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Zhang T, Zhang SW, Xie MY, Li Y. Identifying cooperating cancer driver genes in individual patients through hypergraph random walk. J Biomed Inform 2024; 157:104710. [PMID: 39159864 DOI: 10.1016/j.jbi.2024.104710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Revised: 07/30/2024] [Accepted: 08/14/2024] [Indexed: 08/21/2024]
Abstract
OBJECTIVE Identifying cancer driver genes, especially rare or patient-specific cancer driver genes, is a primary goal in cancer therapy. Although researchers have proposed some methods to tackle this problem, these methods mostly identify cancer driver genes at single gene level, overlooking the cooperative relationship among cancer driver genes. Identifying cooperating cancer driver genes in individual patients is pivotal for understanding cancer etiology and advancing the development of personalized therapies. METHODS Here, we propose a novel Personalized Cooperating cancer Driver Genes (PCoDG) method by using hypergraph random walk to identify the cancer driver genes that cooperatively drive individual patient cancer progression. By leveraging the powerful ability of hypergraph in representing multi-way relationships, PCoDG first employs the personalized hypergraph to depict the complex interactions among mutated genes and differentially expressed genes of an individual patient. Then, a hypergraph random walk algorithm based on hyperedge similarity is utilized to calculate the importance scores of mutated genes, integrating these scores with signaling pathway data to identify the cooperating cancer driver genes in individual patients. RESULTS The experimental results on three TCGA cancer datasets (i.e., BRCA, LUAD, and COADREAD) demonstrate the effectiveness of PCoDG in identifying personalized cooperating cancer driver genes. These genes identified by PCoDG not only offer valuable insights into patient stratification correlating with clinical outcomes, but also provide an useful reference resource for tailoring personalized treatments. CONCLUSION We propose a novel method that can effectively identify cooperating cancer driver genes for individual patients, thereby deepening our understanding of the cooperative relationship among personalized cancer driver genes and advancing the development of precision oncology.
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Affiliation(s)
- Tong Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China; School of Electrical and Mechanical Engineering, Pingdingshan University, Pingdingshan 467000, China
| | - Shao-Wu Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.
| | - Ming-Yu Xie
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Yan Li
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
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2
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Wang T, Zhuo L, Chen Y, Fu X, Zeng X, Zou Q. ECD-CDGI: An efficient energy-constrained diffusion model for cancer driver gene identification. PLoS Comput Biol 2024; 20:e1012400. [PMID: 39213450 DOI: 10.1371/journal.pcbi.1012400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 09/12/2024] [Accepted: 08/10/2024] [Indexed: 09/04/2024] Open
Abstract
The identification of cancer driver genes (CDGs) poses challenges due to the intricate interdependencies among genes and the influence of measurement errors and noise. We propose a novel energy-constrained diffusion (ECD)-based model for identifying CDGs, termed ECD-CDGI. This model is the first to design an ECD-Attention encoder by combining the ECD technique with an attention mechanism. ECD-Attention encoder excels at generating robust gene representations that reveal the complex interdependencies among genes while reducing the impact of data noise. We concatenate topological embedding extracted from gene-gene networks through graph transformers to these gene representations. We conduct extensive experiments across three testing scenarios. Extensive experiments show that the ECD-CDGI model possesses the ability to not only be proficient in identifying known CDGs but also efficiently uncover unknown potential CDGs. Furthermore, compared to the GNN-based approach, the ECD-CDGI model exhibits fewer constraints by existing gene-gene networks, thereby enhancing its capability to identify CDGs. Additionally, ECD-CDGI is open-source and freely available. We have also launched the model as a complimentary online tool specifically crafted to expedite research efforts focused on CDGs identification.
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Affiliation(s)
- Tao Wang
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, China
| | - Linlin Zhuo
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, China
| | - Yifan Chen
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Xiangzheng Fu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Xiangxiang Zeng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
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3
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Bottosso M, Mosele F, Michiels S, Cournède PH, Dogan S, Labaki C, André F. Moving toward precision medicine to predict drug sensitivity in patients with metastatic breast cancer. ESMO Open 2024; 9:102247. [PMID: 38401248 PMCID: PMC10982863 DOI: 10.1016/j.esmoop.2024.102247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 01/03/2024] [Accepted: 01/10/2024] [Indexed: 02/26/2024] Open
Abstract
Tumor heterogeneity represents a major challenge in breast cancer, being associated with disease progression and treatment resistance. Precision medicine has been extensively applied to dissect tumor heterogeneity and, through a deeper molecular understanding of the disease, to personalize therapeutic strategies. In the last years, technological advances have widely improved the understanding of breast cancer biology and several trials have been developed to translate these new insights into clinical practice, with the ultimate aim of improving patients' outcomes. In the era of molecular oncology, genomics analyses and other methodologies are shaping a new treatment algorithm in breast cancer care. In this manuscript, we review the main steps of precision medicine to predict drug sensitivity in breast cancer from a translational point of view. Genomic developments and their clinical implications are discussed, along with technological advancements that could broaden precision medicine applications. Current achievements are put into perspective to provide an overview of the state-of-art of breast cancer precision oncology as well as to identify future research directions.
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Affiliation(s)
- M Bottosso
- INSERM Unit U981, Gustave Roussy Cancer Campus, Villejuif, France; Department of Surgery, Oncology and Gastroenterology, University of Padova, Padova, Italy
| | - F Mosele
- INSERM Unit U981, Gustave Roussy Cancer Campus, Villejuif, France; Department of Medical Oncology, Gustave Roussy, Villejuif
| | - S Michiels
- Gustave Roussy, Department of Biostatistics and Epidemiology, Villejuif; Oncostat U1018, Inserm, Université Paris-Saclay, Ligue Contre le Cancer, Villejuif
| | - P-H Cournède
- Université Paris-Saclay, Centrale Supélec, Laboratory of Mathematics and Computer Science (MICS), Gif-Sur-Yvette, France
| | - S Dogan
- INSERM Unit U981, Gustave Roussy Cancer Campus, Villejuif, France
| | - C Labaki
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston; Department of Medicine, Beth Israel Deaconess Medical Center, Boston, USA
| | - F André
- INSERM Unit U981, Gustave Roussy Cancer Campus, Villejuif, France; Department of Medical Oncology, Gustave Roussy, Villejuif; PRISM, INSERM, Gustave Roussy, Villejuif; Paris Saclay University, Gif Sur-Yvette, France.
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4
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Galant N, Nicoś M, Kuźnar-Kamińska B, Krawczyk P. Variant Allele Frequency Analysis of Circulating Tumor DNA as a Promising Tool in Assessing the Effectiveness of Treatment in Non-Small Cell Lung Carcinoma Patients. Cancers (Basel) 2024; 16:782. [PMID: 38398173 PMCID: PMC10887123 DOI: 10.3390/cancers16040782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 02/06/2024] [Accepted: 02/07/2024] [Indexed: 02/25/2024] Open
Abstract
Despite the different possible paths of treatment, lung cancer remains one of the leading causes of death in oncological patients. New tools guiding the therapeutic process are under scientific investigation, and one of the promising indicators of the effectiveness of therapy in patients with NSCLC is variant allele frequency (VAF) analysis. VAF is a metric characterized as the measurement of the specific variant allele proportion within a genomic locus, and it can be determined using methods based on NGS or PCR. It can be assessed using not only tissue samples but also ctDNA (circulating tumor DNA) isolated from liquid biopsy. The non-invasive characteristic of liquid biopsy enables a more frequent collection of material and increases the potential of VAF analysis in monitoring therapy. Several studies have been performed on patients with NSCLC to evaluate the possibility of VAF usage. The research carried out so far demonstrates that the evaluation of VAF dynamics may be useful in monitoring tumor progression, remission, and recurrence during or after treatment. Moreover, the use of VAF analysis appears to be beneficial in making treatment decisions. However, several issues require better understanding and standardization before VAF testing can be implemented in clinical practice. In this review, we discuss the difficulties in the application of ctDNA VAF analysis in clinical routine, discussing the diagnostic and methodological challenges in VAF measurement in liquid biopsy. We highlight the possible applications of VAF-based measurements that are under consideration in clinical trials in the monitoring of personalized treatments for patients with NSCLC.
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Affiliation(s)
- Natalia Galant
- Department of Pneumonology, Oncology and Allergology, Medical University of Lublin, 20-059 Lublin, Poland
| | - Marcin Nicoś
- Department of Pneumonology, Oncology and Allergology, Medical University of Lublin, 20-059 Lublin, Poland
| | - Barbara Kuźnar-Kamińska
- Department of Pulmonology, Allergology and Respiratory Oncology, Poznan University of Medical Sciences, 61-710 Poznan, Poland;
| | - Paweł Krawczyk
- Department of Pneumonology, Oncology and Allergology, Medical University of Lublin, 20-059 Lublin, Poland
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Cui H, Srinivasan S, Gao Z, Korkin D. The Extent of Edgetic Perturbations in the Human Interactome Caused by Population-Specific Mutations. Biomolecules 2023; 14:40. [PMID: 38254640 PMCID: PMC11154503 DOI: 10.3390/biom14010040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 11/30/2023] [Accepted: 12/03/2023] [Indexed: 01/24/2024] Open
Abstract
Until recently, efforts in population genetics have been focused primarily on people of European ancestry. To attenuate this bias, global population studies, such as the 1000 Genomes Project, have revealed differences in genetic variation across ethnic groups. How many of these differences can be attributed to population-specific traits? To answer this question, the mutation data must be linked with functional outcomes. A new "edgotype" concept has been proposed, which emphasizes the interaction-specific, "edgetic", perturbations caused by mutations in the interacting proteins. In this work, we performed systematic in silico edgetic profiling of ~50,000 non-synonymous SNVs (nsSNVs) from the 1000 Genomes Project by leveraging our semi-supervised learning approach SNP-IN tool on a comprehensive set of over 10,000 protein interaction complexes. We interrogated the functional roles of the variants and their impact on the human interactome and compared the results with the pathogenic variants disrupting PPIs in the same interactome. Our results demonstrated that a considerable number of nsSNVs from healthy populations could rewire the interactome. We also showed that the proteins enriched with interaction-disrupting mutations were associated with diverse functions and had implications in a broad spectrum of diseases. Further analysis indicated that distinct gene edgetic profiles among major populations could shed light on the molecular mechanisms behind the population phenotypic variances. Finally, the network analysis revealed that the disease-associated modules surprisingly harbored a higher density of interaction-disrupting mutations from healthy populations. The variation in the cumulative network damage within these modules could potentially account for the observed disparities in disease susceptibility, which are distinctly specific to certain populations. Our work demonstrates the feasibility of a large-scale in silico edgetic study, and reveals insights into the orchestrated play of population-specific mutations in the human interactome.
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Affiliation(s)
- Hongzhu Cui
- Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, Worcester, MA 01609, USA;
- Chromatography and Mass Spectrometry Division, Thermo Fisher Scientific, San Jose, CA 95134, USA
| | - Suhas Srinivasan
- Data Science Program, Worcester Polytechnic Institute, Worcester, MA 01609, USA;
- Program in Epithelial Biology, Stanford School of Medicine, Stanford, CA 94305, USA
- Center for Personal Dynamic Regulomes, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Ziyang Gao
- Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, Worcester, MA 01609, USA;
| | - Dmitry Korkin
- Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, Worcester, MA 01609, USA;
- Data Science Program, Worcester Polytechnic Institute, Worcester, MA 01609, USA;
- Computer Science Department, Worcester Polytechnic Institute, Worcester, MA 01609, USA
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6
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Boscolo Bielo L, Trapani D, Repetto M, Crimini E, Valenza C, Belli C, Criscitiello C, Marra A, Subbiah V, Curigliano G. Variant allele frequency: a decision-making tool in precision oncology? Trends Cancer 2023; 9:1058-1068. [PMID: 37704501 DOI: 10.1016/j.trecan.2023.08.011] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 08/17/2023] [Accepted: 08/22/2023] [Indexed: 09/15/2023]
Abstract
Precision oncology requires additional predictive biomarkers for targeted therapy selection. Variant allele frequency (VAF), measuring the proportion of variant alleles within a genomic locus, provides insights into tumor clonality in somatic genomic testing, yielding a strong rationale for targeting dominant cancer cell populations. The prognostic and predictive roles of VAF have been evaluated across different studies. Yet, the absence of validated VAF thresholds and a lack of standardization between sequencing assays currently hampers its clinical utility. Therefore, analytical and clinical validation must be further examined. This Review summarizes the evidence regarding the use of VAF as a predictive biomarker and discusses challenges and opportunities for its clinical implementation as a decision-making tool for targeted therapy selection.
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Affiliation(s)
- Luca Boscolo Bielo
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Dario Trapani
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Matteo Repetto
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy; Early Drug Development service, Memorial Sloan-Kettering Cancer Center, New York, USA
| | - Edoardo Crimini
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Carmine Valenza
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Carmen Belli
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan, Italy
| | - Carmen Criscitiello
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Antonio Marra
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan, Italy
| | - Vivek Subbiah
- Drug Development Unit, Sarah Cannon Research Institute, Nashville, TN, USA
| | - Giuseppe Curigliano
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy.
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7
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Pankiw M, Brezden-Masley C, Charames GS. Comprehensive genomic profiling for oncological advancements by precision medicine. Med Oncol 2023; 41:1. [PMID: 37993657 DOI: 10.1007/s12032-023-02228-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 10/25/2023] [Indexed: 11/24/2023]
Abstract
Considerable advancements in next generation sequencing (NGS) techniques have sparked the use of comprehensive genomic profiling (CGP) as a guiding tool for precision-centered oncological treatments. The past two decades have seen the completion of the human genome project, and the consequential invention of NGS. High-throughput sequencing technologies support the discovery and commonplace use of individualized cancer treatments, specifically immune-centered checkpoint inhibitor therapies, and oncogene and tumor suppressor gene targeted therapies. Nevertheless, CGP is not commonly used in all clinical settings. This review investigates the clinically relevant applications of CGP. Studies published between the years 2000-2023 have shown substantial evidence of the benefits of integrating CGP into routine care practice, while also making important comparisons to current-standard oncological treatment strategies. Findings of a comprehensive genomic profile includes predictive, prognostic, and diagnostic biomarkers, together with somatic mutation identification which can indicate the efficacy of immunotherapies and molecularly guided therapies. This review highlights the importance of CGP in identifying driver mutations in tumors that subsequently can be effectively targeted with molecular therapeutics and lead to drug discovery, allowing for increased precision in treating tumors selectively based on their specific genetic mutations, thereby improving patient outcomes.
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Affiliation(s)
- Maya Pankiw
- Department of Medicine, Mount Sinai Hospital, Toronto, ON, Canada
- Department of Pathology and Lab Medicine, Mount Sinai Hospital, Toronto, ON, Canada
| | - Christine Brezden-Masley
- Department of Medicine, Mount Sinai Hospital, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
- Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - George S Charames
- Department of Pathology and Lab Medicine, Mount Sinai Hospital, Toronto, ON, Canada.
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.
- Department of Lab Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada.
- Mount Sinai Services, Toronto, ON, Canada.
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Khalili N, Shooli H, Hosseini N, Fathi Kazerooni A, Familiar A, Bagheri S, Anderson H, Bagley SJ, Nabavizadeh A. Adding Value to Liquid Biopsy for Brain Tumors: The Role of Imaging. Cancers (Basel) 2023; 15:5198. [PMID: 37958372 PMCID: PMC10650848 DOI: 10.3390/cancers15215198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 10/24/2023] [Accepted: 10/26/2023] [Indexed: 11/15/2023] Open
Abstract
Clinical management in neuro-oncology has changed to an integrative approach that incorporates molecular profiles alongside histopathology and imaging findings. While the World Health Organization (WHO) guideline recommends the genotyping of informative alterations as a routine clinical practice for central nervous system (CNS) tumors, the acquisition of tumor tissue in the CNS is invasive and not always possible. Liquid biopsy is a non-invasive approach that provides the opportunity to capture the complex molecular heterogeneity of the whole tumor through the detection of circulating tumor biomarkers in body fluids, such as blood or cerebrospinal fluid (CSF). Despite all of the advantages, the low abundance of tumor-derived biomarkers, particularly in CNS tumors, as well as their short half-life has limited the application of liquid biopsy in clinical practice. Thus, it is crucial to identify the factors associated with the presence of these biomarkers and explore possible strategies that can increase the shedding of these tumoral components into biological fluids. In this review, we first describe the clinical applications of liquid biopsy in CNS tumors, including its roles in the early detection of recurrence and monitoring of treatment response. We then discuss the utilization of imaging in identifying the factors that affect the detection of circulating biomarkers as well as how image-guided interventions such as focused ultrasound can help enhance the presence of tumor biomarkers through blood-brain barrier (BBB) disruption.
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Affiliation(s)
- Nastaran Khalili
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA; (N.K.); (A.F.K.); (A.F.)
| | - Hossein Shooli
- Department of Radiology, Bushehr University of Medical Sciences, Bushehr 75146-33196, Iran
| | - Nastaran Hosseini
- School of Medicine, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran;
| | - Anahita Fathi Kazerooni
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA; (N.K.); (A.F.K.); (A.F.)
- AI2D Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ariana Familiar
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA; (N.K.); (A.F.K.); (A.F.)
| | - Sina Bagheri
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (S.B.); (H.A.)
| | - Hannah Anderson
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (S.B.); (H.A.)
| | - Stephen J. Bagley
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA;
| | - Ali Nabavizadeh
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA; (N.K.); (A.F.K.); (A.F.)
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (S.B.); (H.A.)
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Yi D, Nam JW, Jeong H. Toward the functional interpretation of somatic structural variations: bulk- and single-cell approaches. Brief Bioinform 2023; 24:bbad297. [PMID: 37587831 PMCID: PMC10516374 DOI: 10.1093/bib/bbad297] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 07/05/2023] [Accepted: 07/23/2023] [Indexed: 08/18/2023] Open
Abstract
Structural variants (SVs) are genomic rearrangements that can take many different forms such as copy number alterations, inversions and translocations. During cell development and aging, somatic SVs accumulate in the genome with potentially neutral, deleterious or pathological effects. Generation of somatic SVs is a key mutational process in cancer development and progression. Despite their importance, the detection of somatic SVs is challenging, making them less studied than somatic single-nucleotide variants. In this review, we summarize recent advances in whole-genome sequencing (WGS)-based approaches for detecting somatic SVs at the tissue and single-cell levels and discuss their advantages and limitations. First, we describe the state-of-the-art computational algorithms for somatic SV calling using bulk WGS data and compare the performance of somatic SV detectors in the presence or absence of a matched-normal control. We then discuss the unique features of cutting-edge single-cell-based techniques for analyzing somatic SVs. The advantages and disadvantages of bulk and single-cell approaches are highlighted, along with a discussion of their sensitivity to copy-neutral SVs, usefulness for functional inferences and experimental and computational costs. Finally, computational approaches for linking somatic SVs to their functional readouts, such as those obtained from single-cell transcriptome and epigenome analyses, are illustrated, with a discussion of the promise of these approaches in health and diseases.
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Affiliation(s)
- Dohun Yi
- Department of Life Science, College of Natural Sciences, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
| | - Jin-Wu Nam
- Department of Life Science, College of Natural Sciences, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Research Institute for Convergence of Basic Sciences, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Bio-BigData Center, Hanyang Institute of Bioscience and Biotechnology, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Hanyang Institute of Advanced BioConvergence, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
| | - Hyobin Jeong
- Department of Life Science, College of Natural Sciences, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Bio-BigData Center, Hanyang Institute of Bioscience and Biotechnology, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Hanyang Institute of Advanced BioConvergence, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
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Yang L, Chen R, Melendy T, Goodison S, Sun Y. Identifying Significantly Perturbed Subnetworks in Cancer Using Multiple Protein-Protein Interaction Networks. Cancers (Basel) 2023; 15:4090. [PMID: 37627118 PMCID: PMC10452419 DOI: 10.3390/cancers15164090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 08/03/2023] [Accepted: 08/12/2023] [Indexed: 08/27/2023] Open
Abstract
BACKGROUND The identification of cancer driver genes and key molecular pathways has been the focus of large-scale cancer genome studies. Network-based methods detect significantly perturbed subnetworks as putative cancer pathways by incorporating genomics data with the topological information of PPI networks. However, commonly used PPI networks have distinct topological structures, making the results of the same method vary widely when applied to different networks. Furthermore, emerging context-specific PPI networks often have incomplete topological structures, which pose serious challenges for existing subnetwork detection algorithms. METHODS In this paper, we propose a novel method, referred to as MultiFDRnet, to address the above issues. The basic idea is to model a set of PPI networks as a multiplex network to preserve the topological structure of individual networks, while introducing dependencies among them, and, then, to detect significantly perturbed subnetworks on the modeled multiplex network using all the structural information simultaneously. RESULTS To illustrate the effectiveness of the proposed approach, an extensive benchmark analysis was conducted on both simulated and real cancer data. The experimental results showed that the proposed method is able to detect significantly perturbed subnetworks jointly supported by multiple PPI networks and to identify novel modular structures in context-specific PPI networks.
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Affiliation(s)
- Le Yang
- Department of Microbiology and Immunology, The State University of New York at Buffalo, Buffalo, NY 14203, USA; (L.Y.); (R.C.); (T.M.)
| | - Runpu Chen
- Department of Microbiology and Immunology, The State University of New York at Buffalo, Buffalo, NY 14203, USA; (L.Y.); (R.C.); (T.M.)
| | - Thomas Melendy
- Department of Microbiology and Immunology, The State University of New York at Buffalo, Buffalo, NY 14203, USA; (L.Y.); (R.C.); (T.M.)
| | - Steve Goodison
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL 32224, USA;
| | - Yijun Sun
- Department of Microbiology and Immunology, The State University of New York at Buffalo, Buffalo, NY 14203, USA; (L.Y.); (R.C.); (T.M.)
- Department of Computer Science and Engineering, The State University of New York at Buffalo, Buffalo, NY 14203, USA
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Kiss A, Hariri Akbari F, Marchev A, Papp V, Mirmazloum I. The Cytotoxic Properties of Extreme Fungi's Bioactive Components-An Updated Metabolic and Omics Overview. Life (Basel) 2023; 13:1623. [PMID: 37629481 PMCID: PMC10455657 DOI: 10.3390/life13081623] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 06/28/2023] [Accepted: 06/29/2023] [Indexed: 08/27/2023] Open
Abstract
Fungi are the most diverse living organisms on planet Earth, where their ubiquitous presence in various ecosystems offers vast potential for the research and discovery of new, naturally occurring medicinal products. Concerning human health, cancer remains one of the leading causes of mortality. While extensive research is being conducted on treatments and their efficacy in various stages of cancer, finding cytotoxic drugs that target tumor cells with no/less toxicity toward normal tissue is a significant challenge. In addition, traditional cancer treatments continue to suffer from chemical resistance. Fortunately, the cytotoxic properties of several natural products derived from various microorganisms, including fungi, are now well-established. The current review aims to extract and consolidate the findings of various scientific studies that identified fungi-derived bioactive metabolites with antitumor (anticancer) properties. The antitumor secondary metabolites identified from extremophilic and extremotolerant fungi are grouped according to their biological activity and type. It became evident that the significance of these compounds, with their medicinal properties and their potential application in cancer treatment, is tremendous. Furthermore, the utilization of omics tools, analysis, and genome mining technology to identify the novel metabolites for targeted treatments is discussed. Through this review, we tried to accentuate the invaluable importance of fungi grown in extreme environments and the necessity of innovative research in discovering naturally occurring bioactive compounds for the development of novel cancer treatments.
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Affiliation(s)
- Attila Kiss
- Agro-Food Science Techtransfer and Innovation Centre, Faculty for Agro, Food and Environmental Science, Debrecen University, 4032 Debrecen, Hungary;
| | - Farhad Hariri Akbari
- Department of Biology, Biotechnical Faculty, University of Ljubljana, 1000 Ljubljana, Slovenia;
| | - Andrey Marchev
- Laboratory of Metabolomics, Department of Biotechnology, The Stephan Angeloff Institute of Microbiology, Bulgarian Academy of Sciences, 4000 Plovdiv, Bulgaria
| | - Viktor Papp
- Department of Botany, Hungarian University of Agriculture and Life Sciences, 1118 Budapest, Hungary;
| | - Iman Mirmazloum
- Department of Plant Physiology and Plant Ecology, Institute of Agronomy, Hungarian University of Agriculture and Life Sciences, 1118 Budapest, Hungary
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12
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Mohanty SK, Mishra SK, Amin MB, Agaimy A, Fuchs F. Role of Surgical Pathologist for the Detection of Immuno-oncologic Predictive Factors in Non-small Cell Lung Cancers. Adv Anat Pathol 2023; 30:174-194. [PMID: 37037418 DOI: 10.1097/pap.0000000000000395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2023]
Abstract
Until very recently, surgery, chemotherapy, and radiation therapy have been the mainstay of treatment in non-small cell carcinomas (NSCLCs). However, recent advances in molecular immunology have unveiled some of the complexity of the mechanisms regulating cellular immune responses and led to the successful targeting of immune checkpoints in attempts to enhance antitumor T-cell responses. Immune checkpoint molecules such as cytotoxic T-lymphocyte associated protein-4, programmed cell death protein-1, and programmed death ligand (PD-L) 1 have been shown to play central roles in evading cancer immunity. Thus, these molecules have been targeted by inhibitors for the management of cancers forming the basis of immunotherapy. Advanced NSCLC has been the paradigm for the benefits of immunotherapy in any cancer. Treatment decisions are made based on the expression of PD-L1 on the tumor cells and the presence or absence of driver mutations. Patients with high PD-L1 expression (≥50%) and no driver mutations are treated with single-agent immunotherapy whereas, for all other patients with a lower level of PD-L1 expression, a combination of chemotherapy and immunotherapy is preferred. Thus, PD-L1 blockers are the only immunotherapeutic agents approved in advanced NSCLC without any oncogenic driver mutations. PD-L1 immunohistochemistry, however, may not be the best biomarker in view of its dynamic nature in time and space, and the benefits may be seen regardless of PD -L1 expression. Each immunotherapy molecule is prescribed based on the levels of PD-L1 expression as assessed by a Food and Drug Administration-approved companion diagnostic assay. Other biomarkers that have been studied include tumor mutational burden, the T-effector signature, tumor-infiltrating lymphocytes, radiomic assays, inflammation index, presence or absence of immune-related adverse events and specific driver mutations, and gut as well as local microbiome. At the current time, none of these biomarkers are routinely used in the clinical decision-making process for immunotherapy in NSCLC. However, in individual cases, they can be useful adjuncts to conventional therapy. This review describes our current understanding of the role of biomarkers as predictors of response to immune checkpoint molecules. To begin with a brief on cancer immunology in general and in NSCLC, in particular, is discussed. In the end, recent advancements in laboratory techniques for refining biomarker assays are described.
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Affiliation(s)
- Sambit K Mohanty
- Department of Pathology and Laboratory Medicine, Advanced Medical Research Institute, Bhubaneswar, India and CORE Diagnostics, Gurgaon, HR
| | - Sourav K Mishra
- Department of Medical Oncology, All India Institute of Medical Sciences, DL, India
| | - Mahul B Amin
- Departments of Pathology and Laboratory Medicine and Urology, University of Tennessee Health Science Center, Memphis, TN
| | - Abbas Agaimy
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Florian Fuchs
- Department of Internal Medicine-1, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Erlangen University Hospital and Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany
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13
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Witte HM, Fähnrich A, Künstner A, Riedl J, Fliedner SMJ, Reimer N, Hertel N, von Bubnoff N, Bernard V, Merz H, Busch H, Feller A, Gebauer N. Primary refractory plasmablastic lymphoma: A precision oncology approach. Front Oncol 2023; 13:1129405. [PMID: 36923431 PMCID: PMC10008852 DOI: 10.3389/fonc.2023.1129405] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 02/13/2023] [Indexed: 03/01/2023] Open
Abstract
Introduction Hematologic malignancies are currently underrepresented in multidisciplinary molecular-tumor-boards (MTB). This study assesses the potential of precision-oncology in primary-refractory plasmablastic-lymphoma (prPBL), a highly lethal blood cancer. Methods We evaluated clinicopathological and molecular-genetic data of 14 clinically annotated prPBL-patients from initial diagnosis. For this proof-of-concept study, we employed our certified institutional MTB-pipeline (University-Cancer-Center-Schleswig-Holstein, UCCSH) to annotate a comprehensive dataset within the scope of a virtual MTB-setting, ultimately recommending molecularly stratified therapies. Evidence-levels for MTB-recommendations were defined in accordance with the NCT/DKTK and ESCAT criteria. Results Median age in the cohort was 76.5 years (range 56-91), 78.6% of patients were male, 50% were HIV-positive and clinical outcome was dismal. Comprehensive genomic/transcriptomic analysis revealed potential recommendations of a molecularly stratified treatment option with evidence-levels according to NCT/DKTK of at least m2B/ESCAT of at least IIIA were detected for all 14 prPBL-cases. In addition, immunohistochemical-assessment (CD19/CD30/CD38/CD79B) revealed targeted treatment-recommendations in all 14 cases. Genetic alterations were classified by treatment-baskets proposed by Horak et al. Hereby, we identified tyrosine-kinases (TK; n=4), PI3K-MTOR-AKT-pathway (PAM; n=3), cell-cycle-alterations (CC; n=2), RAF-MEK-ERK-cascade (RME; n=2), immune-evasion (IE; n=2), B-cell-targets (BCT; n=25) and others (OTH; n=4) for targeted treatment-recommendations. The minimum requirement for consideration of a drug within the scope of the study was FDA-fast-track development. Discussion The presented proof-of-concept study demonstrates the clinical potential of precision-oncology, even in prPBL-patients. Due to the aggressive course of the disease, there is an urgent medical-need for personalized treatment approaches, and this population should be considered for MTB inclusion at the earliest time.
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Affiliation(s)
- Hanno M Witte
- Department of Hematology and Oncology, University Hospital of Schleswig-Holstein, Lübeck, Germany.,Department of Hematology and Oncology, Federal Armed Forces Hospital, Ulm, Germany
| | - Anke Fähnrich
- Medical Systems Biology Group, Lübeck Institute of Experimental Dermatology, University of Lübeck, Lübeck, Germany.,Institute for Cardiogenetics, University of Lübeck, Lübeck, Germany.,University Cancer Center Schleswig-Holstein, University Hospital of Schleswig- Holstein, Lübeck, Germany
| | - Axel Künstner
- Medical Systems Biology Group, Lübeck Institute of Experimental Dermatology, University of Lübeck, Lübeck, Germany.,Institute for Cardiogenetics, University of Lübeck, Lübeck, Germany.,University Cancer Center Schleswig-Holstein, University Hospital of Schleswig- Holstein, Lübeck, Germany
| | - Jörg Riedl
- Department of Hematology and Oncology, University Hospital of Schleswig-Holstein, Lübeck, Germany.,Hämatopathologie Lübeck, Reference Centre for Lymph Node Pathology and Hematopathology, Lübeck, Germany
| | - Stephanie M J Fliedner
- Department of Hematology and Oncology, University Hospital of Schleswig-Holstein, Lübeck, Germany.,University Cancer Center Schleswig-Holstein, University Hospital of Schleswig- Holstein, Lübeck, Germany
| | - Niklas Reimer
- Medical Systems Biology Group, Lübeck Institute of Experimental Dermatology, University of Lübeck, Lübeck, Germany.,Institute for Cardiogenetics, University of Lübeck, Lübeck, Germany.,University Cancer Center Schleswig-Holstein, University Hospital of Schleswig- Holstein, Lübeck, Germany
| | - Nadine Hertel
- Department of Hematology and Oncology, University Hospital of Schleswig-Holstein, Lübeck, Germany
| | - Nikolas von Bubnoff
- Department of Hematology and Oncology, University Hospital of Schleswig-Holstein, Lübeck, Germany.,University Cancer Center Schleswig-Holstein, University Hospital of Schleswig- Holstein, Lübeck, Germany
| | - Veronica Bernard
- Hämatopathologie Lübeck, Reference Centre for Lymph Node Pathology and Hematopathology, Lübeck, Germany
| | - Hartmut Merz
- University Cancer Center Schleswig-Holstein, University Hospital of Schleswig- Holstein, Lübeck, Germany
| | - Hauke Busch
- Medical Systems Biology Group, Lübeck Institute of Experimental Dermatology, University of Lübeck, Lübeck, Germany.,Institute for Cardiogenetics, University of Lübeck, Lübeck, Germany.,University Cancer Center Schleswig-Holstein, University Hospital of Schleswig- Holstein, Lübeck, Germany
| | - Alfred Feller
- Hämatopathologie Lübeck, Reference Centre for Lymph Node Pathology and Hematopathology, Lübeck, Germany
| | - Niklas Gebauer
- Department of Hematology and Oncology, University Hospital of Schleswig-Holstein, Lübeck, Germany.,University Cancer Center Schleswig-Holstein, University Hospital of Schleswig- Holstein, Lübeck, Germany
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14
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Wang EJY, Chen IH, Kuo BYT, Yu CC, Lai MT, Lin JT, Lin LYT, Chen CM, Hwang T, Sheu JJC. Alterations of Cytoskeleton Networks in Cell Fate Determination and Cancer Development. Biomolecules 2022; 12:biom12121862. [PMID: 36551290 PMCID: PMC9775460 DOI: 10.3390/biom12121862] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 12/03/2022] [Accepted: 12/10/2022] [Indexed: 12/14/2022] Open
Abstract
Cytoskeleton proteins have been long recognized as structural proteins that provide the necessary mechanical architecture for cell development and tissue homeostasis. With the completion of the cancer genome project, scientists were surprised to learn that huge numbers of mutated genes are annotated as cytoskeletal or associated proteins. Although most of these mutations are considered as passenger mutations during cancer development and evolution, some genes show high mutation rates that can even determine clinical outcomes. In addition, (phospho)proteomics study confirms that many cytoskeleton-associated proteins, e.g., β-catenin, PIK3CA, and MB21D2, are important signaling mediators, further suggesting their biofunctional roles in cancer development. With emerging evidence to indicate the involvement of mechanotransduction in stemness formation and cell differentiation, mutations in these key cytoskeleton components may change the physical/mechanical properties of the cells and determine the cell fate during cancer development. In particular, tumor microenvironment remodeling triggered by such alterations has been known to play important roles in autophagy, metabolism, cancer dormancy, and immune evasion. In this review paper, we will highlight the current understanding of how aberrant cytoskeleton networks affect cancer behaviors and cellular functions through mechanotransduction.
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Affiliation(s)
- Evan Ja-Yang Wang
- Institute of Biomedical Sciences, National Sun Yat-sen University, Kaohsiung 804201, Taiwan
| | - I-Hsuan Chen
- Department of Surgery, Kaohsiung Veterans General Hospital, Kaohsiung 813405, Taiwan
- Department of Pharmacy, College of Pharmacy and Health Care, Tajen University, Pingtung County 907391, Taiwan
- School of Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
| | - Brian Yu-Ting Kuo
- Institute of Biomedical Sciences, National Sun Yat-sen University, Kaohsiung 804201, Taiwan
| | - Chia-Cheng Yu
- Department of Surgery, Kaohsiung Veterans General Hospital, Kaohsiung 813405, Taiwan
- Department of Pharmacy, College of Pharmacy and Health Care, Tajen University, Pingtung County 907391, Taiwan
- School of Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
- Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei 114202, Taiwan
| | - Ming-Tsung Lai
- Department of Pathology, Taichung Hospital, Ministry of Health and Welfare, Taichung 403301, Taiwan
| | - Jen-Tai Lin
- Department of Surgery, Kaohsiung Veterans General Hospital, Kaohsiung 813405, Taiwan
| | - Leo Yen-Ting Lin
- Institute of Biomedical Sciences, National Sun Yat-sen University, Kaohsiung 804201, Taiwan
| | - Chih-Mei Chen
- Human Genetic Center, China Medical University Hospital, Taichung 404327, Taiwan
| | - Tritium Hwang
- Institute of Biomedical Sciences, National Sun Yat-sen University, Kaohsiung 804201, Taiwan
| | - Jim Jinn-Chyuan Sheu
- Institute of Biomedical Sciences, National Sun Yat-sen University, Kaohsiung 804201, Taiwan
- Department of Biotechnology, Kaohsiung Medical University, Kaohsiung 807378, Taiwan
- Institute of Biopharmaceutical Sciences, National Sun Yat-sen University, Kaohsiung 804201, Taiwan
- Institute of Precision Medicine, National Sun Yat-sen University, Kaohsiung 804201, Taiwan
- Correspondence: ; Tel.: +886-7-5252000 (ext. 7102)
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15
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Nussinov R, Tsai CJ, Jang H. A New View of Activating Mutations in Cancer. Cancer Res 2022; 82:4114-4123. [PMID: 36069825 PMCID: PMC9664134 DOI: 10.1158/0008-5472.can-22-2125] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/16/2022] [Accepted: 09/01/2022] [Indexed: 12/14/2022]
Abstract
A vast effort has been invested in the identification of driver mutations of cancer. However, recent studies and observations call into question whether the activating mutations or the signal strength are the major determinant of tumor development. The data argue that signal strength determines cell fate, not the mutation that initiated it. In addition to activating mutations, factors that can impact signaling strength include (i) homeostatic mechanisms that can block or enhance the signal, (ii) the types and locations of additional mutations, and (iii) the expression levels of specific isoforms of genes and regulators of proteins in the pathway. Because signal levels are largely decided by chromatin structure, they vary across cell types, states, and time windows. A strong activating mutation can be restricted by low expression, whereas a weaker mutation can be strengthened by high expression. Strong signals can be associated with cell proliferation, but too strong a signal may result in oncogene-induced senescence. Beyond cancer, moderate signal strength in embryonic neural cells may be associated with neurodevelopmental disorders, and moderate signals in aging may be associated with neurodegenerative diseases, like Alzheimer's disease. The challenge for improving patient outcomes therefore lies in determining signaling thresholds and predicting signal strength.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, NCI, Frederick, Maryland
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Chung-Jung Tsai
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, NCI, Frederick, Maryland
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, NCI, Frederick, Maryland
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16
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The Cancermuts software package for the prioritization of missense cancer variants: a case study of AMBRA1 in melanoma. Cell Death Dis 2022; 13:872. [PMID: 36243772 PMCID: PMC9569343 DOI: 10.1038/s41419-022-05318-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 09/27/2022] [Accepted: 10/03/2022] [Indexed: 11/07/2022]
Abstract
Cancer genomics and cancer mutation databases have made an available wealth of information about missense mutations found in cancer patient samples. Contextualizing by means of annotation and predicting the effect of amino acid change help identify which ones are more likely to have a pathogenic impact. Those can be validated by means of experimental approaches that assess the impact of protein mutations on the cellular functions or their tumorigenic potential. Here, we propose the integrative bioinformatic approach Cancermuts, implemented as a Python package. Cancermuts is able to gather known missense cancer mutations from databases such as cBioPortal and COSMIC, and annotate them with the pathogenicity score REVEL as well as information on their source. It is also able to add annotations about the protein context these mutations are found in, such as post-translational modification sites, structured/unstructured regions, presence of short linear motifs, and more. We applied Cancermuts to the intrinsically disordered protein AMBRA1, a key regulator of many cellular processes frequently deregulated in cancer. By these means, we classified mutations of AMBRA1 in melanoma, where AMBRA1 is highly mutated and displays a tumor-suppressive role. Next, based on REVEL score, position along the sequence, and their local context, we applied cellular and molecular approaches to validate the predicted pathogenicity of a subset of mutations in an in vitro melanoma model. By doing so, we have identified two AMBRA1 mutations which show enhanced tumorigenic potential and are worth further investigation, highlighting the usefulness of the tool. Cancermuts can be used on any protein targets starting from minimal information, and it is available at https://www.github.com/ELELAB/cancermuts as free software.
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17
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Nussinov R, Zhang M, Maloney R, Liu Y, Tsai CJ, Jang H. Allostery: Allosteric Cancer Drivers and Innovative Allosteric Drugs. J Mol Biol 2022; 434:167569. [PMID: 35378118 PMCID: PMC9398924 DOI: 10.1016/j.jmb.2022.167569] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 03/11/2022] [Accepted: 03/25/2022] [Indexed: 01/12/2023]
Abstract
Here, we discuss the principles of allosteric activating mutations, propagation downstream of the signals that they prompt, and allosteric drugs, with examples from the Ras signaling network. We focus on Abl kinase where mutations shift the landscape toward the active, imatinib binding-incompetent conformation, likely resulting in the high affinity ATP outcompeting drug binding. Recent pharmacological innovation extends to allosteric inhibitor (GNF-5)-linked PROTAC, targeting Bcr-Abl1 myristoylation site, and broadly, allosteric heterobifunctional degraders that destroy targets, rather than inhibiting them. Designed chemical linkers in bifunctional degraders can connect the allosteric ligand that binds the target protein and the E3 ubiquitin ligase warhead anchor. The physical properties and favored conformational state of the engineered linker can precisely coordinate the distance and orientation between the target and the recruited E3. Allosteric PROTACs, noncompetitive molecular glues, and bitopic ligands, with covalent links of allosteric ligands and orthosteric warheads, increase the effective local concentration of productively oriented and placed ligands. Through covalent chemical or peptide linkers, allosteric drugs can collaborate with competitive drugs, degrader anchors, or other molecules of choice, driving innovative drug discovery.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD 21702, USA; Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
| | - Mingzhen Zhang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD 21702, USA
| | - Ryan Maloney
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD 21702, USA
| | - Yonglan Liu
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD 21702, 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
| | - 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
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18
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Belikov AV, Vyatkin AD, Leonov SV. Novel Driver Strength Index highlights important cancer genes in TCGA PanCanAtlas patients. PeerJ 2022; 10:e13860. [PMID: 35975235 PMCID: PMC9375969 DOI: 10.7717/peerj.13860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 07/18/2022] [Indexed: 01/18/2023] Open
Abstract
Background Cancer driver genes are usually ranked by mutation frequency, which does not necessarily reflect their driver strength. We hypothesize that driver strength is higher for genes preferentially mutated in patients with few driver mutations overall, because these few mutations should be strong enough to initiate cancer. Methods We propose formulas for the Driver Strength Index (DSI) and the Normalized Driver Strength Index (NDSI), the latter independent of gene mutation frequency. We validate them using TCGA PanCanAtlas datasets, established driver prediction algorithms and custom computational pipelines integrating SNA, CNA and aneuploidy driver contributions at the patient-level resolution. Results DSI and especially NDSI provide substantially different gene rankings compared to the frequency approach. E.g., NDSI prioritized members of specific protein families, including G proteins GNAQ, GNA11 and GNAS, isocitrate dehydrogenases IDH1 and IDH2, and fibroblast growth factor receptors FGFR2 and FGFR3. KEGG analysis shows that top NDSI-ranked genes comprise EGFR/FGFR2/GNAQ/GNA11-NRAS/HRAS/KRAS-BRAF pathway, AKT1-MTOR pathway, and TCEB1-VHL-HIF1A pathway. Conclusion Our indices are able to select for driver gene attributes not selected by frequency sorting, potentially for driver strength. Genes and pathways prioritized are likely the strongest contributors to cancer initiation and progression and should become future therapeutic targets.
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19
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Khalighi S, Joseph P, Babu D, Singh S, LaFramboise T, Guda K, Varadan V. SYSMut: decoding the functional significance of rare somatic mutations in cancer. Brief Bioinform 2022; 23:bbac280. [PMID: 35804437 PMCID: PMC9618165 DOI: 10.1093/bib/bbac280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 06/14/2022] [Accepted: 06/16/2022] [Indexed: 11/13/2022] Open
Abstract
Current tailored-therapy efforts in cancer are largely focused on a small number of highly recurrently mutated driver genes but therapeutic targeting of these oncogenes remains challenging. However, the vast number of genes mutated infrequently across cancers has received less attention, in part, due to a lack of understanding of their biological significance. We present SYSMut, an extendable systems biology platform that can robustly infer the biologic consequences of somatic mutations by integrating routine multiomics profiles in primary tumors. We establish SYSMut's improved performance vis-à-vis state-of-the-art driver gene identification methodologies by recapitulating the functional impact of known driver genes, while additionally identifying novel functionally impactful mutated genes across 29 cancers. Subsequent application of SYSMut on low-frequency gene mutations in head and neck squamous cell (HNSC) cancers, followed by molecular and pharmacogenetic validation, revealed the lipidogenic network as a novel therapeutic vulnerability in aggressive HNSC cancers. SYSMut is thus a robust scalable framework that enables the discovery of new targetable avenues in cancer.
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Affiliation(s)
- Sirvan Khalighi
- Division of General Medical Sciences-Oncology, Case Comprehensive Cancer Center
- Department of Genetics and genome Sciences
| | - Peronne Joseph
- Division of General Medical Sciences-Oncology, Case Comprehensive Cancer Center
| | - Deepak Babu
- Division of General Medical Sciences-Oncology, Case Comprehensive Cancer Center
| | - Salendra Singh
- Division of General Medical Sciences-Oncology, Case Comprehensive Cancer Center
| | | | - Kishore Guda
- Division of General Medical Sciences-Oncology, Case Comprehensive Cancer Center
- Digestive Health Research Institute
- Department of Pathology, Case Western Reserve University School of Medicine, Cleveland, OH-44106 U.S.A
| | - Vinay Varadan
- Division of General Medical Sciences-Oncology, Case Comprehensive Cancer Center
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20
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Abdollahi S, Lin PC, Chiang JH. DiaDeL: An Accurate Deep Learning-Based Model With Mutational Signatures for Predicting Metastasis Stage and Cancer Types. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1336-1343. [PMID: 34570707 DOI: 10.1109/tcbb.2021.3115504] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Mutational signatures help identify cancer-associated genes that are being involved in tumorigenesis pathways. Hence, these pathways guide precision medicine approaches to find appropriate drugs and treatments. The pattern of mutations varies in different cancer types. Some mutations dysregulate protein function so that their accumulation is responsible for cancer development and might be associated with different cancer types. Therefore, mutations as a feature set can be used as an informative candidate to distinguish various cancer types. There are several options for demonstrating mutations. One might employ binary values to demonstrate mutation regions. Another potential method for extracting features is utilizing mutation interpreters. In this study, we investigate the trinucleotide mutational pattern of each cancer type. Moreover, we extract salient NMF-based mutational signatures across various cancer types. Then, we identify cancer-associated genes of a target cancer based on its salient signatures. We evaluate the cancer-associated genes using survival and gene expression analysis in different stages of cancer. Furthermore, we introduce DiaDeL, which is a deep learning-based binary classifier. The DiaDeL model uses mutational signatures as input features and distinct a cancer type from the others. Our proposed model outperforms six state-of-the-art methods with 0.824 and 0.88 for accuracy and AUC, respectively. The source code is available at https://github.com/sabdollahi/DiaDeL.
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21
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Zeng Z, Bromberg Y. Inferring Potential Cancer Driving Synonymous Variants. Genes (Basel) 2022; 13:778. [PMID: 35627162 PMCID: PMC9140830 DOI: 10.3390/genes13050778] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 04/25/2022] [Accepted: 04/26/2022] [Indexed: 02/01/2023] Open
Abstract
Synonymous single nucleotide variants (sSNVs) are often considered functionally silent, but a few cases of cancer-causing sSNVs have been reported. From available databases, we collected four categories of sSNVs: germline, somatic in normal tissues, somatic in cancerous tissues, and putative cancer drivers. We found that screening sSNVs for recurrence among patients, conservation of the affected genomic position, and synVep prediction (synVep is a machine learning-based sSNV effect predictor) recovers cancer driver variants (termed proposed drivers) and previously unknown putative cancer genes. Of the 2.9 million somatic sSNVs found in the COSMIC database, we identified 2111 proposed cancer driver sSNVs. Of these, 326 sSNVs could be further tagged for possible RNA splicing effects, RNA structural changes, and affected RBP motifs. This list of proposed cancer driver sSNVs provides computational guidance in prioritizing the experimental evaluation of synonymous mutations found in cancers. Furthermore, our list of novel potential cancer genes, galvanized by synonymous mutations, may highlight yet unexplored cancer mechanisms.
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Affiliation(s)
- Zishuo Zeng
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ 08873, USA
| | - Yana Bromberg
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ 08873, USA
- Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA
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22
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Hussen BM, Abdullah ST, Salihi A, Sabir DK, Sidiq KR, Rasul MF, Hidayat HJ, Ghafouri-Fard S, Taheri M, Jamali E. The emerging roles of NGS in clinical oncology and personalized medicine. Pathol Res Pract 2022; 230:153760. [PMID: 35033746 DOI: 10.1016/j.prp.2022.153760] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 12/29/2021] [Accepted: 01/06/2022] [Indexed: 02/07/2023]
Abstract
Next-generation sequencing (NGS) has been increasingly popular in genomics studies over the last decade, as new sequencing technology has been created and improved. Recently, NGS started to be used in clinical oncology to improve cancer therapy through diverse modalities ranging from finding novel and rare cancer mutations, discovering cancer mutation carriers to reaching specific therapeutic approaches known as personalized medicine (PM). PM has the potential to minimize medical expenses by shifting the current traditional medical approach of treating cancer and other diseases to an individualized preventive and predictive approach. Currently, NGS can speed up in the early diagnosis of diseases and discover pharmacogenetic markers that help in personalizing therapies. Despite the tremendous growth in our understanding of genetics, NGS holds the added advantage of providing more comprehensive picture of cancer landscape and uncovering cancer development pathways. In this review, we provided a complete overview of potential NGS applications in scientific and clinical oncology, with a particular emphasis on pharmacogenomics in the direction of precision medicine treatment options.
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Affiliation(s)
- Bashdar Mahmud Hussen
- Department Pharmacognosy, College of Pharmacy, Hawler Medical University, Kurdistan Region, Erbil, Iraq; Center of Research and Strategic Studies, Lebanese French University, Kurdistan Region, Erbil, Iraq
| | - Sara Tharwat Abdullah
- Department of Pharmacology and Toxicology, College of Pharmacy, Hawler Medical University, Erbil, Iraq
| | - Abbas Salihi
- Center of Research and Strategic Studies, Lebanese French University, Kurdistan Region, Erbil, Iraq; Department of Biology, College of Science, Salahaddin University, Kurdistan Region, Erbil, Iraq
| | - Dana Khdr Sabir
- Department of Medical Laboratory Sciences, Charmo University, Kurdistan Region, Iraq
| | - Karzan R Sidiq
- Department of Biology, College of Education, University of Sulaimani, Sulaimani 334, Kurdistan, Iraq
| | - Mohammed Fatih Rasul
- Department of Medical Analysis, Faculty of Applied Science, Tishk International University, Kurdistan Region, Erbil, Iraq
| | - Hazha Jamal Hidayat
- Department of Biology, College of Education, Salahaddin University, Kurdistan Region, Erbil, Iraq
| | - Soudeh Ghafouri-Fard
- Department of Medical Genetics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammad Taheri
- Institute of Human Genetics, Jena University Hospital, Jena, Germany; Urology and Nephrology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Elena Jamali
- Skull Base Research Center, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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23
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Russo GI, Musso N, Romano A, Caruso G, Petralia S, Lanzanò L, Broggi G, Camarda M. The Role of Dielectrophoresis for Cancer Diagnosis and Prognosis. Cancers (Basel) 2021; 14:198. [PMID: 35008359 PMCID: PMC8750463 DOI: 10.3390/cancers14010198] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 12/27/2021] [Accepted: 12/30/2021] [Indexed: 12/17/2022] Open
Abstract
Liquid biopsy is emerging as a potential diagnostic tool for prostate cancer (PC) prognosis and diagnosis. Unfortunately, most circulating tumor cells (CTC) technologies, such as AdnaTest or Cellsearch®, critically rely on the epithelial cell adhesion molecule (EpCAM) marker, limiting the possibility of detecting cancer stem-like cells (CSCs) and mesenchymal-like cells (EMT-CTCs) that are present during PC progression. In this context, dielectrophoresis (DEP) is an epCAM independent, label-free enrichment system that separates rare cells simply on the basis of their specific electrical properties. As compared to other technologies, DEP may represent a superior technique in terms of running costs, cell yield and specificity. However, because of its higher complexity, it still requires further technical as well as clinical development. DEP can be improved by the use of microfluid, nanostructured materials and fluoro-imaging to increase its potential applications. In the context of cancer, the usefulness of DEP lies in its capacity to detect CTCs in the bloodstream in their epithelial, mesenchymal, or epithelial-mesenchymal phenotype forms, which should be taken into account when choosing CTC enrichment and analysis methods for PC prognosis and diagnosis.
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Affiliation(s)
| | - Nicolò Musso
- Department of Biomedical and Biotechnological Science (BIOMETEC), University of Catania, 95123 Catania, Italy
- STLab s.r.l., Via Anapo 53, 95126 Catania, Italy;
| | - Alessandra Romano
- Haematological Section, University of Catania, 95125 Catania, Italy;
| | - Giuseppe Caruso
- Department of Drug and Health Sciences, University of Catania, 95125 Catania, Italy; (G.C.); (S.P.)
| | - Salvatore Petralia
- Department of Drug and Health Sciences, University of Catania, 95125 Catania, Italy; (G.C.); (S.P.)
| | - Luca Lanzanò
- Department of Physics and Astronomy “Ettore Majorana”, University of Catania, 95123 Catania, Italy;
| | - Giuseppe Broggi
- Pathology Section, Department of Medical, Surgical Sciences and Advanced Technologies “G.F. Ingrassia”, University of Catania, 95123 Catania, Italy;
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24
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Cutigi JF, Evangelista AF, Reis RM, Simao A. A computational approach for the discovery of significant cancer genes by weighted mutation and asymmetric spreading strength in networks. Sci Rep 2021; 11:23551. [PMID: 34876593 PMCID: PMC8651746 DOI: 10.1038/s41598-021-02671-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Accepted: 10/26/2021] [Indexed: 11/25/2022] Open
Abstract
Identifying significantly mutated genes in cancer is essential for understanding the mechanisms of tumor initiation and progression. This task is a key challenge since large-scale genomic studies have reported an endless number of genes mutated at a shallow frequency. Towards uncovering infrequently mutated genes, gene interaction networks combined with mutation data have been explored. This work proposes Discovering Significant Cancer Genes (DiSCaGe), a computational method for discovering significant genes for cancer. DiSCaGe computes a mutation score for the genes based on the type of mutations they have. The influence received for their neighbors in the network is also considered and obtained through an asymmetric spreading strength applied to a consensus gene network. DiSCaGe produces a ranking of prioritized possible cancer genes. An experimental evaluation with six types of cancer revealed the potential of DiSCaGe for discovering known and possible novel significant cancer genes.
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Affiliation(s)
- Jorge Francisco Cutigi
- Federal Institute of Sao Paulo, Sao Carlos, SP, Brazil.
- University of Sao Paulo, Sao Carlos, SP, Brazil.
| | | | - Rui Manuel Reis
- Molecular Oncology Research Center, Barretos Cancer Hospital, Barretos, SP, Brazil
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25
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Gao B, Zhao Y, Gao Y, Li G, Wu L. Identification of Common Driver Gene Modules and Associations between Cancers through Integrated Network Analysis. GLOBAL CHALLENGES (HOBOKEN, NJ) 2021; 5:2100006. [PMID: 34504716 PMCID: PMC8414517 DOI: 10.1002/gch2.202100006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 04/26/2021] [Indexed: 05/12/2023]
Abstract
High-throughput biological data has created an unprecedented opportunity for illuminating the mechanisms of tumor emergence and evolution. An important and challenging problem in deciphering cancers is to investigate the commonalities of driver genes and pathways and the associations between cancers. Aiming at this problem, a tool ComCovEx is developed to identify common cancer driver gene modules between two cancers by searching for the candidates in local signaling networks using an exclusivity-coverage iteration strategy and outputting those with significant coverage and exclusivity for both cancers. The associations of the cancer pairs are further evaluated by Fisher's exact test. Being applied to 11 TCGA cancer datasets, ComCovEx identifies 13 significantly associated cancer pairs with plenty of biologically significant common gene modules. The novel results of cancer relationship and common gene modules reveal the relevant pathological basis of different cancer types and provide new clues to diagnosis and drug treatment in associated cancers.
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Affiliation(s)
- Bo Gao
- IAMMADISNCMISAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing100190China
- School of MathematicsShandong UniversityJinan250100China
- School of Mathematical SciencesUniversity of Chinese Academy of SciencesBeijing100049China
- School of Public HealthCapital Medical UniversityBeijing100069China
- Beijing Municipal Key Laboratory of Clinical EpidemiologyBeijing100069China
| | - Yue Zhao
- IAMMADISNCMISAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing100190China
- School of Mathematical SciencesUniversity of Chinese Academy of SciencesBeijing100049China
| | - Yonghang Gao
- IAMMADISNCMISAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing100190China
- School of Mathematical SciencesUniversity of Chinese Academy of SciencesBeijing100049China
| | - Guojun Li
- School of MathematicsShandong UniversityJinan250100China
- Research Center for Mathematics and Interdisciplinary SciencesShandong UniversityQingdao266237China
| | - Ling‐Yun Wu
- IAMMADISNCMISAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing100190China
- School of Mathematical SciencesUniversity of Chinese Academy of SciencesBeijing100049China
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26
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Belikov AV, Vyatkin A, Leonov SV. The Erlang distribution approximates the age distribution of incidence of childhood and young adulthood cancers. PeerJ 2021; 9:e11976. [PMID: 34434669 PMCID: PMC8351573 DOI: 10.7717/peerj.11976] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 07/24/2021] [Indexed: 11/20/2022] Open
Abstract
Background It is widely believed that cancers develop upon acquiring a particular number of (epi) mutations in driver genes, but the law governing the kinetics of this process is not known. We have previously shown that the age distribution of incidence for the 20 most prevalent cancers of old age is best approximated by the Erlang probability distribution. The Erlang distribution describes the probability of several successive random events occurring by the given time according to the Poisson process, which allows an estimate for the number of critical driver events. Methods Here we employ a computational grid search method to find global parameter optima for five probability distributions on the CDC WONDER dataset of the age distribution of childhood and young adulthood cancer incidence. Results We show that the Erlang distribution is the only classical probability distribution we found that can adequately model the age distribution of incidence for all studied childhood and young adulthood cancers, in addition to cancers of old age. Conclusions This suggests that the Poisson process governs driver accumulation at any age and that the Erlang distribution can be used to determine the number of driver events for any cancer type. The Poisson process implies the fundamentally random timing of driver events and their constant average rate. As waiting times for the occurrence of the required number of driver events are counted in decades, and most cells do not live this long, it suggests that driver mutations accumulate silently in the longest-living dividing cells in the body—the stem cells.
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Affiliation(s)
- Aleksey V Belikov
- Laboratory of Innovative Medicine, School of Biological and Medical Physics, Moscow Institute of Physics and Technology, Moscow, Russia
| | - Alexey Vyatkin
- Laboratory of Innovative Medicine, School of Biological and Medical Physics, Moscow Institute of Physics and Technology, Moscow, Russia
| | - Sergey V Leonov
- Laboratory of Innovative Medicine, School of Biological and Medical Physics, Moscow Institute of Physics and Technology, Moscow, Russia
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27
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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] [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.
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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.
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28
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Zhang T, Zhang SW, Li Y. Identifying Driver Genes for Individual Patients through Inductive Matrix Completion. Bioinformatics 2021; 37:4477-4484. [PMID: 34175939 DOI: 10.1093/bioinformatics/btab477] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 04/30/2021] [Accepted: 06/25/2021] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION The driver genes play a key role in the evolutionary process of cancer. Effectively identifying these driver genes is crucial to cancer diagnosis and treatment. However, due to the high heterogeneity of cancers, it remains challenging to identify the driver genes for individual patients. Although some computational methods have been proposed to tackle this problem, they seldom consider the fact that the genes functionally similar to the well-established driver genes may likely play similar roles in cancer process, which potentially promotes the driver gene identification. Thus, here we developed a novel approach of IMCDriver to promote the driver gene identification both for cohorts and individual patients. RESULTS IMCDriver first considers the well-established driver genes as prior information, and adopts the using multi-omics data (e.g., somatic mutation, gene expression and protein-protein interaction) to compute the similarity between patients/genes. Then, IMCDriver prioritizes the personalized mutated genes according to their functional similarity to the well-established driver genes via Inductive Matrix Completion. Finally, IMCDriver identifies the highly rank-ordered genes as the personalized driver genes. The results on five cancer datasets from TCGA show that our IMCDriver outperforms other existing state-of-the-art methods both in the cohort and patient-specific driver gene identification. IMCDriver also reveals some novel driver genes that potentially drive cancer development. In addition, even for the driver genes rarely mutated among a population, IMCDriver can still identify them and prioritize them with high priorities. AVAILABILITY Code available at https://github.com/NWPU-903PR/IMCDriver. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Tong Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, China Xi'an.,School of Electrical and Mechanical Engineering, Pingdingshan University, Pingdingshan, China
| | - Shao-Wu Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, China Xi'an
| | - Yan Li
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, China Xi'an
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29
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Park JH, de Lomana ALG, Marzese DM, Juarez T, Feroze A, Hothi P, Cobbs C, Patel AP, Kesari S, Huang S, Baliga NS. A Systems Approach to Brain Tumor Treatment. Cancers (Basel) 2021; 13:3152. [PMID: 34202449 PMCID: PMC8269017 DOI: 10.3390/cancers13133152] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/11/2021] [Accepted: 06/17/2021] [Indexed: 12/12/2022] Open
Abstract
Brain tumors are among the most lethal tumors. Glioblastoma, the most frequent primary brain tumor in adults, has a median survival time of approximately 15 months after diagnosis or a five-year survival rate of 10%; the recurrence rate is nearly 90%. Unfortunately, this prognosis has not improved for several decades. The lack of progress in the treatment of brain tumors has been attributed to their high rate of primary therapy resistance. Challenges such as pronounced inter-patient variability, intratumoral heterogeneity, and drug delivery across the blood-brain barrier hinder progress. A comprehensive, multiscale understanding of the disease, from the molecular to the whole tumor level, is needed to address the intratumor heterogeneity resulting from the coexistence of a diversity of neoplastic and non-neoplastic cell types in the tumor tissue. By contrast, inter-patient variability must be addressed by subtyping brain tumors to stratify patients and identify the best-matched drug(s) and therapies for a particular patient or cohort of patients. Accomplishing these diverse tasks will require a new framework, one involving a systems perspective in assessing the immense complexity of brain tumors. This would in turn entail a shift in how clinical medicine interfaces with the rapidly advancing high-throughput (HTP) technologies that have enabled the omics-scale profiling of molecular features of brain tumors from the single-cell to the tissue level. However, several gaps must be closed before such a framework can fulfill the promise of precision and personalized medicine for brain tumors. Ultimately, the goal is to integrate seamlessly multiscale systems analyses of patient tumors and clinical medicine. Accomplishing this goal would facilitate the rational design of therapeutic strategies matched to the characteristics of patients and their tumors. Here, we discuss some of the technologies, methodologies, and computational tools that will facilitate the realization of this vision to practice.
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Affiliation(s)
- James H. Park
- Institute for Systems Biology, Seattle, WA 98109, USA; (J.H.P.); (S.H.)
| | | | - Diego M. Marzese
- Balearic Islands Health Research Institute (IdISBa), 07010 Palma, Spain;
| | - Tiffany Juarez
- St. John’s Cancer Institute, Santa Monica, CA 90401, USA; (T.J.); (S.K.)
| | - Abdullah Feroze
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA; (A.F.); (A.P.P.)
| | - Parvinder Hothi
- Swedish Neuroscience Institute, Seattle, WA 98122, USA; (P.H.); (C.C.)
- Ben and Catherine Ivy Center for Advanced Brain Tumor Treatment, Seattle, WA 98122, USA
| | - Charles Cobbs
- Swedish Neuroscience Institute, Seattle, WA 98122, USA; (P.H.); (C.C.)
- Ben and Catherine Ivy Center for Advanced Brain Tumor Treatment, Seattle, WA 98122, USA
| | - Anoop P. Patel
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA; (A.F.); (A.P.P.)
- Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
- Brotman-Baty Institute for Precision Medicine, University of Washington, Seattle, WA 98195, USA
| | - Santosh Kesari
- St. John’s Cancer Institute, Santa Monica, CA 90401, USA; (T.J.); (S.K.)
| | - Sui Huang
- Institute for Systems Biology, Seattle, WA 98109, USA; (J.H.P.); (S.H.)
| | - Nitin S. Baliga
- Institute for Systems Biology, Seattle, WA 98109, USA; (J.H.P.); (S.H.)
- Departments of Microbiology, Biology, and Molecular Engineering Sciences, University of Washington, Seattle, WA 98105, USA
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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] [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.
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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.)
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31
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Raimondi D, Passemiers A, Fariselli P, Moreau Y. Current cancer driver variant predictors learn to recognize driver genes instead of functional variants. BMC Biol 2021; 19:3. [PMID: 33441128 PMCID: PMC7807764 DOI: 10.1186/s12915-020-00930-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 11/19/2020] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Identifying variants that drive tumor progression (driver variants) and distinguishing these from variants that are a byproduct of the uncontrolled cell growth in cancer (passenger variants) is a crucial step for understanding tumorigenesis and precision oncology. Various bioinformatics methods have attempted to solve this complex task. RESULTS In this study, we investigate the assumptions on which these methods are based, showing that the different definitions of driver and passenger variants influence the difficulty of the prediction task. More importantly, we prove that the data sets have a construction bias which prevents the machine learning (ML) methods to actually learn variant-level functional effects, despite their excellent performance. This effect results from the fact that in these data sets, the driver variants map to a few driver genes, while the passenger variants spread across thousands of genes, and thus just learning to recognize driver genes provides almost perfect predictions. CONCLUSIONS To mitigate this issue, we propose a novel data set that minimizes this bias by ensuring that all genes covered by the data contain both driver and passenger variants. As a result, we show that the tested predictors experience a significant drop in performance, which should not be considered as poorer modeling, but rather as correcting unwarranted optimism. Finally, we propose a weighting procedure to completely eliminate the gene effects on such predictions, thus precisely evaluating the ability of predictors to model the functional effects of single variants, and we show that indeed this task is still open.
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Affiliation(s)
| | | | | | - Yves Moreau
- ESAT-STADIUS, KU Leuven, Leuven, 3001, Belgium.
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32
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Cheng J, He J, Wang S, Zhao Z, Yan H, Guan Q, Li J, Guo Z, Ao L. Biased Influences of Low Tumor Purity on Mutation Detection in Cancer. Front Mol Biosci 2021; 7:533196. [PMID: 33425983 PMCID: PMC7785586 DOI: 10.3389/fmolb.2020.533196] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Accepted: 10/22/2020] [Indexed: 01/21/2023] Open
Abstract
The non-cancerous components in tumor tissues, e.g., infiltrating stromal cells and immune cells, dilute tumor purity and might confound genomic mutation profile analyses and the identification of pathological biomarkers. It is necessary to systematically evaluate the influence of tumor purity. Here, using public gastric cancer samples from The Cancer Genome Atlas (TCGA), we firstly showed that numbers of mutation, separately called by four algorithms, were significant positively correlated with tumor purities (all p < 0.05, Spearman rank correlation). Similar results were also observed in other nine cancers from TCGA. Notably, the result was further confirmed by six in-house samples from two gastric cancer patients and five in-house samples from two colorectal cancer patients with different tumor purities. Furthermore, the metastasis mechanism of gastric cancer may be incorrectly characterized as numbers of mutation and tumor purities of 248 lymph node metastatic (N + M0) samples were both significantly lower than those of 121 non-metastatic (N0M0) samples (p < 0.05, Wilcoxon rank-sum test). Similar phenomena were also observed that tumor purities could confound the analysis of histological subtypes of cancer and the identification of microsatellite instability status (MSI) in both gastric and colon cancer. Finally, we suggested that the higher tumor purity, such as above 70%, rather than 60%, could be better to meet the requirement of mutation calling. In conclusion, the influence of tumor purity on the genomic mutation profile and pathological analyses should be fully considered in the further study.
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Affiliation(s)
- Jun Cheng
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Jun He
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Shanshan Wang
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Zhangxiang Zhao
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Haidan Yan
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Qingzhou Guan
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Jing Li
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Zheng Guo
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Lu Ao
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
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Yang L, Chen R, Goodison S, Sun Y. An efficient and effective method to identify significantly perturbed subnetworks in cancer. NATURE COMPUTATIONAL SCIENCE 2021; 1:79-88. [PMID: 37346964 PMCID: PMC10284573 DOI: 10.1038/s43588-020-00009-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Accepted: 12/02/2020] [Indexed: 06/23/2023]
Abstract
The identification of key functional biological networks from high-dimensional genomics data is pivotal for cancer research. Here, we introduce FDRnet, a method for the detection of molecular subnetworks in cancer, which addresses several challenges in pathway analysis. FDRnet detects key subnetworks by solving a mixed-integer linear programming problem, using a given upper bound of false discovery rate (FDR) as a budget constraint, and minimizing a conductance score to find dense subgraphs around seed genes. A large-scale benchmark study was performed on both simulation and cancer genomics data. FDRnet outperformed other methods in the ability to detect functionally homogeneous subnetworks in a scale-free biological network, to control FDRs of the genes in detected subnetworks, to improve computational efficiency and to integrate multi-omics data. By overcoming the limitations of existing approaches, FDRnet can facilitate the detection of key functional pathways in cancer and other genetic diseases.
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Affiliation(s)
- Le Yang
- Department of Computer Science and Engineering, The State University of New York at Buffalo, Buffalo, NY, USA
| | - Runpu Chen
- Department of Computer Science and Engineering, The State University of New York at Buffalo, Buffalo, NY, USA
| | - Steve Goodison
- Department of Health Sciences Research, Mayo Clinic, Jacksonville, FL, USA
| | - Yijun Sun
- Department of Computer Science and Engineering, The State University of New York at Buffalo, Buffalo, NY, USA
- Department of Microbiology and Immunology, The State University of New York at Buffalo, Buffalo, NY, USA
- Department of Biostatistics, The State University of New York at Buffalo, Buffalo, NY, USA
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Jovanovic D, Markovic J, Ceriman V, Peric J, Pavlovic S, Soldatovic I. Correlation of genomic alterations and PD-L1 expression in thymoma. J Thorac Dis 2020; 12:7561-7570. [PMID: 33447447 PMCID: PMC7797854 DOI: 10.21037/jtd-2019-thym-13] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Accepted: 06/18/2020] [Indexed: 11/06/2022]
Abstract
Thymic epithelial tumors (TETs) include several anterior mediastinal malignant tumours: thymomas, thymic carcinomas and thymic neuroendocrine cancers. There is significant variety in the biologic features and clinical course of TETs and many attempts have been made to identify target genes for successful therapy of TETs. Next generation sequencing (NGS) represents a huge advancement in diagnostics and these new molecular technologies revealed that thymic neoplasms have the lowest tumor mutation burden among all adult malignant tumours with a different pattern of molecular aberrations in thymomas and thymic carcinomas. As for the PD-L1 expression in tumor cells in thymoma and thymic carcinoma, it varies a lot in published studies, with findings of PD-L1 expression from 23% to 92% in thymoma and 36% to 100% in thymic carcinoma. When correlated PD-L1 expression with disease stage some controversial results were obtained, with no association with tumor stage in most studies. This is, at least in part, explained by the fact that several diverse PD-L1 immunohistochemical tests were used in each trial, with four different antibodies (SP142, SP263, 22C3, and 28-8), different definition of PD-L1 positivity and cutoff values throughout the studies as well. There is a huge interest in using genomic features to produce predictive genomic-based immunotherapy biomarkers, particularly since recent data suggest that certain tumor-specific genomic alterations, either individually or in combination, appear to influence immune checkpoint activity and better responses as the outcome, so as such in some cancer types they may complement existing biomarkers to improve the selection criteria for immunotherapy.
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Affiliation(s)
| | - Jelena Markovic
- Pathology Department, Clinical Center of Serbia, Belgrade, Serbia
| | - Vesna Ceriman
- Clinic for Pulmonology, Clinical Center of Serbia, Belgrade, Serbia
| | - Jelena Peric
- Institute of Molecular Genetics and Genetic Engineering University of Belgrade, Belgrade, Serbia
| | - Sonja Pavlovic
- Institute of Molecular Genetics and Genetic Engineering University of Belgrade, Belgrade, Serbia
| | - Ivan Soldatovic
- Institute of Medical Statistics, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
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Discovering novel driver mutations from pan-cancer analysis of mutational and gene expression profiles. PLoS One 2020; 15:e0242780. [PMID: 33232371 PMCID: PMC7685479 DOI: 10.1371/journal.pone.0242780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Accepted: 11/10/2020] [Indexed: 11/19/2022] Open
Abstract
As the genomic profile across cancers varies from person to person, patient prognosis and treatment may differ based on the mutational signature of each tumour. Thus, it is critical to understand genomic drivers of cancer and identify potential mutational commonalities across tumors originating at diverse anatomical sites. Large-scale cancer genomics initiatives, such as TCGA, ICGC and GENIE have enabled the analysis of thousands of tumour genomes. Our goal was to identify new cancer-causing mutations that may be common across tumour sites using mutational and gene expression profiles. Genomic and transcriptomic data from breast, ovarian, and prostate cancers were aggregated and analysed using differential gene expression methods to identify the effect of specific mutations on the expression of multiple genes. Mutated genes associated with the most differentially expressed genes were considered to be novel candidates for driver mutations, and were validated through literature mining, pathway analysis and clinical data investigation. Our driver selection method successfully identified 116 probable novel cancer-causing genes, with 4 discovered in patients having no alterations in any known driver genes: MXRA5, OBSCN, RYR1, and TG. The candidate genes previously not officially classified as cancer-causing showed enrichment in cancer pathways and in cancer diseases. They also matched expectations pertaining to properties of cancer genes, for instance, showing larger gene and protein lengths, and having mutation patterns suggesting oncogenic or tumor suppressor properties. Our approach allows for the identification of novel putative driver genes that are common across cancer sites using an unbiased approach without any a priori knowledge on pathways or gene interactions and is therefore an agnostic approach to the identification of putative common driver genes acting at multiple cancer sites.
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36
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Comprehensive assessment of TP53 loss of function using multiple combinatorial mutagenesis libraries. Sci Rep 2020; 10:20368. [PMID: 33230179 PMCID: PMC7683535 DOI: 10.1038/s41598-020-74892-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 09/30/2020] [Indexed: 11/23/2022] Open
Abstract
The diagnosis of somatic and germline TP53 mutations in human tumors or in individuals prone to various types of cancer has now reached the clinic. To increase the accuracy of the prediction of TP53 variant pathogenicity, we gathered functional data from three independent large-scale saturation mutagenesis screening studies with experimental data for more than 10,000 TP53 variants performed in different settings (yeast or mammalian) and with different readouts (transcription, growth arrest or apoptosis). Correlation analysis and multidimensional scaling showed excellent agreement between all these variables. Furthermore, we found that some missense mutations localized in TP53 exons led to impaired TP53 splicing as shown by an analysis of the TP53 expression data from the cancer genome atlas. With the increasing availability of genomic, transcriptomic and proteomic data, it is essential to employ both protein and RNA prediction to accurately define variant pathogenicity.
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Khalighi S, Singh S, Varadan V. Untangling a complex web: Computational analyses of tumor molecular profiles to decode driver mechanisms. J Genet Genomics 2020; 47:595-609. [PMID: 33423960 PMCID: PMC7902422 DOI: 10.1016/j.jgg.2020.11.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Revised: 11/04/2020] [Accepted: 11/14/2020] [Indexed: 12/19/2022]
Abstract
Genome-scale studies focusing on molecular profiling of cancers across tissue types have revealed a plethora of aberrations across the genomic, transcriptomic, and epigenomic scales. The significant molecular heterogeneity across individual tumors even within the same tissue context complicates decoding the key etiologic mechanisms of this disease. Furthermore, it is increasingly likely that biologic mechanisms underlying the pathobiology of cancer involve multiple molecular entities interacting across functional scales. This has motivated the development of computational approaches that integrate molecular measurements with prior biological knowledge in increasingly intricate ways to enable the discovery of driver genomic aberrations across cancers. Here, we review diverse methodological approaches that have powered significant advances in our understanding of the genomic underpinnings of cancer at the cohort and at the individual tumor scales. We outline the key advances and challenges in the computational discovery of cancer mechanisms while motivating the development of systems biology approaches to comprehensively decode the biologic drivers of this complex disease.
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Affiliation(s)
- Sirvan Khalighi
- Division of General Medical Sciences-Oncology, Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
| | - Salendra Singh
- Division of General Medical Sciences-Oncology, Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
| | - Vinay Varadan
- Division of General Medical Sciences-Oncology, Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA.
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38
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Deep Neural Network Algorithm Feedback Model with Behavioral Intelligence and Forecast Accuracy. Symmetry (Basel) 2020. [DOI: 10.3390/sym12091465] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
When attempting to apply a large-scale database that holds the behavioral intelligence training data of deep neural networks, the classification accuracy of the artificial intelligence algorithm needs to reflect the behavioral characteristics of the individual. When a change in behavior is recognized, that is, a feedback model based on a data connection model is applied, an analysis of time series data is performed by extracting feature vectors and interpolating data in a deep neural network to overcome the limitations of the existing statistical analysis. Using the results of the first feedback model as inputs to the deep neural network and, furthermore, as the input values of the second feedback model, and interpolating the behavioral intelligence data, that is, context awareness and lifelog data, including physical activities, involves applying the most appropriate conditions. The results of this study show that this method effectively improves the accuracy of the artificial intelligence results. In this paper, through an experiment, after extracting the feature vector of a deep neural network and restoring the missing value, the classification accuracy was verified to improve by about 20% on average. At the same time, by adding behavioral intelligence data to the time series data, a new data connection model, the Deep Neural Network Feedback Model, was proposed, and it was verified that the classification accuracy can be improved by about 8 to 9% on average. Based on the hypothesis, the F (X′) = X model was applied to thoroughly classify the training data set and test data set to present a symmetrical balance between the data connection model and the context-aware data. In addition, behavioral activity data were extrapolated in terms of context-aware and forecasting perspectives to prove the results of the experiment.
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Aganezov S, Raphael BJ. Reconstruction of clone- and haplotype-specific cancer genome karyotypes from bulk tumor samples. Genome Res 2020; 30:1274-1290. [PMID: 32887685 PMCID: PMC7545144 DOI: 10.1101/gr.256701.119] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Accepted: 08/07/2020] [Indexed: 12/25/2022]
Abstract
Many cancer genomes are extensively rearranged with aberrant chromosomal karyotypes. Deriving these karyotypes from high-throughput DNA sequencing of bulk tumor samples is complicated because most tumors are a heterogeneous mixture of normal cells and subpopulations of cancer cells, or clones, that harbor distinct somatic mutations. We introduce a new algorithm, Reconstructing Cancer Karyotypes (RCK), to reconstruct haplotype-specific karyotypes of one or more rearranged cancer genomes from DNA sequencing data from a bulk tumor sample. RCK leverages evolutionary constraints on the somatic mutational process in cancer to reduce ambiguity in the deconvolution of admixed sequencing data into multiple haplotype-specific cancer karyotypes. RCK models mixtures containing an arbitrary number of derived genomes and allows the incorporation of information both from short-read and long-read DNA sequencing technologies. We compare RCK to existing approaches on 17 primary and metastatic prostate cancer samples. We find that RCK infers cancer karyotypes that better explain the DNA sequencing data and conform to a reasonable evolutionary model. RCK's reconstructions of clone- and haplotype-specific karyotypes will aid further studies of the role of intra-tumor heterogeneity in cancer development and response to treatment. RCK is freely available as open source software.
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Affiliation(s)
- Sergey Aganezov
- Department of Computer Science, Princeton University, Princeton, New Jersey 08540, USA
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, New Jersey 08540, USA
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40
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Cutigi JF, Evangelista AF, Simao A. Approaches for the identification of driver mutations in cancer: A tutorial from a computational perspective. J Bioinform Comput Biol 2020; 18:2050016. [PMID: 32698724 DOI: 10.1142/s021972002050016x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Cancer is a complex disease caused by the accumulation of genetic alterations during the individual's life. Such alterations are called genetic mutations and can be divided into two groups: (1) Passenger mutations, which are not responsible for cancer and (2) Driver mutations, which are significant for cancer and responsible for its initiation and progression. Cancer cells undergo a large number of mutations, of which most are passengers, and few are drivers. The identification of driver mutations is a key point and one of the biggest challenges in Cancer Genomics. Many computational methods for such a purpose have been developed in Cancer Bioinformatics. Such computational methods are complex and are usually described in a high level of abstraction. This tutorial details some classical computational methods, from a computational perspective, with the transcription in an algorithmic format towards an easy access by researchers.
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Affiliation(s)
- Jorge Francisco Cutigi
- Federal Institute of São Paulo (IFSP), São Carlos, SP, Brazil.,University of São Paulo (USP), São Carlos, SP, Brazil
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41
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Grossmann P, Cristea S, Beerenwinkel N. Clonal evolution driven by superdriver mutations. BMC Evol Biol 2020; 20:89. [PMID: 32689942 PMCID: PMC7370525 DOI: 10.1186/s12862-020-01647-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Accepted: 06/29/2020] [Indexed: 11/10/2022] Open
Abstract
Background Tumors are widely recognized to progress through clonal evolution by sequentially acquiring selectively advantageous genetic alterations that significantly contribute to tumorigenesis and thus are termned drivers. Some cancer drivers, such as TP53 point mutation or EGFR copy number gain, provide exceptional fitness gains, which, in time, can be sufficient to trigger the onset of cancer with little or no contribution from additional genetic alterations. These key alterations are called superdrivers. Results In this study, we employ a Wright-Fisher model to study the interplay between drivers and superdrivers in tumor progression. We demonstrate that the resulting evolutionary dynamics follow global clonal expansions of superdrivers with periodic clonal expansions of drivers. We find that the waiting time to the accumulation of a set of superdrivers and drivers in the tumor cell population can be approximated by the sum of the individual waiting times. Conclusions Our results suggest that superdriver dynamics dominate over driver dynamics in tumorigenesis. Furthermore, our model allows studying the interplay between superdriver and driver mutations both empirically and theoretically.
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Affiliation(s)
- Patrick Grossmann
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058, Basel, Switzerland
| | - Simona Cristea
- Department of Biostatistics & Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Harvard Department of Stem Cell and Regenerative Biology, Cambridge, MA, USA
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058, Basel, Switzerland. .,SIB Swiss Institute of Bioinformatics, 4058, Basel, Switzerland.
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Huang J, Zhang Y, Ma Q, Zhang Y, Wang M, Zhou Y, Xing Z, Jin M, Hu L, Kong X. Natural Selection on Exonic SNPs Shapes Allelic Expression Imbalance (AEI) Adaptability in Lung Cancer Progression. Front Genet 2020; 11:665. [PMID: 32670357 PMCID: PMC7327089 DOI: 10.3389/fgene.2020.00665] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 06/01/2020] [Indexed: 01/28/2023] Open
Abstract
Tumors are driven by a sequence of genetic and epigenetic alterations. Previous studies have mostly focused on the roles of somatic mutations in tumorigenesis, but how germline variants act is largely unknown. In this study, we hypothesized that allelic expression imbalance (AEI) participated in the process of germline variants on tumorigenesis. We screened single-nucleotide polymorphisms (SNPs) as representative germline variants. By using 127 patients’ RNA sequencing data from paired lung cancer and adjacent normal tissues from public databases, we analyzed the effects of the functional consequence of SNPs, function and conservativeness on genes with AEI. We found that natural selection can affect AEI. Functional adaptability of genes with a high frequency of AEI and a correlation of the incidence of AEI with conservativeness were observed in both adjacent tissues and tumor tissues. Moreover, we observed a higher incidence of AEI in genes with non-synonymous SNPs than in those with synonymous SNPs. However, we also found that AEI was affected by allele expression noise, especially in tumor tissues, which led to an increased proportion of AEI, weakened the effect of natural selection and eliminated the influence of the functional consequence of SNPs on AEI. We unveiled a previously unknown adaptive regulatory mechanism in which the effect of natural selection on SNPs can be reflected in allelic expression, which provides insight into a better understanding of cancer evolution.
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Affiliation(s)
- Jinfei Huang
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yuchao Zhang
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Qingyang Ma
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yuhang Zhang
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Meng Wang
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - You Zhou
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Zhihao Xing
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Meiling Jin
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Landian Hu
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Xiangyin Kong
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
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43
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Peric J, Samaradzic N, Skodric Trifunovic V, Tosic N, Stojsic J, Pavlovic S, Jovanovic D. Genomic profiling of thymoma using a targeted high-throughput approach. Arch Med Sci 2020; 20:909-917. [PMID: 39050176 PMCID: PMC11264071 DOI: 10.5114/aoms.2020.96537] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Accepted: 12/21/2019] [Indexed: 07/27/2024] Open
Abstract
Introduction Thymomas and thymic carcinoma (TC) are the most common neoplasms localised in the thymus. These diseases are poorly understood, but progress made in next-generation sequencing (NGS) technology has provided novel data on their molecular pathology. Material and methods Genomic DNA was isolated from formalin-fixed paraffin-embedded tumour tissue. We investigated somatic variants in 35 thymoma patients using amplicon-based TruSeq Amplicon Cancer Panel (TSACP) that covers 48 cancer related genes. We also analysed three samples from healthy individuals by TSACP platform and 32 healthy controls using exome sequencing. Results The total number of detected variants was 4447, out of which 2906 were in the coding region (median per patient 83, range: 2-300) and 1541 were in the non-coding area (median per patient 44, range: 0-172). We identified four genes, APC, ATM, ERBB4, and SMAD4, having more than 100 protein-changing variants. Additionally, more than 70% of the analysed cases harboured protein-changing variants in SMAD4, APC, ATM, PTEN, KDR, and TP53. Moreover, this study revealed 168 recurrent variants, out of which 15 were shown to be pathogenic. Comparison to controls revealed that the variants we reported in this study were somatic thymoma-specific variants. Additionally, we found that the presence of variants in SMAD4 gene predicted shorter overall survival in thymoma patients. Conclusions The most frequently mutated genes in thymoma samples analysed in this study belong to the EGFR, ATM, and TP53 signalling pathways, regulating cell cycle check points, gene expression, and apoptosis. The results of our study complement the knowledge of thymoma molecular pathogenesis.
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Affiliation(s)
- Jelena Peric
- Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, Belgrade, Serbia
| | - Natalija Samaradzic
- University Hospital of Pulmonology, Clinical Centre of Serbia, Belgrade, Serbia
| | - Vesna Skodric Trifunovic
- University Hospital of Pulmonology, Clinical Centre of Serbia, Belgrade, Serbia
- School of Medicine, University of Belgrade, Belgrade, Serbia
| | - Natasa Tosic
- Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, Belgrade, Serbia
| | - Jelena Stojsic
- Department of Thoracopulmonary Pathology, Service of Pathology, Clinical Centre of Serbia, Serbia
| | - Sonja Pavlovic
- Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, Belgrade, Serbia
| | - Dragana Jovanovic
- University Hospital of Pulmonology, Clinical Centre of Serbia, Belgrade, Serbia
- School of Medicine, University of Belgrade, Belgrade, Serbia
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44
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Wei PJ, Wu FX, Xia J, Su Y, Wang J, Zheng CH. Prioritizing Cancer Genes Based on an Improved Random Walk Method. Front Genet 2020; 11:377. [PMID: 32411180 PMCID: PMC7198854 DOI: 10.3389/fgene.2020.00377] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Accepted: 03/26/2020] [Indexed: 12/18/2022] Open
Abstract
Identifying driver genes that contribute to cancer progression from numerous passenger genes, although a central goal, is a major challenge. The protein-protein interaction network provides convenient and reasonable assistance for driver gene discovery. Random walk-based methods have been widely used to prioritize nodes in social or biological networks. However, most studies select the next arriving node uniformly from the random walker's neighbors. Few consider transiting preference according to the degree of random walker's neighbors. In this study, based on the random walk method, we propose a novel approach named Driver_IRW (Driver genes discovery with Improved Random Walk method), to prioritize cancer genes in cancer-related network. The key idea of Driver_IRW is to assign different transition probabilities for different edges of a constructed cancer-related network in accordance with the degree of the nodes' neighbors. Furthermore, the global centrality (here is betweenness centrality) and Katz feedback centrality are incorporated into the framework to evaluate the probability to walk to the seed nodes. Experimental results on four cancer types indicate that Driver_IRW performs more efficiently than some previously published methods for uncovering known cancer-related genes. In conclusion, our method can aid in prioritizing cancer-related genes and complement traditional frequency and network-based methods.
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Affiliation(s)
- Pi-Jing Wei
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, College of Computer Science and Technology, Anhui University, Hefei, China
| | - Fang-Xiang Wu
- Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK, Canada
- Department of Computer Sciences, University of Saskatchewan, Saskatoon, SK, Canada
- Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK, Canada
| | - Junfeng Xia
- Institutes of Physical Science and Information Technology, Anhui University, Hefei, China
| | - Yansen Su
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, College of Computer Science and Technology, Anhui University, Hefei, China
| | - Jing Wang
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, College of Computer Science and Technology, Anhui University, Hefei, China
- College of Computer and Information Engineering, Fuyang Normal University, Fuyang, China
| | - Chun-Hou Zheng
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, College of Computer Science and Technology, Anhui University, Hefei, China
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45
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Corona RI, Seo JH, Lin X, Hazelett DJ, Reddy J, Fonseca MAS, Abassi F, Lin YG, Mhawech-Fauceglia PY, Shah SP, Huntsman DG, Gusev A, Karlan BY, Berman BP, Freedman ML, Gayther SA, Lawrenson K. Non-coding somatic mutations converge on the PAX8 pathway in ovarian cancer. Nat Commun 2020; 11:2020. [PMID: 32332753 PMCID: PMC7181647 DOI: 10.1038/s41467-020-15951-0] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 03/31/2020] [Indexed: 02/07/2023] Open
Abstract
The functional consequences of somatic non-coding mutations in ovarian cancer (OC) are unknown. To identify regulatory elements (RE) and genes perturbed by acquired non-coding variants, here we establish epigenomic and transcriptomic landscapes of primary OCs using H3K27ac ChIP-seq and RNA-seq, and then integrate these with whole genome sequencing data from 232 OCs. We identify 25 frequently mutated regulatory elements, including an enhancer at 6p22.1 which associates with differential expression of ZSCAN16 (P = 6.6 × 10-4) and ZSCAN12 (P = 0.02). CRISPR/Cas9 knockout of this enhancer induces downregulation of both genes. Globally, there is an enrichment of single nucleotide variants in active binding sites for TEAD4 (P = 6 × 10-11) and its binding partner PAX8 (P = 2×10-10), a known lineage-specific transcription factor in OC. In addition, the collection of cis REs associated with PAX8 comprise the most frequently mutated set of enhancers in OC (P = 0.003). These data indicate that non-coding somatic mutations disrupt the PAX8 transcriptional network during OC development.
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Affiliation(s)
- Rosario I Corona
- Cedars-Sinai Women's Cancer Program at the Samuel Oschin Cancer Center, Los Angeles, CA, USA
- Center for Bioinformatics and Functional Genomics, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Ji-Heui Seo
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Xianzhi Lin
- Cedars-Sinai Women's Cancer Program at the Samuel Oschin Cancer Center, Los Angeles, CA, USA
| | - Dennis J Hazelett
- Center for Bioinformatics and Functional Genomics, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jessica Reddy
- Cedars-Sinai Women's Cancer Program at the Samuel Oschin Cancer Center, Los Angeles, CA, USA
| | - Marcos A S Fonseca
- Cedars-Sinai Women's Cancer Program at the Samuel Oschin Cancer Center, Los Angeles, CA, USA
| | - Forough Abassi
- Cedars-Sinai Women's Cancer Program at the Samuel Oschin Cancer Center, Los Angeles, CA, USA
| | - Yvonne G Lin
- Department of Obstetrics and Gynecology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | | | - Sohrab P Shah
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
- Department of Molecular Oncology, BC Cancer Agency, Vancouver, BC, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - David G Huntsman
- Department of Molecular Oncology, BC Cancer Agency, Vancouver, BC, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
- Department of Gynecology and Obstetrics, University of British Columbia, Vancouver, BC, Canada
| | - Alexander Gusev
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- McGraw/Patterson Center for Population Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Beth Y Karlan
- Cedars-Sinai Women's Cancer Program at the Samuel Oschin Cancer Center, Los Angeles, CA, USA
| | - Benjamin P Berman
- Center for Bioinformatics and Functional Genomics, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Matthew L Freedman
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA.
- The Eli and Edythe L. Broad Institute, Cambridge, MA, USA.
| | - Simon A Gayther
- Cedars-Sinai Women's Cancer Program at the Samuel Oschin Cancer Center, Los Angeles, CA, USA.
- Center for Bioinformatics and Functional Genomics, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
| | - Kate Lawrenson
- Cedars-Sinai Women's Cancer Program at the Samuel Oschin Cancer Center, Los Angeles, CA, USA.
- Center for Bioinformatics and Functional Genomics, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
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DiNardo Z, Tomlinson K, Ritz A, Oesper L. Distance measures for tumor evolutionary trees. Bioinformatics 2020; 36:2090-2097. [PMID: 31750900 PMCID: PMC7141873 DOI: 10.1093/bioinformatics/btz869] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Revised: 09/04/2019] [Accepted: 11/19/2019] [Indexed: 12/14/2022] Open
Abstract
MOTIVATION There has been recent increased interest in using algorithmic methods to infer the evolutionary tree underlying the developmental history of a tumor. Quantitative measures that compare such trees are vital to a number of different applications including benchmarking tree inference methods and evaluating common inheritance patterns across patients. However, few appropriate distance measures exist, and those that do have low resolution for differentiating trees or do not fully account for the complex relationship between tree topology and the inheritance of the mutations labeling that topology. RESULTS Here, we present two novel distance measures, Common Ancestor Set distance (CASet) and Distinctly Inherited Set Comparison distance (DISC), that are specifically designed to account for the subclonal mutation inheritance patterns characteristic of tumor evolutionary trees. We apply CASet and DISC to multiple simulated datasets and two breast cancer datasets and show that our distance measures allow for more nuanced and accurate delineation between tumor evolutionary trees than existing distance measures. AVAILABILITY AND IMPLEMENTATION Implementations of CASet and DISC are freely available at: https://bitbucket.org/oesperlab/stereodist. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zach DiNardo
- Department of Computer Science, Carleton College, Northfield, MN 55057, USA
| | - Kiran Tomlinson
- Department of Computer Science, Carleton College, Northfield, MN 55057, USA
- Department of Computer Science, Cornell University, Ithaca, NY 14853, USA
| | - Anna Ritz
- Department of Biology, Reed College, Portland, OR 97202, USA
| | - Layla Oesper
- Department of Computer Science, Carleton College, Northfield, MN 55057, USA
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Stafford IS, Kellermann M, Mossotto E, Beattie RM, MacArthur BD, Ennis S. A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases. NPJ Digit Med 2020; 3:30. [PMID: 32195365 PMCID: PMC7062883 DOI: 10.1038/s41746-020-0229-3] [Citation(s) in RCA: 102] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Accepted: 01/17/2020] [Indexed: 02/07/2023] Open
Abstract
Autoimmune diseases are chronic, multifactorial conditions. Through machine learning (ML), a branch of the wider field of artificial intelligence, it is possible to extract patterns within patient data, and exploit these patterns to predict patient outcomes for improved clinical management. Here, we surveyed the use of ML methods to address clinical problems in autoimmune disease. A systematic review was conducted using MEDLINE, embase and computers and applied sciences complete databases. Relevant papers included "machine learning" or "artificial intelligence" and the autoimmune diseases search term(s) in their title, abstract or key words. Exclusion criteria: studies not written in English, no real human patient data included, publication prior to 2001, studies that were not peer reviewed, non-autoimmune disease comorbidity research and review papers. 169 (of 702) studies met the criteria for inclusion. Support vector machines and random forests were the most popular ML methods used. ML models using data on multiple sclerosis, rheumatoid arthritis and inflammatory bowel disease were most common. A small proportion of studies (7.7% or 13/169) combined different data types in the modelling process. Cross-validation, combined with a separate testing set for more robust model evaluation occurred in 8.3% of papers (14/169). The field may benefit from adopting a best practice of validation, cross-validation and independent testing of ML models. Many models achieved good predictive results in simple scenarios (e.g. classification of cases and controls). Progression to more complex predictive models may be achievable in future through integration of multiple data types.
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Affiliation(s)
- I. S. Stafford
- Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK
- Institute for Life Sciences, University of Southampton, Southampton, UK
| | - M. Kellermann
- Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK
| | - E. Mossotto
- Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK
- Institute for Life Sciences, University of Southampton, Southampton, UK
| | - R. M. Beattie
- Department of Paediatric Gastroenterology, Southampton Children’s Hospital, Southampton, UK
| | - B. D. MacArthur
- Institute for Life Sciences, University of Southampton, Southampton, UK
| | - S. Ennis
- Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK
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Xi J, Li A, Wang M. HetRCNA: A Novel Method to Identify Recurrent Copy Number Alternations from Heterogeneous Tumor Samples Based on Matrix Decomposition Framework. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:422-434. [PMID: 29994262 DOI: 10.1109/tcbb.2018.2846599] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
A common strategy to discovering cancer associated copy number aberrations (CNAs) from a cohort of cancer samples is to detect recurrent CNAs (RCNAs). Although the previous methods can successfully identify communal RCNAs shared by nearly all tumor samples, detecting subgroup-specific RCNAs and their related subgroup samples from cancer samples with heterogeneity is still invalid for these existing approaches. In this paper, we introduce a novel integrated method called HetRCNA, which can identify statistically significant subgroup-specific RCNAs and their related subgroup samples. Based on matrix decomposition framework with weight constraint, HetRCNA can successfully measure the subgroup samples by coefficients of left vectors with weight constraint and subgroup-specific RCNAs by coefficients of the right vectors and significance test. When we evaluate HetRCNA on simulated dataset, the results show that HetRCNA gives the best performances among the competing methods and is robust to the noise factors of the simulated data. When HetRCNA is applied on a real breast cancer dataset, our approach successfully identifies a bunch of RCNA regions and the result is highly correlated with the results of the other two investigated approaches. Notably, the genomic regions identified by HetRCNA harbor many breast cancer related genes reported by previous researches.
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Tan Z, Zhu J, Stemmer PM, Sun L, Yang Z, Schultz K, Gaffrey MJ, Cesnik AJ, Yi X, Hao X, Shortreed MR, Shi T, Lubman DM. Comprehensive Detection of Single Amino Acid Variants and Evaluation of Their Deleterious Potential in a PANC-1 Cell Line. J Proteome Res 2020; 19:1635-1646. [PMID: 32058723 DOI: 10.1021/acs.jproteome.9b00840] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Identifying single amino acid variants (SAAVs) in cancer is critical for precision oncology. Several advanced algorithms are now available to identify SAAVs, but attempts to combine different algorithms and optimize them on large data sets to achieve a more comprehensive coverage of SAAVs have not been implemented. Herein, we report an expanded detection of SAAVs in the PANC-1 cell line using three different strategies, which results in the identification of 540 SAAVs in the mass spectrometry data. Among the set of 540 SAAVs, 79 are evaluated as deleterious SAAVs based on analysis using the novel AssVar software in which one of the driver mutations found in each protein of KRAS, TP53, and SLC37A4 is further validated using independent selected reaction monitoring (SRM) analysis. Our study represents the most comprehensive discovery of SAAVs to date and the first large-scale detection of deleterious SAAVs in the PANC-1 cell line. This work may serve as the basis for future research in pancreatic cancer and personal immunotherapy and treatment.
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Affiliation(s)
- Zhijing Tan
- Department of Surgery, The University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Jianhui Zhu
- Department of Surgery, The University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Paul M Stemmer
- Institute of Environmental Health Sciences, Wayne State University, Detroit, Michigan 48202, United States
| | - Liangliang Sun
- Department of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States
| | - Zhichang Yang
- Department of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States
| | - Kendall Schultz
- Integrative Omics Group, Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Matthew J Gaffrey
- Integrative Omics Group, Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Anthony J Cesnik
- Department of Genetics, Stanford University, Stanford, California 94305, United States
| | - Xinpei Yi
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, Texas 77030, United States
| | - Xiaohu Hao
- Shanghai Institutes for Biological Science, Chinese Academy of Science, Shanghai 200031, China
| | - Michael R Shortreed
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Tujin Shi
- Integrative Omics Group, Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - David M Lubman
- Department of Surgery, The University of Michigan, Ann Arbor, Michigan 48109, United States
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50
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Multimodal genomic features predict outcome of immune checkpoint blockade in non-small-cell lung cancer. ACTA ACUST UNITED AC 2020; 1:99-111. [PMID: 32984843 DOI: 10.1038/s43018-019-0008-8] [Citation(s) in RCA: 119] [Impact Index Per Article: 29.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Despite progress in immunotherapy, identifying patients that respond has remained a challenge. Through analysis of whole-exome and targeted sequence data from 5,449 tumors, we found a significant correlation between tumor mutation burden (TMB) and tumor purity, suggesting that low tumor purity tumors are likely to have inaccurate TMB estimates. We developed a new method to estimate a corrected TMB (cTMB) that was adjusted for tumor purity and more accurately predicted outcome to immune checkpoint blockade (ICB). To identify improved predictive markers together with cTMB, we performed whole-exome sequencing for 104 lung tumors treated with ICB. Through comprehensive analyses of sequence and structural alterations, we discovered a significant enrichment in activating mutations in receptor tyrosine kinase (RTK) genes in nonresponding tumors in three immunotherapy treated cohorts. An integrated multivariable model incorporating cTMB, RTK mutations, smoking-related mutational signature and human leukocyte antigen status provided an improved predictor of response to immunotherapy that was independently validated.
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