology & Physiology, UCLA, Los Angeles, CA, USA
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA
| | - Ning Wang
- Department of Integrative Biology & Physiology, UCLA, Los Angeles, CA, USA
| | - Chao Peng
- Department of Neurology, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Xia Yang
- Department of Integrative Biology & Physiology, UCLA, Los Angeles, CA, USA
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA
- Institute for Quantitative and Computational Biosciences (QCBio), Los Angeles, CA, USA
- Molecular Biology Institute (MBI), Los Angeles, CA, USA
- Brain Research Institute (BRI), Los Angeles, CA, USA
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Huang M, Zhong F, Chen M, Hong L, Chen W, Abudukeremu X, She F, Chen Y. CEP55 as a promising biomarker and therapeutic target on gallbladder cancer. Front Oncol 2023; 13:1156177. [PMID: 37274251 PMCID: PMC10232967 DOI: 10.3389/fonc.2023.1156177] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 05/05/2023] [Indexed: 06/06/2023] Open
Abstract
Introduction Gallbladder cancer (GBC) is a highly malignant biliary tumor with a poor prognosis. As existing therapies for advanced metastatic GBC are rarely effective, there is an urgent need to identify more effective targets for treatment. Methods Hub genes of GBC were identified by bioinformatics analysis and their expression in GBC was analyzed by tissue validation. The biological role of CEP55 in GBC cell and the underlying mechanism of the anticancer effect of CEP55 knockdown were evaluated via CCK8, colony formation assay, EDU staining, flow cytometry, western blot, immunofluorescence, and an alkaline comet assay. Results We screened out five hub genes of GBC, namely PLK1, CEP55, FANCI, NEK2 and PTTG1. CEP55 is not only overexpressed in the GBC but also correlated with advanced TNM stage, differentiation grade and poorer survival. After CEP55 knockdown, the proliferation of GBC cells was inhibited with cell cycle arrest in G2/M phase and DNA damage. There was a marked increase in the apoptosis of GBC cells in the siCEP55 group. Besides, in vivo, CEP55 inhibition attenuated the growth and promoted apoptosis of GBC cells. Mechanically, the tumor suppressor effect of CEP55 knockdown is associated with dysregulation of the AKT and ERK signaling networks. Discussion These data not only demonstrate that CEP55 is identified as a potential independent predictor crucial to the diagnosis and prognosis of gallbladder cancer but also reveal the possibility for CEP55 to be used as a promising target in the treatment of GBC.
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Affiliation(s)
- Maotuan Huang
- Department of Hepatobiliary Surgery and Fujian Institute of Hepatobiliary Surgery, Fujian Medical University Union Hospital, Fujian Medical University, Fuzhou, China
- Fujian Medical University Cancer Center, Fujian Medical University, Fuzhou, China
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Tumor Microbiology, Department of Medical Microbiology, Fujian Medical University, Fuzhou, China
| | - Fuxiu Zhong
- Department of Hepatobiliary Surgery and Fujian Institute of Hepatobiliary Surgery, Fujian Medical University Union Hospital, Fujian Medical University, Fuzhou, China
- Fujian Medical University Cancer Center, Fujian Medical University, Fuzhou, China
- Department of Nursing, Fujian Medical University Union Hospital, Fujian Medical University, Fuzhou, China
| | - Mingyuan Chen
- Department of Hepatobiliary Surgery and Fujian Institute of Hepatobiliary Surgery, Fujian Medical University Union Hospital, Fujian Medical University, Fuzhou, China
- Fujian Medical University Cancer Center, Fujian Medical University, Fuzhou, China
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Tumor Microbiology, Department of Medical Microbiology, Fujian Medical University, Fuzhou, China
| | - Lingju Hong
- Department of Hepatobiliary Surgery and Fujian Institute of Hepatobiliary Surgery, Fujian Medical University Union Hospital, Fujian Medical University, Fuzhou, China
- Fujian Medical University Cancer Center, Fujian Medical University, Fuzhou, China
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Tumor Microbiology, Department of Medical Microbiology, Fujian Medical University, Fuzhou, China
| | - Weihong Chen
- Department of Hepatobiliary Surgery and Fujian Institute of Hepatobiliary Surgery, Fujian Medical University Union Hospital, Fujian Medical University, Fuzhou, China
- Fujian Medical University Cancer Center, Fujian Medical University, Fuzhou, China
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Tumor Microbiology, Department of Medical Microbiology, Fujian Medical University, Fuzhou, China
| | - Xiahenazi Abudukeremu
- Department of Hepatobiliary Surgery and Fujian Institute of Hepatobiliary Surgery, Fujian Medical University Union Hospital, Fujian Medical University, Fuzhou, China
- Fujian Medical University Cancer Center, Fujian Medical University, Fuzhou, China
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Tumor Microbiology, Department of Medical Microbiology, Fujian Medical University, Fuzhou, China
| | - Feifei She
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Tumor Microbiology, Department of Medical Microbiology, Fujian Medical University, Fuzhou, China
| | - Yanling Chen
- Department of Hepatobiliary Surgery and Fujian Institute of Hepatobiliary Surgery, Fujian Medical University Union Hospital, Fujian Medical University, Fuzhou, China
- Fujian Medical University Cancer Center, Fujian Medical University, Fuzhou, China
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Tumor Microbiology, Department of Medical Microbiology, Fujian Medical University, Fuzhou, China
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Ding H, Yu JH, Ge G, Ma YY, Wang JC, Zhang J, Liu J. RASAL2 Deficiency Attenuates Hepatic Steatosis by Promoting Hepatic VLDL Secretion via the AKT/TET1/MTTP Axis. J Clin Transl Hepatol 2023; 11:261-272. [PMID: 36643045 PMCID: PMC9817063 DOI: 10.14218/jcth.2022.00042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 05/03/2022] [Accepted: 05/10/2022] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND AND AIMS RAS protein activator like 2 (RASAL2) is a newly discovered metabolic regulator involved in energy homeostasis and adipogenesis. However, whether RASAL2 is involved in hepatic lipid metabolism remains undetermined. This study explored the function of RASAL2 and elucidated its potential mechanisms in nonalcoholic fatty liver disease (NAFLD). METHODS NAFLD models were established either by feeding mice a high-fat diet or by incubation of hepatocytes with 1 mM free fatty acids (oleic acid:palmitic acid=2:1). Pathological changes were observed by hematoxylin and eosin staining. Lipid accumulation was assessed by Oil Red O staining, BODIPY493/503 staining, and triglyceride quantification. The in vivo secretion rate of very low-density lipoprotein was determined by intravenous injection of tyloxapol. Gene regulation was analyzed by chromatin immunoprecipitation assays and hydroxymethylated DNA immunoprecipitation combined with real-time polymerase chain reaction. RESULTS RASAL2 deficiency ameliorated hepatic steatosis both in vivo and in vitro. Mechanistically, RASAL2 deficiency upregulated hepatic TET1 expression by activating the AKT signaling pathway and thereby promoted MTTP expression by DNA hydroxymethylation, leading to increased production and secretion of very low-density lipoprotein, which is the major carrier of triglycerides exported from the liver to distal tissues. CONCLUSIONS Our study reports the first evidence that RASAL2 deficiency ameliorates hepatic steatosis by regulating lipid metabolism through the AKT/TET1/MTTP axis. These findings will help understand the pathogenesis of NAFLD and highlight the potency of RASAL2 as a new molecular target for NAFLD.
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Affiliation(s)
- Hao Ding
- Department of Digestive Diseases, Huashan Hospital, Fudan University, Shanghai, China
| | - Jiang-Hong Yu
- Department of Digestive Diseases, Huashan Hospital, Fudan University, Shanghai, China
- Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Ge Ge
- Department of Dermatology, The Seventh Medical Center of PLA General Hospital, Beijing, China
| | - Yan-Yun Ma
- Human Phenome Institute, Fudan University, Shanghai, China
- Ministry of Education Key Laboratory of Contemporary Anthropology and State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
- Six-sector Industrial Research Institute, Fudan University, Shanghai, China
| | - Jiu-Cun Wang
- Human Phenome Institute, Fudan University, Shanghai, China
| | - Jun Zhang
- Department of Digestive Diseases, Huashan Hospital, Fudan University, Shanghai, China
| | - Jie Liu
- Department of Digestive Diseases, Huashan Hospital, Fudan University, Shanghai, China
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11
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Pan-cancer clinical impact of latent drivers from double mutations. Commun Biol 2023; 6:202. [PMID: 36808143 PMCID: PMC9941481 DOI: 10.1038/s42003-023-04519-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 01/23/2023] [Indexed: 02/22/2023] Open
Abstract
Here, we discover potential 'latent driver' mutations in cancer genomes. Latent drivers have low frequencies and minor observable translational potential. As such, to date they have escaped identification. Their discovery is important, since when paired in cis, latent driver mutations can drive cancer. Our comprehensive statistical analysis of the pan-cancer mutation profiles of ~60,000 tumor sequences from the TCGA and AACR-GENIE cohorts identifies significantly co-occurring potential latent drivers. We observe 155 same gene double mutations of which 140 individual components are cataloged as latent drivers. Evaluation of cell lines and patient-derived xenograft response data to drug treatment indicate that in certain genes double mutations may have a prominent role in increasing oncogenic activity, hence obtaining a better drug response, as in PIK3CA. Taken together, our comprehensive analyses indicate that same-gene double mutations are exceedingly rare phenomena but are a signature for some cancer types, e.g., breast, and lung cancers. The relative rarity of doublets can be explained by the likelihood of strong signals resulting in oncogene-induced senescence, and by doublets consisting of non-identical single residue components populating the background mutational load, thus not identified.
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12
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Khan T, Raza S. Exploration of Computational Aids for Effective Drug Designing and Management of Viral Diseases: A Comprehensive Review. Curr Top Med Chem 2023; 23:1640-1663. [PMID: 36725827 DOI: 10.2174/1568026623666230201144522] [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: 06/21/2022] [Revised: 11/14/2022] [Accepted: 12/19/2022] [Indexed: 02/03/2023]
Abstract
BACKGROUND Microbial diseases, specifically originating from viruses are the major cause of human mortality all over the world. The current COVID-19 pandemic is a case in point, where the dynamics of the viral-human interactions are still not completely understood, making its treatment a case of trial and error. Scientists are struggling to devise a strategy to contain the pandemic for over a year and this brings to light the lack of understanding of how the virus grows and multiplies in the human body. METHODS This paper presents the perspective of the authors on the applicability of computational tools for deep learning and understanding of host-microbe interaction, disease progression and management, drug resistance and immune modulation through in silico methodologies which can aid in effective and selective drug development. The paper has summarized advances in the last five years. The studies published and indexed in leading databases have been included in the review. RESULTS Computational systems biology works on an interface of biology and mathematics and intends to unravel the complex mechanisms between the biological systems and the inter and intra species dynamics using computational tools, and high-throughput technologies developed on algorithms, networks and complex connections to simulate cellular biological processes. CONCLUSION Computational strategies and modelling integrate and prioritize microbial-host interactions and may predict the conditions in which the fine-tuning attenuates. These microbial-host interactions and working mechanisms are important from the aspect of effective drug designing and fine- tuning the therapeutic interventions.
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Affiliation(s)
- Tahmeena Khan
- Department of Chemistry, Integral University, Lucknow, 226026, U.P., India
| | - Saman Raza
- Department of Chemistry, Isabella Thoburn College, Lucknow, 226007, U.P., India
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13
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Pinet S, Durand S, Perani A, Darnaud L, Amadjikpe F, Yon M, Darbas T, Vergnenegre A, Egenod T, Simonneau Y, Le Brun-Ly V, Pestre J, Venat L, Thuillier F, Chaunavel A, Duchesne M, Fermeaux V, Guyot A, Lacorre S, Bessette B, Lalloué F, Durand K, Deluche E. Clinical management of molecular alterations identified by high throughput sequencing in patients with advanced solid tumors in treatment failure: Real-world data from a French hospital. Front Oncol 2023; 13:1104659. [PMID: 36923436 PMCID: PMC10009270 DOI: 10.3389/fonc.2023.1104659] [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: 11/21/2022] [Accepted: 02/07/2023] [Indexed: 03/03/2023] Open
Abstract
Background In the context of personalized medicine, screening patients to identify targetable molecular alterations is essential for therapeutic decisions such as inclusion in clinical trials, early access to therapies, or compassionate treatment. The objective of this study was to determine the real-world impact of routine incorporation of FoundationOne analysis in cancers with a poor prognosis and limited treatment options, or in those progressing after at least one course of standard therapy. Methods A FoundationOneCDx panel for solid tumor or liquid biopsy samples was offered to 204 eligible patients. Results Samples from 150 patients were processed for genomic testing, with a data acquisition success rate of 93%. The analysis identified 2419 gene alterations, with a median of 11 alterations per tumor (range, 0-86). The most common or likely pathogenic variants were on TP53, TERT, PI3KCA, CDKN2A/B, KRAS, CCDN1, FGF19, FGF3, and SMAD4. The median tumor mutation burden was three mutations/Mb (range, 0-117) in 143 patients with available data. Of 150 patients with known or likely pathogenic actionable alterations, 13 (8.6%) received matched targeted therapy. Sixty-nine patients underwent Molecular Tumor Board, which resulted in recommendations in 60 cases. Treatment with genotype-directed therapy had no impact on overall survival (13 months vs. 14 months; p = 0.95; hazard ratio = 1.04 (95% confidence interval, 0.48-2.26)]. Conclusions This study highlights that an organized center with a Multidisciplinary Molecular Tumor Board and an NGS screening system can obtain satisfactory results comparable with those of large centers for including patients in clinical trials.
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Affiliation(s)
- Sandra Pinet
- Medical Oncology Department, Dupuytren University Hospital, Limoges, France
| | - Stéphanie Durand
- The National Institute for Health and Medical Research (INSERM) U1308 - CAPTuR "Control Of Cell Activation, Tumor Progression and Therapeutic Resistance", Faculty of Medicine, University of Limoges, Limoges, France
| | - Alexandre Perani
- Cytogenetic, Medical Genetic and Reproductive Biology, Dupuytren University Hospital, Limoges, France
| | - Léa Darnaud
- Department of Pathology, Dupuytren University Hospital, Limoges, France
| | - Fifame Amadjikpe
- Department of Pathology, Dupuytren University Hospital, Limoges, France
| | - Mathieu Yon
- Department of Pathology, Dupuytren University Hospital, Limoges, France
| | - Tiffany Darbas
- Medical Oncology Department, Dupuytren University Hospital, Limoges, France
| | | | - Thomas Egenod
- Chest Department, Dupuytren University Hospital, Limoges, France
| | | | - Valérie Le Brun-Ly
- Medical Oncology Department, Dupuytren University Hospital, Limoges, France
| | - Julia Pestre
- Medical Oncology Department, Dupuytren University Hospital, Limoges, France
| | - Laurence Venat
- Medical Oncology Department, Dupuytren University Hospital, Limoges, France
| | - Frédéric Thuillier
- Medical Oncology Department, Dupuytren University Hospital, Limoges, France
| | - Alain Chaunavel
- The National Institute for Health and Medical Research (INSERM) U1308 - CAPTuR "Control Of Cell Activation, Tumor Progression and Therapeutic Resistance", Faculty of Medicine, University of Limoges, Limoges, France.,Department of Pathology, Dupuytren University Hospital, Limoges, France
| | - Mathilde Duchesne
- Department of Pathology, Dupuytren University Hospital, Limoges, France.,Research Unit (UR) 20218 - NEURIT "Neuropathies et Innovations Thérapeutiques", Faculty of Medicine, University of Limoges, Limoges, France
| | | | - Anne Guyot
- Department of Pathology, Dupuytren University Hospital, Limoges, France
| | - Sylvain Lacorre
- Department of Pathology, Dupuytren University Hospital, Limoges, France
| | - Barbara Bessette
- The National Institute for Health and Medical Research (INSERM) U1308 - CAPTuR "Control Of Cell Activation, Tumor Progression and Therapeutic Resistance", Faculty of Medicine, University of Limoges, Limoges, France
| | - Fabrice Lalloué
- The National Institute for Health and Medical Research (INSERM) U1308 - CAPTuR "Control Of Cell Activation, Tumor Progression and Therapeutic Resistance", Faculty of Medicine, University of Limoges, Limoges, France
| | - Karine Durand
- The National Institute for Health and Medical Research (INSERM) U1308 - CAPTuR "Control Of Cell Activation, Tumor Progression and Therapeutic Resistance", Faculty of Medicine, University of Limoges, Limoges, France.,Department of Pathology, Dupuytren University Hospital, Limoges, France
| | - Elise Deluche
- Medical Oncology Department, Dupuytren University Hospital, Limoges, France.,The National Institute for Health and Medical Research (INSERM) U1308 - CAPTuR "Control Of Cell Activation, Tumor Progression and Therapeutic Resistance", Faculty of Medicine, University of Limoges, Limoges, France
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14
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Nussinov R, Tsai CJ, Jang H. A New View of Activating Mutations in Cancer. Cancer Res 2022; 82:4114-4123. [PMID: 36069825 PMCID: PMC9664134 DOI: 10.1158/0008-5472.can-22-2125] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/16/2022] [Accepted: 09/01/2022] [Indexed: 12/14/2022]
Abstract
A vast effort has been invested in the identification of driver mutations of cancer. However, recent studies and observations call into question whether the activating mutations or the signal strength are the major determinant of tumor development. The data argue that signal strength determines cell fate, not the mutation that initiated it. In addition to activating mutations, factors that can impact signaling strength include (i) homeostatic mechanisms that can block or enhance the signal, (ii) the types and locations of additional mutations, and (iii) the expression levels of specific isoforms of genes and regulators of proteins in the pathway. Because signal levels are largely decided by chromatin structure, they vary across cell types, states, and time windows. A strong activating mutation can be restricted by low expression, whereas a weaker mutation can be strengthened by high expression. Strong signals can be associated with cell proliferation, but too strong a signal may result in oncogene-induced senescence. Beyond cancer, moderate signal strength in embryonic neural cells may be associated with neurodevelopmental disorders, and moderate signals in aging may be associated with neurodegenerative diseases, like Alzheimer's disease. The challenge for improving patient outcomes therefore lies in determining signaling thresholds and predicting signal strength.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, NCI, Frederick, Maryland
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Chung-Jung Tsai
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, NCI, Frederick, Maryland
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, NCI, Frederick, Maryland
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15
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Nussinov R, Zhang M, Liu Y, Jang H. AlphaFold, Artificial Intelligence (AI), and Allostery. J Phys Chem B 2022; 126:6372-6383. [PMID: 35976160 PMCID: PMC9442638 DOI: 10.1021/acs.jpcb.2c04346] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 08/03/2022] [Indexed: 02/08/2023]
Abstract
AlphaFold has burst into our lives. A powerful algorithm that underscores the strength of biological sequence data and artificial intelligence (AI). AlphaFold has appended projects and research directions. The database it has been creating promises an untold number of applications with vast potential impacts that are still difficult to surmise. AI approaches can revolutionize personalized treatments and usher in better-informed clinical trials. They promise to make giant leaps toward reshaping and revamping drug discovery strategies, selecting and prioritizing combinations of drug targets. Here, we briefly overview AI in structural biology, including in molecular dynamics simulations and prediction of microbiota-human protein-protein interactions. We highlight the advancements accomplished by the deep-learning-powered AlphaFold in protein structure prediction and their powerful impact on the life sciences. At the same time, AlphaFold does not resolve the decades-long protein folding challenge, nor does it identify the folding pathways. The models that AlphaFold provides do not capture conformational mechanisms like frustration and allostery, which are rooted in ensembles, and controlled by their dynamic distributions. Allostery and signaling are properties of populations. AlphaFold also does not generate ensembles of intrinsically disordered proteins and regions, instead describing them by their low structural probabilities. Since AlphaFold generates single ranked structures, rather than conformational ensembles, it cannot elucidate the mechanisms of allosteric activating driver hotspot mutations nor of allosteric drug resistance. However, by capturing key features, deep learning techniques can use the single predicted conformation as the basis for generating a diverse ensemble.
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Affiliation(s)
- Ruth Nussinov
- Computational
Structural Biology Section, Frederick National
Laboratory for Cancer Research, Frederick, Maryland 21702, United States
- Department
of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Mingzhen Zhang
- Computational
Structural Biology Section, Frederick National
Laboratory for Cancer Research, Frederick, Maryland 21702, United States
| | - Yonglan Liu
- Cancer
Innovation Laboratory, National Cancer Institute, Frederick, Maryland 21702, United States
| | - Hyunbum Jang
- Computational
Structural Biology Section, Frederick National
Laboratory for Cancer Research, Frederick, Maryland 21702, United States
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16
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Berezovsky IN, Nussinov R. Multiscale Allostery: Basic Mechanisms and Versatility in Diagnostics and Drug Design. J Mol Biol 2022; 434:167751. [PMID: 35863488 DOI: 10.1016/j.jmb.2022.167751] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Affiliation(s)
- Igor N Berezovsky
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, Singapore 138671, Singapore; Department of Biological Sciences (DBS), National University of Singapore (NUS), 8 Medical Drive, 117579, Singapore.
| | - Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboraory, National Cancer Institute, Frederick, MD 21702, USA; Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
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17
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Crawford J, Christensen BC, Chikina M, Greene CS. Widespread redundancy in -omics profiles of cancer mutation states. Genome Biol 2022; 23:137. [PMID: 35761387 PMCID: PMC9238138 DOI: 10.1186/s13059-022-02705-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 06/14/2022] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND In studies of cellular function in cancer, researchers are increasingly able to choose from many -omics assays as functional readouts. Choosing the correct readout for a given study can be difficult, and which layer of cellular function is most suitable to capture the relevant signal remains unclear. RESULTS We consider prediction of cancer mutation status (presence or absence) from functional -omics data as a representative problem that presents an opportunity to quantify and compare the ability of different -omics readouts to capture signals of dysregulation in cancer. From the TCGA Pan-Cancer Atlas that contains genetic alteration data, we focus on RNA sequencing, DNA methylation arrays, reverse phase protein arrays (RPPA), microRNA, and somatic mutational signatures as -omics readouts. Across a collection of genes recurrently mutated in cancer, RNA sequencing tends to be the most effective predictor of mutation state. We find that one or more other data types for many of the genes are approximately equally effective predictors. Performance is more variable between mutations than that between data types for the same mutation, and there is little difference between the top data types. We also find that combining data types into a single multi-omics model provides little or no improvement in predictive ability over the best individual data type. CONCLUSIONS Based on our results, for the design of studies focused on the functional outcomes of cancer mutations, there are often multiple -omics types that can serve as effective readouts, although gene expression seems to be a reasonable default option.
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Affiliation(s)
- Jake Crawford
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Brock C Christensen
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA
- Department of Molecular and Systems Biology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA
| | - Maria Chikina
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Casey S Greene
- Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora, CO, USA.
- Center for Health AI, University of Colorado School of Medicine, Aurora, CO, USA.
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18
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Basseville A, Violet PC, Safari M, Sourbier C, Linehan WM, Robey RW, Levine M, Sackett DL, Bates SE. A Histone Deacetylase Inhibitor Induces Acetyl-CoA Depletion Leading to Lethal Metabolic Stress in RAS-Pathway Activated Cells. Cancers (Basel) 2022; 14:2643. [PMID: 35681624 PMCID: PMC9179484 DOI: 10.3390/cancers14112643] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 05/17/2022] [Accepted: 05/18/2022] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND The mechanism of action of romidepsin and other histone deacetylase inhibitors is still not fully explained. Our goal was to gain a mechanistic understanding of the RAS-linked phenotype associated with romidepsin sensitivity. METHODS The NCI60 dataset was screened for molecular clues to romidepsin sensitivity. Histone acetylation, DNA damage, ROS production, metabolic state (real-time measurement and metabolomics), and gene expression alterations (transcriptomics) were determined in KRAS-WT versus KRAS-mutant cell groups. The search for biomarkers in response to HDACi was implemented by supervised machine learning analysis on a 608-cell transcriptomic dataset and validated in a clinical dataset. RESULTS Romidepsin treatment induced depletion in acetyl-CoA in all tested cell lines, which led to oxidative stress, metabolic stress, and increased death-particularly in KRAS-mutant cell lines. Romidepsin-induced stresses and death were rescued by acetyl-CoA replenishment. Two acetyl-CoA gene expression signatures associated with HDACi sensitivity were derived from machine learning analysis in the CCLE (Cancer Cell Line Encyclopedia) cell panel. Signatures were then validated in the training cohort for seven HDACi, and in an independent 13-patient cohort treated with belinostat. CONCLUSIONS Our study reveals the importance of acetyl-CoA metabolism in HDAC sensitivity, and it highlights acetyl-CoA generation pathways as potential targets to combine with HDACi.
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Affiliation(s)
- Agnes Basseville
- Developmental Therapeutics Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA;
- Omics Data Science Unit, Institut de Cancérologie de l’Ouest, 49055 Angers, France
| | - Pierre-Christian Violet
- Molecular and Clinical Nutrition Section, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892, USA; (P.-C.V.); (M.L.)
| | - Maryam Safari
- Division of Hematology/Oncology, Department of Medicine, Columbia University Medical Center, New York, NY 10032, USA;
| | - Carole Sourbier
- Urology Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA; (C.S.); (W.M.L.)
- Office of Biotechnology Products, Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD 20903, USA
| | - W. Marston Linehan
- Urology Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA; (C.S.); (W.M.L.)
| | - Robert W. Robey
- Developmental Therapeutics Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA;
- Laboratory of Cell Biology, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Mark Levine
- Molecular and Clinical Nutrition Section, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892, USA; (P.-C.V.); (M.L.)
| | - Dan L. Sackett
- Division of Basic and Translational Biophysics, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892, USA;
| | - Susan E. Bates
- Developmental Therapeutics Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA;
- Division of Hematology/Oncology, Department of Medicine, Columbia University Medical Center, New York, NY 10032, USA;
- Hematology/Oncology Research Department, James J. Peters Department of Veterans Affairs Medical Center, New York, NY 10468, USA
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19
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Ragusa F, Ferrari SM, Elia G, Paparo SR, Balestri E, Botrini C, Patrizio A, Mazzi V, Guglielmi G, Foddis R, Spinelli C, Ulisse S, Antonelli A, Fallahi P. Combination Strategies Involving Immune Checkpoint Inhibitors and Tyrosine Kinase or BRAF Inhibitors in Aggressive Thyroid Cancer. Int J Mol Sci 2022; 23:ijms23105731. [PMID: 35628540 PMCID: PMC9144613 DOI: 10.3390/ijms23105731] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 05/10/2022] [Accepted: 05/18/2022] [Indexed: 02/01/2023] Open
Abstract
Thyroid cancer is the most common (~90%) type of endocrine-system tumor, accounting for 70% of the deaths from endocrine cancers. In the last years, the high-throughput genomics has been able to identify pathways/molecular targets involved in survival and tumor progression. Targeted therapy and immunotherapy individually have many limitations. Regarding the first one, although it greatly reduces the size of the cancer, clinical responses are generally transient and often lead to cancer relapse after initial treatment. For the second one, although it induces longer-lasting responses in cancer patients than targeted therapy, its response rate is lower. The individual limitations of these two different types of therapies can be overcome by combining them. Here, we discuss MAPK pathway inhibitors, i.e., BRAF and MEK inhibitors, combined with checkpoint inhibitors targeting PD-1, PD-L1, and CTLA-4. Several mutations make tumors resistant to treatments. Therefore, more studies are needed to investigate the patient's individual tumor mutation burden in order to overcome the problem of resistance to therapy and to develop new combination therapies.
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Affiliation(s)
- Francesca Ragusa
- Department of Surgical, Medical and Molecular Pathology and Critical Area, University of Pisa, 56126 Pisa, Italy; (F.R.); (G.E.); (S.R.P.); (E.B.); (C.B.); (V.M.); (C.S.)
| | - Silvia Martina Ferrari
- Department of Clinical and Experimental Medicine, University of Pisa, 56126 Pisa, Italy;
| | - Giusy Elia
- Department of Surgical, Medical and Molecular Pathology and Critical Area, University of Pisa, 56126 Pisa, Italy; (F.R.); (G.E.); (S.R.P.); (E.B.); (C.B.); (V.M.); (C.S.)
| | - Sabrina Rosaria Paparo
- Department of Surgical, Medical and Molecular Pathology and Critical Area, University of Pisa, 56126 Pisa, Italy; (F.R.); (G.E.); (S.R.P.); (E.B.); (C.B.); (V.M.); (C.S.)
| | - Eugenia Balestri
- Department of Surgical, Medical and Molecular Pathology and Critical Area, University of Pisa, 56126 Pisa, Italy; (F.R.); (G.E.); (S.R.P.); (E.B.); (C.B.); (V.M.); (C.S.)
| | - Chiara Botrini
- Department of Surgical, Medical and Molecular Pathology and Critical Area, University of Pisa, 56126 Pisa, Italy; (F.R.); (G.E.); (S.R.P.); (E.B.); (C.B.); (V.M.); (C.S.)
| | - Armando Patrizio
- Department of Emergency Medicine, Azienda Ospedaliero-Universitaria Pisana, 56124 Pisa, Italy;
| | - Valeria Mazzi
- Department of Surgical, Medical and Molecular Pathology and Critical Area, University of Pisa, 56126 Pisa, Italy; (F.R.); (G.E.); (S.R.P.); (E.B.); (C.B.); (V.M.); (C.S.)
| | - Giovanni Guglielmi
- U.O. Medicina Preventiva Del Lavoro, Azienda Ospedaliero-Universitaria Pisana, 56124 Pisa, Italy;
| | - Rudy Foddis
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy; (R.F.); (P.F.)
| | - Claudio Spinelli
- Department of Surgical, Medical and Molecular Pathology and Critical Area, University of Pisa, 56126 Pisa, Italy; (F.R.); (G.E.); (S.R.P.); (E.B.); (C.B.); (V.M.); (C.S.)
| | - Salvatore Ulisse
- Department of Surgical Sciences, ‘Sapienza’ University of Rome, 00161 Rome, Italy;
| | - Alessandro Antonelli
- Department of Surgical, Medical and Molecular Pathology and Critical Area, University of Pisa, 56126 Pisa, Italy; (F.R.); (G.E.); (S.R.P.); (E.B.); (C.B.); (V.M.); (C.S.)
- Correspondence: ; Tel.: +39-050-992318
| | - Poupak Fallahi
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy; (R.F.); (P.F.)
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20
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Shiihara M, Furukawa T. Application of Patient-Derived Cancer Organoids to Personalized Medicine. J Pers Med 2022; 12:jpm12050789. [PMID: 35629212 PMCID: PMC9146789 DOI: 10.3390/jpm12050789] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 05/12/2022] [Accepted: 05/12/2022] [Indexed: 01/27/2023] Open
Abstract
Cell models are indispensable for the research and development of cancer therapies. Cancer medications have evolved with the establishment of various cell models. Patient-derived cell lines are very useful for identifying characteristic phenotypes and susceptibilities to anticancer drugs as well as molecularly targeted therapies for tumors. However, conventional 2-dimensional (2D) cell cultures have several drawbacks in terms of engraftment rate and phenotypic changes during culture. The organoid is a recently developed in vitro model with cultured cells that form a three-dimensional structure in the extracellular matrix. Organoids have the capacity to self-renew and can organize themselves to resemble the original organ or tumor in terms of both structure and function. Patient-derived cancer organoids are more suitable for the investigation of cancer biology and clinical medicine than conventional 2D cell lines or patient-derived xenografts. With recent advances in genetic analysis technology, the genetic information of various tumors has been clarified, and personalized medicine based on genetic information has become clinically available. Here, we have reviewed the recent advances in the development and application of patient-derived cancer organoids in cancer biology studies and personalized medicine. We have focused on the potential of organoids as a platform for the identification and development of novel targeted medicines for pancreatobiliary cancer, which is the most intractable cancer.
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Affiliation(s)
| | - Toru Furukawa
- Correspondence: ; Tel.: +81-22-717-8149; Fax: +81-22-717-8053
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21
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Abstract
Three-dimensional protein structural data at the molecular level are pivotal for successful precision medicine. Such data are crucial not only for discovering drugs that act to block the active site of the target mutant protein but also for clarifying to the patient and the clinician how the mutations harbored by the patient work. The relative paucity of structural data reflects their cost, challenges in their interpretation, and lack of clinical guidelines for their utilization. Rapid technological advancements in experimental high-resolution structural determination increasingly generate structures. Computationally, modeling algorithms, including molecular dynamics simulations, are becoming more powerful, as are compute-intensive hardware, particularly graphics processing units, overlapping with the inception of the exascale era. Accessible, freely available, and detailed structural and dynamical data can be merged with big data to powerfully transform personalized pharmacology. Here we review protein and emerging genome high-resolution data, along with means, applications, and examples underscoring their usefulness in precision medicine. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, Maryland, USA; .,Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, Maryland, USA;
| | - Guy Nir
- Department of Biochemistry and Molecular Biology, Department of Neuroscience, Cell Biology and Anatomy, and Sealy Center for Structural Biology and Molecular Biophysics, University of Texas Medical Branch, Galveston, Texas, USA
| | - Chung-Jung Tsai
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, Maryland, USA;
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA.,Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, USA.,Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
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22
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Zhang X, Qian Z, Wang Y, Zhang Q, Yu K, Zheng Y, Liu Z, Zhao Q, Liu ZX. DrugCVar: a platform for evidence-based drug annotation for genetic variants in cancer. Bioinformatics 2022; 38:3094-3098. [DOI: 10.1093/bioinformatics/btac273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 03/18/2022] [Accepted: 04/11/2022] [Indexed: 11/12/2022] Open
Abstract
Abstract
Motivation
Targeted therapy for cancer-related genetic variants is critical for precision medicine. Although several databases including The Clinical Interpretation of Variants in Cancer (CIViC), The Oncology Knowledge Base (OncoKB), The Cancer Genome Interpreter (CGI), My Cancer Genome (MCG) provide clinical interpretations of variants in cancer, the clinical evidence was limited and miscellaneous. In this study, we developed the DrugCVar database, which integrated our manually curated cancer variant-drug targeting evidence from literature and the interpretations from the public resources.
Results
In total, 7,830 clinical evidences for cancer variant-drug targeting were integrated and classified into ten evidence tiers. Searching and browsing functions were provided for quick queries of cancer variant-drug targeting evidence. Also, batch annotation module was developed for user-provided massive genetic variants in various formats. Details such as the mutation function, location of the variants in gene and protein structures, and mutation statistics of queried genes in various tumor types were also provided for further investigations. Thus, DrugCVar could serve as a comprehensive annotation tool to interpret potential drugs for cancer variants especially the massive ones from clinical cancer genomics studies.
Availability and implementation
The database is available at http://drugcvar.omicsbio.info.
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xiaolong Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China
| | - Zhikai Qian
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China
| | - Ye Wang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China
| | - Qingfeng Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China
| | - Kai Yu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China
| | - Yongqiang Zheng
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China
| | - Zekun Liu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China
| | - Qi Zhao
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China
| | - Ze-Xian Liu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China
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23
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Chen D, Zhang X, Zhu L, Liu C, Li Z. All on size-coded single bead set: a modular enrich-amplify-amplify strategy for attomolar level multi-immunoassay. Chem Sci 2022; 13:3501-3506. [PMID: 35432875 PMCID: PMC8943839 DOI: 10.1039/d1sc07048g] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Accepted: 02/17/2022] [Indexed: 12/04/2022] Open
Abstract
Ultrasensitive protein analysis is of great significance for early diagnosis and biological studies. The core challenge is that many critical protein markers at extremely low aM to fM levels are difficult to accurately quantify because the target-induced weak signal may be easily masked by the surrounding background. Hence, we propose herein an ultrasensitive immunoassay based on a modular Single Bead Enrich-Amplify-Amplify (SBEAA) strategy. The highly efficient enrichment of targets on only a single bead (enrich) could confine the target-responsive signal output within a limited tiny space. Furthermore, a cascade tyramide signal amplification design enables remarkable in situ signal enhancement just affixed to the target. As a result, the efficient but space-confined fluorescence deposition on a single bead will significantly exceed the background and provide a wide dynamic range. Importantly, the SBEAA system can be modularly combined to meet different levels of clinical need regarding the detection sensitivity from aM to nM. Finally, a size-coded SBEAA set (SC-SBEAA) is also designed that allows ultrasensitive multi-immunoassay for rare samples in a single tube.
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Affiliation(s)
- Desheng Chen
- Key Laboratory of Applied Surface and Colloid Chemistry, Ministry of Education, Key Laboratory of Analytical Chemistry for Life Science of Shaanxi Province, School of Chemistry and Chemical Engineering, Shaanxi Normal University Xi'an 710119 Shaanxi Province P. R. China
- Beijing Key Laboratory for Bioengineering and Sensing Technology, School of Chemistry and Biological Engineering, University of Science and Technology Beijing 30 Xueyuan Road, Haidian District Beijing 100083 P. R. China
| | - Xiaobo Zhang
- Key Laboratory of Applied Surface and Colloid Chemistry, Ministry of Education, Key Laboratory of Analytical Chemistry for Life Science of Shaanxi Province, School of Chemistry and Chemical Engineering, Shaanxi Normal University Xi'an 710119 Shaanxi Province P. R. China
| | - Liping Zhu
- Key Laboratory of Applied Surface and Colloid Chemistry, Ministry of Education, Key Laboratory of Analytical Chemistry for Life Science of Shaanxi Province, School of Chemistry and Chemical Engineering, Shaanxi Normal University Xi'an 710119 Shaanxi Province P. R. China
| | - Chenghui Liu
- Key Laboratory of Applied Surface and Colloid Chemistry, Ministry of Education, Key Laboratory of Analytical Chemistry for Life Science of Shaanxi Province, School of Chemistry and Chemical Engineering, Shaanxi Normal University Xi'an 710119 Shaanxi Province P. R. China
| | - Zhengping Li
- Key Laboratory of Applied Surface and Colloid Chemistry, Ministry of Education, Key Laboratory of Analytical Chemistry for Life Science of Shaanxi Province, School of Chemistry and Chemical Engineering, Shaanxi Normal University Xi'an 710119 Shaanxi Province P. R. China
- Beijing Key Laboratory for Bioengineering and Sensing Technology, School of Chemistry and Biological Engineering, University of Science and Technology Beijing 30 Xueyuan Road, Haidian District Beijing 100083 P. R. China
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24
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Lu H, Martí J. Predicting the conformational variability of oncogenic GTP-bound G12D mutated KRas-4B proteins at zwitterionic model cell membranes. NANOSCALE 2022; 14:3148-3158. [PMID: 35142321 DOI: 10.1039/d1nr07622a] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
KRas proteins are the largest family of mutated Ras isoforms, participating in a wide variety of cancers. Due to their importance, large effort is being carried out on drug development by small-molecule inhibitors. However, understanding protein conformational variability remains a challenge in drug discovery. In the case of the Ras family, their multiple conformational states can affect the binding of potential drug inhibitors. To overcome this challenge, we propose a computational framework based on combined all-atom Molecular Dynamics and Metadynamics simulations in order to accurately access conformational variants of the target protein. We tested the methodology using a G12D mutated GTP bound oncogenic KRas-4B protein located at the interface of a DOPC/DOPS/cholesterol model anionic cell membrane. Two main orientations of KRas-4B at the anionic membrane have been determined. The corresponding torsional angles are taken as reliable reaction coordinates so that free-energy landscapes are obtained by well-tempered metadynamics simulations, revealing local and global minima of the free-energy hypersurface and unveiling reactive paths of the system between the two preferential orientations. We have observed that GTP-binding to KRas-4B has huge influence on the stabilisation of the protein and it can potentially help to open Switch I/II druggable pockets, lowering energy barriers between stable states and resulting in cumulative conformers of KRas-4B. This may highlight new opportunities for targeting the unique meta-stable states through the design of new efficient drugs.
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Affiliation(s)
- Huixia Lu
- School of Pharmacy, Shanghai Jiao Tong University, Shanghai, China.
| | - Jordi Martí
- Department of Physics, Polytechnical University of Catalonia-Barcelona Tech, B5-209 Northern Campus, Jordi Girona 1-3, 08034 Barcelona, Catalonia, Spain.
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25
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Jiao F, Guo R, Beckmann JS, Yan Z, Yang Y, Hu J, Wang X, Xie S. Great future or greedy venture: Precision medicine needs philosophy. Health Sci Rep 2021; 4:e376. [PMID: 34541334 PMCID: PMC8439431 DOI: 10.1002/hsr2.376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 08/06/2021] [Accepted: 08/16/2021] [Indexed: 11/07/2022] Open
Abstract
INTRODUCTION Over the past decade, we have witnessed the initiation and implementation of precision medicine (PM), a discipline that promises to individualize and personalize medical management and treatment, rendering them ultimately more precise and effective. Despite of the continuing advances and numerous clinical applications, the potential of PM remains highly controversial, sparking heated debates about its future. METHOD The present article reviews the philosophical issues and practical challenges that are critical to the feasibility and implementation of PM. OUTCOME The explanation and argument about the relations between PM and computability, uncertainty as well as complexity, show that key foundational assumptions of PM might not be fully validated. CONCLUSION The present analysis suggests that our current understanding of PM is probably oversimplified and too superficial. More efforts are needed to realize the hope that PM has elicited, rather than make the term just as a hype.
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Affiliation(s)
- Fei Jiao
- Department of Biochemistry and Molecular BiologyBinzhou Medical UniversityYantaiChina
| | - Ruoyu Guo
- Department of Biochemistry and Molecular BiologyBinzhou Medical UniversityYantaiChina
| | | | - Zhonghai Yan
- Department of Medicine, College of Physicians and SurgeonsColumbia UniversityNew YorkNew YorkUSA
| | - Yun Yang
- Department of Biochemistry and Molecular BiologyBinzhou Medical UniversityYantaiChina
| | - Jinxia Hu
- Department of Biochemistry and Molecular BiologyBinzhou Medical UniversityYantaiChina
| | - Xin Wang
- Department of Clinical Laboratory & Center of Health Service Training970 Hospital of the PLA Joint Logistic Support ForceYantaiChina
| | - Shuyang Xie
- Department of Biochemistry and Molecular BiologyBinzhou Medical UniversityYantaiChina
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26
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Othman H, da Rocha JEB, Hazelhurst S. Single Nucleotide Polymorphism Induces Divergent Dynamic Patterns in CYP3A5: A Microsecond Scale Biomolecular Simulation of Variants Identified in Sub-Saharan African Populations. Int J Mol Sci 2021; 22:7786. [PMID: 34360551 PMCID: PMC8346100 DOI: 10.3390/ijms22157786] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 06/19/2021] [Accepted: 06/23/2021] [Indexed: 11/17/2022] Open
Abstract
Pharmacogenomics aims to reveal variants associated with drug response phenotypes. Genes whose roles involve the absorption, distribution, metabolism, and excretion of drugs, are highly polymorphic between populations. High coverage whole genome sequencing showed that a large proportion of the variants for these genes are rare in African populations. This study investigated the impact of such variants on protein structure to assess their functional importance. We used genetic data of CYP3A5 from 458 individuals from sub-Saharan Africa to conduct a structural bioinformatics analysis. Five missense variants were modeled and microsecond scale molecular dynamics simulations were conducted for each, as well as for the CYP3A5 wildtype and the Y53C variant, which has a known deleterious impact on enzyme activity. The binding of ritonavir and artemether to CYP3A5 variant structures was also evaluated. Our results showed different conformational characteristics between all the variants. No significant structural changes were noticed. However, the genetic variability seemed to act on the plasticity of the protein. The impact on drug binding might be drug dependant. We concluded that rare variants hold relevance in determining the pharmacogenomics properties of populations. This could have a significant impact on precision medicine applications in sub-Saharan Africa.
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Affiliation(s)
- Houcemeddine Othman
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg 2001, South Africa; (J.E.B.d.R.); (S.H.)
| | - Jorge E. B. da Rocha
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg 2001, South Africa; (J.E.B.d.R.); (S.H.)
- Division of Human Genetics, National Health Laboratory Service, School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg 2001, South Africa
| | - Scott Hazelhurst
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg 2001, South Africa; (J.E.B.d.R.); (S.H.)
- School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg 2001, South Africa
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27
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González-Sánchez JC, Ibrahim MFR, Leist IC, Weise K, Russell R. Mechnetor: a web server for exploring protein mechanism and the functional context of genetic variants. Nucleic Acids Res 2021; 49:W366-W374. [PMID: 34076240 PMCID: PMC8262711 DOI: 10.1093/nar/gkab399] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 04/06/2021] [Accepted: 05/31/2021] [Indexed: 01/02/2023] Open
Abstract
Advances in DNA sequencing and proteomics mean that researchers must now regularly interrogate thousands of positional gene/protein changes in order to find those relevant for potential clinical application or biological insights. The abundance of already known information on protein interactions, mechanism, and tertiary structure provides the possible means to understand these changes rapidly, though a careful and systematic integration of these diverse datasets is first needed. For this purpose, we developed Mechnetor, a tool that allows users to quickly explore and visualize integrated mechanistic data for proteins or interactions of interest. Central to the system is a careful cataloguing of diverse sources of protein interaction mechanism, and an efficient means to visualize interactions between relevant and/or known protein regions. The result is a finer resolution interaction network that provides more immediate clues as to points of intervention or mechanistic understanding. Users can import protein, interactions, genetic variants or post-translational modifications and see these data in the best known mechanistic context. We demonstrate the tool with topical examples in human genetic diseases and cancer genomics. The tool is freely available at: mechnetor.russelllab.org.
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Affiliation(s)
- Juan Carlos González-Sánchez
- BioQuant, Heidelberg University, Heidelberg 69120, Germany
- Biochemistry Center (BZH), Heidelberg University, Heidelberg 69120, Germany
| | - Mustafa F R Ibrahim
- BioQuant, Heidelberg University, Heidelberg 69120, Germany
- Biochemistry Center (BZH), Heidelberg University, Heidelberg 69120, Germany
| | - Ivo C Leist
- BioQuant, Heidelberg University, Heidelberg 69120, Germany
- Biochemistry Center (BZH), Heidelberg University, Heidelberg 69120, Germany
| | - Kyle R Weise
- BioQuant, Heidelberg University, Heidelberg 69120, Germany
- Biochemistry Center (BZH), Heidelberg University, Heidelberg 69120, Germany
| | - Robert B Russell
- BioQuant, Heidelberg University, Heidelberg 69120, Germany
- Biochemistry Center (BZH), Heidelberg University, Heidelberg 69120, Germany
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28
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Gussow AB, Koonin EV, Auslander N. Identification of combinations of somatic mutations that predict cancer survival and immunotherapy benefit. NAR Cancer 2021; 3:zcab017. [PMID: 34027407 PMCID: PMC8127965 DOI: 10.1093/narcan/zcab017] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 04/18/2021] [Accepted: 04/28/2021] [Indexed: 11/14/2022] Open
Abstract
Cancer evolves through the accumulation of somatic mutations over time. Although several methods have been developed to characterize mutational processes in cancers, these have not been specifically designed to identify mutational patterns that predict patient prognosis. Here we present CLICnet, a method that utilizes mutational data to cluster patients by survival rate. CLICnet employs Restricted Boltzmann Machines, a type of generative neural network, which allows for the capture of complex mutational patterns associated with patient survival in different cancer types. For some cancer types, clustering produced by CLICnet also predicts benefit from anti-PD1 immune checkpoint blockade therapy, whereas for other cancer types, the mutational processes associated with survival are different from those associated with the improved anti-PD1 survival benefit. Thus, CLICnet has the ability to systematically identify and catalogue combinations of mutations that predict cancer survival, unveiling intricate associations between mutations, survival, and immunotherapy benefit.
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Affiliation(s)
- Ayal B Gussow
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | - Eugene V Koonin
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | - Noam Auslander
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
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29
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Baptiste M, Moinuddeen SS, Soliz CL, Ehsan H, Kaneko G. Making Sense of Genetic Information: The Promising Evolution of Clinical Stratification and Precision Oncology Using Machine Learning. Genes (Basel) 2021; 12:722. [PMID: 34065872 PMCID: PMC8151328 DOI: 10.3390/genes12050722] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 05/07/2021] [Accepted: 05/08/2021] [Indexed: 12/16/2022] Open
Abstract
Precision medicine is a medical approach to administer patients with a tailored dose of treatment by taking into consideration a person's variability in genes, environment, and lifestyles. The accumulation of omics big sequence data led to the development of various genetic databases on which clinical stratification of high-risk populations may be conducted. In addition, because cancers are generally caused by tumor-specific mutations, large-scale systematic identification of single nucleotide polymorphisms (SNPs) in various tumors has propelled significant progress of tailored treatments of tumors (i.e., precision oncology). Machine learning (ML), a subfield of artificial intelligence in which computers learn through experience, has a great potential to be used in precision oncology chiefly to help physicians make diagnostic decisions based on tumor images. A promising venue of ML in precision oncology is the integration of all available data from images to multi-omics big data for the holistic care of patients and high-risk healthy subjects. In this review, we provide a focused overview of precision oncology and ML with attention to breast cancer and glioma as well as the Bayesian networks that have the flexibility and the ability to work with incomplete information. We also introduce some state-of-the-art attempts to use and incorporate ML and genetic information in precision oncology.
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Affiliation(s)
| | | | | | | | - Gen Kaneko
- School of Arts & Sciences, University of Houston-Victoria, Victoria, TX 77901, USA; (M.B.); (S.S.M.); (C.L.S.); (H.E.)
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30
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Zhao W, Yang J, Wu J, Cai G, Zhang Y, Haltom J, Su W, Dong MJ, Chen S, Wu J, Zhou Z, Gu X. CanDriS: posterior profiling of cancer-driving sites based on two-component evolutionary model. Brief Bioinform 2021; 22:6238585. [PMID: 33876217 DOI: 10.1093/bib/bbab131] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 03/17/2021] [Accepted: 03/18/2021] [Indexed: 12/12/2022] Open
Abstract
Current cancer genomics databases have accumulated millions of somatic mutations that remain to be further explored. Due to the over-excess mutations unrelated to cancer, the great challenge is to identify somatic mutations that are cancer-driven. Under the notion that carcinogenesis is a form of somatic-cell evolution, we developed a two-component mixture model: while the ground component corresponds to passenger mutations, the rapidly evolving component corresponds to driver mutations. Then, we implemented an empirical Bayesian procedure to calculate the posterior probability of a site being cancer-driven. Based on these, we developed a software CanDriS (Cancer Driver Sites) to profile the potential cancer-driving sites for thousands of tumor samples from the Cancer Genome Atlas and International Cancer Genome Consortium across tumor types and pan-cancer level. As a result, we identified that approximately 1% of the sites have posterior probabilities larger than 0.90 and listed potential cancer-wide and cancer-specific driver mutations. By comprehensively profiling all potential cancer-driving sites, CanDriS greatly enhances our ability to refine our knowledge of the genetic basis of cancer and might guide clinical medication in the upcoming era of precision medicine. The results were displayed in a database CandrisDB (http://biopharm.zju.edu.cn/candrisdb/).
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Affiliation(s)
- Wenyi Zhao
- College of Pharmaceutical Sciences & College of Computer Science and Technology, Zhejiang University, China
| | - Jingwen Yang
- MOE Key Laboratory of Contemporary Anthropology, Human Phenome Institute, School of Life Sciences, Fudan University, China
| | - Jingcheng Wu
- College of Pharmaceutical Sciences, Zhejiang University, China
| | - Guoxing Cai
- College of Pharmaceutical Sciences, Zhejiang University, China
| | - Yao Zhang
- College of Pharmaceutical Sciences, Zhejiang University, China
| | - Jeffrey Haltom
- Department of Genetics, Development and Cell Biology, Iowa State University, Ames, 12 Iowa 50011, USA
| | - Weijia Su
- Department of Genetics, Development and Cell Biology, Iowa State University, Ames, 12 Iowa 50011, USA
| | - Michael J Dong
- Department of Genetics, Development and Cell Biology, Iowa State University, Ames, 12 Iowa 50011, USA
| | - Shuqing Chen
- College of Pharmaceutical Sciences, Zhejiang University, China
| | - Jian Wu
- College of Computer Science and Technology & School of Medicine, Zhejiang University, China
| | - Zhan Zhou
- College of Pharmaceutical Sciences, Innovation Institute for Artificial Intelligence in Medicine, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Zhejiang University, China
| | - Xun Gu
- Department of Genetics, Development and Cell Biology, Iowa State University, Ames, 12 Iowa 50011, USA
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31
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Tee WV, Tan ZW, Lee K, Guarnera E, Berezovsky IN. Exploring the Allosteric Territory of Protein Function. J Phys Chem B 2021; 125:3763-3780. [PMID: 33844527 DOI: 10.1021/acs.jpcb.1c00540] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
While the pervasiveness of allostery in proteins is commonly accepted, we further show the generic nature of allosteric mechanisms by analyzing here transmembrane ion-channel viroporin 3a and RNA-dependent RNA polymerase (RdRp) from SARS-CoV-2 along with metabolic enzymes isocitrate dehydrogenase 1 (IDH1) and fumarate hydratase (FH) implicated in cancers. Using the previously developed structure-based statistical mechanical model of allostery (SBSMMA), we share our experience in analyzing the allosteric signaling, predicting latent allosteric sites, inducing and tuning targeted allosteric response, and exploring the allosteric effects of mutations. This, yet incomplete list of phenomenology, forms a complex and unique allosteric territory of protein function, which should be thoroughly explored. We propose a generic computational framework, which not only allows one to obtain a comprehensive allosteric control over proteins but also provides an opportunity to approach the fragment-based design of allosteric effectors and drug candidates. The advantages of allosteric drugs over traditional orthosteric compounds, complemented by the emerging role of the allosteric effects of mutations in the expansion of the cancer mutational landscape and in the increased mutability of viral proteins, leave no choice besides further extensive studies of allosteric mechanisms and their biomedical implications.
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Affiliation(s)
- Wei-Ven Tee
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, 138671, Singapore.,Department of Biological Sciences (DBS), National University of Singapore (NUS), 8 Medical Drive, 117597, Singapore
| | - Zhen Wah Tan
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, 138671, Singapore
| | - Keene Lee
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, 138671, Singapore
| | - Enrico Guarnera
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, 138671, Singapore
| | - Igor N Berezovsky
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, 138671, Singapore.,Department of Biological Sciences (DBS), National University of Singapore (NUS), 8 Medical Drive, 117597, Singapore
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32
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Pham VVH, Liu L, Bracken C, Goodall G, Li J, Le TD. Computational methods for cancer driver discovery: A survey. Am J Cancer Res 2021; 11:5553-5568. [PMID: 33859763 PMCID: PMC8039954 DOI: 10.7150/thno.52670] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 01/20/2021] [Indexed: 12/21/2022] Open
Abstract
Identifying the genes responsible for driving cancer is of critical importance for directing treatment. Accordingly, multiple computational tools have been developed to facilitate this task. Due to the different methods employed by these tools, different data considered by the tools, and the rapidly evolving nature of the field, the selection of an appropriate tool for cancer driver discovery is not straightforward. This survey seeks to provide a comprehensive review of the different computational methods for discovering cancer drivers. We categorise the methods into three groups; methods for single driver identification, methods for driver module identification, and methods for identifying personalised cancer drivers. In addition to providing a “one-stop” reference of these methods, by evaluating and comparing their performance, we also provide readers the information about the different capabilities of the methods in identifying biologically significant cancer drivers. The biologically relevant information identified by these tools can be seen through the enrichment of discovered cancer drivers in GO biological processes and KEGG pathways and through our identification of a small cancer-driver cohort that is capable of stratifying patient survival.
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33
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Mészáros B, Hajdu-Soltész B, Zeke A, Dosztányi Z. Mutations of Intrinsically Disordered Protein Regions Can Drive Cancer but Lack Therapeutic Strategies. Biomolecules 2021; 11:biom11030381. [PMID: 33806614 PMCID: PMC8000335 DOI: 10.3390/biom11030381] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 02/22/2021] [Accepted: 02/24/2021] [Indexed: 12/22/2022] Open
Abstract
Many proteins contain intrinsically disordered regions (IDRs) which carry out important functions without relying on a single well-defined conformation. IDRs are increasingly recognized as critical elements of regulatory networks and have been also associated with cancer. However, it is unknown whether mutations targeting IDRs represent a distinct class of driver events associated with specific molecular and system-level properties, cancer types and treatment options. Here, we used an integrative computational approach to explore the direct role of intrinsically disordered protein regions driving cancer. We showed that around 20% of cancer drivers are primarily targeted through a disordered region. These IDRs can function in multiple ways which are distinct from the functional mechanisms of ordered drivers. Disordered drivers play a central role in context-dependent interaction networks and are enriched in specific biological processes such as transcription, gene expression regulation and protein degradation. Furthermore, their modulation represents an alternative mechanism for the emergence of all known cancer hallmarks. Importantly, in certain cancer patients, mutations of disordered drivers represent key driving events. However, treatment options for such patients are currently severely limited. The presented study highlights a largely overlooked class of cancer drivers associated with specific cancer types that need novel therapeutic options.
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Affiliation(s)
- Bálint Mészáros
- Department of Biochemistry, ELTE Eötvös Loránd University, H-1117 Budapest, Hungary; (B.M.); (B.H.-S.)
- EMBL Heidelberg, Meyerhofstraße 1, 69117 Heidelberg, Germany
| | - Borbála Hajdu-Soltész
- Department of Biochemistry, ELTE Eötvös Loránd University, H-1117 Budapest, Hungary; (B.M.); (B.H.-S.)
| | - András Zeke
- Institute of Enzymology, RCNS, P.O. Box 7, H-1518 Budapest, Hungary;
| | - Zsuzsanna Dosztányi
- Department of Biochemistry, ELTE Eötvös Loránd University, H-1117 Budapest, Hungary; (B.M.); (B.H.-S.)
- Correspondence: ; Tel.: +36-1-372 2500/8537
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34
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Cava C, Sabetian S, Castiglioni I. Patient-Specific Network for Personalized Breast Cancer Therapy with Multi-Omics Data. ENTROPY (BASEL, SWITZERLAND) 2021; 23:225. [PMID: 33670375 PMCID: PMC7918754 DOI: 10.3390/e23020225] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 02/04/2021] [Accepted: 02/09/2021] [Indexed: 01/06/2023]
Abstract
The development of new computational approaches that are able to design the correct personalized drugs is the crucial therapeutic issue in cancer research. However, tumor heterogeneity is the main obstacle to developing patient-specific single drugs or combinations of drugs that already exist in clinics. In this study, we developed a computational approach that integrates copy number alteration, gene expression, and a protein interaction network of 73 basal breast cancer samples. 2509 prognostic genes harboring a copy number alteration were identified using survival analysis, and a protein-protein interaction network considering the direct interactions was created. Each patient was described by a specific combination of seven altered hub proteins that fully characterize the 73 basal breast cancer patients. We suggested the optimal combination therapy for each patient considering drug-protein interactions. Our approach is able to confirm well-known cancer related genes and suggest novel potential drug target genes. In conclusion, we presented a new computational approach in breast cancer to deal with the intra-tumor heterogeneity towards personalized cancer therapy.
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Affiliation(s)
- Claudia Cava
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Via F.Cervi 93, Segrate, 20090 Milan, Italy
| | - Soudabeh Sabetian
- Infertility Research Center, Shiraz University of Medical Sciences, Shiraz, Iran;
| | - Isabella Castiglioni
- Department of Physics “Giuseppe Occhialini”, University of Milan-Bicocca Piazza dell’Ateneo Nuovo, 20126 Milan, Italy;
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Supplitt S, Karpinski P, Sasiadek M, Laczmanska I. Current Achievements and Applications of Transcriptomics in Personalized Cancer Medicine. Int J Mol Sci 2021; 22:1422. [PMID: 33572595 PMCID: PMC7866970 DOI: 10.3390/ijms22031422] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 01/19/2021] [Accepted: 01/21/2021] [Indexed: 12/12/2022] Open
Abstract
Over the last decades, transcriptome profiling emerged as one of the most powerful approaches in oncology, providing prognostic and predictive utility for cancer management. The development of novel technologies, such as revolutionary next-generation sequencing, enables the identification of cancer biomarkers, gene signatures, and their aberrant expression affecting oncogenesis, as well as the discovery of molecular targets for anticancer therapies. Transcriptomics contribute to a change in the holistic understanding of cancer, from histopathological and organic to molecular classifications, opening a more personalized perspective for tumor diagnostics and therapy. The further advancement on transcriptome profiling may allow standardization and cost reduction of its analysis, which will be the next step for transcriptomics to become a canon of contemporary cancer medicine.
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Affiliation(s)
- Stanislaw Supplitt
- Department of Genetics, Wroclaw Medical University, Marcinkowskiego 1, 50-368 Wroclaw, Poland; (P.K.); (M.S.); (I.L.)
| | - Pawel Karpinski
- Department of Genetics, Wroclaw Medical University, Marcinkowskiego 1, 50-368 Wroclaw, Poland; (P.K.); (M.S.); (I.L.)
- Laboratory of Genomics and Bioinformatics, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Weigla 12, 53-114 Wroclaw, Poland
| | - Maria Sasiadek
- Department of Genetics, Wroclaw Medical University, Marcinkowskiego 1, 50-368 Wroclaw, Poland; (P.K.); (M.S.); (I.L.)
| | - Izabela Laczmanska
- Department of Genetics, Wroclaw Medical University, Marcinkowskiego 1, 50-368 Wroclaw, Poland; (P.K.); (M.S.); (I.L.)
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Zhou Y, Zhao J, Fang J, Martin W, Li L, Nussinov R, Chan TA, Eng C, Cheng F. My personal mutanome: a computational genomic medicine platform for searching network perturbing alleles linking genotype to phenotype. Genome Biol 2021; 22:53. [PMID: 33514395 PMCID: PMC7845113 DOI: 10.1186/s13059-021-02269-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Accepted: 01/13/2021] [Indexed: 12/12/2022] Open
Abstract
Massive genome sequencing data have inspired new challenges in personalized treatments and facilitated oncological drug discovery. We present a comprehensive database, My Personal Mutanome (MPM), for accelerating the development of precision cancer medicine protocols. MPM contains 490,245 mutations from over 10,800 tumor exomes across 33 cancer types in The Cancer Genome Atlas mapped to 94,563 structure-resolved/predicted protein-protein interaction interfaces ("edgetic") and 311,022 functional sites ("nodetic"), including ligand-protein binding sites and 8 types of protein posttranslational modifications. In total, 8884 survival results and 1,271,132 drug responses are obtained for these mapped interactions. MPM is available at https://mutanome.lerner.ccf.org .
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Affiliation(s)
- Yadi Zhou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
| | - Junfei Zhao
- Department of Systems Biology, Herbert Irving Comprehensive Center, Columbia University, New York, NY, 10032, USA
- Department of Biomedical Informatics, Columbia University, New York, NY, 10032, USA
| | - Jiansong Fang
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
| | - William Martin
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
| | - Lang Li
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH, 43210, USA
| | - Ruth Nussinov
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, MD, 21702, USA
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, 69978, Tel Aviv, Israel
| | - Timothy A Chan
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
- Center for Immunotherapy and Precision Immuno-Oncology, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA
- Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
| | - Charis Eng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA
- Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
- Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA.
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, 44195, USA.
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA.
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Bányai L, Trexler M, Kerekes K, Csuka O, Patthy L. Use of signals of positive and negative selection to distinguish cancer genes and passenger genes. eLife 2021; 10:e59629. [PMID: 33427197 PMCID: PMC7877913 DOI: 10.7554/elife.59629] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 01/10/2021] [Indexed: 12/14/2022] Open
Abstract
A major goal of cancer genomics is to identify all genes that play critical roles in carcinogenesis. Most approaches focused on genes positively selected for mutations that drive carcinogenesis and neglected the role of negative selection. Some studies have actually concluded that negative selection has no role in cancer evolution. We have re-examined the role of negative selection in tumor evolution through the analysis of the patterns of somatic mutations affecting the coding sequences of human genes. Our analyses have confirmed that tumor suppressor genes are positively selected for inactivating mutations, oncogenes, however, were found to display signals of both negative selection for inactivating mutations and positive selection for activating mutations. Significantly, we have identified numerous human genes that show signs of strong negative selection during tumor evolution, suggesting that their functional integrity is essential for the growth and survival of tumor cells.
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Affiliation(s)
- László Bányai
- Institute of Enzymology, Research Centre for Natural SciencesBudapestHungary
| | - Maria Trexler
- Institute of Enzymology, Research Centre for Natural SciencesBudapestHungary
| | - Krisztina Kerekes
- Institute of Enzymology, Research Centre for Natural SciencesBudapestHungary
| | - Orsolya Csuka
- Department of Pathogenetics, National Institute of OncologyBudapestHungary
| | - László Patthy
- Institute of Enzymology, Research Centre for Natural SciencesBudapestHungary
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Nussinov R, Jang H, Nir G, Tsai CJ, Cheng F. A new precision medicine initiative at the dawn of exascale computing. Signal Transduct Target Ther 2021; 6:3. [PMID: 33402669 PMCID: PMC7785737 DOI: 10.1038/s41392-020-00420-3] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 10/27/2020] [Accepted: 10/30/2020] [Indexed: 12/14/2022] Open
Abstract
Which signaling pathway and protein to select to mitigate the patient's expected drug resistance? The number of possibilities facing the physician is massive, and the drug combination should fit the patient status. Here, we briefly review current approaches and data and map an innovative patient-specific strategy to forecast drug resistance targets that centers on parallel (or redundant) proliferation pathways in specialized cells. It considers the availability of each protein in each pathway in the specific cell, its activating mutations, and the chromatin accessibility of its encoding gene. The construction of the resulting Proliferation Pathway Network Atlas will harness the emerging exascale computing and advanced artificial intelligence (AI) methods for therapeutic development. Merging the resulting set of targets, pathways, and proteins, with current strategies will augment the choice for the attending physicians to thwart resistance.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD, 21702, USA.
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, 69978, Israel.
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD, 21702, USA
| | - Guy Nir
- Department of Genetics, Harvard Medical School, Boston, MA, 02115, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, 02115, USA
- Department of Biochemistry & Molecular Biology, Department of Neuroscience, Cell Biology and Anatomy, Sealy Center for Structural Biology and Molecular Biophysics, University of Texas Medical Branch, Galveston, TX, 77555, USA
| | - Chung-Jung Tsai
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD, 21702, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44106, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, 44195, USA
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40
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Roither B, Oostenbrink C, Schreiner W. Molecular dynamics of the immune checkpoint programmed cell death protein I, PD-1: conformational changes of the BC-loop upon binding of the ligand PD-L1 and the monoclonal antibody nivolumab. BMC Bioinformatics 2020; 21:557. [PMID: 33308148 PMCID: PMC7734776 DOI: 10.1186/s12859-020-03904-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 11/24/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND The immune checkpoint receptor programmed cell death protein I (PD-1) has been identified as a key target in immunotherapy. PD-1 reduces the risk of autoimmunity by inducing apoptosis in antigen-specific T cells upon interaction with programmed cell death protein ligand I (PD-L1). Various cancer types overexpress PD-L1 to evade the immune system by inducing apoptosis in tumor-specific CD8+ T cells. The clinically used blocking antibody nivolumab binds to PD-1 and inhibits the immunosuppressive interaction with PD-L1. Even though PD-1 is already used as a drug target, the exact mechanism of the receptor is still a matter of debate. For instance, it is hypothesized that the signal transduction is based on an active conformation of PD-1. RESULTS Here we present the results of the first molecular dynamics simulations of PD-1 with a complete extracellular domain with a focus on the role of the BC-loop of PD-1 upon binding PD-L1 or nivolumab. We could demonstrate that the BC-loop can form three conformations. Nivolumab binds to the BC-loop according to the conformational selection model whereas PD-L1 induces allosterically a conformational change of the BC-loop. CONCLUSION Due to the structural differences of the BC-loop, a signal transduction based on active conformation cannot be ruled out. These findings will have an impact on drug design and will help to refine immunotherapy blocking antibodies.
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Affiliation(s)
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Bernhard Roither
- Institute of Biosimulation and Bioinformatics, Medical University of Vienna, Spitalgasse 23/88.04.510, 1090 Vienna, Austria
| | - Chris Oostenbrink
- Institute of Molecular Modeling and Simulation, University of Natural Resources and Life Science, Vienna, Muthgasse 18, 1190 Vienna, Austria
| | - Wolfgang Schreiner
- Institute of Biosimulation and Bioinformatics, Medical University of Vienna, Spitalgasse 23/88.04.510, 1090 Vienna, Austria
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Lu H, Martí J. Long-lasting Salt Bridges Provide the Anchoring Mechanism of Oncogenic Kirsten Rat Sarcoma Proteins at Cell Membranes. J Phys Chem Lett 2020; 11:9938-9945. [PMID: 33170712 DOI: 10.1021/acs.jpclett.0c02809] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
RAS proteins work as GDP-GTP binary switches and regulate cytoplasmic signaling networks that are able to control several cellular processes, playing an essential role in signal transduction pathways involved in cell growth, differentiation, and survival, so that overacting RAS signaling can lead to cancer. One of the hardest challenges to face is the design of mutation-selective therapeutic strategies. In this work, a G12D-mutated farnesylated GTP-bound Kirsten RAt sarcoma (KRAS) protein has been simulated at the interface of a DOPC/DOPS/cholesterol model anionic cell membrane. A specific long-lasting salt bridge connection between farnesyl and the hypervariable region of the protein has been identified as the main mechanism responsible for the binding of oncogenic farnesylated KRAS-4B to the cell membrane. Free-energy landscapes allowed us to characterize local and global minima of KRAS-4B binding to the cell membrane, revealing the main pathways between anchored and released states.
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Affiliation(s)
- Huixia Lu
- Department of Physics, Technical University of Catalonia-Barcelona Tech, B4-B5 Northern Campus, Barcelona, Catalonia, Spain
| | - Jordi Martí
- Department of Physics, Technical University of Catalonia-Barcelona Tech, B4-B5 Northern Campus, Barcelona, Catalonia, Spain
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42
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Xu Y, Xue D, Bankhead A, Neamati N. Why All the Fuss about Oxidative Phosphorylation (OXPHOS)? J Med Chem 2020; 63:14276-14307. [PMID: 33103432 DOI: 10.1021/acs.jmedchem.0c01013] [Citation(s) in RCA: 89] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Certain subtypes of cancer cells require oxidative phosphorylation (OXPHOS) to survive. Increased OXPHOS dependency is frequently a hallmark of cancer stem cells and cells resistant to chemotherapy and targeted therapies. Suppressing the OXPHOS function might also influence the tumor microenvironment by alleviating hypoxia and improving the antitumor immune response. Thus, targeting OXPHOS is a promising strategy to treat various cancers. A growing arsenal of therapeutic agents is under development to inhibit this biological process. This Perspective provides an overview of the structure and function of OXPHOS complexes, their biological functions in cancer, relevant research tools and models, as well as the limitations of OXPHOS as drug targets. We also focus on the current development status of OXPHOS inhibitors and potential therapeutic strategies to strengthen their clinical applications.
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Affiliation(s)
- Yibin Xu
- Department of Medicinal Chemistry, College of Pharmacy, Rogel Cancer Center, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Ding Xue
- Department of Medicinal Chemistry, College of Pharmacy, Rogel Cancer Center, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Armand Bankhead
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan 48109, United States.,Department of Biostatistics, University of Michigan, School of Public Health, Ann Arbor, Michigan 48109, United States
| | - Nouri Neamati
- Department of Medicinal Chemistry, College of Pharmacy, Rogel Cancer Center, University of Michigan, Ann Arbor, Michigan 48109, United States
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Identification of the Sixth Complement Component as Potential Key Genes in Hepatocellular Carcinoma via Bioinformatics Analysis. BIOMED RESEARCH INTERNATIONAL 2020; 2020:7042124. [PMID: 33083480 PMCID: PMC7556077 DOI: 10.1155/2020/7042124] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Revised: 08/17/2020] [Accepted: 09/14/2020] [Indexed: 12/24/2022]
Abstract
The present study is designed to determine potential target genes involved in hepatocellular carcinoma (HCC) and provide possible underlying mechanisms of action. Several studies (GSE112790, GSE87630, and GSE56140) from the GEO database looking at molecular characteristics in HCC were screened and analyzed by GEO2R, which led to the identification of a total of 93 differentially expressed genes (DEGs). From the protein–protein interaction (PPI) network, we selected 13 key genes with high degree of variability in expression in HCC. Expression of three key genes (NQO1, CYP2C9, and C6) presented with poor overall survival (OS) in HCC patients by UALCAN. C6, which is a complement component, was found by ONCOMINE and TIMER to have low expression in many solid cancers including HCC. Besides, Kaplan-Meier plotter and UALCAN database analysis to access diseases prognosis suggested that low expression of C6 is significantly related to worse OS in LIHC patients, especially in advanced HCC patients. Finally, the TIMER analysis suggested that the C6 expression showed significant negative correlation with infiltrating levels of six immune cells. The somatic copy number alterations (SCNAs) of C6 were associated with CD4+ T cell infiltration in HCC. Taken together, these results together identified C6 as a potential key gene in the diagnosis and prognosis of HCC.
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Tan ZW, Guarnera E, Tee WV, Berezovsky IN. AlloSigMA 2: paving the way to designing allosteric effectors and to exploring allosteric effects of mutations. Nucleic Acids Res 2020; 48:W116-W124. [PMID: 32392302 PMCID: PMC7319554 DOI: 10.1093/nar/gkaa338] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 04/09/2020] [Accepted: 04/22/2020] [Indexed: 12/21/2022] Open
Abstract
The AlloSigMA 2 server provides an interactive platform for exploring the allosteric signaling caused by ligand binding and/or mutations, for analyzing the allosteric effects of mutations and for detecting potential cancer drivers and pathogenic nsSNPs. It can also be used for searching latent allosteric sites and for computationally designing allosteric effectors for these sites with required agonist/antagonist activity. The server is based on the implementation of the Structure-Based Statistical Mechanical Model of Allostery (SBSMMA), which allows one to evaluate the allosteric free energy as a result of the perturbation at per-residue resolution. The Allosteric Signaling Map (ASM) providing a comprehensive residue-by-residue allosteric control over the protein activity can be obtained for any structure of interest. The Allosteric Probing Map (APM), in turn, allows one to perform the fragment-based-like computational design experiment aimed at finding leads for potential allosteric effectors. The server can be instrumental in elucidating of allosteric mechanisms and actions of allosteric mutations, and in the efforts on design of new elements of allosteric control. The server is freely available at: http://allosigma.bii.a-star.edu.sg.
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Affiliation(s)
- Zhen Wah Tan
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, 138671, Singapore
| | - Enrico Guarnera
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, 138671, Singapore
| | - Wei-Ven Tee
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, 138671, Singapore.,Department of Biological Sciences (DBS), National University of Singapore (NUS), 8 Medical Drive, 117579, Singapore
| | - Igor N Berezovsky
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, 138671, Singapore.,Department of Biological Sciences (DBS), National University of Singapore (NUS), 8 Medical Drive, 117579, Singapore
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Jafari M, Wang Y, Amiryousefi A, Tang J. Unsupervised Learning and Multipartite Network Models: A Promising Approach for Understanding Traditional Medicine. Front Pharmacol 2020; 11:1319. [PMID: 32982738 PMCID: PMC7479204 DOI: 10.3389/fphar.2020.01319] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 08/07/2020] [Indexed: 12/11/2022] Open
Abstract
The ultimate goal of precision medicine is to determine right treatment for right patients based on precise diagnosis. To achieve this goal, correct stratification of patients using molecular features and clinical phenotypes is crucial. During the long history of medical science, our understanding on disease classification has been improved greatly by chemistry and molecular biology. Nowadays, we gain access to large scale patient-derived data by high-throughput technologies, generating a greater need for data science including unsupervised learning and network modeling. Unsupervised learning methods such as clustering could be a better solution to stratify patients when there is a lack of predefined classifiers. In network modularity analysis, clustering methods can be also applied to elucidate the complex structure of biological and disease networks at the systems level. In this review, we went over the main points of clustering analysis and network modeling, particularly in the context of Traditional Chinese medicine (TCM). We showed that this approach can provide novel insights on the rationale of classification for TCM herbs. In a case study, using a modularity analysis of multipartite networks, we illustrated that the TCM classifications are associated with the chemical properties of the herb ingredients. We concluded that multipartite network modeling may become a suitable data integration tool for understanding the mechanisms of actions of traditional medicine.
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Affiliation(s)
- Mohieddin Jafari
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Yinyin Wang
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Ali Amiryousefi
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Jing Tang
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
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Sanavia T, Birolo G, Montanucci L, Turina P, Capriotti E, Fariselli P. Limitations and challenges in protein stability prediction upon genome variations: towards future applications in precision medicine. Comput Struct Biotechnol J 2020; 18:1968-1979. [PMID: 32774791 PMCID: PMC7397395 DOI: 10.1016/j.csbj.2020.07.011] [Citation(s) in RCA: 85] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2020] [Revised: 07/10/2020] [Accepted: 07/14/2020] [Indexed: 12/13/2022] Open
Abstract
Protein stability predictions are becoming essential in medicine to develop novel immunotherapeutic agents and for drug discovery. Despite the large number of computational approaches for predicting the protein stability upon mutation, there are still critical unsolved problems: 1) the limited number of thermodynamic measurements for proteins provided by current databases; 2) the large intrinsic variability of ΔΔG values due to different experimental conditions; 3) biases in the development of predictive methods caused by ignoring the anti-symmetry of ΔΔG values between mutant and native protein forms; 4) over-optimistic prediction performance, due to sequence similarity between proteins used in training and test datasets. Here, we review these issues, highlighting new challenges required to improve current tools and to achieve more reliable predictions. In addition, we provide a perspective of how these methods will be beneficial for designing novel precision medicine approaches for several genetic disorders caused by mutations, such as cancer and neurodegenerative diseases.
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Affiliation(s)
- Tiziana Sanavia
- Department of Medical Sciences, University of Torino, Via Santena 19, 10126 Torino, Italy
| | - Giovanni Birolo
- Department of Medical Sciences, University of Torino, Via Santena 19, 10126 Torino, Italy
| | - Ludovica Montanucci
- Department of Comparative Biomedicine and Food Science (BCA), University of Padova, Viale dell'Università 16, 35020 Legnaro, Italy
| | - Paola Turina
- Department of Pharmacy and Biotechnology (FaBiT), University of Bologna, Via F. Selmi 3, 40126 Bologna, Italy
| | - Emidio Capriotti
- Department of Pharmacy and Biotechnology (FaBiT), University of Bologna, Via F. Selmi 3, 40126 Bologna, Italy
| | - Piero Fariselli
- Department of Medical Sciences, University of Torino, Via Santena 19, 10126 Torino, Italy
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Yang X, Fu H, Ivanov AA. Online informatics resources to facilitate cancer target and chemical probe discovery. RSC Med Chem 2020; 11:611-624. [PMID: 33479663 PMCID: PMC7429978 DOI: 10.1039/d0md00012d] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Accepted: 03/30/2020] [Indexed: 12/20/2022] Open
Abstract
The advances in cancer genomics, chemical biology, high-throughput screening technologies, and synthetic medicinal chemistry have tremendously expanded the biological space of cancer targets and chemical space of bioactive small molecules to interrogate oncogenic signaling. To explore and leverage these exponentially growing cancer-associated data, a great number of computational tools, databases, and algorithms have been developed. This review summarizes recent cancer-related web resources that allow researchers working at the interface of chemical, biological, and cancer genomics fields to integrate clinical and genomics data for specific actionable targets and selective chemical compounds to facilitate cancer therapeutic discovery.
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Affiliation(s)
- Xuan Yang
- Department of Pharmacology and Chemical Biology , Emory University School of Medicine , Emory University , 1510 Clifton Road , Atlanta , GA 30322 , USA . ; ; Tel: +1 404 727 6343
- Emory Chemical Biology Discovery Center , Emory University School of Medicine , Emory University , Atlanta , GA , USA
| | - Haian Fu
- Department of Pharmacology and Chemical Biology , Emory University School of Medicine , Emory University , 1510 Clifton Road , Atlanta , GA 30322 , USA . ; ; Tel: +1 404 727 6343
- Emory Chemical Biology Discovery Center , Emory University School of Medicine , Emory University , Atlanta , GA , USA
- Winship Cancer Institute , Emory University , Atlanta , GA , USA
- Department of Hematology & Medical Oncology , Emory University , Atlanta , GA , USA
| | - Andrey A Ivanov
- Department of Pharmacology and Chemical Biology , Emory University School of Medicine , Emory University , 1510 Clifton Road , Atlanta , GA 30322 , USA . ; ; Tel: +1 404 727 6343
- Emory Chemical Biology Discovery Center , Emory University School of Medicine , Emory University , Atlanta , GA , USA
- Winship Cancer Institute , Emory University , Atlanta , GA , USA
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Wei PJ, Wu FX, Xia J, Su Y, Wang J, Zheng CH. Prioritizing Cancer Genes Based on an Improved Random Walk Method. Front Genet 2020; 11:377. [PMID: 32411180 PMCID: PMC7198854 DOI: 10.3389/fgene.2020.00377] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Accepted: 03/26/2020] [Indexed: 12/18/2022] Open
Abstract
Identifying driver genes that contribute to cancer progression from numerous passenger genes, although a central goal, is a major challenge. The protein-protein interaction network provides convenient and reasonable assistance for driver gene discovery. Random walk-based methods have been widely used to prioritize nodes in social or biological networks. However, most studies select the next arriving node uniformly from the random walker's neighbors. Few consider transiting preference according to the degree of random walker's neighbors. In this study, based on the random walk method, we propose a novel approach named Driver_IRW (Driver genes discovery with Improved Random Walk method), to prioritize cancer genes in cancer-related network. The key idea of Driver_IRW is to assign different transition probabilities for different edges of a constructed cancer-related network in accordance with the degree of the nodes' neighbors. Furthermore, the global centrality (here is betweenness centrality) and Katz feedback centrality are incorporated into the framework to evaluate the probability to walk to the seed nodes. Experimental results on four cancer types indicate that Driver_IRW performs more efficiently than some previously published methods for uncovering known cancer-related genes. In conclusion, our method can aid in prioritizing cancer-related genes and complement traditional frequency and network-based methods.
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Affiliation(s)
- Pi-Jing Wei
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, College of Computer Science and Technology, Anhui University, Hefei, China
| | - Fang-Xiang Wu
- Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK, Canada
- Department of Computer Sciences, University of Saskatchewan, Saskatoon, SK, Canada
- Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK, Canada
| | - Junfeng Xia
- Institutes of Physical Science and Information Technology, Anhui University, Hefei, China
| | - Yansen Su
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, College of Computer Science and Technology, Anhui University, Hefei, China
| | - Jing Wang
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, College of Computer Science and Technology, Anhui University, Hefei, China
- College of Computer and Information Engineering, Fuyang Normal University, Fuyang, China
| | - Chun-Hou Zheng
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, College of Computer Science and Technology, Anhui University, Hefei, China
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Shin MH, Kim J, Lim SA, Kim J, Lee KM. Current Insights into Combination Therapies with MAPK Inhibitors and Immune Checkpoint Blockade. Int J Mol Sci 2020; 21:E2531. [PMID: 32260561 PMCID: PMC7177307 DOI: 10.3390/ijms21072531] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2020] [Revised: 04/03/2020] [Accepted: 04/03/2020] [Indexed: 12/31/2022] Open
Abstract
The recent development of high-throughput genomics has revolutionized personalized medicine by identifying key pathways and molecular targets controlling tumor progression and survival. Mitogen-activated protein kinase (MAPK) pathways are examples of such targets, and inhibitors against these pathways have shown promising clinical responses in patients with melanoma, non-small-cell lung cancer, colorectal cancer, pancreatic cancer, and thyroid cancer. Although MAPK pathway-targeted therapies have resulted in significant clinical responses in a large proportion of cancer patients, the rate of tumor recurrence is high due to the development of resistance. Conversely, immunotherapies have shown limited clinical responses, but have led to durable tumor regression in patients, and complete responses. Recent evidence indicates that MAPK-targeted therapies may synergize with immune cells, thus providing rationale for the development of combination therapies. Here, we review the current status of ongoing clinical trials investigating MAPK pathway inhibitors, such as BRAF and MAPK/ERK kinase (MEK) inhibitors, in combination with checkpoint inhibitors targeting programmed death protein 1 (PD-1), programmed death-ligand 1 (PD-L1), and cytotoxic T cell associated antigen-4 (CTLA-4). A better understanding of an individual drug's mechanism of action, patterns of acquired resistance, and the influence on immune cells will be critical for the development of novel combination therapies.
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Affiliation(s)
| | | | | | | | - Kyung-Mi Lee
- Department of Biochemistry and Molecular Biology, College of Medicine, Korea University, Seoul 02841, Korea
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Aramini B, Banchelli F, Bettelli S, Manfredini S, D'Amico R, Masciale V, Pinelli M, Moretti M, Stefani A, Bertolini F, Dominici M, Morandi U, Maiorana A. Overall survival in patients with lung adenocarcinoma harboring "niche" mutations: an observational study. Oncotarget 2020; 11:550-559. [PMID: 32082488 PMCID: PMC7007296 DOI: 10.18632/oncotarget.27472] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Accepted: 01/21/2020] [Indexed: 12/11/2022] Open
Abstract
Objective: In addition to the most common somatic lung cancer mutations (i. e., KRAS and EGFR mutations), other genes may harbor mutations that could be relevant for lung cancer. We defined BRAF, c-MET, DDR2, HER2, MAP2K1, NRAS, PIK3CA, and RET mutations as “niche” mutations and analyzed. The aim of this retrospective cohort study was to assess the differences in the overall survival (OS) of patients with lung adenocarcinoma harboring niche somatic mutations.
Results: Data were gathered for 252 patients. Mutations were observed in all genes studied, except c-MET, DDR2, MAP2K1, and RET. The multivariable analysis showed that 1) niche mutations had a higher mortality than EGFR mutations (HR = 2.3; 95% CI = 1.2–4.4; p = 0.009); 2) KRAS mutations had a higher mortality than EGFR mutations (HR = 2.5; 95% CI = 1.4–4.5; p = 0.003); 3) niche mutations presented a similar mortality to KRAS mutations (HR = 0.9; 95% CI = 0.6–1.5; p = 0.797).
Methods: Three cohorts of mutations were selected from patients with lung adenocarcinoma and their OS was compared. Mutations that were searched for, were 1) BRAF, c-MET, DDR2, HER2, MAP2K1, NRAS, PIK3CA, and RET; 2) K-RAS; and 3) EGFR. Differences in OS between these three cohorts were assessed by means of a multivariable Cox model that adjusted for age, sex, smoking habits, clinical stages, and treatments.
Conclusions: Niche mutations exhibited an increased risk of death when compared with EGFR mutations and a similar risk of death when compared with KRAS mutations.
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Affiliation(s)
- Beatrice Aramini
- Division of Thoracic Surgery, Department of Medical and Surgical Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Federico Banchelli
- Center of Statistics, Department of Medical and Surgical Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Stefania Bettelli
- Institute of Pathology, Department of Medical and Surgical Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Samantha Manfredini
- Institute of Pathology, Department of Medical and Surgical Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Roberto D'Amico
- Center of Statistics, Department of Medical and Surgical Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Valentina Masciale
- Division of Thoracic Surgery, Department of Medical and Surgical Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Massimo Pinelli
- Division of Plastic Surgery, Department of Medical and Surgical Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Margherita Moretti
- Division of Thoracic Surgery, Department of Medical and Surgical Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Alessandro Stefani
- Division of Thoracic Surgery, Department of Medical and Surgical Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Federica Bertolini
- Division of Oncology, Department of Medical and Surgical Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Massimo Dominici
- Division of Oncology, Department of Medical and Surgical Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Uliano Morandi
- Division of Thoracic Surgery, Department of Medical and Surgical Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Antonino Maiorana
- Institute of Pathology, Department of Medical and Surgical Sciences, University of Modena and Reggio Emilia, Modena, Italy
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