1
|
Xu X, Qi Z, Wang L, Zhang M, Geng Z, Han X. Gsw-fi: a GLM model incorporating shrinkage and double-weighted strategies for identifying cancer driver genes with functional impact. BMC Bioinformatics 2024; 25:99. [PMID: 38448819 PMCID: PMC10916024 DOI: 10.1186/s12859-024-05707-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 02/16/2024] [Indexed: 03/08/2024] Open
Abstract
BACKGROUND Cancer, a disease with high morbidity and mortality rates, poses a significant threat to human health. Driver genes, which harbor mutations accountable for the initiation and progression of tumors, play a crucial role in cancer development. Identifying driver genes stands as a paramount objective in cancer research and precision medicine. RESULTS In the present work, we propose a method for identifying driver genes using a Generalized Linear Regression Model (GLM) with Shrinkage and double-Weighted strategies based on Functional Impact, which is named GSW-FI. Firstly, an estimating model is proposed for assessing the background functional impacts of genes based on GLM, utilizing gene features as predictors. Secondly, the shrinkage and double-weighted strategies as two revising approaches are integrated to ensure the rationality of the identified driver genes. Lastly, a statistical method of hypothesis testing is designed to identify driver genes by leveraging the estimated background function impacts. Experimental results conducted on 31 The Cancer Genome Altas datasets demonstrate that GSW-FI outperforms ten other prediction methods in terms of the overlap fraction with well-known databases and consensus predictions among different methods. CONCLUSIONS GSW-FI presents a novel approach that efficiently identifies driver genes with functional impact mutations using computational methods, thereby advancing the development of precision medicine for cancer.
Collapse
Affiliation(s)
- Xiaolu Xu
- School of Computer and Artificial Intelligence, Liaoning Normal University, Dalian, China
| | - Zitong Qi
- Department of Statistics, University of Washington, Seattle, USA
| | - Lei Wang
- Center for Reproductive and Genetic Medicine, Dalian Women and Children's Medical Group, Dalian, China.
| | - Meiwei Zhang
- Center for Reproductive and Genetic Medicine, Dalian Women and Children's Medical Group, Dalian, China.
| | - Zhaohong Geng
- Department of Cardiology, Second Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Xiumei Han
- College of Artificial Intelligence, Dalian Maritime University, Dalian, China
| |
Collapse
|
2
|
Ostroverkhova D, Tyryshkin K, Beach AK, Moore EA, Masoudi-Sobhanzadeh Y, Barbari SR, Rogozin IB, Shaitan KV, Panchenko AR, Shcherbakova PV. DNA polymerase ε and δ variants drive mutagenesis in polypurine tracts in human tumors. Cell Rep 2024; 43:113655. [PMID: 38219146 PMCID: PMC10830898 DOI: 10.1016/j.celrep.2023.113655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 11/07/2023] [Accepted: 12/19/2023] [Indexed: 01/16/2024] Open
Abstract
Alterations in the exonuclease domain of DNA polymerase ε cause ultramutated cancers. These cancers accumulate AGA>ATA transversions; however, their genomic features beyond the trinucleotide motifs are obscure. We analyze the extended DNA context of ultramutation using whole-exome sequencing data from 524 endometrial and 395 colorectal tumors. We find that G>T transversions in POLE-mutant tumors predominantly affect sequences containing at least six consecutive purines, with a striking preference for certain positions within polypurine tracts. Using this signature, we develop a machine-learning classifier to identify tumors with hitherto unknown POLE drivers and validate two drivers, POLE-E978G and POLE-S461L, by functional assays in yeast. Unlike other pathogenic variants, the E978G substitution affects the polymerase domain of Pol ε. We further show that tumors with POLD1 drivers share the extended signature of POLE ultramutation. These findings expand the understanding of ultramutation mechanisms and highlight peculiar mutagenic properties of polypurine tracts in the human genome.
Collapse
Affiliation(s)
- Daria Ostroverkhova
- Department of Pathology and Molecular Medicine, School of Medicine, Queen's University, Kingston, ON, Canada
| | - Kathrin Tyryshkin
- Department of Pathology and Molecular Medicine, School of Medicine, Queen's University, Kingston, ON, Canada
| | - Annette K Beach
- Eppley Institute for Research in Cancer and Allied Diseases, Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, NE, USA
| | - Elizabeth A Moore
- Eppley Institute for Research in Cancer and Allied Diseases, Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, NE, USA
| | - Yosef Masoudi-Sobhanzadeh
- Department of Pathology and Molecular Medicine, School of Medicine, Queen's University, Kingston, ON, Canada
| | - Stephanie R Barbari
- Eppley Institute for Research in Cancer and Allied Diseases, Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, NE, USA
| | - Igor B Rogozin
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | | | - Anna R Panchenko
- Department of Pathology and Molecular Medicine, School of Medicine, Queen's University, Kingston, ON, Canada.
| | - Polina V Shcherbakova
- Eppley Institute for Research in Cancer and Allied Diseases, Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, NE, USA.
| |
Collapse
|
3
|
Nourbakhsh M, Degn K, Saksager A, Tiberti M, Papaleo E. Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks. Brief Bioinform 2024; 25:bbad519. [PMID: 38261338 PMCID: PMC10805075 DOI: 10.1093/bib/bbad519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 11/27/2023] [Accepted: 12/11/2023] [Indexed: 01/24/2024] Open
Abstract
The vast amount of available sequencing data allows the scientific community to explore different genetic alterations that may drive cancer or favor cancer progression. Software developers have proposed a myriad of predictive tools, allowing researchers and clinicians to compare and prioritize driver genes and mutations and their relative pathogenicity. However, there is little consensus on the computational approach or a golden standard for comparison. Hence, benchmarking the different tools depends highly on the input data, indicating that overfitting is still a massive problem. One of the solutions is to limit the scope and usage of specific tools. However, such limitations force researchers to walk on a tightrope between creating and using high-quality tools for a specific purpose and describing the complex alterations driving cancer. While the knowledge of cancer development increases daily, many bioinformatic pipelines rely on single nucleotide variants or alterations in a vacuum without accounting for cellular compartments, mutational burden or disease progression. Even within bioinformatics and computational cancer biology, the research fields work in silos, risking overlooking potential synergies or breakthroughs. Here, we provide an overview of databases and datasets for building or testing predictive cancer driver tools. Furthermore, we introduce predictive tools for driver genes, driver mutations, and the impact of these based on structural analysis. Additionally, we suggest and recommend directions in the field to avoid silo-research, moving towards integrative frameworks.
Collapse
Affiliation(s)
- Mona Nourbakhsh
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Kristine Degn
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Astrid Saksager
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Matteo Tiberti
- Cancer Structural Biology, Danish Cancer Institute, 2100 Copenhagen, Denmark
| | - Elena Papaleo
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
- Cancer Structural Biology, Danish Cancer Institute, 2100 Copenhagen, Denmark
| |
Collapse
|
4
|
Casolino R, Beer PA, Chakravarty D, Davis MB, Malapelle U, Mazzarella L, Normanno N, Pauli C, Subbiah V, Turnbull C, Westphalen CB, Biankin AV. Interpreting and integrating genomic tests results in clinical cancer care: Overview and practical guidance. CA Cancer J Clin 2024. [PMID: 38174605 DOI: 10.3322/caac.21825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 11/07/2023] [Accepted: 11/27/2023] [Indexed: 01/05/2024] Open
Abstract
The last decade has seen rapid progress in the use of genomic tests, including gene panels, whole-exome sequencing, and whole-genome sequencing, in research and clinical cancer care. These advances have created expansive opportunities to characterize the molecular attributes of cancer, revealing a subset of cancer-associated aberrations called driver mutations. The identification of these driver mutations can unearth vulnerabilities of cancer cells to targeted therapeutics, which has led to the development and approval of novel diagnostics and personalized interventions in various malignancies. The applications of this modern approach, often referred to as precision oncology or precision cancer medicine, are already becoming a staple in cancer care and will expand exponentially over the coming years. Although genomic tests can lead to better outcomes by informing cancer risk, prognosis, and therapeutic selection, they remain underutilized in routine cancer care. A contributing factor is a lack of understanding of their clinical utility and the difficulty of results interpretation by the broad oncology community. Practical guidelines on how to interpret and integrate genomic information in the clinical setting, addressed to clinicians without expertise in cancer genomics, are currently limited. Building upon the genomic foundations of cancer and the concept of precision oncology, the authors have developed practical guidance to aid the interpretation of genomic test results that help inform clinical decision making for patients with cancer. They also discuss the challenges that prevent the wider implementation of precision oncology.
Collapse
Affiliation(s)
- Raffaella Casolino
- Wolfson Wohl Cancer Research Center, School of Cancer Sciences, University of Glasgow, Glasgow, UK
| | - Philip A Beer
- Wolfson Wohl Cancer Research Center, School of Cancer Sciences, University of Glasgow, Glasgow, UK
- Hull York Medical School, York, UK
| | | | - Melissa B Davis
- Department of Surgery, Weill Cornell Medicine, New York City, New York, USA
| | - Umberto Malapelle
- Department of Public Health, University of Naples Federico II, Naples, Italy
| | - Luca Mazzarella
- Laboratory of Translational Oncology and Division of Gastrointestinal Medical Oncology and Neuroendocrine Tumors IEO European Institute of Oncology, IRCCS, Milan, Italy
| | - Nicola Normanno
- Cell Biology and Biotherapy Unit, Istituto Nazionale Tumori, IRCCS "Fondazione G. Pascale", Naples, Italy
| | - Chantal Pauli
- Department of Pathology and Molecular Pathology, University Hospital Zurich, Zurich, Switzerland
| | - Vivek Subbiah
- Sarah Cannon Research Institute, Nashville, Tennessee, USA
| | - Clare Turnbull
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
- National Cancer Registration and Analysis Service, National Health Service (NHS) England, London, UK
- Cancer Genetics Unit, The Royal Marsden NHS Foundation Trust, London, UK
| | - C Benedikt Westphalen
- Department of Medicine III, Ludwig Maximilians University (LMU) Hospital Munich, Munich, Germany
- Comprehensive Cancer Center, LMU Hospital Munich, Munich, Germany
- German Cancer Consortium, LMU Hospital Munich, Munich, Germany
| | - Andrew V Biankin
- Wolfson Wohl Cancer Research Center, School of Cancer Sciences, University of Glasgow, Glasgow, UK
- West of Scotland Pancreatic Unit, Glasgow Royal Infirmary, Glasgow, UK
- South Western Sydney Clinical School, Liverpool, New South Wales, Australia
| |
Collapse
|
5
|
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] [What about the content of this article? (0)] [Affiliation(s)] [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.
Collapse
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
| |
Collapse
|
6
|
Gonzalez-Salinas F, Herrera-Gamboa J, Rojo R, Trevino V. Heterozygous Knockout of ARID4B Using CRISPR/Cas9 Attenuates Some Aggressive Phenotypes in a Breast Cancer Cell Line. Genes (Basel) 2023; 14:2184. [PMID: 38137006 PMCID: PMC10743217 DOI: 10.3390/genes14122184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 11/17/2023] [Accepted: 11/20/2023] [Indexed: 12/24/2023] Open
Abstract
Breast cancer is one of the leading causes of death in women around the world. Over time, many genes and mutations that are associated with the development of this disease have been identified. However, the specific role of many genes has not yet been fully elucidated. Higher ARID4B expression has been identified as a risk factor for diverse cancer types. Silencing experiments also showed that ARID4B is associated with developing cancer-associated characteristics. However, no transcriptomic studies have shown the overall cellular effect of loss of function in breast cancer in humans. This study addresses the impact of loss-of-function mutations in breast cancer MCF-7 cells. Using the CRISPR/Cas9 system, we generated mutations that caused heterozygous truncated proteins, isolating three monoclonal lines carrying insertions and deletions in ARID4B. We observed reduced proliferation and migration in in vitro experiments. In addition, from RNA-seq assays, a differential expression analysis shows known and novel deregulated cancer-associate pathways in mutated cells supporting the impact of ARID4B. For example, we found the AKT-PI3K pathway to be altered at the transcript level but through different genes than those reported for ARID4B. Our transcriptomic results also suggest new insights into the role of ARID4B in aggressiveness by the epithelial-to-mesenchymal transition and TGF-β pathways and in metabolism through cholesterol and mevalonate pathways. We also performed exome sequencing to show that no off-target effects were apparent. In conclusion, the ARID4B gene is associated with some aggressive phenotypes in breast cancer cells.
Collapse
Affiliation(s)
- Fernando Gonzalez-Salinas
- Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Ave. Morones Prieto 3000, Monterrey 64710, Nuevo Leon, Mexico; (F.G.-S.); (J.H.-G.); (R.R.)
| | - Jessica Herrera-Gamboa
- Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Ave. Morones Prieto 3000, Monterrey 64710, Nuevo Leon, Mexico; (F.G.-S.); (J.H.-G.); (R.R.)
- Instituto de Biotecnología, Facultad de Ciencias Biológicas, Universidad Autónoma de Nuevo Leon, San Nicolas de los Garza 66455, Nuevo Leon, Mexico
| | - Rocio Rojo
- Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Ave. Morones Prieto 3000, Monterrey 64710, Nuevo Leon, Mexico; (F.G.-S.); (J.H.-G.); (R.R.)
- Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Mexico City 14380, Mexico
| | - Victor Trevino
- Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Ave. Morones Prieto 3000, Monterrey 64710, Nuevo Leon, Mexico; (F.G.-S.); (J.H.-G.); (R.R.)
- Tecnologico de Monterrey, The Institute for Obesity Research, Eugenio Garza Sada Avenue 2501, Monterrey 64849, Nuevo Leon, Mexico
- Tecnologico de Monterrey, oriGen Project, Eugenio Garza Sada Avenue 2501, Monterrey 64849, Nuevo Leon, Mexico
| |
Collapse
|
7
|
Austin BK, Firooz A, Valafar H, Blenda AV. An Updated Overview of Existing Cancer Databases and Identified Needs. Biology (Basel) 2023; 12:1152. [PMID: 37627037 PMCID: PMC10452211 DOI: 10.3390/biology12081152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 07/26/2023] [Accepted: 08/14/2023] [Indexed: 08/27/2023]
Abstract
Our search of existing cancer databases aimed to assess the current landscape and identify key needs. We analyzed 71 databases, focusing on genomics, proteomics, lipidomics, and glycomics. We found a lack of cancer-related lipidomic and glycomic databases, indicating a need for further development in these areas. Proteomic databases dedicated to cancer research were also limited. To assess overall progress, we included human non-cancer databases in proteomics, lipidomics, and glycomics for comparison. This provided insights into advancements in these fields over the past eight years. We also analyzed other types of cancer databases, such as clinical trial databases and web servers. Evaluating user-friendliness, we used the FAIRness principle to assess findability, accessibility, interoperability, and reusability. This ensured databases were easily accessible and usable. Our search summary highlights significant growth in cancer databases while identifying gaps and needs. These insights are valuable for researchers, clinicians, and database developers, guiding efforts to enhance accessibility, integration, and usability. Addressing these needs will support advancements in cancer research and benefit the wider cancer community.
Collapse
Affiliation(s)
- Brittany K. Austin
- Department of Biomedical Sciences, School of Medicine Greenville, University of South Carolina, Greenville, SC 29605, USA;
| | - Ali Firooz
- Department of Computer Science and Engineering, College of Engineering and Computing, University of South Carolina, Columbia, SC 29208, USA;
| | - Homayoun Valafar
- Department of Computer Science and Engineering, College of Engineering and Computing, University of South Carolina, Columbia, SC 29208, USA;
| | - Anna V. Blenda
- Department of Biomedical Sciences, School of Medicine Greenville, University of South Carolina, Greenville, SC 29605, USA;
- Prisma Health Cancer Institute, Prisma Health, Greenville, SC 29605, USA
| |
Collapse
|
8
|
Kondrashov A, Sapkota S, Sharma A, Riano I, Kurzrock R, Adashek JJ. Antibody-Drug Conjugates in Solid Tumor Oncology: An Effectiveness Payday with a Targeted Payload. Pharmaceutics 2023; 15:2160. [PMID: 37631374 PMCID: PMC10459723 DOI: 10.3390/pharmaceutics15082160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 08/10/2023] [Accepted: 08/16/2023] [Indexed: 08/27/2023] Open
Abstract
Antibody-drug conjugates (ADCs) are at the forefront of the drug development revolution occurring in oncology. Formed from three main components-an antibody, a linker molecule, and a cytotoxic agent ("payload"), ADCs have the unique ability to deliver cytotoxic agents to cells expressing a specific antigen, a great leap forward from traditional chemotherapeutic approaches that cause widespread effects without specificity. A variety of payloads can be used, including most frequently microtubular inhibitors (auristatins and maytansinoids), as well as topoisomerase inhibitors and alkylating agents. Finally, linkers play a critical role in the ADCs' effect, as cleavable moieties that serve as linkers impact site-specific activation as well as bystander killing effects, an upshot that is especially important in solid tumors that often express a variety of antigens. While ADCs were initially used in hematologic malignancies, their utility has been demonstrated in multiple solid tumor malignancies, including breast, gastrointestinal, lung, cervical, ovarian, and urothelial cancers. Currently, six ADCs are FDA-approved for the treatment of solid tumors: ado-trastuzumab emtansine and trastuzumab deruxtecan, both anti-HER2; enfortumab-vedotin, targeting nectin-4; sacituzuzmab govitecan, targeting Trop2; tisotumab vedotin, targeting tissue factor; and mirvetuximab soravtansine, targeting folate receptor-alpha. Although they demonstrate utility and tolerable safety profiles, ADCs may become ineffective as tumor cells undergo evolution to avoid expressing the specific antigen being targeted. Furthermore, the current cost of ADCs can be limiting their reach. Here, we review the structure and functions of ADCs, as well as ongoing clinical investigations into novel ADCs and their potential as treatments of solid malignancies.
Collapse
Affiliation(s)
- Aleksei Kondrashov
- Department of Internal Medicine, Saint Agnes Hospital, Baltimore, MD 21229, USA; (A.K.); (S.S.)
| | - Surendra Sapkota
- Department of Internal Medicine, Saint Agnes Hospital, Baltimore, MD 21229, USA; (A.K.); (S.S.)
| | - Aditya Sharma
- Department of Internal Medicine, Dartmouth Health, Geisel School of Medicine at Dartmouth, Lebanon, NH 03756, USA; (A.S.); (I.R.)
| | - Ivy Riano
- Department of Internal Medicine, Dartmouth Health, Geisel School of Medicine at Dartmouth, Lebanon, NH 03756, USA; (A.S.); (I.R.)
- Division of Hematology and Oncology, Dartmouth Cancer Center, Lebanon, NH 03755, USA
| | - Razelle Kurzrock
- WIN Consortium, 94550 Paris, France;
- MCW Cancer Center, Milwaukee, WI 53226, USA
- Division of Oncology and Hematology, University of Nebraska, Omaha, NE 68198, USA
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins Hospital, Baltimore, MD 21287, USA
| | - Jacob J. Adashek
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins Hospital, Baltimore, MD 21287, USA
| |
Collapse
|
9
|
Ostroverkhova D, Espiritu D, Aristizabal MJ, Panchenko AR. Leveraging Gene Redundancy to Find New Histone Drivers in Cancer. Cancers (Basel) 2023; 15:3437. [PMID: 37444547 DOI: 10.3390/cancers15133437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 06/22/2023] [Accepted: 06/28/2023] [Indexed: 07/15/2023] Open
Abstract
Histones play a critical role in chromatin function but are susceptible to mutagenesis. In fact, numerous mutations have been observed in several cancer types, and a few of them have been associated with carcinogenesis. Histones are peculiar, as they are encoded by a large number of genes, and the majority of them are clustered in three regions of the human genome. In addition, their replication and expression are tightly regulated in a cell. Understanding the etiology of cancer mutations in histone genes is impeded by their functional and sequence redundancy, their unusual genomic organization, and the necessity to be rapidly produced during cell division. Here, we collected a large data set of histone gene mutations in cancer and used it to investigate their distribution over 96 human histone genes and 68 different cancer types. This analysis allowed us to delineate the factors influencing the probability of mutation accumulation in histone genes and to detect new histone gene drivers. Although no significant difference in observed mutation rates between different histone types was detected for the majority of cancer types, several cancers demonstrated an excess or depletion of mutations in histone genes. As a consequence, we identified seven new histone genes as potential cancer-specific drivers. Interestingly, mutations were found to be distributed unevenly in several histone genes encoding the same protein, pointing to different factors at play, which are specific to histone function and genomic organization. Our study also elucidated mutational processes operating in genomic regions harboring histone genes, highlighting POLE as a factor of potential interest.
Collapse
Affiliation(s)
- Daria Ostroverkhova
- Department of Pathology and Molecular Medicine, Queen's University, Kingston, ON K7L 3N6, Canada
| | - Daniel Espiritu
- Department of Pathology and Molecular Medicine, Queen's University, Kingston, ON K7L 3N6, Canada
| | | | - Anna R Panchenko
- Department of Pathology and Molecular Medicine, Queen's University, Kingston, ON K7L 3N6, Canada
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON K7L 3N6, Canada
- School of Computing, Queen's University, Kingston, ON K7L 3N6, Canada
- Ontario Institute of Cancer Research, Toronto, ON M5G 0A3, Canada
| |
Collapse
|
10
|
Ostroverkhova D, Przytycka TM, Panchenko AR. Cancer driver mutations: predictions and reality. Trends Mol Med 2023:S1471-4914(23)00067-9. [PMID: 37076339 DOI: 10.1016/j.molmed.2023.03.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/17/2023] [Accepted: 03/23/2023] [Indexed: 04/21/2023]
Abstract
Cancer cells accumulate many genetic alterations throughout their lifetime, but only a few of them drive cancer progression, termed driver mutations. Driver mutations may vary between cancer types and patients, can remain latent for a long time and become drivers at particular cancer stages, or may drive oncogenesis only in conjunction with other mutations. The high mutational, biochemical, and histological tumor heterogeneity makes driver mutation identification very challenging. In this review we summarize recent efforts to identify driver mutations in cancer and annotate their effects. We underline the success of computational methods to predict driver mutations in finding novel cancer biomarkers, including in circulating tumor DNA (ctDNA). We also report on the boundaries of their applicability in clinical research.
Collapse
Affiliation(s)
- Daria Ostroverkhova
- Department of Pathology and Molecular Medicine, Queen's University, Kingston, ON, Canada
| | - Teresa M Przytycka
- National Library of Medicine, National Institutes of Health (NIH), Bethesda, MD, USA.
| | - Anna R Panchenko
- Department of Pathology and Molecular Medicine, Queen's University, Kingston, ON, Canada; Department of Biology and Molecular Sciences, Queen's University, Kingston, ON, Canada; School of Computing, Queen's University, Kingston, ON, Canada; Ontario Institute of Cancer Research, Toronto, ON, Canada.
| |
Collapse
|
11
|
Yavuz BR, Tsai CJ, Nussinov R, Tuncbag N. Pan-cancer clinical impact of latent drivers from double mutations. Commun Biol 2023; 6:202. [PMID: 36808143 DOI: 10.1038/s42003-023-04519-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [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.
Collapse
|
12
|
Landau J, Tsaban L, Yaacov A, Ben Cohen G, Rosenberg S. Shared Cancer Dataset Analysis Identifies and Predicts the Quantitative Effects of Pan-Cancer Somatic Driver Variants. Cancer Res 2023; 83:74-88. [PMID: 36264175 DOI: 10.1158/0008-5472.can-22-1038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 08/02/2022] [Accepted: 10/18/2022] [Indexed: 02/03/2023]
Abstract
Driver mutations endow tumors with selective advantages and produce an array of pathogenic effects. Determining the function of somatic variants is important for understanding cancer biology and identifying optimal therapies. Here, we compiled a shared dataset from several cancer genomic databases. Two measures were applied to 535 cancer genes based on observed and expected frequencies of driver variants as derived from cancer-specific rates of somatic mutagenesis. The first measure comprised a binary classifier based on a binomial test; the second was tumor variant amplitude (TVA), a continuous measure representing the selective advantage of individual variants. TVA outperformed all other computational tools in terms of its correlation with experimentally derived functional scores of cancer mutations. TVA also highly correlated with drug response, overall survival, and other clinical implications in relevant cancer genes. This study demonstrates how a selective advantage measure based on a large cancer dataset significantly impacts our understanding of the spectral effect of driver variants in cancer. The impact of this information will increase as cancer treatment becomes more precise and personalized to tumor-specific mutations. SIGNIFICANCE A new selective advantage estimation assists in oncogenic driver identification and relative effect measurements, enabling better prognostication, therapy selection, and prioritization.
Collapse
Affiliation(s)
- Jakob Landau
- Gaffin Center for Neuro-Oncology, Sharett Institute for Oncology, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.,The Wohl Institute for Translational Medicine, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Linoy Tsaban
- Gaffin Center for Neuro-Oncology, Sharett Institute for Oncology, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.,The Wohl Institute for Translational Medicine, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Adar Yaacov
- Gaffin Center for Neuro-Oncology, Sharett Institute for Oncology, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.,The Wohl Institute for Translational Medicine, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.,Department of Microbiology and Molecular Genetics, IMRIC, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Gil Ben Cohen
- Gaffin Center for Neuro-Oncology, Sharett Institute for Oncology, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.,The Wohl Institute for Translational Medicine, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Shai Rosenberg
- Gaffin Center for Neuro-Oncology, Sharett Institute for Oncology, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.,The Wohl Institute for Translational Medicine, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| |
Collapse
|
13
|
Gopal P, Yard BD, Petty A, Lal JC, Bera TK, Hoang TQ, Buhimschi AD, Abazeed ME. The Mutational Landscape of Cancer's Vulnerability to Ionizing Radiation. Clin Cancer Res 2022; 28:5343-5358. [PMID: 36222846 PMCID: PMC9751780 DOI: 10.1158/1078-0432.ccr-22-1914] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 08/24/2022] [Accepted: 10/10/2022] [Indexed: 01/24/2023]
Abstract
PURPOSE Large-scale sequencing efforts have established that cancer-associated genetic alterations are highly diverse, posing a challenge to the identification of variants that regulate complex phenotypes like radiation sensitivity. The impact of the vast majority of rare or common genetic variants on the sensitivity of cancers to radiotherapy remains largely unknown. EXPERIMENTAL DESIGN We developed a scalable gene editing and irradiation platform to assess the role of categories of variants in cells. Variants were prioritized on the basis of genotype-phenotype associations from a previously completed large-scale cancer cell line radiation profiling study. Altogether, 488 alleles (396 unique single-nucleotide variants) from 92 genes were generated and profiled in an immortalized lung cell line, BEAS-2B. We validated our results in other cell lines (TRT-HU1 and NCI-H520), in vivo via the use of both cell line and patient-derived murine xenografts, and in clinical cohorts. RESULTS We show that resistance to radiation is characterized by substantial inter- and intra-gene allelic variation. Some genes (e.g., KEAP1) demonstrated significant intragenic allelic variation in the magnitude of conferred resistance and other genes (e.g., CTNNB1) displayed both resistance and sensitivity in a protein domain-dependent manner. We combined results from our platform with gene expression and metabolite features and identified the upregulation of amino acid transporters that facilitate oxidative reductive capacity and cell-cycle deregulation as key regulators of radiation sensitivity. CONCLUSIONS Our results reveal new insights into the genetic determinants of tumor sensitivity to radiotherapy and nominate a multitude of cancer mutations that are predicted to impact treatment efficacy.
Collapse
Affiliation(s)
- Priyanka Gopal
- Department of Radiation Oncology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Brian D. Yard
- Department of Translational Hematology Oncology Research, Cleveland Clinic, Cleveland, Ohio
| | - Aaron Petty
- Department of Translational Hematology Oncology Research, Cleveland Clinic, Cleveland, Ohio
| | - Jessica C. Lal
- Department of Translational Hematology Oncology Research, Cleveland Clinic, Cleveland, Ohio
| | - Titas K. Bera
- Department of Radiation Oncology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Trung Q. Hoang
- Department of Radiation Oncology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Alexandru D. Buhimschi
- Department of Radiation Oncology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Mohamed E. Abazeed
- Department of Radiation Oncology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois.,Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, Illinois.,Corresponding Author: Mohamed E. Abazeed, Feinberg School of Medicine, Northwestern University, 303 E. Superior St/Lurie 7-115, Chicago, IL 60611. Phone: 312-926-2520; Fax: 312-926-6524; E-mail:
| |
Collapse
|
14
|
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.
Collapse
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
| |
Collapse
|
15
|
Kim JH, Hwang S, Son H, Kim D, Kim IB, Kim MH, Sim NS, Kim DS, Ha YJ, Lee J, Kang HC, Lee JH, Kim S. Analysis of low-level somatic mosaicism reveals stage and tissue-specific mutational features in human development. PLoS Genet 2022; 18:e1010404. [PMID: 36121845 PMCID: PMC9560606 DOI: 10.1371/journal.pgen.1010404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 10/13/2022] [Accepted: 08/31/2022] [Indexed: 11/18/2022] Open
Abstract
Most somatic mutations that arise during normal development are present at low levels in single or multiple tissues depending on the developmental stage and affected organs. However, the effect of human developmental stages or mutations of different organs on the features of somatic mutations is still unclear. Here, we performed a systemic and comprehensive analysis of low-level somatic mutations using deep whole-exome sequencing (average read depth ~500×) of 498 multiple organ tissues with matched controls from 190 individuals. Our results showed that early clone-forming mutations shared between multiple organs were lower in number but showed higher allele frequencies than late clone-forming mutations [0.54 vs. 5.83 variants per individual; 6.17% vs. 1.5% variant allele frequency (VAF)] along with less nonsynonymous mutations and lower functional impacts. Additionally, early and late clone-forming mutations had unique mutational signatures that were distinct from mutations that originated from tumors. Compared with early clone-forming mutations that showed a clock-like signature across all organs or tissues studied, late clone-forming mutations showed organ, tissue, and cell-type specificity in the mutation counts, VAFs, and mutational signatures. In particular, analysis of brain somatic mutations showed a bimodal occurrence and temporal-lobe-specific signature. These findings provide new insights into the features of somatic mosaicism that are dependent on developmental stage and brain regions. Most somatic mutations that arise during normal development are present at low levels in single or multiple tissues, and often show a degree of clonality depending on the time and origin of the mutation. Recent studies have identified the characteristics of postzygotic variants of somatic mutations at the single-cell or mono-clonal levels. However, the results may not be fully representative of the mutational processes involved. Here, we describe a comprehensive analysis of low-level somatic mutations identified after deep whole-exome sequencing in peripheral and brain tissues. We found that clone-forming mutations are uniquely defined by early and late-stage aspects in the mutational profiles. Thus, we identified reliable spatiotemporal characteristics of mosaic variants. Additionally, we found low-level clone-forming mosaic variants across multiple stages and tissues, and identified their intrinsic features.
Collapse
Affiliation(s)
- Ja Hye Kim
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Shinwon Hwang
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
- Department of Medicine, Physician-Scientist Program, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Hyeonju Son
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
- Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Dongsun Kim
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
- Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Il Bin Kim
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
- Department of Psychiatry, Hanyang University Guri Hospital, Guri, Republic of Korea
| | - Myeong-Heui Kim
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
- SoVarGen Inc., Daejeon, Republic of Korea
| | - Nam Suk Sim
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
- Department of Otorhinolaryngology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Dong Seok Kim
- Department of Neurosurgery, Pediatric Neurosurgery, Severance Children’s Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Yoo-Jin Ha
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
- Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Junehawk Lee
- Center for Supercomputing Applications, National Institute of Supercomputing and Networking, Korea Institute of Science and Technology Information, Daejeon, Republic of Korea
| | - Hoon-Chul Kang
- Division of Pediatric Neurology, Department of Pediatrics, Pediatric Epilepsy Clinics, Severance Children’s Hospital, Epilepsy Research Institute, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jeong Ho Lee
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
- SoVarGen Inc., Daejeon, Republic of Korea
- * E-mail: (J.H.L.); (S.K.)
| | - Sangwoo Kim
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
- Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, Republic of Korea
- * E-mail: (J.H.L.); (S.K.)
| |
Collapse
|
16
|
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.
Collapse
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
| |
Collapse
|
17
|
Zou J, Qin W. Comprehensive analysis of the cancer driver genes constructs a seven-gene signature for prediction of survival and tumor immunity in hepatocellular carcinoma. Front Genet 2022; 13:937948. [PMID: 36017503 PMCID: PMC9395598 DOI: 10.3389/fgene.2022.937948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 07/05/2022] [Indexed: 12/02/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is a highly malignant and heterogeneous tumor with poor prognosis. Cancer driver genes (CDGs) play an important role in the carcinogenesis and progression of HCC. In this study, we comprehensively investigated the expression, mutation, and prognostic significance of 568 CDGs in HCC. A prognostic risk model was constructed based on seven CDGs (CDKN2C, HRAS, IRAK1, LOX, MYCN, NRAS, and PABPC1) and verified to be an independent prognostic factor in both TCGA and ICGC cohorts. The low-score group, which showed better prognosis, had a high proportion of CD8+ T cells and elevated expression of interferon-related signaling pathways. Additionally, we constructed a nomogram to extend the clinical applicability of the prognostic model, which exhibits excellent predictive accuracy for survival. Our study showed the important role of CDGs in HCC and provides a novel prognostic indicator for HCC.
Collapse
Affiliation(s)
- Jun Zou
- Department of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wan Qin
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- *Correspondence: Wan Qin,
| |
Collapse
|
18
|
Böldicke T. Therapeutic Potential of Intrabodies for Cancer Immunotherapy: Current Status and Future Directions. Antibodies (Basel) 2022; 11:antib11030049. [PMID: 35892709 PMCID: PMC9326752 DOI: 10.3390/antib11030049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 06/29/2022] [Accepted: 07/12/2022] [Indexed: 12/04/2022] Open
Abstract
Tumor cells are characterized by overexpressed tumor-associated antigens or mutated neoantigens, which are expressed on the cell surface or intracellularly. One strategy of cancer immunotherapy is to target cell-surface-expressed tumor-associated antigens (TAAs) with therapeutic antibodies. For targeting TAAs or neoantigens, adoptive T-cell therapies with activated autologous T cells from cancer patients transduced with novel recombinant TCRs or chimeric antigen receptors have been successfully applied. Many TAAs and most neoantigens are expressed in the cytoplasm or nucleus of tumor cells. As alternative to adoptive T-cell therapy, the mRNA of intracellular tumor antigens can be depleted by RNAi, the corresponding genes or proteins deleted by CRISPR-Cas or inactivated by kinase inhibitors or by intrabodies, respectively. Intrabodies are suitable to knockdown TAAs and neoantigens without off-target effects. RNA sequencing and proteome analysis of single tumor cells combined with computational methods is bringing forward the identification of new neoantigens for the selection of anti-cancer intrabodies, which can be easily performed using phage display antibody repertoires. For specifically delivering intrabodies into tumor cells, the usage of new capsid-modified adeno-associated viruses and lipid nanoparticles coupled with specific ligands to cell surface receptors can be used and might bring cancer intrabodies into the clinic.
Collapse
Affiliation(s)
- Thomas Böldicke
- Department Structure and Function of Proteins, Helmholtz Centre for Infection Research, 38124 Braunschweig, Germany
| |
Collapse
|
19
|
Mani DR, Krug K, Zhang B, Satpathy S, Clauser KR, Ding L, Ellis M, Gillette MA, Carr SA. Cancer proteogenomics: current impact and future prospects. Nat Rev Cancer 2022; 22:298-313. [PMID: 35236940 DOI: 10.1038/s41568-022-00446-5] [Citation(s) in RCA: 64] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/21/2022] [Indexed: 02/07/2023]
Abstract
Genomic analyses in cancer have been enormously impactful, leading to the identification of driver mutations and development of targeted therapies. But the functions of the vast majority of somatic mutations and copy number variants in tumours remain unknown, and the causes of resistance to targeted therapies and methods to overcome them are poorly defined. Recent improvements in mass spectrometry-based proteomics now enable direct examination of the consequences of genomic aberrations, providing deep and quantitative characterization of tumour tissues. Integration of proteins and their post-translational modifications with genomic, epigenomic and transcriptomic data constitutes the new field of proteogenomics, and is already leading to new biological and diagnostic knowledge with the potential to improve our understanding of malignant transformation and therapeutic outcomes. In this Review we describe recent developments in proteogenomics and key findings from the proteogenomic analysis of a wide range of cancers. Considerations relevant to the selection and use of samples for proteogenomics and the current technologies used to generate, analyse and integrate proteomic with genomic data are described. Applications of proteogenomics in translational studies and immuno-oncology are rapidly emerging, and the prospect for their full integration into therapeutic trials and clinical care seems bright.
Collapse
Affiliation(s)
- D R Mani
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA.
| | - Karsten Krug
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Bing Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA
| | - Shankha Satpathy
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Karl R Clauser
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Li Ding
- Department of Medicine and Genetics, Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Matthew Ellis
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA
| | - Michael A Gillette
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Steven A Carr
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA.
| |
Collapse
|
20
|
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.
Collapse
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
| |
Collapse
|
21
|
Andrades R, Recamonde-Mendoza M. Machine learning methods for prediction of cancer driver genes: a survey paper. Brief Bioinform 2022; 23:6551145. [PMID: 35323900 DOI: 10.1093/bib/bbac062] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 02/06/2022] [Accepted: 02/08/2022] [Indexed: 12/21/2022] Open
Abstract
Identifying the genes and mutations that drive the emergence of tumors is a critical step to improving our understanding of cancer and identifying new directions for disease diagnosis and treatment. Despite the large volume of genomics data, the precise detection of driver mutations and their carrying genes, known as cancer driver genes, from the millions of possible somatic mutations remains a challenge. Computational methods play an increasingly important role in discovering genomic patterns associated with cancer drivers and developing predictive models to identify these elements. Machine learning (ML), including deep learning, has been the engine behind many of these efforts and provides excellent opportunities for tackling remaining gaps in the field. Thus, this survey aims to perform a comprehensive analysis of ML-based computational approaches to identify cancer driver mutations and genes, providing an integrated, panoramic view of the broad data and algorithmic landscape within this scientific problem. We discuss how the interactions among data types and ML algorithms have been explored in previous solutions and outline current analytical limitations that deserve further attention from the scientific community. We hope that by helping readers become more familiar with significant developments in the field brought by ML, we may inspire new researchers to address open problems and advance our knowledge towards cancer driver discovery.
Collapse
Affiliation(s)
- Renan Andrades
- Institute of Informatics, Universidade Federal do Rio Grande do Sul, Porto Alegre/RS, Brazil.,Bioinformatics Core, Hospital de Clínicas de Porto Alegre, Porto Alegre/RS, Brazil
| | - Mariana Recamonde-Mendoza
- Institute of Informatics, Universidade Federal do Rio Grande do Sul, Porto Alegre/RS, Brazil.,Bioinformatics Core, Hospital de Clínicas de Porto Alegre, Porto Alegre/RS, Brazil
| |
Collapse
|
22
|
Abstract
Driver mutations promote initiation and progression of cancer. Pharmacological treatment can inhibit the action of the mutant protein; however, drug resistance almost invariably emerges. Multiple studies revealed that cancer drug resistance is based upon a plethora of distinct mechanisms. Drug resistance mutations can occur in the same protein or in different proteins; as well as in the same pathway or in parallel pathways, bypassing the intercepted signaling. The dilemma that the clinical oncologist is facing is that not all the genomic alterations as well as alterations in the tumor microenvironment that facilitate cancer cell proliferation are known, and neither are the alterations that are likely to promote metastasis. For example, the common KRasG12C driver mutation emerges in different cancers. Most occur in NSCLC, but some occur, albeit to a lower extent, in colorectal cancer and pancreatic ductal carcinoma. The responses to KRasG12C inhibitors are variable and fall into three categories, (i) new point mutations in KRas, or multiple copies of KRAS G12C which lead to higher expression level of the mutant protein; (ii) mutations in genes other than KRAS; (iii) original cancer transitioning to other cancer(s). Resistance to adagrasib, an experimental antitumor agent exerting its cytotoxic effect as a covalent inhibitor of the G12C KRas, indicated that half of the cases present multiple KRas mutations as well as allele amplification. Redundant or parallel pathways included MET amplification; emerging driver mutations in NRAS, BRAF, MAP2K1, and RET; gene fusion events in ALK, RET, BRAF, RAF1, and FGFR3; and loss-of-function mutations in NF1 and PTEN tumor suppressors. In the current review we discuss the molecular mechanisms underlying drug resistance while focusing on those emerging to common targeted cancer drivers. We also address questions of why cancers with a common driver mutation are unlikely to evolve a common drug resistance mechanism, and whether one can predict the likely mechanisms that the tumor cell may develop. These vastly important and tantalizing questions in drug discovery, and broadly in precision medicine, are the focus of our present review. We end with our perspective, which calls for target combinations to be selected and prioritized with the help of the emerging massive compute power which enables artificial intelligence, and the increased gathering of data to overcome its insatiable needs.
Collapse
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.
| | - 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
| | - 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
| |
Collapse
|
23
|
Alsulami AF, Torres PHM, Moghul I, Arif SM, Chaplin AK, Vedithi SC, Blundell TL. COSMIC Cancer Gene Census 3D database: understanding the impacts of mutations on cancer targets. Brief Bioinform 2021; 22:bbab220. [PMID: 34137435 PMCID: PMC8574963 DOI: 10.1093/bib/bbab220] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 05/20/2021] [Accepted: 05/21/2021] [Indexed: 01/25/2023] Open
Abstract
Mutations in hallmark genes are believed to be the main drivers of cancer progression. These mutations are reported in the Catalogue of Somatic Mutations in Cancer (COSMIC). Structural appreciation of where these mutations appear, in protein-protein interfaces, active sites or deoxyribonucleic acid (DNA) interfaces, and predicting the impacts of these mutations using a variety of computational tools are crucial for successful drug discovery and development. Currently, there are 723 genes presented in the COSMIC Cancer Gene Census. Due to the complexity of the gene products, structures of only 87 genes have been solved experimentally with structural coverage between 90% and 100%. Here, we present a comprehensive, user-friendly, web interface (https://cancer-3d.com/) of 714 modelled cancer-related genes, including homo-oligomers, hetero-oligomers, transmembrane proteins and complexes with DNA, ribonucleic acid, ligands and co-factors. Using SDM and mCSM software, we have predicted the impacts of reported mutations on protein stability, protein-protein interfaces affinity and protein-nucleic acid complexes affinity. Furthermore, we also predicted intrinsically disordered regions using DISOPRED3.
Collapse
Affiliation(s)
- Ali F Alsulami
- Department of Biochemistry at the University of Cambridge, Cambridge CB2 1GA, UK
| | - Pedro H M Torres
- Laboratório de Modelagem e Dinâmica Molecular, Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brasil
| | | | | | - Amanda K Chaplin
- Department of Biochemistry, University of Cambridge, Cambridge CB2 1GA, UK
| | | | - Tom L Blundell
- Department of Biochemistry, University of Cambridge, Cambridge CB2 1GA, UK
| |
Collapse
|
24
|
Hasbullah HH, Musa M. Gene Therapy Targeting p53 and KRAS for Colorectal Cancer Treatment: A Myth or the Way Forward? Int J Mol Sci 2021; 22:11941. [PMID: 34769370 PMCID: PMC8584926 DOI: 10.3390/ijms222111941] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 10/27/2021] [Accepted: 10/28/2021] [Indexed: 12/12/2022] Open
Abstract
Colorectal cancer (CRC) is the third most commonly diagnosed malignancy worldwide and is responsible as one of the main causes of mortality in both men and women. Despite massive efforts to raise public awareness on early screening and significant advancements in the treatment for CRC, the majority of cases are still being diagnosed at the advanced stage. This contributes to low survivability due to this cancer. CRC patients present various genetic changes and epigenetic modifications. The most common genetic alterations associated with CRC are p53 and KRAS mutations. Gene therapy targeting defect genes such as TP53 (tumor suppressor gene encodes for p53) and KRAS (oncogene) in CRC potentially serves as an alternative treatment avenue for the disease in addition to the standard therapy. For the last decade, significant developments have been seen in gene therapy for translational purposes in treating various cancers. This includes the development of vectors as delivery vehicles. Despite the optimism revolving around targeted gene therapy for cancer treatment, it also has various limitations, such as a lack of availability of related technology, high cost of the involved procedures, and ethical issues. This article will provide a review on the potentials and challenges of gene therapy targeting p53 and KRAS for the treatment of CRC.
Collapse
Affiliation(s)
| | - Marahaini Musa
- Human Genome Centre, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia;
| |
Collapse
|
25
|
Dalurzo ML, Avilés-Salas A, Soares FA, Hou Y, Li Y, Stroganova A, Öz B, Abdillah A, Wan H, Choi YL. Testing for EGFR Mutations and ALK Rearrangements in Advanced Non-Small-Cell Lung Cancer: Considerations for Countries in Emerging Markets. Onco Targets Ther 2021; 14:4671-4692. [PMID: 34511936 PMCID: PMC8420791 DOI: 10.2147/ott.s313669] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 08/08/2021] [Indexed: 12/24/2022] Open
Abstract
The treatment of patients with advanced non-small-cell lung cancer (NSCLC) in recent years has been increasingly guided by biomarker testing. Testing has centered on driver genetic alterations involving the epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK) rearrangements. The presence of these mutations is predictive of response to targeted therapies such as EGFR tyrosine kinase inhibitors (TKIs) and ALK TKIs. However, there are substantial challenges for the implementation of biomarker testing, particularly in emerging countries. Understanding the barriers to testing in NSCLC will be key to improving molecular testing rates worldwide and patient outcomes as a result. In this article, we review EGFR mutations and ALK rearrangements as predictive biomarkers for NSCLC, discuss a selection of appropriate tests and review the literature with respect to the global uptake of EGFR and ALK testing. To help improve testing rates and unify procedures, we review our experiences with biomarker testing in China, South Korea, Russia, Turkey, Brazil, Argentina and Mexico, and propose a set of recommendations that pathologists from emerging countries can apply to assist with the diagnosis of NSCLC.
Collapse
Affiliation(s)
- Mercedes L Dalurzo
- Department of Pathology, Hospital Italiano de Buenos Aires, Buenos Aires, Argentina
| | | | | | - Yingyong Hou
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, People’s Republic of China
| | - Yuan Li
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, People’s Republic of China
| | - Anna Stroganova
- N.N. Blokhin National Medical Research Centre of Oncology, Russian Academy of Medical Sciences, Moscow, Russia
| | - Büge Öz
- Cerrahpaşa School of Medicine, Istanbul University-Cerrahpaşa, Istanbul, Turkey
| | - Arif Abdillah
- Takeda Pharmaceuticals International AG – Singapore Branch, Singapore, Singapore
| | - Hui Wan
- Takeda Pharmaceuticals International AG – Singapore Branch, Singapore, Singapore
| | - Yoon-La Choi
- Department of Pathology and Translational Genomics, Samsung Medical Centre, Sungkyunkwan University School of Medicine, Seoul, South Korea
| |
Collapse
|
26
|
Lu X, Wang X, Ding L, Li J, Gao Y, He K. frDriver: A Functional Region Driver Identification for Protein Sequence. IEEE/ACM Trans Comput Biol Bioinform 2021; 18:1773-1783. [PMID: 32870797 DOI: 10.1109/tcbb.2020.3020096] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Identifying cancer drivers is a crucial challenge to explain the underlying mechanisms of cancer development. There are many methods to identify cancer drivers based on the single mutation site or the entire gene. But they ignore a large number of functional elements with medium in size. It is hypothesized that mutations occurring in different regions of the protein sequence have different effects on the progression of cancer. Here, we develop a novel functional region driver(frDriver) identification method based on Bayesian probability and multiple linear regression models to identify protein regions that can regulate gene expression levels and have high functional impact potential. Combining gene expression data and somatic mutation data, with functional impact scores(SIFT, PROVEAN) as a priori knowledge, we identified cancer driver regions that are most accurate in predicting gene expression levels. We evaluated the performance of frDriver on the BRCA and GBM datasets from TCGA. The results showed that frDriver identified known cancer drivers and outperformed the other three state-of-the-art methods(eDriver, ActiveDriver and OncodriveCLUST). In addition, we performed KEGG pathway and GO term enrichment analysis, and the results indicated that the cancer drivers predicted by frDriver were related to processes such as cancer formation and gene regulation.
Collapse
|
27
|
Grillo E, Ravelli C, Corsini M, Zammataro L, Mitola S. Protein domain-based approaches for the identification and prioritization of therapeutically actionable cancer variants. Biochim Biophys Acta Rev Cancer 2021; 1876:188614. [PMID: 34403770 DOI: 10.1016/j.bbcan.2021.188614] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 08/11/2021] [Accepted: 08/11/2021] [Indexed: 01/04/2023]
Abstract
The tremendous number of cancer variants that can be detected by NGS analyses has required the development of computational approaches to prioritize mutations on the basis of their biological and clinical significance. Standard strategies take a gene-centric approach to the problem, allowing exclusively the identification of highly frequent variants. On the contrary, protein domain (PD)-based approaches allow to identify functionally relevant low frequency variants by searching for mutations that recur on analogous residues across homologous proteins (i.e. containing the same PD). Such approaches enable to transfer information about the effects and druggability from one known mutation to unknown ones. Here we describe how PD-based strategies work, and discuss how they could be exploited for mutation prioritization. The principle that mutations clustered on specific residues of PDs have the same functional consequences and are therapeutically actionable in a similar manner could help the choice of patient-specific targeted drugs, eventually improving the management of cancer patients.
Collapse
Affiliation(s)
- Elisabetta Grillo
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy.
| | - Cosetta Ravelli
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Michela Corsini
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Luca Zammataro
- Division of Artificial Intelligence Systems for Immunoinformatics, Kiromic BioPharma, Inc., Houston, USA
| | - Stefania Mitola
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy.
| |
Collapse
|
28
|
Dorantes-Gilardi R, García-Cortés D, Hernández-Lemus E, Espinal-Enríquez J. k-core genes underpin structural features of breast cancer. Sci Rep 2021; 11:16284. [PMID: 34381069 PMCID: PMC8358063 DOI: 10.1038/s41598-021-95313-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 07/12/2021] [Indexed: 02/07/2023] Open
Abstract
Gene co-expression networks (GCNs) have been developed as relevant analytical tools for the study of the gene expression patterns behind complex phenotypes. Determining the association between structure and function in GCNs is a current challenge in biomedical research. Several structural differences between GCNs of breast cancer and healthy phenotypes have been reported. In a previous study, using co-expression multilayer networks, we have shown that there are abrupt differences in the connectivity patterns of the GCN of basal-like breast cancer between top co-expressed gene-pairs and the remaining gene-pairs. Here, we compared the top-100,000 interactions networks for the four breast cancer phenotypes (Luminal-A, Luminal-B, Her2+ and Basal), in terms of structural properties. For this purpose, we used the graph-theoretical k-core of a network (maximal sub-network with nodes of degree at least k). We developed a comprehensive analysis of the network k-core ([Formula: see text]) structures in cancer, and its relationship with biological functions. We found that in the Top-100,000-edges networks, the majority of interactions in breast cancer networks are intra-chromosome, meanwhile inter-chromosome interactions serve as connecting bridges between clusters. Moreover, core genes in the healthy network are strongly associated with processes such as metabolism and cell cycle. In breast cancer, only the core of Luminal A is related to those processes, and genes in its core are over-expressed. The intersection of the core nodes in all subtypes of cancer is composed only by genes in the chr8q24.3 region. This region has been observed to be highly amplified in several cancers before, and its appearance in the intersection of the four breast cancer k-cores, may suggest that local co-expression is a conserved phenomenon in cancer. Considering the many intricacies associated with these phenomena and the vast amount of research in epigenomic regulation which is currently undergoing, there is a need for further research on the epigenomic effects on the structure and function of gene co-expression networks in cancer.
Collapse
Affiliation(s)
- Rodrigo Dorantes-Gilardi
- grid.261112.70000 0001 2173 3359Network Science Institute and Department of Physics, Northeastern University, Boston, MA 02115 USA ,grid.462201.3El Colegio de México, Tlalpan, Mexico City, 14110 Mexico ,grid.452651.10000 0004 0627 7633Computational Genomics Division, National Institute of Genomic Medicine (INMEGEN), Mexico City, 14610 Mexico
| | - Diana García-Cortés
- grid.452651.10000 0004 0627 7633Computational Genomics Division, National Institute of Genomic Medicine (INMEGEN), Mexico City, 14610 Mexico
| | - Enrique Hernández-Lemus
- grid.452651.10000 0004 0627 7633Computational Genomics Division, National Institute of Genomic Medicine (INMEGEN), Mexico City, 14610 Mexico ,grid.9486.30000 0001 2159 0001Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México (UNAM), Mexico City, 04510 Mexico
| | - Jesús Espinal-Enríquez
- grid.452651.10000 0004 0627 7633Computational Genomics Division, National Institute of Genomic Medicine (INMEGEN), Mexico City, 14610 Mexico ,grid.9486.30000 0001 2159 0001Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México (UNAM), Mexico City, 04510 Mexico
| |
Collapse
|
29
|
Hosseinkhan N, Honardoost M, Blighe K, Moore T, Khamseh ME. Large contribution of copy number alterations in early stage of Papillary Thyroid Carcinoma. Comput Biol Med 2021; 135:104584. [PMID: 34171638 DOI: 10.1016/j.compbiomed.2021.104584] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 06/10/2021] [Accepted: 06/13/2021] [Indexed: 11/23/2022]
Abstract
Papillary Thyroid Carcinoma (PTC) accounts for approximately 85% of patients with thyroid cancer. Despite its indolent nature, progression to higher stages is expected in a subgroup of patients. Hence, genomic characterization of the early stages of PTC may help to identify this subgroup, leading to better clinical management. Here, we conducted a comprehensive mutational and somatic copy number alteration (SCNA) investigation on 277 stage one PTC from TCGA. SCNA analysis revealed amplification and deletion of several cancer related genes. We found amplification of 60 oncogenes (Oncs), from which 15 were recurrently observed. Deletion of 58 tumor suppressors (TSs) was also detected. MAPK, PI3K-Akt, Rap1 and Ras were the signaling pathways with large numbers of amplified Oncs. On the other hand, deleted TSs belonged mostly to cell cycle, PI3K-Akt, mTOR and cellular senescence pathways. This suggests that despite heterogeneity in SCNA events, the final results would be the activation/deactivation of a few cancer signaling pathways. Of note, despite large amounts of heterogeneity in stage one PTC, recurrent broad deletion on Chr22 was detected in 21 individuals, leading to deletion of several tumor suppressors. In parallel, the oncogenic/pathogenic mutations in the RTK-RAS and PI3k-Akt pathways were detected. However, no pathogenic mutation was identified in known tumor suppressor genes. In order to identify a potential subgroup of BRAF (V600E) positive patients, who might progress to higher stages, low frequency mutations accompanying BRAF (V600E) were also identified. In conclusion, our findings imply that SCNA have a substantial contribution to early stages of PTC. Experimental validation of the observed genomic alterations could help to stratify patients at the time of diagnosis, and to move toward precision medicine in PTC.
Collapse
|
30
|
Dotolo S, Marabotti A, Rachiglio AM, Esposito Abate R, Benedetto M, Ciardiello F, De Luca A, Normanno N, Facchiano A, Tagliaferri R. A multiple network-based bioinformatics pipeline for the study of molecular mechanisms in oncological diseases for personalized medicine. Brief Bioinform 2021; 22:6287337. [PMID: 34050359 PMCID: PMC8574709 DOI: 10.1093/bib/bbab180] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 03/17/2021] [Accepted: 04/20/2021] [Indexed: 01/03/2023] Open
Abstract
Motivation Assessment of genetic mutations is an essential element in the modern era of personalized cancer treatment. Our strategy is focused on ‘multiple network analysis’ in which we try to improve cancer diagnostics by using biological networks. Genetic alterations in some important hubs or in driver genes such as BRAF and TP53 play a critical role in regulating many important molecular processes. Most of the studies are focused on the analysis of the effects of single mutations, while tumors often carry mutations of multiple driver genes. The aim of this work is to define an innovative bioinformatics pipeline focused on the design and analysis of networks (such as biomedical and molecular networks), in order to: (1) improve the disease diagnosis; (2) identify the patients that could better respond to a given drug treatment; and (3) predict what are the primary and secondary effects of gene mutations involved in human diseases. Results By using our pipeline based on a multiple network approach, it has been possible to demonstrate and validate what are the joint effects and changes of the molecular profile that occur in patients with metastatic colorectal carcinoma (mCRC) carrying mutations in multiple genes. In this way, we can identify the most suitable drugs for the therapy for the individual patient. This information is useful to improve precision medicine in cancer patients. As an application of our pipeline, the clinically significant case studies of a cohort of mCRC patients with the BRAF V600E-TP53 I195N missense combined mutation were considered. Availability The procedures used in this paper are part of the Cytoscape Core, available at (www.cytoscape.org). Data used here on mCRC patients have been published in [55]. Supplementary Information A supplementary file containing a more detailed discussion of this case study and other cases is available at the journal site as Supplementary Data.
Collapse
Affiliation(s)
- Serena Dotolo
- Dipartimento di Scienze Aziendali, Management & Innovation Systems, Università degli Studi di Salerno, Fisciano (SA), Italy
| | - Anna Marabotti
- Dipartimento di Chimica e Biologia "A. Zambelli", Università degli Studi di Salerno, Fisciano (SA), Italy
| | - Anna Maria Rachiglio
- Cell Biology and Biotherapy Unit, Istituto Nazionale Tumori - IRCCS - Fondazione G. Pascale, Naples, Italy
| | - Riziero Esposito Abate
- Cell Biology and Biotherapy Unit, Istituto Nazionale Tumori -IRCCS - Fondazione G. Pascale, Naples, Italy
| | | | - Fortunato Ciardiello
- Dipartimento di Medicina di Precisione, Università degli Studi della Campania "Luigi Vanvitelli", Naples, Italy
| | - Antonella De Luca
- Cell Biology and Biotherapy Unit, Istituto Nazionale Tumori - IRCCS - Fondazione G. Pascale, Naples, Italy
| | - Nicola Normanno
- Cell Biology and Biotherapy Unit, Istituto Nazionale Tumori - IRCCS - Fondazione G. Pascale, Naples, Italy
| | - Angelo Facchiano
- Institute of Food Sciences, Italian National Research Council (CNR), Avellino, Italy
| | - Roberto Tagliaferri
- Dipartimento di Scienze Aziendali, Management & Innovation Systems, Università degli Studi di Salerno, Fisciano (SA), Italy
| |
Collapse
|
31
|
Rogozin IB, Roche-Lima A, Tyryshkin K, Carrasquillo-Carrión K, Lada AG, Poliakov LY, Schwartz E, Saura A, Yurchenko V, Cooper DN, Panchenko AR, Pavlov YI. DNA Methylation, Deamination, and Translesion Synthesis Combine to Generate Footprint Mutations in Cancer Driver Genes in B-Cell Derived Lymphomas and Other Cancers. Front Genet 2021; 12:671866. [PMID: 34093666 PMCID: PMC8170131 DOI: 10.3389/fgene.2021.671866] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 04/21/2021] [Indexed: 11/13/2022] Open
Abstract
Cancer genomes harbor numerous genomic alterations and many cancers accumulate thousands of nucleotide sequence variations. A prominent fraction of these mutations arises as a consequence of the off-target activity of DNA/RNA editing cytosine deaminases followed by the replication/repair of edited sites by DNA polymerases (pol), as deduced from the analysis of the DNA sequence context of mutations in different tumor tissues. We have used the weight matrix (sequence profile) approach to analyze mutagenesis due to Activation Induced Deaminase (AID) and two error-prone DNA polymerases. Control experiments using shuffled weight matrices and somatic mutations in immunoglobulin genes confirmed the power of this method. Analysis of somatic mutations in various cancers suggested that AID and DNA polymerases η and θ contribute to mutagenesis in contexts that almost universally correlate with the context of mutations in A:T and G:C sites during the affinity maturation of immunoglobulin genes. Previously, we demonstrated that AID contributes to mutagenesis in (de)methylated genomic DNA in various cancers. Our current analysis of methylation data from malignant lymphomas suggests that driver genes are subject to different (de)methylation processes than non-driver genes and, in addition to AID, the activity of pols η and θ contributes to the establishment of methylation-dependent mutation profiles. This may reflect the functional importance of interplay between mutagenesis in cancer and (de)methylation processes in different groups of genes. The resulting changes in CpG methylation levels and chromatin modifications are likely to cause changes in the expression levels of driver genes that may affect cancer initiation and/or progression.
Collapse
Affiliation(s)
- Igor B Rogozin
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, United States
| | - Abiel Roche-Lima
- Center for Collaborative Research in Health Disparities - RCMI Program, University of Puerto Rico, San Juan, Puerto Rico
| | - Kathrin Tyryshkin
- Department of Pathology and Molecular Medicine, School of Medicine, Queen's University, Kingston, ON, Canada
| | | | - Artem G Lada
- Department Microbiology and Molecular Genetics, University of California, Davis, Davis, CA, United States
| | - Lennard Y Poliakov
- Life Science Research Centre, Faculty of Science, University of Ostrava, Ostrava, Czechia
| | - Elena Schwartz
- Coordinating Center for Clinical Trials, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Andreu Saura
- Life Science Research Centre, Faculty of Science, University of Ostrava, Ostrava, Czechia
| | - Vyacheslav Yurchenko
- Life Science Research Centre, Faculty of Science, University of Ostrava, Ostrava, Czechia.,Martsinovsky Institute of Medical Parasitology, Tropical and Vector Borne Diseases, Sechenov First Moscow State Medical University, Moscow, Russia
| | - David N Cooper
- Institute of Medical Genetics, Cardiff University, Cardiff, United Kingdom
| | - Anna R Panchenko
- Department of Pathology and Molecular Medicine, School of Medicine, Queen's University, Kingston, ON, Canada
| | - Youri I Pavlov
- Eppley Institute for Research in Cancer and Allied Diseases, Omaha, NE, United States.,Department of Microbiology and Pathology, Biochemistry and Molecular Biology, Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE, United States.,Department of Genetics and Biotechnology, Saint-Petersburg State University, Saint-Petersburg, Russia
| |
Collapse
|
32
|
Banerjee S, Raman K, Ravindran B. Sequence Neighborhoods Enable Reliable Prediction of Pathogenic Mutations in Cancer Genomes. Cancers (Basel) 2021; 13:cancers13102366. [PMID: 34068918 PMCID: PMC8156421 DOI: 10.3390/cancers13102366] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 04/30/2021] [Indexed: 12/11/2022] Open
Abstract
Simple Summary Cancer is caused by the accumulation of somatic mutations, some of which are responsible for the disease’s progression (drivers) while others are functionally neutral (passengers). Although several methods have been developed to distinguish between the two classes of mutations, very few have concentrated on using the neighborhood nucleotide sequences as potential discrimination features. In this study, we show that driver mutations’ neighborhood is significantly different from that of passengers. We further develop a novel machine learning tool, NBDriver, which is highly efficient at identifying pathogenic variants from multiple independent test datasets. Efficient and accurate identification of novel pathogenic variants from sequenced cancer genomes would help facilitate more effective therapies tailored to patients’ mutational profiles. Abstract Identifying cancer-causing mutations from sequenced cancer genomes hold much promise for targeted therapy and precision medicine. “Driver” mutations are primarily responsible for cancer progression, while “passengers” are functionally neutral. Although several computational approaches have been developed for distinguishing between driver and passenger mutations, very few have concentrated on using the raw nucleotide sequences surrounding a particular mutation as potential features for building predictive models. Using experimentally validated cancer mutation data in this study, we explored various string-based feature representation techniques to incorporate information on the neighborhood bases immediately 5′ and 3′ from each mutated position. Density estimation methods showed significant distributional differences between the neighborhood bases surrounding driver and passenger mutations. Binary classification models derived using repeated cross-validation experiments provided comparable performances across all window sizes. Integrating sequence features derived from raw nucleotide sequences with other genomic, structural, and evolutionary features resulted in the development of a pan-cancer mutation effect prediction tool, NBDriver, which was highly efficient in identifying pathogenic variants from five independent validation datasets. An ensemble predictor obtained by combining the predictions from NBDriver with three other commonly used driver prediction tools (FATHMM (cancer), CONDEL, and MutationTaster) significantly outperformed existing pan-cancer models in prioritizing a literature-curated list of driver and passenger mutations. Using the list of true positive mutation predictions derived from NBDriver, we identified a list of 138 known driver genes with functional evidence from various sources. Overall, our study underscores the efficacy of using raw nucleotide sequences as features to distinguish between driver and passenger mutations from sequenced cancer genomes.
Collapse
Affiliation(s)
- Shayantan Banerjee
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), Indian Institute of Technology (IIT) Madras, Chennai 600 036, India;
- Initiative for Biological Systems Engineering, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
| | - Karthik Raman
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), Indian Institute of Technology (IIT) Madras, Chennai 600 036, India;
- Initiative for Biological Systems Engineering, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Correspondence: (K.R.); (B.R.)
| | - Balaraman Ravindran
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), Indian Institute of Technology (IIT) Madras, Chennai 600 036, India;
- Initiative for Biological Systems Engineering, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Correspondence: (K.R.); (B.R.)
| |
Collapse
|
33
|
Woods K, Perry C, Brühlmann F, Olias P. Theileria's Strategies and Effector Mechanisms for Host Cell Transformation: From Invasion to Immortalization. Front Cell Dev Biol 2021; 9:662805. [PMID: 33959614 PMCID: PMC8096294 DOI: 10.3389/fcell.2021.662805] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 03/30/2021] [Indexed: 12/11/2022] Open
Abstract
One of the first events that follows invasion of leukocytes by Theileria sporozoites is the destruction of the surrounding host cell membrane and the rapid association of the intracellular parasite with host microtubules. This is essential for the parasite to establish its niche within the cytoplasm of the invaded leukocyte and sets Theileria spp. apart from other members of the apicomplexan phylum such as Toxoplasma gondii and Plasmodium spp., which reside within the confines of a host-derived parasitophorous vacuole. After establishing infection, transforming Theileria species (T. annulata, T. parva) significantly rewire the signaling pathways of their bovine host cell, causing continual proliferation and resistance to ligand-induced apoptosis, and conferring invasive properties on the parasitized cell. Having transformed its target cell, Theileria hijacks the mitotic machinery to ensure its persistence in the cytoplasm of the dividing cell. Some of the parasite and bovine proteins involved in parasite-microtubule interactions have been fairly well characterized, and the schizont expresses at least two proteins on its membrane that contain conserved microtubule binding motifs. Theileria-encoded proteins have been shown to be translocated to the host cell cytoplasm and nucleus where they have the potential to directly modify signaling pathways and host gene expression. However, little is known about their mode of action, and even less about how these proteins are secreted by the parasite and trafficked to their target location. In this review we explore the strategies employed by Theileria to transform leukocytes, from sporozoite invasion until immortalization of the host cell has been established. We discuss the recent description of nuclear pore-like complexes that accumulate on membranes close to the schizont surface. Finally, we consider putative mechanisms of protein and nutrient exchange that might occur between the parasite and the host. We focus in particular on differences and similarities with recent discoveries in T. gondii and Plasmodium species.
Collapse
Affiliation(s)
- Kerry Woods
- Institute of Animal Pathology, Vetsuisse Faculty, University of Bern, Bern, Switzerland
| | - Carmen Perry
- Institute of Animal Pathology, Vetsuisse Faculty, University of Bern, Bern, Switzerland
| | - Francis Brühlmann
- Institute of Animal Pathology, Vetsuisse Faculty, University of Bern, Bern, Switzerland
| | - Philipp Olias
- Institute of Animal Pathology, Vetsuisse Faculty, University of Bern, Bern, Switzerland
| |
Collapse
|
34
|
Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer ImmunoMetabolism, National Cancer Institute, Frederick U.S.A.,Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University Israel
| | - Mingzhen Zhang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer ImmunoMetabolism, National Cancer Institute, Frederick U.S.A
| | - Ryan Maloney
- Computational Structural Biology Section, Laboratory of Cancer ImmunoMetabolism, National Cancer Institute, Frederick USA
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer ImmunoMetabolism, National Cancer Institute, Frederick U.S.A
| |
Collapse
|
35
|
Espiritu D, Gribkova AK, Gupta S, Shaytan AK, Panchenko AR. Molecular Mechanisms of Oncogenesis through the Lens of Nucleosomes and Histones. J Phys Chem B 2021; 125:3963-3976. [PMID: 33769808 DOI: 10.1021/acs.jpcb.1c00694] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
At the cellular level, cancer is the disease of both the genome and the epigenome, and the interplay between genetic mutations and epigenetic states may occur at the level of elementary chromatin units, the nucleosomes. They are formed by a segment of DNA wrapped around an octamer of histone proteins. In this review, we survey various mechanisms of cancer etiology and progression mediated by histones and nucleosomes. In particular, we discuss the effects of mutations in histones, changes in their expression and slicing on epigenetic dysregulation and carcinogenesis. The links between cancer phenotypes and differential expression of histone variants and isoforms are summarized. Finally, we discourse the geometric and steric effects of DNA compaction in nucleosomes on DNA mutation rate, interactions with transcription factors, including pioneer transcription factors, and prospects of cancer cells' genome and epigenome editing.
Collapse
Affiliation(s)
- Daniel Espiritu
- Department of Pathology and Molecular Medicine, School of Medicine, Queen's University, Kingston, Ontario, Canada
| | - Anna K Gribkova
- Department of Biology, Lomonosov Moscow State University, 1-12 Leninskie Gory, Moscow, 119991, Russia.,Sirius University of Science and Technology, 1 Olympic Avenue, Sochi, 354340, Russia
| | - Shubhangi Gupta
- Department of Pathology and Molecular Medicine, School of Medicine, Queen's University, Kingston, Ontario, Canada
| | - Alexey K Shaytan
- Department of Biology, Lomonosov Moscow State University, 1-12 Leninskie Gory, Moscow, 119991, Russia.,Sirius University of Science and Technology, 1 Olympic Avenue, Sochi, 354340, Russia.,Bioinformatics Lab, Faculty of Computer Science, HSE University, 11 Pokrovsky Boulevard, Moscow, 109028, Russia
| | - Anna R Panchenko
- Department of Pathology and Molecular Medicine, School of Medicine, Queen's University, Kingston, Ontario, Canada.,Ontario Institute of Cancer Research, Toronto, Ontario, Canada
| |
Collapse
|
36
|
Liu Y, Wu Y, Liu S, Dai Y. Long Non-Coding RNA TRIM52-AS1 Promotes Growth and Metastasis via miR-218-5p/ROBO1 in Hepatocellular Carcinoma. Cancer Manag Res 2021; 13:547-558. [PMID: 33519234 PMCID: PMC7837577 DOI: 10.2147/cmar.s286205] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 12/26/2020] [Indexed: 12/13/2022] Open
Abstract
Background Hepatocellular carcinoma (HCC) is a malignant disease with a high mortality among primary HCC patients worldwide. Lots of studies have shown that lncRNAs are known as the biomarkers in diagnosis, treatment and prognosis of hepatocellular carcinoma. Therefore, clarifying the detailed function and mechanism of the lncRNA in the HCC progressing seems particularly important. Methods The TCGA and GEO database and RT-qPCR were used to analyse the expression of TRIM52-AS1 in HCC tissues and cell lines. Clinical data were collected to further analyze the correlation between indicators of clinical samples and the expression of TRIM52-AS1. CCK-8, plate clone and transwell assays were employed to evaluate the role of TRIM52-AS1 on cell proliferation, migration and invasion. Then, bioinformatics prediction, luciferase reporter, RNA immunoprecipitation (RIP), and RT-qPCR were employed to analyze the direct interaction among TRIM52-AS1, miR-218-5p and ROBO1. Additionally, the rescue function assays were used to verify that miR-218-5p/ROBO1 was the function downstream of TRIM52-AS1. Results TRIM52-AS1 was overexpressed in HCC according to the TCGA database and RT-qPCR assay. The expression of TRIM52-AS1 was higher in the metastatic foci compared with primary tumor according to the GEO database. Additionally, TRIM52-AS1 knockdown inhibited the proliferation and metastasis of HCC cells. TRIM52-AS1 could act as competitive endogenous RNA to regulate ROBO1 through miR-218-5p, then promoted the HCC cell progression. Conclusion TRIM52-AS1 is overexpressed in HCC and can promote the proliferation and metastasis of HCC cells through miR-218-5p/ROBO1 axis, then drives the HCC cell progression.
Collapse
Affiliation(s)
- Yuanjun Liu
- Department of Hepatobiliary Surgery, Suining Central Hospital, Suining 629000, Sichuan Province, People's Republic of China
| | - Yakun Wu
- Department of Hepatobiliary Surgery, Suining Central Hospital, Suining 629000, Sichuan Province, People's Republic of China
| | - Shuang Liu
- Department of Hepatobiliary Surgery, Suining Central Hospital, Suining 629000, Sichuan Province, People's Republic of China
| | - Yi Dai
- Department of Hepatobiliary Surgery, Suining Central Hospital, Suining 629000, Sichuan Province, People's Republic of China
| |
Collapse
|
37
|
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: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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.
Collapse
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
| |
Collapse
|
38
|
Lischer C, Vera-González J. The Road to Effective Cancer Immunotherapy—A Computational Perspective on Tumor Epitopes in Anti-Cancer Immunotherapy. Systems Medicine 2021. [DOI: 10.1016/b978-0-12-801238-3.11605-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
|
39
|
Chiara F, Indraccolo S, Trevisan A. Filling the gap between risk assessment and molecular determinants of tumor onset. Carcinogenesis 2020; 42:507-516. [PMID: 33319226 DOI: 10.1093/carcin/bgaa135] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 11/22/2020] [Accepted: 12/11/2020] [Indexed: 12/30/2022] Open
Abstract
In the past two decades, a ponderous epidemiological literature has causally linked tumor onset to environmental exposure to carcinogens. As consequence, risk assessment studies have been carried out with the aim to identify both predictive models of estimating cancer risks within exposed populations and establishing rules for minimizing hazard when handling carcinogenic compounds. The central assumption of these works is that neoplastic transformation is directly related to the mutational burden of the cell without providing further mechanistic clues to explain increased cancer onset after carcinogen exposure. Nevertheless, in the last few years, a growing number of studies have implemented the traditional models of cancer etiology, proposing that neoplastic transformation is a complex process in which several parameters and crosstalk between tumor and microenvironmental cells must be taken into account and integrated with mutagenesis. In this conceptual framework, the current strategies of risk assessment that are solely based on the 'mutator model' require an urgent update and revision to keep pace with advances in our understanding of cancer biology. We will approach this topic revising the most recent theories on the biological mechanisms involved in tumor formation in order to envision a roadmap leading to a future regulatory framework for a new, protective policy of risk assessment.
Collapse
Affiliation(s)
- Federica Chiara
- Department of Surgery, Oncology and Gastroenterology, University of Padova, Via Giustiniani, Padua, Italy
| | | | - Andrea Trevisan
- Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Via Giustiniani, Padua, Italy
| |
Collapse
|
40
|
Nussinov R, Zhang M, Tsai CJ, Jang H. Phosphorylation and Driver Mutations in PI3Kα and PTEN Autoinhibition. Mol Cancer Res 2020; 19:543-548. [PMID: 33288731 DOI: 10.1158/1541-7786.mcr-20-0818] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 10/29/2020] [Accepted: 12/03/2020] [Indexed: 11/16/2022]
Abstract
PI3K and PTEN are the second and third most highly mutated proteins in cancer following only p53. Their actions oppose each other. PI3K phosphorylates signaling lipid PIP2 to PIP3 PTEN dephosphorylates it back. Driver mutations in both proteins accrue PIP3 PIP3 recruits AKT and PDK1 to the membrane, promoting cell-cycle progression. Here we review phosphorylation events and mutations in autoinhibition in PI3K and PTEN from the structural standpoint. Our purpose is to clarify how they control the autoinhibited states. In autoinhibition, a segment or a subunit of the protein occludes its functional site. Protein-protein interfaces are often only marginally stable, making them sensitive to changes in conditions in living cells. Phosphorylation can stabilize or destabilize the interfaces. Driver mutations commonly destabilize them. In analogy to "passenger mutations," we coin "passenger phosphorylation" to emphasize that the presence of a phosphorylation recognition sequence logo does not necessarily imply function. Rather, it may simply reflect a statistical occurrence. In both PI3K and PTEN, autoinhibiting phosphorylation events are observed in the occluding "piece." In PI3Kα, the "piece" is the p85α subunit. In PTEN, it is the C-terminal segment. In both enzymes the stabilized interface covers the domain that attaches to the membrane. Driver mutations that trigger rotation of the occluding piece or its deletion prompt activation. To date, both enzymes lack specific, potent drugs. We discuss the implications of detailed structural and mechanistic insight into oncogenic activation and how it can advance allosteric precision oncology.
Collapse
Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, NCI, Frederick, Maryland. .,Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Mingzhen Zhang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, NCI, Frederick, Maryland
| | - Chung-Jung Tsai
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, NCI, Frederick, Maryland
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, NCI, Frederick, Maryland
| |
Collapse
|
41
|
Harris KL, Walia V, Gong B, McKim KL, Myers MB, Xu J, Parsons BL. Quantification of cancer driver mutations in human breast and lung DNA using targeted, error-corrected CarcSeq. Environ Mol Mutagen 2020; 61:872-889. [PMID: 32940377 PMCID: PMC7756507 DOI: 10.1002/em.22409] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 09/04/2020] [Accepted: 09/12/2020] [Indexed: 05/14/2023]
Abstract
There is a need for scientifically-sound, practical approaches to improve carcinogenicity testing. Advances in DNA sequencing technology and knowledge of events underlying cancer development have created an opportunity for progress in this area. The long-term goal of this work is to develop variation in cancer driver mutation (CDM) levels as a metric of clonal expansion of cells carrying CDMs because these important early events could inform carcinogenicity testing. The first step toward this goal was to develop and validate an error-corrected next-generation sequencing method to analyze panels of hotspot cancer driver mutations (hCDMs). The "CarcSeq" method that was developed uses unique molecular identifier sequences to construct single-strand consensus sequences for error correction. CarcSeq was used for mutational analysis of 13 amplicons encompassing >20 hotspot CDMs in normal breast, normal lung, ductal carcinomas, and lung adenocarcinomas. The approach was validated by detecting expected differences related to tissue type (normal vs. tumor and breast vs. lung) and mutation spectra. CarcSeq mutant fractions (MFs) correlated strongly with previously obtained ACB-PCR mutant fraction (MF) measurements from the same samples. A reconstruction experiment, in conjunction with other analyses, showed CarcSeq accurately quantifies MFs ≥10-4 . CarcSeq MF measurements were correlated with tissue donor age and breast cancer risk. CarcSeq MF measurements were correlated with deviation from median MFs analyzed to assess clonal expansion. Thus, CarcSeq is a promising approach to advance cancer risk assessment and carcinogenicity testing practices. Paradigms that should be investigated to advance this strategy for carcinogenicity testing are proposed.
Collapse
Affiliation(s)
- Kelly L. Harris
- US Food and Drug Administration, National Center for Toxicological ResearchDivision of Genetic and Molecular ToxicologyJeffersonArkansasUSA
| | - Vijay Walia
- US Food and Drug Administration, National Center for Toxicological ResearchDivision of Genetic and Molecular ToxicologyJeffersonArkansasUSA
- Present address:
USA
| | - Binsheng Gong
- US Food and Drug AdministrationNational Center for Toxicological Research, Division of Bioinformatics and BiostatisticsJeffersonArkansasUSA
| | - Karen L. McKim
- US Food and Drug Administration, National Center for Toxicological ResearchDivision of Genetic and Molecular ToxicologyJeffersonArkansasUSA
| | - Meagan B. Myers
- US Food and Drug Administration, National Center for Toxicological ResearchDivision of Genetic and Molecular ToxicologyJeffersonArkansasUSA
| | - Joshua Xu
- US Food and Drug AdministrationNational Center for Toxicological Research, Division of Bioinformatics and BiostatisticsJeffersonArkansasUSA
| | - Barbara L. Parsons
- US Food and Drug Administration, National Center for Toxicological ResearchDivision of Genetic and Molecular ToxicologyJeffersonArkansasUSA
| |
Collapse
|
42
|
Abstract
Although not all somatic mutations are cancer drivers, their mutational signatures, i.e. the patterns of genomic alterations at a genome-wide scale, provide insights into past exposure to mutagens, DNA damage and repair processes. Computational deconvolution of somatic mutation patterns and expert curation pan-cancer studies have identified a number of mutational signatures associated with point mutations, dinucleotide substitutions, insertions and deletions, and rearrangements, and have established etiologies for a subset of these signatures. However, the mechanisms underlying nearly one-third of all mutational signatures are not yet understood. The signatures with established etiology and those with hitherto unknown origin appear to have some differences in strand bias, GC content and nucleotide context diversity. It is possible that some of the hitherto ‘unknown’ signatures predominantly occur outside gene regions. While nucleotide contexts might be adequate to establish etiologies of some mutational signatures, in other cases additional features, such as broader (epi)genomic contexts, including chromatin, replication timing, processivity and local mutational patterns, may help fully understand the underlying DNA damage and repair processes. Nonetheless, remarkable progress in characterization of mutational signatures has provided fundamental insights into the biology of cancer, informed disease etiology and opened up new opportunities for cancer prevention, risk management, and therapeutic decision making.
Collapse
Affiliation(s)
- Xiaoju Hu
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901, USA
| | - Zhuxuan Xu
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901, USA
| | - Subhajyoti De
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901, USA
| |
Collapse
|
43
|
Babbitt GA, Lynch ML, McCoy M, Fokoue EP, Hudson AO. Function and evolution of B-Raf loop dynamics relevant to cancer recurrence under drug inhibition. J Biomol Struct Dyn 2020; 40:468-483. [DOI: 10.1080/07391102.2020.1815578] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Affiliation(s)
- Gregory A. Babbitt
- Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, New York, USA
| | - Miranda L. Lynch
- Hauptmann-Woodward Medical Research Institute, Buffalo, New York, USA
| | - Matthew McCoy
- Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, USA
| | - Ernest P. Fokoue
- School of Mathematical Sciences, Rochester Institute of Technology, Rochester, New York, USA
| | - André O. Hudson
- Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, New York, USA
| |
Collapse
|
44
|
Wong ETC, So V, Guron M, Kuechler ER, Malhis N, Bui JM, Gsponer J. Protein-Protein Interactions Mediated by Intrinsically Disordered Protein Regions Are Enriched in Missense Mutations. Biomolecules 2020; 10:E1097. [PMID: 32722039 DOI: 10.3390/biom10081097] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Revised: 07/15/2020] [Accepted: 07/20/2020] [Indexed: 12/27/2022] Open
Abstract
Because proteins are fundamental to most biological processes, many genetic diseases can be traced back to single nucleotide variants (SNVs) that cause changes in protein sequences. However, not all SNVs that result in amino acid substitutions cause disease as each residue is under different structural and functional constraints. Influential studies have shown that protein–protein interaction interfaces are enriched in disease-associated SNVs and depleted in SNVs that are common in the general population. These studies focus primarily on folded (globular) protein domains and overlook the prevalent class of protein interactions mediated by intrinsically disordered regions (IDRs). Therefore, we investigated the enrichment patterns of missense mutation-causing SNVs that are associated with disease and cancer, as well as those present in the healthy population, in structures of IDR-mediated interactions with comparisons to classical globular interactions. When comparing the different categories of interaction interfaces, division of the interface regions into solvent-exposed rim residues and buried core residues reveal distinctive enrichment patterns for the various types of missense mutations. Most notably, we demonstrate a strong enrichment at the interface core of interacting IDRs in disease mutations and its depletion in neutral ones, which supports the view that the disruption of IDR interactions is a mechanism underlying many diseases. Intriguingly, we also found an asymmetry across the IDR interaction interface in the enrichment of certain missense mutation types, which may hint at an increased variant tolerance and urges further investigations of IDR interactions.
Collapse
|
45
|
Arbab M, Shen MW, Mok B, Wilson C, Matuszek Ż, Cassa CA, Liu DR. Determinants of Base Editing Outcomes from Target Library Analysis and Machine Learning. Cell 2020; 182:463-480.e30. [PMID: 32533916 PMCID: PMC7384975 DOI: 10.1016/j.cell.2020.05.037] [Citation(s) in RCA: 140] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2019] [Revised: 04/09/2020] [Accepted: 05/19/2020] [Indexed: 12/26/2022]
Abstract
Although base editors are widely used to install targeted point mutations, the factors that determine base editing outcomes are not well understood. We characterized sequence-activity relationships of 11 cytosine and adenine base editors (CBEs and ABEs) on 38,538 genomically integrated targets in mammalian cells and used the resulting outcomes to train BE-Hive, a machine learning model that accurately predicts base editing genotypic outcomes (R ≈ 0.9) and efficiency (R ≈ 0.7). We corrected 3,388 disease-associated SNVs with ≥90% precision, including 675 alleles with bystander nucleotides that BE-Hive correctly predicted would not be edited. We discovered determinants of previously unpredictable C-to-G, or C-to-A editing and used these discoveries to correct coding sequences of 174 pathogenic transversion SNVs with ≥90% precision. Finally, we used insights from BE-Hive to engineer novel CBE variants that modulate editing outcomes. These discoveries illuminate base editing, enable editing at previously intractable targets, and provide new base editors with improved editing capabilities.
Collapse
Affiliation(s)
- Mandana Arbab
- Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA; Howard Hughes Medical Institute, Harvard University, Cambridge, MA 02138, USA
| | - Max W Shen
- Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA; Howard Hughes Medical Institute, Harvard University, Cambridge, MA 02138, USA; Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Beverly Mok
- Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA; Howard Hughes Medical Institute, Harvard University, Cambridge, MA 02138, USA
| | - Christopher Wilson
- Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA; Howard Hughes Medical Institute, Harvard University, Cambridge, MA 02138, USA
| | - Żaneta Matuszek
- Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA; Howard Hughes Medical Institute, Harvard University, Cambridge, MA 02138, USA; Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | - Christopher A Cassa
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - David R Liu
- Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA; Howard Hughes Medical Institute, Harvard University, Cambridge, MA 02138, USA.
| |
Collapse
|
46
|
Binatti A, Bresolin S, Bortoluzzi S, Coppe A. iWhale: a computational pipeline based on Docker and SCons for detection and annotation of somatic variants in cancer WES data. Brief Bioinform 2020; 22:5840042. [PMID: 32436933 PMCID: PMC8557746 DOI: 10.1093/bib/bbaa065] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Revised: 03/27/2020] [Accepted: 03/30/2020] [Indexed: 12/11/2022] Open
Abstract
Whole exome sequencing (WES) is a powerful approach for discovering sequence variants in cancer cells but its time effectiveness is limited by the complexity and issues of WES data analysis. Here we present iWhale, a customizable pipeline based on Docker and SCons, reliably detecting somatic variants by three complementary callers (MuTect2, Strelka2 and VarScan2). The results are combined to obtain a single variant call format file for each sample and variants are annotated by integrating a wide range of information extracted from several reference databases, ultimately allowing variant and gene prioritization according to different criteria. iWhale allows users to conduct a complex series of WES analyses with a powerful yet customizable and easy-to-use tool, running on most operating systems (macOs, GNU/Linux and Windows). iWhale code is freely available at https://github.com/alexcoppe/iWhale and the docker image is downloadable from https://hub.docker.com/r/alexcoppe/iwhale.
Collapse
Affiliation(s)
| | | | - Stefania Bortoluzzi
- Corresponding authors: Stefania Bortoluzzi, Department of Molecular Medicine, University of Padova, Padova, Italy. E-mail: ; Alessandro Coppe, Department of Women's and Children's Health, Department of Biology, University of Padova and Department of Biology, Padova, Italy. Tel.: +39 049 8276502; E-mail:
| | - Alessandro Coppe
- Corresponding authors: Stefania Bortoluzzi, Department of Molecular Medicine, University of Padova, Padova, Italy. E-mail: ; Alessandro Coppe, Department of Women's and Children's Health, Department of Biology, University of Padova and Department of Biology, Padova, Italy. Tel.: +39 049 8276502; E-mail:
| |
Collapse
|
47
|
Franco-Luzón L, García-Mulero S, Sanz-Pamplona R, Melen G, Ruano D, Lassaletta Á, Madero L, González-Murillo Á, Ramírez M. Genetic and Immune Changes Associated with Disease Progression under the Pressure of Oncolytic Therapy in A Neuroblastoma Outlier Patient. Cancers (Basel) 2020; 12:cancers12051104. [PMID: 32354143 PMCID: PMC7281487 DOI: 10.3390/cancers12051104] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 04/25/2020] [Accepted: 04/26/2020] [Indexed: 12/19/2022] Open
Abstract
Little is known about the effect of oncolytic adenovirotherapy on pediatric tumors. Here we present the clinical case of a refractory neuroblastoma that responded positively to Celyvir (ICOVIR-5 oncolytic adenovirus delivered by autologous mesenchymal stem cells) for several months. We analyzed samples during tumor evolution in order to identify molecular and mutational features that could explain the interactions between treatment and tumor and how the balance between both of them evolved. We identified a higher adaptive immune infiltration during stabilized disease compared to progression, and also a higher mutational rate and T-cell receptor (TCR) diversity during disease progression. Our results indicate an initial active role of the immune system controlling tumor growth during Celyvir therapy. The tumor eventually escaped from the control exerted by virotherapy through acquisition of resistance by the tumor microenvironment that exhausted the initial T cell response.
Collapse
Affiliation(s)
- Lidia Franco-Luzón
- Children Oncohematology Foundation, 28079 Madrid, Spain; (L.F.-L.); (L.M.)
| | - Sandra García-Mulero
- Department of Clinical Sciences, Faculty of Medicine and Health Sciences, University of Barcelona, 08036 Barcelona, Spain;
- Unit of Biomarkers and Susceptibility, Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL) and CIBERESP, L’Hospitalet de Llobregat, 08908 Barcelona, Spain;
| | - Rebeca Sanz-Pamplona
- Unit of Biomarkers and Susceptibility, Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL) and CIBERESP, L’Hospitalet de Llobregat, 08908 Barcelona, Spain;
| | - Gustavo Melen
- Biomedical Research Foundation, Niño Jesús Children Hospital, 28009 Madrid, Spain; (G.M.); (Á.G.-M.)
- La Princesa Institute of Health Research, 28006 Madrid, Spain; (D.R.); (Á.L.)
| | - David Ruano
- La Princesa Institute of Health Research, 28006 Madrid, Spain; (D.R.); (Á.L.)
| | - Álvaro Lassaletta
- La Princesa Institute of Health Research, 28006 Madrid, Spain; (D.R.); (Á.L.)
| | - Luís Madero
- Children Oncohematology Foundation, 28079 Madrid, Spain; (L.F.-L.); (L.M.)
- La Princesa Institute of Health Research, 28006 Madrid, Spain; (D.R.); (Á.L.)
- Oncohematology Unit, Hospital Infantil Universitario Niño Jesús, 28009 Madrid, Spain
| | - África González-Murillo
- Biomedical Research Foundation, Niño Jesús Children Hospital, 28009 Madrid, Spain; (G.M.); (Á.G.-M.)
- La Princesa Institute of Health Research, 28006 Madrid, Spain; (D.R.); (Á.L.)
| | - Manuel Ramírez
- Biomedical Research Foundation, Niño Jesús Children Hospital, 28009 Madrid, Spain; (G.M.); (Á.G.-M.)
- La Princesa Institute of Health Research, 28006 Madrid, Spain; (D.R.); (Á.L.)
- Correspondence: ; Tel.: +34-9150-35938
| |
Collapse
|
48
|
Abstract
With advancements in biomarkers and momentum in precision medicine, biomarker-guided trials such as basket trials and umbrella trials have been developed under the master protocol framework. A master protocol refers to a single, overarching design developed to evaluate multiple hypotheses with the general goal of improving the efficiency of trial evaluation. One type of master protocol is the basket trial, in which a targeted therapy is evaluated for multiple diseases that share common molecular alterations or risk factors that may help predict whether the patients will respond to the given therapy. Another variant of a master protocol is the umbrella trial, in which multiple targeted therapies are evaluated for a single disease that is stratified into multiple subgroups based on different molecular or other predictive risk factors. Both designs follow the core principle of precision medicine-to tailor intervention strategies based on the patient's risk factor(s) that can help predict whether they will respond to a specific treatment. There have been increasing numbers of basket and umbrella trials, but they are still poorly understood. This article reviews common characteristics of basket and umbrella trials, key trials and recent US Food and Drug Administration approvals for precision oncology, and important considerations for clinical readers when critically evaluating future publications on basket trials and umbrella trials and for researchers when designing these clinical trials.
Collapse
Affiliation(s)
- Jay J. H. Park
- Experimental Medicine, Department of MedicineUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Grace Hsu
- Department of Health Research Methodology, Evidence, and ImpactMcMaster UniversityHamiltonOntarioCanada
| | - Ellie G. Siden
- Experimental Medicine, Department of MedicineUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Kristian Thorlund
- Department of Health Research Methodology, Evidence, and ImpactMcMaster UniversityHamiltonOntarioCanada
- Cytel IncVancouverBritish ColumbiaCanada
| | - Edward J. Mills
- Department of Health Research Methodology, Evidence, and ImpactMcMaster UniversityHamiltonOntarioCanada
- Cytel IncVancouverBritish ColumbiaCanada
| |
Collapse
|
49
|
Somarelli JA, Gardner H, Cannataro VL, Gunady EF, Boddy AM, Johnson NA, Fisk JN, Gaffney SG, Chuang JH, Li S, Ciccarelli FD, Panchenko AR, Megquier K, Kumar S, Dornburg A, DeGregori J, Townsend JP. Molecular Biology and Evolution of Cancer: From Discovery to Action. Mol Biol Evol 2020; 37:320-326. [PMID: 31642480 PMCID: PMC6993850 DOI: 10.1093/molbev/msz242] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Cancer progression is an evolutionary process. During this process, evolving cancer cell populations encounter restrictive ecological niches within the body, such as the primary tumor, circulatory system, and diverse metastatic sites. Efforts to prevent or delay cancer evolution-and progression-require a deep understanding of the underlying molecular evolutionary processes. Herein we discuss a suite of concepts and tools from evolutionary and ecological theory that can inform cancer biology in new and meaningful ways. We also highlight current challenges to applying these concepts, and propose ways in which incorporating these concepts could identify new therapeutic modes and vulnerabilities in cancer.
Collapse
Affiliation(s)
- Jason A Somarelli
- Department of Medicine, Duke University Medical Center, Durham, NC
- Duke Cancer Institute, Duke University Medical Center, Durham, NC
| | - Heather Gardner
- Sackler School of Graduate Biomedical Sciences, Tufts University, Medford, MA
| | | | - Ella F Gunady
- Department of Medicine, Duke University Medical Center, Durham, NC
| | - Amy M Boddy
- Department of Anthropology, University of California, Santa Barbara, CA
| | | | | | - Stephen G Gaffney
- Department of Biostatistics, Yale School of Public Health, New Haven, CT
| | | | - Sheng Li
- The Jackson Laboratory for Genomic Medicine, Farmington, CT
| | - Francesca D Ciccarelli
- Cancer Systems Biology Laboratory, The Francis Crick Institute, London, United Kingdom
- King’s College London, London, United Kingdom
| | - Anna R Panchenko
- Department of Pathology and Molecular Medicine, School of Medicine, Queen’s University, Kingston, ON, Canada
- Ontario Institute of Cancer Research, Toronto, ON, Canada
| | - Kate Megquier
- Broad Institute, Massachusettes Institute of Technology and Harvard University
| | - Sudhir Kumar
- Institute for Genomics and Evolutionary Medicine, and Department of Biology, Temple University, Philadelphia, PA
| | - Alex Dornburg
- North Carolina Museum of Natural Sciences, Raleigh, NC
| | - James DeGregori
- Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Jeffrey P Townsend
- Department of Biostatistics, Yale School of Public Health, New Haven, CT
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT
| |
Collapse
|
50
|
Abstract
Understanding why driver mutations that promote cancer are sometimes rare is important for precision medicine since it would help in their identification. Driver mutations are largely discovered through their frequencies. Thus, rare mutations often escape detection. Unlike high-frequency drivers, low-frequency drivers can be tissue specific; rare drivers have extremely low frequencies. Here, we discuss rare drivers and strategies to discover them. We suggest that allosteric driver mutations shift the protein ensemble from the inactive to the active state. Rare allosteric drivers are statistically rare since, to switch the protein functional state, they cooperate with additional mutations, and these are not considered in the patient cancer-specific protein sequence analysis. A complete landscape of mutations that drive cancer will reveal tumor-specific therapeutic vulnerabilities.
Collapse
Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA; Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
| | - Chung-Jung Tsai
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
| | - Hyunbum Jang
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
| |
Collapse
|