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Huang J, Mao L, Lei Q, Guo AY. Bioinformatics tools and resources for cancer and application. Chin Med J (Engl) 2024:00029330-990000000-01162. [PMID: 39075637 DOI: 10.1097/cm9.0000000000003254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2024] [Indexed: 07/31/2024] Open
Abstract
ABSTRACT Tumor bioinformatics plays an important role in cancer research and precision medicine. The primary focus of traditional cancer research has been molecular and clinical studies of a number of fundamental pathways and genes. In recent years, driven by breakthroughs in high-throughput technologies, large-scale cancer omics data have accumulated rapidly. How to effectively utilize and share these data is particularly important. To address this crucial task, many computational tools and databases have been developed over the past few years. To help researchers quickly learn and understand the functions of these tools, in this review, we summarize publicly available bioinformatics tools and resources for pan-cancer multi-omics analysis, regulatory analysis of tumorigenesis, tumor treatment and prognosis, immune infiltration analysis, immune repertoire analysis, cancer driver gene and driver mutation analysis, and cancer single-cell analysis, which may further help researchers find more suitable tools for their research.
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Affiliation(s)
- Jin Huang
- Department of Thoracic Surgery, West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Lingzi Mao
- Hubei Bioinformatics & Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Qian Lei
- Department of Thoracic Surgery, West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - An-Yuan Guo
- Department of Thoracic Surgery, West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
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2
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Besedina E, Supek F. Copy number losses of oncogenes and gains of tumor suppressor genes generate common driver mutations. Nat Commun 2024; 15:6139. [PMID: 39033140 PMCID: PMC11271286 DOI: 10.1038/s41467-024-50552-1] [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: 08/24/2023] [Accepted: 07/11/2024] [Indexed: 07/23/2024] Open
Abstract
Cancer driver genes can undergo positive selection for various types of genetic alterations, including gain-of-function or loss-of-function mutations and copy number alterations (CNA). We investigated the landscape of different types of alterations affecting driver genes in 17,644 cancer exomes and genomes. We find that oncogenes may simultaneously exhibit signatures of positive selection and also negative selection in different gene segments, suggesting a method to identify additional tumor types where an oncogene is a driver or a vulnerability. Next, we characterize the landscape of CNA-dependent selection effects, revealing a general trend of increased positive selection on oncogene mutations not only upon CNA gains but also upon CNA deletions. Similarly, we observe a positive interaction between mutations and CNA gains in tumor suppressor genes. Thus, two-hit events involving point mutations and CNA are universally observed regardless of the type of CNA and may signal new therapeutic opportunities. An analysis with focus on the somatic CNA two-hit events can help identify additional driver genes relevant to a tumor type. By a global inference of point mutation and CNA selection signatures and interactions thereof across genes and tissues, we identify 9 evolutionary archetypes of driver genes, representing different mechanisms of (in)activation by genetic alterations.
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Affiliation(s)
- Elizaveta Besedina
- Institute for Research in Biomedicine (IRB Barcelona), 08028, Barcelona, Spain
| | - Fran Supek
- Institute for Research in Biomedicine (IRB Barcelona), 08028, Barcelona, Spain.
- Biotech Research and Innovation Centre (BRIC), University of Copenhagen, 2200, Copenhagen, Denmark.
- Catalan Institution for Research and Advanced Studies (ICREA), 08010, Barcelona, Spain.
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3
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Rojas-Rodriguez F, Schmidt MK, Canisius S. Assessing the validity of driver gene identification tools for targeted genome sequencing data. BIOINFORMATICS ADVANCES 2024; 4:vbae073. [PMID: 38808071 PMCID: PMC11132814 DOI: 10.1093/bioadv/vbae073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 04/16/2024] [Accepted: 05/22/2024] [Indexed: 05/30/2024]
Abstract
Motivation Most cancer driver gene identification tools have been developed for whole-exome sequencing data. Targeted sequencing is a popular alternative to whole-exome sequencing for large cancer studies due to its greater depth at a lower cost per tumor. Unlike whole-exome sequencing, targeted sequencing only enables mutation calling for a selected subset of genes. Whether existing driver gene identification tools remain valid in that context has not previously been studied. Results We evaluated the validity of seven popular driver gene identification tools when applied to targeted sequencing data. Based on whole-exome data of 14 different cancer types from TCGA, we constructed matching targeted datasets by keeping only the mutations overlapping with the pan-cancer MSK-IMPACT panel and, in the case of breast cancer, also the breast-cancer-specific B-CAST panel. We then compared the driver gene predictions obtained on whole-exome and targeted mutation data for each of the seven tools. Differences in how the tools model background mutation rates were the most important determinant of their validity on targeted sequencing data. Based on our results, we recommend OncodriveFML, OncodriveCLUSTL, 20/20+, dNdSCv, and ActiveDriver for driver gene identification in targeted sequencing data, whereas MutSigCV and DriverML are best avoided in that context. Availability and implementation Code for the analyses is available at https://github.com/SchmidtGroupNKI/TGSdrivergene_validity.
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Affiliation(s)
- Felipe Rojas-Rodriguez
- Division of Molecular Pathology, The Netherlands Cancer Institute—Antoni van Leeuwenhoek Hospital, 1066 CX Amsterdam, The Netherlands
| | - Marjanka K Schmidt
- Division of Molecular Pathology, The Netherlands Cancer Institute—Antoni van Leeuwenhoek Hospital, 1066 CX Amsterdam, The Netherlands
- Department of Clinical Genetics, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands
- Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute—Antoni van Leeuwenhoek Hospital, 1066 CX Amsterdam, The Netherlands
| | - Sander Canisius
- Division of Molecular Pathology, The Netherlands Cancer Institute—Antoni van Leeuwenhoek Hospital, 1066 CX Amsterdam, The Netherlands
- Division of Molecular Carcinogenesis, The Netherlands Cancer Institute—Antoni van Leeuwenhoek Hospital, 1066 CX Amsterdam, The Netherlands
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4
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Cao X, Huber S, Ahari AJ, Traube FR, Seifert M, Oakes CC, Secheyko P, Vilov S, Scheller IF, Wagner N, Yépez VA, Blombery P, Haferlach T, Heinig M, Wachutka L, Hutter S, Gagneur J. Analysis of 3760 hematologic malignancies reveals rare transcriptomic aberrations of driver genes. Genome Med 2024; 16:70. [PMID: 38769532 PMCID: PMC11103968 DOI: 10.1186/s13073-024-01331-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 04/04/2024] [Indexed: 05/22/2024] Open
Abstract
BACKGROUND Rare oncogenic driver events, particularly affecting the expression or splicing of driver genes, are suspected to substantially contribute to the large heterogeneity of hematologic malignancies. However, their identification remains challenging. METHODS To address this issue, we generated the largest dataset to date of matched whole genome sequencing and total RNA sequencing of hematologic malignancies from 3760 patients spanning 24 disease entities. Taking advantage of our dataset size, we focused on discovering rare regulatory aberrations. Therefore, we called expression and splicing outliers using an extension of the workflow DROP (Detection of RNA Outliers Pipeline) and AbSplice, a variant effect predictor that identifies genetic variants causing aberrant splicing. We next trained a machine learning model integrating these results to prioritize new candidate disease-specific driver genes. RESULTS We found a median of seven expression outlier genes, two splicing outlier genes, and two rare splice-affecting variants per sample. Each category showed significant enrichment for already well-characterized driver genes, with odds ratios exceeding three among genes called in more than five samples. On held-out data, our integrative modeling significantly outperformed modeling based solely on genomic data and revealed promising novel candidate driver genes. Remarkably, we found a truncated form of the low density lipoprotein receptor LRP1B transcript to be aberrantly overexpressed in about half of hairy cell leukemia variant (HCL-V) samples and, to a lesser extent, in closely related B-cell neoplasms. This observation, which was confirmed in an independent cohort, suggests LRP1B as a novel marker for a HCL-V subclass and a yet unreported functional role of LRP1B within these rare entities. CONCLUSIONS Altogether, our census of expression and splicing outliers for 24 hematologic malignancy entities and the companion computational workflow constitute unique resources to deepen our understanding of rare oncogenic events in hematologic cancers.
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Affiliation(s)
- Xueqi Cao
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
- Graduate School of Quantitative Biosciences (QBM), Munich, Germany
| | - Sandra Huber
- Munich Leukemia Laboratory (MLL), Munich, Germany
| | - Ata Jadid Ahari
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Franziska R Traube
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Stuttgart, Germany
| | - Marc Seifert
- Department of Haematology, Oncology and Clinical Immunology, University Hospital Düsseldorf, Düsseldorf, Germany
| | - Christopher C Oakes
- Division of Hematology, Department of Internal Medicine, The Ohio State University, Columbus, OH, USA
| | - Polina Secheyko
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
- Faculty of Biology, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Sergey Vilov
- Computational Health Center, Helmholtz Center Munich, Neuherberg, Germany
| | - Ines F Scheller
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
- Computational Health Center, Helmholtz Center Munich, Neuherberg, Germany
| | - Nils Wagner
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
- Helmholtz Association - Munich School for Data Science (MUDS), Munich, Germany
| | - Vicente A Yépez
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Piers Blombery
- Peter MacCallum Cancer Centre, Melbourne, Australia
- University of Melbourne, Melbourne, Australia
- Torsten Haferlach Leukämiediagnostik Stiftung, Munich, Germany
| | | | - Matthias Heinig
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
- Computational Health Center, Helmholtz Center Munich, Neuherberg, Germany
| | - Leonhard Wachutka
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
| | | | - Julien Gagneur
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
- Graduate School of Quantitative Biosciences (QBM), Munich, Germany.
- Computational Health Center, Helmholtz Center Munich, Neuherberg, Germany.
- Institute of Human Genetics, School of Medicine and Health, Technical University of Munich, Munich, Germany.
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Echeverría-Garcés G, Ramos-Medina MJ, Vargas R, Cabrera-Andrade A, Altamirano-Colina A, Freire MP, Montalvo-Guerrero J, Rivera-Orellana S, Echeverría-Espinoza P, Quiñones LA, López-Cortés A. Gastric cancer actionable genomic alterations across diverse populations worldwide and pharmacogenomics strategies based on precision oncology. Front Pharmacol 2024; 15:1373007. [PMID: 38756376 PMCID: PMC11096557 DOI: 10.3389/fphar.2024.1373007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 04/10/2024] [Indexed: 05/18/2024] Open
Abstract
Introduction: Gastric cancer is one of the most prevalent types of cancer worldwide. The World Health Organization (WHO), the International Agency for Research on Cancer (IARC), and the Global Cancer Statistics (GLOBOCAN) reported an age standardized global incidence rate of 9.2 per 100,000 individuals for gastric cancer in 2022, with a mortality rate of 6.1. Despite considerable progress in precision oncology through the efforts of international consortia, understanding the genomic features and their influence on the effectiveness of anti-cancer treatments across diverse ethnic groups remains essential. Methods: Our study aimed to address this need by conducting integrated in silico analyses to identify actionable genomic alterations in gastric cancer driver genes, assess their impact using deleteriousness scores, and determine allele frequencies across nine global populations: European Finnish, European non-Finnish, Latino, East Asian, South Asian, African, Middle Eastern, Ashkenazi Jewish, and Amish. Furthermore, our goal was to prioritize targeted therapeutic strategies based on pharmacogenomics clinical guidelines, in silico drug prescriptions, and clinical trial data. Results: Our comprehensive analysis examined 275,634 variants within 60 gastric cancer driver genes from 730,947 exome sequences and 76,215 whole-genome sequences from unrelated individuals, identifying 13,542 annotated and predicted oncogenic variants. We prioritized the most prevalent and deleterious oncogenic variants for subsequent pharmacogenomics testing. Additionally, we discovered actionable genomic alterations in the ARID1A, ATM, BCOR, ERBB2, ERBB3, CDKN2A, KIT, PIK3CA, PTEN, NTRK3, TP53, and CDKN2A genes that could enhance the efficacy of anti-cancer therapies, as suggested by in silico drug prescription analyses, reviews of current pharmacogenomics clinical guidelines, and evaluations of phase III and IV clinical trials targeting gastric cancer driver proteins. Discussion: These findings underline the urgency of consolidating efforts to devise effective prevention measures, invest in genomic profiling for underrepresented populations, and ensure the inclusion of ethnic minorities in future clinical trials and cancer research in developed countries.
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Affiliation(s)
- Gabriela Echeverría-Garcés
- Centro de Referencia Nacional de Genómica, Secuenciación y Bioinformática, Instituto Nacional de Investigación en Salud Pública “Leopoldo Izquieta Pérez”, Quito, Ecuador
- Latin American Network for the Implementation and Validation of Clinical Pharmacogenomics Guidelines (RELIVAF-CYTED), Santiago, Chile
| | - María José Ramos-Medina
- German Cancer Research Center (DKFZ), Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Rodrigo Vargas
- Latin American Network for the Implementation and Validation of Clinical Pharmacogenomics Guidelines (RELIVAF-CYTED), Santiago, Chile
- Department of Molecular Biology, Galileo University, Guatemala City, Guatemala
| | - Alejandro Cabrera-Andrade
- Escuela de Enfermería, Facultad de Ciencias de La Salud, Universidad de Las Américas, Quito, Ecuador
- Grupo de Bio-Quimioinformática, Universidad de Las Américas, Quito, Ecuador
| | | | - María Paula Freire
- Cancer Research Group (CRG), Faculty of Medicine, Universidad de Las Américas, Quito, Ecuador
| | | | | | | | - Luis A. Quiñones
- Latin American Network for the Implementation and Validation of Clinical Pharmacogenomics Guidelines (RELIVAF-CYTED), Santiago, Chile
- Laboratory of Chemical Carcinogenesis and Pharmacogenetics, Department of Basic-Clinical Oncology (DOBC), Faculty of Medicine, University of Chile, Santiago, Chile
- Department of Pharmaceutical Sciences and Technology, Faculty of Chemical and Pharmaceutical Sciences, University of Chile, Santiago, Chile
| | - Andrés López-Cortés
- Cancer Research Group (CRG), Faculty of Medicine, Universidad de Las Américas, Quito, Ecuador
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Munquad S, Das AB. Uncovering the subtype-specific disease module and the development of drug response prediction models for glioma. Heliyon 2024; 10:e27190. [PMID: 38468932 PMCID: PMC10926146 DOI: 10.1016/j.heliyon.2024.e27190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 02/24/2024] [Accepted: 02/26/2024] [Indexed: 03/13/2024] Open
Abstract
The poor prognosis of glioma patients brought attention to the need for effective therapeutic approaches for precision therapy. Here, we deployed algorithms relying on network medicine and artificial intelligence to design the framework for subtype-specific target identification and drug response prediction in glioma. We identified the driver mutations that were differentially expressed in each subtype of lower-grade glioma and glioblastoma multiforme and were linked to cancer-specific processes. Driver mutations that were differentially expressed were also subjected to subtype-specific disease module identification. The drugs from the drug bank database were retrieved to target these disease modules. However, the efficacy of anticancer drugs depends on the molecular profile of the cancer and varies among cancer patients due to intratumor heterogeneity. Hence, we developed a deep-learning-based drug response prediction framework using the experimental drug screening data. Models for 30 drugs that can target the disease module were developed, where drug response measured by IC50 was considered a response and gene expression and mutation data were considered predictor variables. The model construction consists of three steps: feature selection, data integration, and classification. We observed the consistent performance of the models in training, test, and validation datasets. Drug responses were predicted for particular cell lines derived from distinct subtypes of gliomas. We found that subtypes of gliomas respond differently to the drug, highlighting the importance of subtype-specific drug response prediction. Therefore, the development of personalized therapy by integrating network medicine and a deep learning-based approach can lead to cancer-specific treatment and improved patient care.
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Affiliation(s)
- Sana Munquad
- Department of Biotechnology, National Institute of Technology Warangal, Warangal, 506004, Telangana, India
| | - Asim Bikas Das
- Department of Biotechnology, National Institute of Technology Warangal, Warangal, 506004, Telangana, India
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7
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Nourbakhsh M, Degn K, Saksager A, Tiberti M, Papaleo E. Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks. Brief Bioinform 2024; 25:bbad519. [PMID: 38261338 PMCID: PMC10805075 DOI: 10.1093/bib/bbad519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 11/27/2023] [Accepted: 12/11/2023] [Indexed: 01/24/2024] Open
Abstract
The vast amount of available sequencing data allows the scientific community to explore different genetic alterations that may drive cancer or favor cancer progression. Software developers have proposed a myriad of predictive tools, allowing researchers and clinicians to compare and prioritize driver genes and mutations and their relative pathogenicity. However, there is little consensus on the computational approach or a golden standard for comparison. Hence, benchmarking the different tools depends highly on the input data, indicating that overfitting is still a massive problem. One of the solutions is to limit the scope and usage of specific tools. However, such limitations force researchers to walk on a tightrope between creating and using high-quality tools for a specific purpose and describing the complex alterations driving cancer. While the knowledge of cancer development increases daily, many bioinformatic pipelines rely on single nucleotide variants or alterations in a vacuum without accounting for cellular compartments, mutational burden or disease progression. Even within bioinformatics and computational cancer biology, the research fields work in silos, risking overlooking potential synergies or breakthroughs. Here, we provide an overview of databases and datasets for building or testing predictive cancer driver tools. Furthermore, we introduce predictive tools for driver genes, driver mutations, and the impact of these based on structural analysis. Additionally, we suggest and recommend directions in the field to avoid silo-research, moving towards integrative frameworks.
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Affiliation(s)
- Mona Nourbakhsh
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Kristine Degn
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Astrid Saksager
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Matteo Tiberti
- Cancer Structural Biology, Danish Cancer Institute, 2100 Copenhagen, Denmark
| | - Elena Papaleo
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
- Cancer Structural Biology, Danish Cancer Institute, 2100 Copenhagen, Denmark
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Demajo S, Ramis-Zaldivar JE, Muiños F, Grau ML, Andrianova M, López-Bigas N, González-Pérez A. Identification of Clonal Hematopoiesis Driver Mutations through In Silico Saturation Mutagenesis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.13.23299893. [PMID: 38168256 PMCID: PMC10760256 DOI: 10.1101/2023.12.13.23299893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Clonal hematopoiesis (CH) is a phenomenon of clonal expansion of hematopoietic stem cells driven by somatic mutations affecting certain genes. Recently, CH has been linked to the development of a number of hematologic malignancies, cardiovascular diseases and other conditions. Although the most frequently mutated CH driver genes have been identified, a systematic landscape of the mutations capable of initiating this phenomenon is still lacking. Here, we train high-quality machine-learning models for 12 of the most recurrent CH driver genes to identify their driver mutations. These models outperform an experimental base-editing approach and expert-curated rules based on prior knowledge of the function of these genes. Moreover, their application to identify CH driver mutations across almost half a million donors of the UK Biobank reproduces known associations between CH driver mutations and age, and the prevalence of several diseases and conditions. We thus propose that these models support the accurate identification of CH across healthy individuals.
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Affiliation(s)
- Santiago Demajo
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac, 10, 08028 Barcelona, Spain
- Centro de Investigación Biomédica en Red en Cáncer (CIBERONC), Instituto de Salud Carlos III, Madrid, Spain
| | - Joan Enric Ramis-Zaldivar
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac, 10, 08028 Barcelona, Spain
| | - Ferran Muiños
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac, 10, 08028 Barcelona, Spain
- Centro de Investigación Biomédica en Red en Cáncer (CIBERONC), Instituto de Salud Carlos III, Madrid, Spain
| | - Miguel L Grau
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac, 10, 08028 Barcelona, Spain
| | - Maria Andrianova
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac, 10, 08028 Barcelona, Spain
| | - Núria López-Bigas
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac, 10, 08028 Barcelona, Spain
- Centro de Investigación Biomédica en Red en Cáncer (CIBERONC), Instituto de Salud Carlos III, Madrid, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Abel González-Pérez
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac, 10, 08028 Barcelona, Spain
- Centro de Investigación Biomédica en Red en Cáncer (CIBERONC), Instituto de Salud Carlos III, Madrid, Spain
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Li L, Sheng Q, Zeng H, Li W, Wang Q, Ma G, Xu X, Qiu M, Zhang W, Shan C. Specific genetic aberrations of parathyroid in Chinese patients with tertiary hyperparathyroidism using whole-exome sequencing. Front Endocrinol (Lausanne) 2023; 14:1221060. [PMID: 37854190 PMCID: PMC10579901 DOI: 10.3389/fendo.2023.1221060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 09/07/2023] [Indexed: 10/20/2023] Open
Abstract
Background Tertiary hyperparathyroidism (THPT) is a peculiar subtype of hyperparathyroidism that usually develops from chronic kidney disease (CKD) and persists even after kidney transplantation. Unlike its precursor, secondary hyperparathyroidism (SHPT), THPT is characterized by uncontrolled high levels of calcium in the blood, which suggests the monoclonal or oligoclonal proliferation of parathyroid cells. However, the molecular abnormalities leading to THPT have not yet been fully understood. Methods In this study, we analyzed DNA samples from hyperplastic parathyroid and corresponding blood cells of 11 patients with THPT using whole-exome sequencing (WES). We identified somatic single nucleotide variants (SNV) and insertions or deletions variants (INDEL) and performed driver mutation analysis, KEGG pathway, and GO functional enrichment analysis. To confirm the impact of selected driver mutated genes, we also tested their expression level in these samples using qRT-PCR. Results Following quality control and mutation filtering, we identified 17,401 mutations, comprising 6690 missense variants, 3078 frameshift variants, 2005 stop-gained variants, and 1630 synonymous variants. Copy number variants (CNV) analysis showed that chromosome 22 copy number deletion was frequently observed in 6 samples. Driver mutation analysis identified 179 statistically significant mutated genes, including recurrent missense mutations on TBX20, ATAD5, ZNF669, and NOX3 genes in 3 different patients. KEGG pathway analysis revealed two enriched pathways: non-homologous end-joining and cell cycle, with a sole gene, PRKDC, involved. GO analysis demonstrated significant enrichment of various cellular components and cytobiological processes associated with four genes, including GO items of positive regulation of developmental growth, protein ubiquitination, and positive regulation of the apoptotic process. Compared to blood samples, THPT samples exhibited lower expression levels of PRKDC, TBX20, ATAD5, and NOX3 genes. THPT samples with exon mutations had relatively lower expression levels of PRKDC, TBX20, and NOX3 genes compared to those without mutations, although the difference was not statistically significant. Conclusion This study provides a comprehensive landscape of the genetic characteristics of hyperplastic parathyroids in THPT, highlighting the involvement of multiple genes and pathways in the development and progression of this disease. The dominant mutations identified in our study depicted new insights into the pathogenesis and molecular characteristics of THPT.
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Affiliation(s)
- Lei Li
- Department of Thyroid, Breast and Hernia Surgery of Changzheng Hospital Affiliated with Naval Military Medical University, Shanghai, China
- Department of General Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qixuan Sheng
- Department of Thyroid, Breast and Hernia Surgery of Changzheng Hospital Affiliated with Naval Military Medical University, Shanghai, China
| | - Huajin Zeng
- Department of Thyroid, Breast and Hernia Surgery of Changzheng Hospital Affiliated with Naval Military Medical University, Shanghai, China
| | - Wei Li
- Department of Thyroid, Breast and Hernia Surgery of Changzheng Hospital Affiliated with Naval Military Medical University, Shanghai, China
| | - Qiang Wang
- Department of Thyroid, Breast and Hernia Surgery of Changzheng Hospital Affiliated with Naval Military Medical University, Shanghai, China
| | - Guanjun Ma
- Department of Thyroid, Breast and Hernia Surgery of Changzheng Hospital Affiliated with Naval Military Medical University, Shanghai, China
| | - Xinyun Xu
- Department of Thyroid, Breast and Hernia Surgery of Changzheng Hospital Affiliated with Naval Military Medical University, Shanghai, China
| | - Ming Qiu
- Department of Thyroid, Breast and Hernia Surgery of Changzheng Hospital Affiliated with Naval Military Medical University, Shanghai, China
| | - Wei Zhang
- Department of Thyroid, Breast and Hernia Surgery of Changzheng Hospital Affiliated with Naval Military Medical University, Shanghai, China
| | - Chengxiang Shan
- Department of Thyroid, Breast and Hernia Surgery of Changzheng Hospital Affiliated with Naval Military Medical University, Shanghai, China
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10
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Li Y, Zhang SW, Xie MY, Zhang T. PhenoDriver: interpretable framework for studying personalized phenotype-associated driver genes in breast cancer. Brief Bioinform 2023; 24:bbad291. [PMID: 37738403 DOI: 10.1093/bib/bbad291] [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: 04/02/2023] [Revised: 07/12/2023] [Accepted: 07/27/2023] [Indexed: 09/24/2023] Open
Abstract
Identifying personalized cancer driver genes and further revealing their oncogenic mechanisms is critical for understanding the mechanisms of cell transformation and aiding clinical diagnosis. Almost all existing methods primarily focus on identifying driver genes at the cohort or individual level but fail to further uncover their underlying oncogenic mechanisms. To fill this gap, we present an interpretable framework, PhenoDriver, to identify personalized cancer driver genes, elucidate their roles in cancer development and uncover the association between driver genes and clinical phenotypic alterations. By analyzing 988 breast cancer patients, we demonstrate the outstanding performance of PhenoDriver in identifying breast cancer driver genes at the cohort level compared to other state-of-the-art methods. Otherwise, our PhenoDriver can also effectively identify driver genes with both recurrent and rare mutations in individual patients. We further explore and reveal the oncogenic mechanisms of some known and unknown breast cancer driver genes (e.g. TP53, MAP3K1, HTT, etc.) identified by PhenoDriver, and construct their subnetworks for regulating clinical abnormal phenotypes. Notably, most of our findings are consistent with existing biological knowledge. Based on the personalized driver profiles, we discover two existing and one unreported breast cancer subtypes and uncover their molecular mechanisms. These results intensify our understanding for breast cancer mechanisms, guide therapeutic decisions and assist in the development of targeted anticancer therapies.
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Affiliation(s)
- Yan Li
- School of Automation from Northwestern Polytechnical University, China
| | - Shao-Wu Zhang
- School of Automation from Northwestern Polytechnical University, China
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, China
| | - Ming-Yu Xie
- School of Automation from Northwestern Polytechnical University, China
| | - Tong Zhang
- School of Automation from Northwestern Polytechnical University, China
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11
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Ravindran F, Jain A, Desai S, Menon N, Srivastava K, Bawa PS, Sateesh K, Srivatsa N, Raghunath SK, Srinivasan S, Choudhary B. Whole-exome sequencing of Indian prostate cancer reveals a novel therapeutic target: POLQ. J Cancer Res Clin Oncol 2023; 149:2451-2462. [PMID: 35737091 DOI: 10.1007/s00432-022-04111-0] [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: 04/21/2022] [Accepted: 06/03/2022] [Indexed: 11/25/2022]
Abstract
PURPOSE Prostate cancer is the second most common cancer diagnosed worldwide and the third most common cancer among men in India. This study's objective was to characterise the mutational landscape of Indian prostate cancer using whole-exome sequencing to identify population-specific polymorphisms. METHODS Whole-exome sequencing was performed of 58 treatment-naive primary prostate tumors of Indian origin. Multiple computational and statistical analyses were used to profile the known common mutations, other deleterious mutations, driver genes, prognostic biomarkers, and gene signatures unique to each clinical parameter. Cox analysis was performed to validate survival-associated genes. McNemar test identified genes significant to recurrence and receiver-operating characteristic (ROC) analysis was conducted to determine its accuracy. OncodriveCLUSTL algorithm was used to deduce driver genes. The druggable target identified was modeled with its known inhibitor using Autodock. RESULTS TP53 was the most commonly mutated gene in our cohort. Three novel deleterious variants unique to the Indian prostate cancer subtype were identified: POLQ, FTHL17, and OR8G1. COX regression analysis identified ACSM5, a mitochondrial gene responsible for survival. CYLC1 gene, which encodes for sperm head cytoskeletal protein, was identified as an unfavorable prognostic biomarker indicative of recurrence. The novel POLQ mutant, also identified as a driver gene, was evaluated as the druggable target in this study. POLQ, a DNA repair enzyme implicated in various cancer types, is overexpressed and is associated with a poor prognosis. The mutant POLQ was subjected to structural analysis and modeled with its known inhibitor novobiocin resulting in decreased binding efficiency necessitating the development of a better drug. CONCLUSION In this pilot study, the molecular profiling using multiple computational and statistical analyses revealed distinct polymorphisms in the Indian prostate cancer cohort. The mutational signatures identified provide a valuable resource for prognostic stratification and targeted treatment strategies for Indian prostate cancer patients. The DNA repair enzyme, POLQ, was identified as the druggable target in this study.
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Affiliation(s)
- Febina Ravindran
- Institute of Bioinformatics and Applied Biotechnology, Electronic City Phase 1, Bangalore, Karnataka, India
| | - Anika Jain
- Institute of Bioinformatics and Applied Biotechnology, Electronic City Phase 1, Bangalore, Karnataka, India
| | - Sagar Desai
- Institute of Bioinformatics and Applied Biotechnology, Electronic City Phase 1, Bangalore, Karnataka, India
- Manipal Academy of Higher Education, Manipal, India
| | - Navjoth Menon
- Institute of Bioinformatics and Applied Biotechnology, Electronic City Phase 1, Bangalore, Karnataka, India
| | - Kriti Srivastava
- Institute of Bioinformatics and Applied Biotechnology, Electronic City Phase 1, Bangalore, Karnataka, India
| | - Pushpinder Singh Bawa
- Institute of Bioinformatics and Applied Biotechnology, Electronic City Phase 1, Bangalore, Karnataka, India
| | - K Sateesh
- Healthcare Global Enterprises Ltd, Cancer Centre, Bangalore, India
| | - N Srivatsa
- Healthcare Global Enterprises Ltd, Cancer Centre, Bangalore, India
| | - S K Raghunath
- Healthcare Global Enterprises Ltd, Cancer Centre, Bangalore, India
| | - Subhashini Srinivasan
- Institute of Bioinformatics and Applied Biotechnology, Electronic City Phase 1, Bangalore, Karnataka, India
| | - Bibha Choudhary
- Institute of Bioinformatics and Applied Biotechnology, Electronic City Phase 1, Bangalore, Karnataka, India.
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12
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Li S, Chen X, Chen J, Wu B, Liu J, Guo Y, Li M, Pu X. Multi-omics integration analysis of GPCRs in pan-cancer to uncover inter-omics relationships and potential driver genes. Comput Biol Med 2023; 161:106988. [PMID: 37201441 DOI: 10.1016/j.compbiomed.2023.106988] [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: 03/14/2023] [Revised: 03/30/2023] [Accepted: 04/27/2023] [Indexed: 05/20/2023]
Abstract
G protein-coupled receptors (GPCRs) are the largest drug target family. Unfortunately, applications of GPCRs in cancer therapy are scarce due to very limited knowledge regarding their correlations with cancers. Multi-omics data enables systematic investigations of GPCRs, yet their effective integration remains a challenge due to the complexity of the data. Here, we adopt two types of integration strategies, multi-staged and meta-dimensional approaches, to fully characterize somatic mutations, somatic copy number alterations (SCNAs), DNA methylations, and mRNA expressions of GPCRs in 33 cancers. Results from the multi-staged integration reveal that GPCR mutations cannot well predict expression dysregulation. The correlations between expressions and SCNAs are primarily positive, while correlations of the methylations with expressions and SCNAs are bimodal with negative correlations predominating. Based on these correlations, 32 and 144 potential cancer-related GPCRs driven by aberrant SCNA and methylation are identified, respectively. In addition, the meta-dimensional integration analysis is carried out by using deep learning models, which predict more than one hundred GPCRs as potential oncogenes. When comparing results between the two integration strategies, 165 cancer-related GPCRs are common in both, suggesting that they should be prioritized in future studies. However, 172 GPCRs emerge in only one, indicating that the two integration strategies should be considered concurrently to complement the information missed by the other such that obtain a more comprehensive understanding. Finally, correlation analysis further reveals that GPCRs, in particular for the class A and adhesion receptors, are generally immune-related. In a whole, the work is for the first time to reveal the associations between different omics layers and highlight the necessity of combing the two strategies in identifying cancer-related GPCRs.
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Affiliation(s)
- Shiqi Li
- College of Chemistry, Sichuan University, Chengdu, 610064, China.
| | - Xin Chen
- College of Chemistry, Sichuan University, Chengdu, 610064, China.
| | - Jianfang Chen
- College of Chemistry, Sichuan University, Chengdu, 610064, China.
| | - Binjian Wu
- College of Chemistry, Sichuan University, Chengdu, 610064, China.
| | - Jing Liu
- College of Chemistry, Sichuan University, Chengdu, 610064, China.
| | - Yanzhi Guo
- College of Chemistry, Sichuan University, Chengdu, 610064, China.
| | - Menglong Li
- College of Chemistry, Sichuan University, Chengdu, 610064, China.
| | - Xuemei Pu
- College of Chemistry, Sichuan University, Chengdu, 610064, China.
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13
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Pan-cancer clinical impact of latent drivers from double mutations. Commun Biol 2023; 6:202. [PMID: 36808143 PMCID: PMC9941481 DOI: 10.1038/s42003-023-04519-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 01/23/2023] [Indexed: 02/22/2023] Open
Abstract
Here, we discover potential 'latent driver' mutations in cancer genomes. Latent drivers have low frequencies and minor observable translational potential. As such, to date they have escaped identification. Their discovery is important, since when paired in cis, latent driver mutations can drive cancer. Our comprehensive statistical analysis of the pan-cancer mutation profiles of ~60,000 tumor sequences from the TCGA and AACR-GENIE cohorts identifies significantly co-occurring potential latent drivers. We observe 155 same gene double mutations of which 140 individual components are cataloged as latent drivers. Evaluation of cell lines and patient-derived xenograft response data to drug treatment indicate that in certain genes double mutations may have a prominent role in increasing oncogenic activity, hence obtaining a better drug response, as in PIK3CA. Taken together, our comprehensive analyses indicate that same-gene double mutations are exceedingly rare phenomena but are a signature for some cancer types, e.g., breast, and lung cancers. The relative rarity of doublets can be explained by the likelihood of strong signals resulting in oncogene-induced senescence, and by doublets consisting of non-identical single residue components populating the background mutational load, thus not identified.
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14
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Álvarez-Prado ÁF, Maas RR, Soukup K, Klemm F, Kornete M, Krebs FS, Zoete V, Berezowska S, Brouland JP, Hottinger AF, Daniel RT, Hegi ME, Joyce JA. Immunogenomic analysis of human brain metastases reveals diverse immune landscapes across genetically distinct tumors. Cell Rep Med 2023; 4:100900. [PMID: 36652909 PMCID: PMC9873981 DOI: 10.1016/j.xcrm.2022.100900] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 09/20/2022] [Accepted: 12/19/2022] [Indexed: 01/19/2023]
Abstract
Brain metastases (BrMs) are the most common form of brain tumors in adults and frequently originate from lung and breast primary cancers. BrMs are associated with high mortality, emphasizing the need for more effective therapies. Genetic profiling of primary tumors is increasingly used as part of the effort to guide targeted therapies against BrMs, and immune-based strategies for the treatment of metastatic cancer are gaining momentum. However, the tumor immune microenvironment (TIME) of BrM is extremely heterogeneous, and whether specific genetic profiles are associated with distinct immune states remains unknown. Here, we perform an extensive characterization of the immunogenomic landscape of human BrMs by combining whole-exome/whole-genome sequencing, RNA sequencing of immune cell populations, flow cytometry, immunofluorescence staining, and tissue imaging analyses. This revealed unique TIME phenotypes in genetically distinct lung- and breast-BrMs, thereby enabling the development of personalized immunotherapies tailored by the genetic makeup of the tumors.
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Affiliation(s)
- Ángel F. Álvarez-Prado
- Department of Oncology, University of Lausanne, 1011 Lausanne, Switzerland,Ludwig Institute for Cancer Research, University of Lausanne, 1011 Lausanne, Switzerland,Agora Cancer Research Center, 1011 Lausanne, Switzerland,L. Lundin and Family Brain Tumor Research Center, Departments of Oncology and Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, 1011 Lausanne, Switzerland
| | - Roeltje R. Maas
- Department of Oncology, University of Lausanne, 1011 Lausanne, Switzerland,Ludwig Institute for Cancer Research, University of Lausanne, 1011 Lausanne, Switzerland,Agora Cancer Research Center, 1011 Lausanne, Switzerland,L. Lundin and Family Brain Tumor Research Center, Departments of Oncology and Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, 1011 Lausanne, Switzerland,Neuroscience Research Center, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland,Department of Neurosurgery, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Klara Soukup
- Department of Oncology, University of Lausanne, 1011 Lausanne, Switzerland,Ludwig Institute for Cancer Research, University of Lausanne, 1011 Lausanne, Switzerland,Agora Cancer Research Center, 1011 Lausanne, Switzerland
| | - Florian Klemm
- Department of Oncology, University of Lausanne, 1011 Lausanne, Switzerland,Ludwig Institute for Cancer Research, University of Lausanne, 1011 Lausanne, Switzerland,Agora Cancer Research Center, 1011 Lausanne, Switzerland
| | - Mara Kornete
- Department of Oncology, University of Lausanne, 1011 Lausanne, Switzerland,Ludwig Institute for Cancer Research, University of Lausanne, 1011 Lausanne, Switzerland,Agora Cancer Research Center, 1011 Lausanne, Switzerland
| | - Fanny S. Krebs
- Department of Oncology, University of Lausanne, 1011 Lausanne, Switzerland,Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Vincent Zoete
- Department of Oncology, University of Lausanne, 1011 Lausanne, Switzerland,Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Sabina Berezowska
- Department of Pathology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Jean-Philippe Brouland
- Department of Pathology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Andreas F. Hottinger
- Department of Oncology, University of Lausanne, 1011 Lausanne, Switzerland,L. Lundin and Family Brain Tumor Research Center, Departments of Oncology and Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, 1011 Lausanne, Switzerland,Brain and Spine Tumor Center, Departments of Clinical Neurosciences and Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Roy T. Daniel
- L. Lundin and Family Brain Tumor Research Center, Departments of Oncology and Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, 1011 Lausanne, Switzerland,Department of Neurosurgery, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Monika E. Hegi
- L. Lundin and Family Brain Tumor Research Center, Departments of Oncology and Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, 1011 Lausanne, Switzerland,Neuroscience Research Center, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland,Department of Neurosurgery, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Johanna A. Joyce
- Department of Oncology, University of Lausanne, 1011 Lausanne, Switzerland,Ludwig Institute for Cancer Research, University of Lausanne, 1011 Lausanne, Switzerland,Agora Cancer Research Center, 1011 Lausanne, Switzerland,L. Lundin and Family Brain Tumor Research Center, Departments of Oncology and Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, 1011 Lausanne, Switzerland,Corresponding author
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15
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Newell F, Johansson PA, Wilmott JS, Nones K, Lakis V, Pritchard AL, Lo SN, Rawson RV, Kazakoff SH, Colebatch AJ, Koufariotis LT, Ferguson PM, Wood S, Leonard C, Law MH, Brooks KM, Broit N, Palmer JM, Couts KL, Vergara IA, Long GV, Barbour AP, Nieweg OE, Shivalingam B, Robinson WA, Stretch JR, Spillane AJ, Saw RP, Shannon KF, Thompson JF, Mann GJ, Pearson JV, Scolyer RA, Waddell N, Hayward NK. Comparative Genomics Provides Etiologic and Biological Insight into Melanoma Subtypes. Cancer Discov 2022; 12:2856-2879. [PMID: 36098958 PMCID: PMC9716259 DOI: 10.1158/2159-8290.cd-22-0603] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 08/01/2022] [Accepted: 09/02/2022] [Indexed: 01/12/2023]
Abstract
Melanoma is a cancer of melanocytes, with multiple subtypes based on body site location. Cutaneous melanoma is associated with skin exposed to ultraviolet radiation; uveal melanoma occurs in the eyes; mucosal melanoma occurs in internal mucous membranes; and acral melanoma occurs on the palms, soles, and nail beds. Here, we present the largest whole-genome sequencing study of melanoma to date, with 570 tumors profiled, as well as methylation and RNA sequencing for subsets of tumors. Uveal melanoma is genomically distinct from other melanoma subtypes, harboring the lowest tumor mutation burden and with significantly mutated genes in the G-protein signaling pathway. Most cutaneous, acral, and mucosal melanomas share alterations in components of the MAPK, PI3K, p53, p16, and telomere pathways. However, the mechanism by which these pathways are activated or inactivated varies between melanoma subtypes. Additionally, we identify potential novel germline predisposition genes for some of the less common melanoma subtypes. SIGNIFICANCE This is the largest whole-genome analysis of melanoma to date, comprehensively comparing the genomics of the four major melanoma subtypes. This study highlights both similarities and differences between the subtypes, providing insights into the etiology and biology of melanoma. This article is highlighted in the In This Issue feature, p. 2711.
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Affiliation(s)
- Felicity Newell
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.,Corresponding Authors: Felicity Newell, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006, Australia. Phone: 61-7-3845-3965; E-mail: ; Richard A. Scolyer, Melanoma Institute Australia, 40 Rockland Road, Wollstonecraft, Sydney, NSW 2065, Australia. Phone: 61-2-9515-7011; E-mail: ; and Nicola Waddell, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006, Australia. Phone: 61-7-3845-3538;
| | - Peter A. Johansson
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - James S. Wilmott
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.,Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.,Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - Katia Nones
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Vanessa Lakis
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Antonia L. Pritchard
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.,Department of Genetics and Immunology, Division of Biomedical Science, University of the Highlands and Islands, Inverness, Scotland, United Kingdom
| | - Serigne N. Lo
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia
| | - Robert V. Rawson
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.,Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.,Department of Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital and NSW Health Pathology, Sydney, New South Wales, Australia
| | | | - Andrew J. Colebatch
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.,Department of Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital and NSW Health Pathology, Sydney, New South Wales, Australia
| | | | - Peter M. Ferguson
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.,Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.,Department of Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital and NSW Health Pathology, Sydney, New South Wales, Australia
| | - Scott Wood
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Conrad Leonard
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Matthew H. Law
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.,Faculty of Health, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Kelly M. Brooks
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Natasa Broit
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.,Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia.,Q-Gen Cell Therapeutics, Brisbane, Queensland, Australia
| | - Jane M. Palmer
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Kasey L. Couts
- Center for Rare Melanomas, University of Colorado Cancer Center, Aurora, Colorado
| | - Ismael A. Vergara
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.,Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.,Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - Georgina V. Long
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.,Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.,Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia.,Mater Hospital, North Sydney, New South Wales, Australia.,Royal North Shore Hospital, St Leonards, New South Wales, Australia
| | - Andrew P. Barbour
- Diamantina Institute, The University of Queensland, Brisbane, Queensland, Australia
| | - Omgo E. Nieweg
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.,Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
| | - Brindha Shivalingam
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.,Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.,Mater Hospital, North Sydney, New South Wales, Australia.,Department of Neurosurgery, Chris O'Brien Lifehouse, Camperdown, New South Wales, Australia.,Department of Neurosurgery, Royal Prince Alfred Hospital, Camperdown, New South Wales, Australia
| | - William A. Robinson
- Center for Rare Melanomas, University of Colorado Cancer Center, Aurora, Colorado
| | - Jonathan R. Stretch
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.,Mater Hospital, North Sydney, New South Wales, Australia.,Department of Melanoma and Surgical Oncology, Royal Prince Alfred Hospital, Camperdown, New South Wales, Australia
| | - Andrew J. Spillane
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.,Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.,Mater Hospital, North Sydney, New South Wales, Australia.,Royal North Shore Hospital, St Leonards, New South Wales, Australia
| | - Robyn P.M. Saw
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.,Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.,Mater Hospital, North Sydney, New South Wales, Australia.,Department of Melanoma and Surgical Oncology, Royal Prince Alfred Hospital, Camperdown, New South Wales, Australia
| | - Kerwin F. Shannon
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.,Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.,Department of Melanoma and Surgical Oncology, Royal Prince Alfred Hospital, Camperdown, New South Wales, Australia
| | - John F. Thompson
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.,Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.,Mater Hospital, North Sydney, New South Wales, Australia.,Department of Melanoma and Surgical Oncology, Royal Prince Alfred Hospital, Camperdown, New South Wales, Australia
| | - Graham J. Mann
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.,Centre for Cancer Research, Westmead Institute for Medical Research, The University of Sydney, Westmead, New South Wales, Australia.,John Curtin School of Medical Research, Australian National University, Canberra, Australian Capital Territory, Australia
| | - John V. Pearson
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Richard A. Scolyer
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.,Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.,Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia.,Department of Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital and NSW Health Pathology, Sydney, New South Wales, Australia.,Corresponding Authors: Felicity Newell, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006, Australia. Phone: 61-7-3845-3965; E-mail: ; Richard A. Scolyer, Melanoma Institute Australia, 40 Rockland Road, Wollstonecraft, Sydney, NSW 2065, Australia. Phone: 61-2-9515-7011; E-mail: ; and Nicola Waddell, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006, Australia. Phone: 61-7-3845-3538;
| | - Nicola Waddell
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.,Corresponding Authors: Felicity Newell, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006, Australia. Phone: 61-7-3845-3965; E-mail: ; Richard A. Scolyer, Melanoma Institute Australia, 40 Rockland Road, Wollstonecraft, Sydney, NSW 2065, Australia. Phone: 61-2-9515-7011; E-mail: ; and Nicola Waddell, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006, Australia. Phone: 61-7-3845-3538;
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16
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Abstract
Mutations in genes that confer a selective advantage to hematopoietic stem cells (HSCs) drive clonal hematopoiesis (CH). While some CH drivers have been identified, the compendium of all genes able to drive CH upon mutations in HSCs remains incomplete. Exploiting signals of positive selection in blood somatic mutations may be an effective way to identify CH driver genes, analogously to cancer. Using the tumor sample in blood/tumor pairs as reference, we identify blood somatic mutations across more than 12,000 donors from two large cancer genomics cohorts. The application of IntOGen, a driver discovery pipeline, to both cohorts, and more than 24,000 targeted sequenced samples yields a list of close to 70 genes with signals of positive selection in CH, available at http://www.intogen.org/ch. This approach recovers known CH genes, and discovers other candidates. Identifying the genetic drivers of clonal haematopoiesis (CH) has been challenging due to their low frequencies and a lack of adequate tools. Here, the authors use a reverse calling to detect blood somatic mutations and the IntOGen pipeline to identify CH drivers in large cancer genomics data sets based on signals of positive selection.
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17
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Tabata J, Nakaoku T, Araki M, Yoshino R, Kohsaka S, Otsuka A, Ikegami M, Ui A, Kanno SI, Miyoshi K, Matsumoto S, Sagae Y, Yasui A, Sekijima M, Mano H, Okuno Y, Okamoto A, Kohno T. Novel Calcium-Binding Ablating Mutations Induce Constitutive RET Activity and Drive Tumorigenesis. Cancer Res 2022; 82:3751-3762. [PMID: 36166639 PMCID: PMC9574375 DOI: 10.1158/0008-5472.can-22-0834] [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: 03/12/2022] [Revised: 07/13/2022] [Accepted: 08/11/2022] [Indexed: 01/07/2023]
Abstract
Distinguishing oncogenic mutations from variants of unknown significance (VUS) is critical for precision cancer medicine. Here, computational modeling of 71,756 RET variants for positive selection together with functional assays of 110 representative variants identified a three-dimensional cluster of VUSs carried by multiple human cancers that cause amino acid substitutions in the calmodulin-like motif (CaLM) of RET. Molecular dynamics simulations indicated that CaLM mutations decrease interactions between Ca2+ and its surrounding residues and induce conformational distortion of the RET cysteine-rich domain containing the CaLM. RET-CaLM mutations caused ligand-independent constitutive activation of RET kinase by homodimerization mediated by illegitimate disulfide bond formation. RET-CaLM mutants possessed oncogenic and tumorigenic activities that could be suppressed by tyrosine kinase inhibitors targeting RET. This study identifies calcium-binding ablating mutations as a novel type of oncogenic mutation of RET and indicates that in silico-driven annotation of VUSs of druggable oncogenes is a promising strategy to identify targetable driver mutations. SIGNIFICANCE Comprehensive proteogenomic and in silico analyses of a vast number of VUSs identify a novel set of oncogenic and druggable mutations in the well-characterized RET oncogene.
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Affiliation(s)
- Junya Tabata
- Division of Genome Biology, National Cancer Center Research Institute, Tokyo, Japan.,Department of Obstetrics and Gynecology, The Jikei University School of Medicine, Tokyo, Japan
| | - Takashi Nakaoku
- Division of Genome Biology, National Cancer Center Research Institute, Tokyo, Japan.,Corresponding Authors: Takashi Nakaoku, Division of Genome Biology, National Cancer Center Research Institute, 5-1-1, Tsukiji, Chuo-ku, Tokyo 104-0045, Japan. Phone: 813-3542-2511; E-mail: ; and Takashi Kohno, Division of Genome Biology, National Cancer Center Research Institute, 5-1-1, Tsukiji, Chuo-ku, Tokyo 104-0045, Japan. Phone: 813-3547-5272; E-mail:
| | - Mitsugu Araki
- Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Ryunosuke Yoshino
- Transborder Medical Research Center, University of Tsukuba, Ibaraki, Japan
| | - Shinji Kohsaka
- Division of Cellular Signaling, National Cancer Center Research Institute, Tokyo, Japan
| | - Ayaka Otsuka
- Division of Genome Biology, National Cancer Center Research Institute, Tokyo, Japan
| | - Masachika Ikegami
- Division of Cellular Signaling, National Cancer Center Research Institute, Tokyo, Japan
| | - Ayako Ui
- Department of Molecular Oncology, Institute of Development, Aging, and Cancer, Tohoku University, Sendai, Japan
| | - Shin-ichiro Kanno
- Department of Molecular Oncology, Institute of Development, Aging, and Cancer, Tohoku University, Sendai, Japan
| | - Keiko Miyoshi
- Division of Genome Biology, National Cancer Center Research Institute, Tokyo, Japan
| | | | - Yukari Sagae
- Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Akira Yasui
- IDAC Fellow Laboratory, Institute of Development, Aging, and Cancer, Tohoku University, Sendai, Japan
| | - Masakazu Sekijima
- Department of Computer Science, Tokyo Institute of Technology, Yokohama, Japan
| | - Hiroyuki Mano
- Division of Cellular Signaling, National Cancer Center Research Institute, Tokyo, Japan
| | - Yasushi Okuno
- Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Aikou Okamoto
- Department of Obstetrics and Gynecology, The Jikei University School of Medicine, Tokyo, Japan
| | - Takashi Kohno
- Division of Genome Biology, National Cancer Center Research Institute, Tokyo, Japan.,Corresponding Authors: Takashi Nakaoku, Division of Genome Biology, National Cancer Center Research Institute, 5-1-1, Tsukiji, Chuo-ku, Tokyo 104-0045, Japan. Phone: 813-3542-2511; E-mail: ; and Takashi Kohno, Division of Genome Biology, National Cancer Center Research Institute, 5-1-1, Tsukiji, Chuo-ku, Tokyo 104-0045, Japan. Phone: 813-3547-5272; E-mail:
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18
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Torrens L, Puigvehí M, Torres-Martín M, Wang H, Maeda M, Haber PK, Leonel T, García-López M, Esteban-Fabró R, Leow WQ, Montironi C, Torrecilla S, Varadarajan AR, Taik P, Campreciós G, Enkhbold C, Taivanbaatar E, Yerbolat A, Villanueva A, Pérez-del-Pulgar S, Thung S, Chinburen J, Letouzé E, Zucman-Rossi J, Uzilov A, Neely J, Forns X, Roayaie S, Sia D, Llovet JM. Hepatocellular Carcinoma in Mongolia Delineates Unique Molecular Traits and a Mutational Signature Associated with Environmental Agents. Clin Cancer Res 2022; 28:4509-4520. [PMID: 35998012 PMCID: PMC7613712 DOI: 10.1158/1078-0432.ccr-22-0632] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 05/25/2022] [Accepted: 08/19/2022] [Indexed: 12/14/2022]
Abstract
PURPOSE Mongolia has the world's highest incidence of hepatocellular carcinoma (HCC), with ∼100 cases/100,000 inhabitants, although the reasons for this have not been thoroughly delineated. EXPERIMENTAL DESIGN We performed a molecular characterization of Mongolian (n = 192) compared with Western (n = 187) HCCs by RNA sequencing and whole-exome sequencing to unveil distinct genomic and transcriptomic features associated with environmental factors in this population. RESULTS Mongolian patients were younger, with higher female prevalence, and with predominantly HBV-HDV coinfection etiology. Mongolian HCCs presented significantly higher rates of protein-coding mutations (121 vs. 70 mutations per tumor in Western), and in specific driver HCC genes (i.e., APOB and TSC2). Four mutational signatures characterized Mongolian samples, one of which was novel (SBS Mongolia) and present in 25% of Mongolian HCC cases. This signature showed a distinct substitution profile with a high proportion of T>G substitutions and was significantly associated with a signature of exposure to the environmental agent dimethyl sulfate (71%), a 2A carcinogenic associated with coal combustion. Transcriptomic-based analysis delineated three molecular clusters, two not present in Western HCC; one with a highly inflamed profile and the other significantly associated with younger female patients. CONCLUSIONS Mongolian HCC has unique molecular traits with a high mutational burden and a novel mutational signature associated with genotoxic environmental factors present in this country.
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Affiliation(s)
- Laura Torrens
- Liver Cancer Program, Division of Liver Diseases, Tisch Cancer Institute, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Translational research in Hepatic Oncology, Liver Unit, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Hospital Clínic, University of Barcelona, Barcelona, Spain
| | - Marc Puigvehí
- Liver Cancer Program, Division of Liver Diseases, Tisch Cancer Institute, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Hepatology Section, Gastroenterology Department, Parc de Salut Mar, IMIM (Hospital del Mar Medical Research Institute), Barcelona, Catalonia, Spain
| | - Miguel Torres-Martín
- Liver Cancer Program, Division of Liver Diseases, Tisch Cancer Institute, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Translational research in Hepatic Oncology, Liver Unit, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Hospital Clínic, University of Barcelona, Barcelona, Spain
| | | | - Miho Maeda
- Liver Cancer Program, Division of Liver Diseases, Tisch Cancer Institute, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Philipp K. Haber
- Liver Cancer Program, Division of Liver Diseases, Tisch Cancer Institute, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Thais Leonel
- Liver Unit, Hospital Clínic, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), CIBEREHD, University of Barcelona, Barcelona, Spain
| | - Mireia García-López
- Liver Unit, Hospital Clínic, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), CIBEREHD, University of Barcelona, Barcelona, Spain
| | - Roger Esteban-Fabró
- Liver Cancer Program, Division of Liver Diseases, Tisch Cancer Institute, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Translational research in Hepatic Oncology, Liver Unit, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Hospital Clínic, University of Barcelona, Barcelona, Spain
| | - Wei Qiang Leow
- Liver Cancer Program, Division of Liver Diseases, Tisch Cancer Institute, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Anatomical Pathology, Singapore General Hospital, Singapore, Singapore
| | - Carla Montironi
- Liver Cancer Program, Division of Liver Diseases, Tisch Cancer Institute, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Translational research in Hepatic Oncology, Liver Unit, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Hospital Clínic, University of Barcelona, Barcelona, Spain
| | - Sara Torrecilla
- Translational research in Hepatic Oncology, Liver Unit, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Hospital Clínic, University of Barcelona, Barcelona, Spain
| | | | | | - Genís Campreciós
- Liver Cancer Program, Division of Liver Diseases, Tisch Cancer Institute, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Liver Unit, Hospital Clínic, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), CIBEREHD, University of Barcelona, Barcelona, Spain
| | - Chinbold Enkhbold
- Hepato-Pancreatico-Biliary Surgery Department, National Cancer Center, Ulaanbaatar, Mongolia
| | | | | | - Augusto Villanueva
- Liver Cancer Program, Division of Liver Diseases, Tisch Cancer Institute, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Sofía Pérez-del-Pulgar
- Liver Unit, Hospital Clínic, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), CIBEREHD, University of Barcelona, Barcelona, Spain
| | - Swan Thung
- Liver Cancer Program, Division of Liver Diseases, Tisch Cancer Institute, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | - Eric Letouzé
- Centre de Recherche des Cordeliers, Sorbonne Université, Inserm, Université de Paris, Université Paris 13, Functional Genomics of Solid Tumors laboratory, F-75006, Paris, France
| | - Jessica Zucman-Rossi
- Centre de Recherche des Cordeliers, Sorbonne Université, Inserm, Université de Paris, Université Paris 13, Functional Genomics of Solid Tumors laboratory, F-75006, Paris, France
| | - Andrew Uzilov
- Sema4, Stamford, Connecticut, USA
- Department of Genetics and Genomic Sciences and Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Jaclyn Neely
- Bristol Myers Squibb, Princeton, New Jersey, USA
| | - Xavier Forns
- Liver Unit, Hospital Clínic, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), CIBEREHD, University of Barcelona, Barcelona, Spain
| | - Sasan Roayaie
- Department of Surgery, White Plains Hospital, White Plains, New York, USA
| | - Daniela Sia
- Liver Cancer Program, Division of Liver Diseases, Tisch Cancer Institute, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Josep M. Llovet
- Liver Cancer Program, Division of Liver Diseases, Tisch Cancer Institute, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Translational research in Hepatic Oncology, Liver Unit, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Hospital Clínic, University of Barcelona, Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain
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19
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Deng Y, Huang H, Shi J, Jin H. Identification of Candidate Genes in Breast Cancer Induced by Estrogen Plus Progestogens Using Bioinformatic Analysis. Int J Mol Sci 2022; 23:ijms231911892. [PMID: 36233194 PMCID: PMC9569986 DOI: 10.3390/ijms231911892] [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: 06/28/2022] [Revised: 09/28/2022] [Accepted: 10/04/2022] [Indexed: 11/16/2022] Open
Abstract
Menopausal hormone therapy (MHT) was widely used to treat menopause-related symptoms in menopausal women. However, MHT therapies were controversial with the increased risk of breast cancer because of different estrogen and progestogen combinations, and the molecular basis behind this phenomenon is currently not understood. To address this issue, we identified differentially expressed genes (DEGs) between the estrogen plus progestogens treatment (EPT) and estrogen treatment (ET) using the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) data. As a result, a total of 96 upregulated DEGs were first identified. Seven DEGs related to the cell cycle (CCNE2, CDCA5, RAD51, TCF19, KNTC1, MCM10, and NEIL3) were validated by RT-qPCR. Specifically, these seven DEGs were increased in EPT compared to ET (p < 0.05) and had higher expression levels in breast cancer than adjacent normal tissues (p < 0.05). Next, we found that estrogen receptor (ER)-positive breast cancer patients with a higher CNNE2 expression have a shorter overall survival time (p < 0.05), while this effect was not observed in the other six DEGs (p > 0.05). Interestingly, the molecular docking results showed that CCNE2 might bind to 17β-estradiol (−6.791 kcal/mol), progesterone (−6.847 kcal/mol), and medroxyprogesterone acetate (−6.314 kcal/mol) with a relatively strong binding affinity, respectively. Importantly, CNNE2 protein level could be upregulated with EPT and attenuated by estrogen receptor antagonist, acolbifene and had interactions with cancer driver genes (AKT1 and KRAS) and high mutation frequency gene (TP53 and PTEN) in breast cancer patients. In conclusion, the current study showed that CCNE2, CDCA5, RAD51, TCF19, KNTC1, MCM10, and NEIL3 might contribute to EPT-related tumorigenesis in breast cancer, with CCNE2 might be a sensitive risk indicator of breast cancer risk in women using MHT.
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Affiliation(s)
- Yu Deng
- Department of Obstetrics and Gynecology, Peking University First Hospital, No. 8 Xishiku Street, Beijing 100034, China
| | - He Huang
- Department of Obstetrics and Gynecology, Peking University First Hospital, No. 8 Xishiku Street, Beijing 100034, China
| | - Jiangcheng Shi
- School of Life Sciences, Tiangong University, Tianjin 300387, China
| | - Hongyan Jin
- Department of Obstetrics and Gynecology, Peking University First Hospital, No. 8 Xishiku Street, Beijing 100034, China
- Correspondence:
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20
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Improvements in Quality Control and Library Preparation for Targeted Sequencing Allowed Detection of Potentially Pathogenic Alterations in Circulating Cell-Free DNA Derived from Plasma of Brain Tumor Patients. Cancers (Basel) 2022; 14:cancers14163902. [PMID: 36010895 PMCID: PMC9405692 DOI: 10.3390/cancers14163902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 08/03/2022] [Accepted: 08/09/2022] [Indexed: 11/17/2022] Open
Abstract
Malignant gliomas are the most frequent primary brain tumors in adults. They are genetically heterogenous and invariably recur due to incomplete surgery and therapy resistance. Circulating tumor DNA (ctDNA) is a component of circulating cell-free DNA (ccfDNA) and represents genetic material that originates from the primary tumor or metastasis. Brain tumors are frequently located in the eloquent brain regions, which makes biopsy difficult or impossible due to severe postoperative complications. The analysis of ccfDNA from a patient's blood presents a plausible and noninvasive alternative. In this study, freshly frozen tumors and corresponding blood samples were collected from 84 brain tumor patients and analyzed by targeted next-generation sequencing (NGS). The cohort included 80 glioma patients, 2 metastatic cancer patients, and 2 primary CNS lymphoma (PCNSL) patients. We compared the pattern of genetic alterations in the tumor DNA (tDNA) with that of ccfDNA. The implemented technical improvements in quality control and library preparation allowed for the detection of ctDNA in 8 out of 84 patients, including 5 out of 80 glioma patients. In 32 out of 84 patients, we found potentially pathogenic genetic alterations in ccfDNA that were not detectable in tDNA. While sequencing ccfDNA from plasma has a low efficacy as a diagnostic tool for glioma patients, we concluded that further improvements in sample processing and library preparation can make liquid biopsy a valuable diagnostic tool for glioma patients.
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21
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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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22
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Garcia-Prieto CA, Martínez-Jiménez F, Valencia A, Porta-Pardo E. Detection of oncogenic and clinically actionable mutations in cancer genomes critically depends on variant calling tools. Bioinformatics 2022; 38:3181-3191. [PMID: 35512388 PMCID: PMC9191211 DOI: 10.1093/bioinformatics/btac306] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 02/09/2022] [Accepted: 05/01/2022] [Indexed: 11/22/2022] Open
Abstract
Motivation The analysis of cancer genomes provides fundamental information about its etiology, the processes driving cell transformation or potential treatments. While researchers and clinicians are often only interested in the identification of oncogenic mutations, actionable variants or mutational signatures, the first crucial step in the analysis of any tumor genome is the identification of somatic variants in cancer cells (i.e. those that have been acquired during their evolution). For that purpose, a wide range of computational tools have been developed in recent years to detect somatic mutations in sequencing data from tumor samples. While there have been some efforts to benchmark somatic variant calling tools and strategies, the extent to which variant calling decisions impact the results of downstream analyses of tumor genomes remains unknown. Results Here, we quantify the impact of variant calling decisions by comparing the results obtained in three important analyses of cancer genomics data (identification of cancer driver genes, quantification of mutational signatures and detection of clinically actionable variants) when changing the somatic variant caller (MuSE, MuTect2, SomaticSniper and VarScan2) or the strategy to combine them (Consensus of two, Consensus of three and Union) across all 33 cancer types from The Cancer Genome Atlas. Our results show that variant calling decisions have a significant impact on these analyses, creating important differences that could even impact treatment decisions for some patients. Moreover, the Consensus of three calling strategy to combine the output of multiple variant calling tools, a very widely used strategy by the research community, can lead to the loss of some cancer driver genes and actionable mutations. Overall, our results highlight the limitations of widespread practices within the cancer genomics community and point to important differences in critical analyses of tumor sequencing data depending on variant calling, affecting even the identification of clinically actionable variants. Availability and implementation Code is available at https://github.com/carlosgarciaprieto/VariantCallingClinicalBenchmark. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Carlos A Garcia-Prieto
- Josep Carreras Leukaemia Research Institute (IJC), Badalona, Spain.,Barcelona Supercomputing Center (BSC), Barcelona, Spain
| | - Francisco Martínez-Jiménez
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Alfonso Valencia
- Josep Carreras Leukaemia Research Institute (IJC), Badalona, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Eduard Porta-Pardo
- Josep Carreras Leukaemia Research Institute (IJC), Badalona, Spain.,Barcelona Supercomputing Center (BSC), Barcelona, Spain
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23
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Zou B, Guo D, Kong P, Wang Y, Cheng X, Cui Y. Integrative Genomic Analyses of 1,145 Patient Samples Reveal New Biomarkers in Esophageal Squamous Cell Carcinoma. Front Mol Biosci 2022; 8:792779. [PMID: 35127817 PMCID: PMC8814608 DOI: 10.3389/fmolb.2021.792779] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 12/02/2021] [Indexed: 12/23/2022] Open
Abstract
Due to the lack of effective diagnostic markers and therapeutic targets, esophageal squamous cell carcinoma (ESCC) shows a poor 5 years survival rate of less than 30%. To explore the potential therapeutic targets of ESCC, we integrated and reanalyzed the mutation data of WGS (whole genome sequencing) or WES (whole exome sequencing) from a total of 1,145 samples in 7 large ESCC cohorts, including 270 ESCC gene expression data. Two new mutation signatures and 20 driver genes were identified in our study. Among them, AP3S1, MUC16, and RPS15 were reported for the first time. We also discovered that the KMT2D was associated with the multiple clinical characteristics of ESCC, and KMT2D knockdown cells showed enhanced cell migration and cell invasion. Furthermore, a few neoantigens were shared between ESCC patients. For ESCC, compared to TMB, neoantigen might be treated as a better immunotherapy biomarker. Our research expands the understanding of ESCC mutations and helps the identification of ESCC biomarkers, especially for immunotherapy biomarkers.
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Affiliation(s)
- Binbin Zou
- Key Laboratory of Cellular Physiology of the Ministry of Education, Shanxi Medical University, Taiyuan, China
- Department of Pathology, Shanxi Medical University, Taiyuan, China
| | - Dinghe Guo
- Key Laboratory of Cellular Physiology of the Ministry of Education, Shanxi Medical University, Taiyuan, China
- Department of Pathology, Shanxi Medical University, Taiyuan, China
| | - Pengzhou Kong
- Key Laboratory of Cellular Physiology of the Ministry of Education, Shanxi Medical University, Taiyuan, China
- Department of Pathology, Shanxi Medical University, Taiyuan, China
| | - Yanqiang Wang
- Key Laboratory of Cellular Physiology of the Ministry of Education, Shanxi Medical University, Taiyuan, China
- Department of Pathology, Shanxi Medical University, Taiyuan, China
| | - Xiaolong Cheng
- Key Laboratory of Cellular Physiology of the Ministry of Education, Shanxi Medical University, Taiyuan, China
- Department of Pathology, Shanxi Medical University, Taiyuan, China
- *Correspondence: Xiaolong Cheng, ; Yongping Cui,
| | - Yongping Cui
- Key Laboratory of Cellular Physiology of the Ministry of Education, Shanxi Medical University, Taiyuan, China
- Department of Pathology, Shanxi Medical University, Taiyuan, China
- Shenzhen Peking University-Hong Kong University of Science and Technology (PKU-HKUST) Medical Center, Peking University Shenzhen Hospital, Shenzhen, China
- *Correspondence: Xiaolong Cheng, ; Yongping Cui,
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24
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Comprehensive patient-level classification and quantification of driver events in TCGA PanCanAtlas cohorts. PLoS Genet 2022; 18:e1009996. [PMID: 35030162 PMCID: PMC8759692 DOI: 10.1371/journal.pgen.1009996] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 12/14/2021] [Indexed: 12/14/2022] Open
Abstract
There is a growing need to develop novel therapeutics for targeted treatment of cancer. The prerequisite to success is the knowledge about which types of molecular alterations are predominantly driving tumorigenesis. To shed light onto this subject, we have utilized the largest database of human cancer mutations–TCGA PanCanAtlas, multiple established algorithms for cancer driver prediction (2020plus, CHASMplus, CompositeDriver, dNdScv, DriverNet, HotMAPS, OncodriveCLUSTL, OncodriveFML) and developed four novel computational pipelines: SNADRIF (Single Nucleotide Alteration DRIver Finder), GECNAV (Gene Expression-based Copy Number Alteration Validator), ANDRIF (ANeuploidy DRIver Finder) and PALDRIC (PAtient-Level DRIver Classifier). A unified workflow integrating all these pipelines, algorithms and datasets at cohort and patient levels was created. We have found that there are on average 12 driver events per tumour, of which 0.6 are single nucleotide alterations (SNAs) in oncogenes, 1.5 are amplifications of oncogenes, 1.2 are SNAs in tumour suppressors, 2.1 are deletions of tumour suppressors, 1.5 are driver chromosome losses, 1 is a driver chromosome gain, 2 are driver chromosome arm losses, and 1.5 are driver chromosome arm gains. The average number of driver events per tumour increases with age (from 7 to 15) and cancer stage (from 10 to 15) and varies strongly between cancer types (from 1 to 24). Patients with 1 and 7 driver events per tumour are the most frequent, and there are very few patients with more than 40 events. In tumours having only one driver event, this event is most often an SNA in an oncogene. However, with increasing number of driver events per tumour, the contribution of SNAs decreases, whereas the contribution of copy-number alterations and aneuploidy events increases. By analysing genomic and transcriptomic data from 10000 cancer patients through our custom-built computational pipelines and previously established third-party algorithms, we have found that half of all driver events in a patient’s tumour appear to be gains and losses of chromosomal arms and whole chromosomes. We therefore suggest that future therapeutics development efforts should be focused on targeting aneuploidy. We have also found that approximately a third of driver events in a patient are whole gene amplifications and deletions. Thus, therapies aimed at copy-number alterations also appear very promising. On the other hand, drugs aiming at point mutations are predicted to be less successful, as these alterations are responsible for just a couple of drivers per tumour. One notable exception are patients having only one driver event in their tumours, as this event is almost always a single nucleotide alteration in an oncogene.
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25
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Desai SS, K RR, Jain A, Bawa PS, Dutta P, Atre G, Subhash A, Rao VUS, J S, Srinivasan S, Choudhary B. Multidimensional Mutational Profiling of the Indian HNSCC Sub-Population Provides IRAK1, a Novel Driver Gene and Potential Druggable Target. Front Oncol 2021; 11:723162. [PMID: 34796107 PMCID: PMC8593415 DOI: 10.3389/fonc.2021.723162] [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: 06/10/2021] [Accepted: 09/28/2021] [Indexed: 12/30/2022] Open
Abstract
Head and neck squamous cell carcinomas (HNSCC) include heterogeneous group of tumors, classified according to their anatomical site. It is the sixth most prevalent cancer globally. Among South Asian countries, India accounts for 40% of HNC malignancies with significant morbidity and mortality. In the present study, we have performed exome sequencing and analysis of 51 Head and Neck squamous cell carcinoma samples. Besides known mutations in the oncogenes and tumour suppressors, we have identified novel gene signatures differentiating buccal, alveolar, and tongue cancers. Around 50% of the patients showed mutation in tumour suppressor genes TP53 and TP63. Apart from the known mutations, we report novel mutations in the genes AKT1, SPECC1, and LRP1B, which are linked with tumour progression and patient survival. A highly curated process was developed to identify survival signatures. 36 survival-related genes were identified based on the correlation of functional impact of variants identified using exome-seq with gene expression from transcriptome data (GEPIA database) and survival. An independent LASSO regression analysis was also performed. Survival signatures common to both the methods led to identification of 4 dead and 3 alive gene signatures, the accuracy of which was confirmed by performing a ROC analysis (AUC=0.79 and 0.91, respectively). Also, machine learning-based driver gene prediction tool resulted in the identification of IRAK1 as the driver (p-value = 9.7 e-08) and also as an actionable mutation. Modelling of the IRAK1 mutation showed a decrease in its binding to known IRAK1 inhibitors.
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Affiliation(s)
- Sagar Sanjiv Desai
- Department of Biotechnology and Bioinformatics, Institute of Bioinformatics and Applied Biotechnology, Bangalore, India.,Graduate Student Registered Under Manipal Academy of Higher Education, Manipal, India
| | - Raksha Rao K
- Department of Biotechnology and Bioinformatics, Institute of Bioinformatics and Applied Biotechnology, Bangalore, India
| | - Anika Jain
- Department of Biotechnology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore Campus, Katpadi, Vellore, India
| | - Pushpinder Singh Bawa
- Department of Biotechnology and Bioinformatics, Institute of Bioinformatics and Applied Biotechnology, Bangalore, India
| | - Priyatam Dutta
- Department of Biotechnology and Bioinformatics, Institute of Bioinformatics and Applied Biotechnology, Bangalore, India
| | - Gaurav Atre
- Department of Biotechnology and Bioinformatics, Institute of Bioinformatics and Applied Biotechnology, Bangalore, India
| | - Anand Subhash
- Healthcare Global Enterprises Ltd, Cancer Centre, Bangalore, India
| | - Vishal U S Rao
- Healthcare Global Enterprises Ltd, Cancer Centre, Bangalore, India
| | - Suvratha J
- Department of Biotechnology and Bioinformatics, Institute of Bioinformatics and Applied Biotechnology, Bangalore, India
| | - Subhashini Srinivasan
- Department of Biotechnology and Bioinformatics, Institute of Bioinformatics and Applied Biotechnology, Bangalore, India
| | - Bibha Choudhary
- Department of Biotechnology and Bioinformatics, Institute of Bioinformatics and Applied Biotechnology, Bangalore, India
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26
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Hu H, Zhang Q, Huang R, Gao Z, Yuan Z, Tang Q, Gao F, Wang M, Zhang W, Ma T, Qiao T, Jin Y, Wang G. Genomic Analysis Reveals Heterogeneity Between Lesions in Synchronous Primary Right-Sided and Left-Sided Colon Cancer. Front Mol Biosci 2021; 8:689466. [PMID: 34422903 PMCID: PMC8371635 DOI: 10.3389/fmolb.2021.689466] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 06/07/2021] [Indexed: 01/05/2023] Open
Abstract
Background: The synchronous primary right-sided and left-sided colon cancer (sRL-CC) is a peculiar subtype of colorectal cancer. However, the genomic landscape of sRL-CC remains elusive. Methods: Twenty-eight paired tumor samples and their corresponding normal mucosa samples from 14 patients were collected from the Second Affiliated Hospital of Harbin Medical University from 2011 to 2018. The clinical-pathological data were obtained, and whole-exome sequencing was performed based on formalin-fixed and paraffin-embedded samples of these patients, and then, comprehensive bioinformatic analyses were conducted. Results: Both the lesions of sRL-CC presented dissimilar histological grade and differentiation. Based on sequencing data, few overlapping SNV signatures, onco-driver gene mutations, and SMGs were identified. Moreover, the paired lesions harbored a different distribution of copy number variants (CNVs) and loss of heterozygosity. The clonal architecture analysis demonstrated the polyclonal origin of sRL-CC and inter-cancerous heterogeneity between two lesions. Conclusion: Our work provides evidence that lesions of sRL-CC share few overlapping mutational signatures and CNVs, and may originate from different clones.
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Affiliation(s)
- Hanqing Hu
- Colorectal Cancer Surgery Department, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Qian Zhang
- Colorectal Cancer Surgery Department, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
| | - Rui Huang
- Colorectal Cancer Surgery Department, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Zhifeng Gao
- Department of Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Ziming Yuan
- Colorectal Cancer Surgery Department, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Qingchao Tang
- Colorectal Cancer Surgery Department, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Feng Gao
- Colorectal Cancer Surgery Department, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Meng Wang
- Colorectal Cancer Surgery Department, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
| | - Weiyuan Zhang
- Colorectal Cancer Surgery Department, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Tianyi Ma
- Colorectal Cancer Surgery Department, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Tianyu Qiao
- Colorectal Cancer Surgery Department, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yinghu Jin
- Colorectal Cancer Surgery Department, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Guiyu Wang
- Colorectal Cancer Surgery Department, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
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27
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Varela NM, Guevara-Ramírez P, Acevedo C, Zambrano T, Armendáriz-Castillo I, Guerrero S, Quiñones LA, López-Cortés A. A New Insight for the Identification of Oncogenic Variants in Breast and Prostate Cancers in Diverse Human Populations, With a Focus on Latinos. Front Pharmacol 2021; 12:630658. [PMID: 33912047 PMCID: PMC8072346 DOI: 10.3389/fphar.2021.630658] [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: 11/18/2020] [Accepted: 01/25/2021] [Indexed: 12/24/2022] Open
Abstract
Background: Breast cancer (BRCA) and prostate cancer (PRCA) are the most commonly diagnosed cancer types in Latin American women and men, respectively. Although in recent years large-scale efforts from international consortia have focused on improving precision oncology, a better understanding of genomic features of BRCA and PRCA in developing regions and racial/ethnic minority populations is still required. Methods: To fill in this gap, we performed integrated in silico analyses to elucidate oncogenic variants from BRCA and PRCA driver genes; to calculate their deleteriousness scores and allele frequencies from seven human populations worldwide, including Latinos; and to propose the most effective therapeutic strategies based on precision oncology. Results: We analyzed 339,100 variants belonging to 99 BRCA and 82 PRCA driver genes and identified 18,512 and 15,648 known/predicted oncogenic variants, respectively. Regarding known oncogenic variants, we prioritized the most frequent and deleterious variants of BRCA (n = 230) and PRCA (n = 167) from Latino, African, Ashkenazi Jewish, East Asian, South Asian, European Finnish, and European non-Finnish populations, to incorporate them into pharmacogenomics testing. Lastly, we identified which oncogenic variants may shape the response to anti-cancer therapies, detailing the current status of pharmacogenomics guidelines and clinical trials involved in BRCA and PRCA cancer driver proteins. Conclusion: It is imperative to unify efforts where developing countries might invest in obtaining databases of genomic profiles of their populations, and developed countries might incorporate racial/ethnic minority populations in future clinical trials and cancer researches with the overall objective of fomenting pharmacogenomics in clinical practice and public health policies.
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Affiliation(s)
- Nelson M Varela
- Laboratory of Chemical Carcinogenesis and Pharmacogenetics, Department of Basic and Clinical Oncology, Faculty of Medicine, University of Chile, Santiago, Chile.,Latin American Network for the Implementation and Validation of Clinical Pharmacogenomics Guidelines (RELIVAF-CYTED), Madrid, Spain
| | - Patricia Guevara-Ramírez
- Latin American Network for the Implementation and Validation of Clinical Pharmacogenomics Guidelines (RELIVAF-CYTED), Madrid, Spain
| | - Cristian Acevedo
- Laboratory of Chemical Carcinogenesis and Pharmacogenetics, Department of Basic and Clinical Oncology, Faculty of Medicine, University of Chile, Santiago, Chile.,Department of Basic and Clinical Oncology, Clinical Hospital University of Chile, Santiago, Chile
| | - Tomás Zambrano
- Department of Medical Technology, Faculty of Medicine, University of Chile, Santiago, Chile
| | - Isaac Armendáriz-Castillo
- Centro de Investigación Genética y Genómica, Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Quito, Ecuador
| | - Santiago Guerrero
- Centro de Investigación Genética y Genómica, Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Quito, Ecuador
| | - Luis A Quiñones
- Laboratory of Chemical Carcinogenesis and Pharmacogenetics, Department of Basic and Clinical Oncology, Faculty of Medicine, University of Chile, Santiago, Chile.,Latin American Network for the Implementation and Validation of Clinical Pharmacogenomics Guidelines (RELIVAF-CYTED), Madrid, Spain
| | - Andrés López-Cortés
- Latin American Network for the Implementation and Validation of Clinical Pharmacogenomics Guidelines (RELIVAF-CYTED), Madrid, Spain.,Centro de Investigación Genética y Genómica, Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Quito, Ecuador.,Department of Computer Science and Information Technologies, Faculty of Computer Science, University of A Coruna, A Coruña, Spain
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28
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Nguyen QH, Le DH. Improving existing analysis pipeline to identify and analyze cancer driver genes using multi-omics data. Sci Rep 2020; 10:20521. [PMID: 33239644 PMCID: PMC7688645 DOI: 10.1038/s41598-020-77318-1] [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: 08/19/2020] [Accepted: 10/26/2020] [Indexed: 12/18/2022] Open
Abstract
The cumulative of genes carrying mutations is vital for the establishment and development of cancer. However, this driver gene exploring research line has selected and used types of tools and models of analysis unsystematically and discretely. Also, the previous studies may have neglected low-frequency drivers and seldom predicted subgroup specificities of identified driver genes. In this study, we presented an improved driver gene identification and analysis pipeline that comprises the four most widely focused analyses for driver genes: enrichment analysis, clinical feature association with expression profiles of identified driver genes as well as with their functional modules, and patient stratification by existing advanced computational tools integrating multi-omics data. The improved pipeline's general usability was demonstrated straightforwardly for breast cancer, validated by some independent databases. Accordingly, 31 validated driver genes, including four novel ones, were discovered. Subsequently, we detected cancer-related significantly enriched gene ontology terms and pathways, probable drug targets, two co-expressed modules associated significantly with several clinical features, such as number of positive lymph nodes, Nottingham prognostic index, and tumor stage, and two biologically distinct groups of BRCA patients. Data and source code of the case study can be downloaded at https://github.com/hauldhut/drivergene.
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Affiliation(s)
- Quang-Huy Nguyen
- Department of Computational Biomedicine, Vingroup Big Data Institute, Hanoi, Vietnam.,Faculty of Pharmacy, Dainam University, Hanoi, Vietnam
| | - Duc-Hau Le
- Department of Computational Biomedicine, Vingroup Big Data Institute, Hanoi, Vietnam. .,College of Engineering and Computer Science, VinUniversity, Hanoi, Vietnam.
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29
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Martínez-Jiménez F, Muiños F, Sentís I, Deu-Pons J, Reyes-Salazar I, Arnedo-Pac C, Mularoni L, Pich O, Bonet J, Kranas H, Gonzalez-Perez A, Lopez-Bigas N. A compendium of mutational cancer driver genes. Nat Rev Cancer 2020; 20:555-572. [PMID: 32778778 DOI: 10.1038/s41568-020-0290-x] [Citation(s) in RCA: 523] [Impact Index Per Article: 130.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/02/2020] [Indexed: 12/11/2022]
Abstract
A fundamental goal in cancer research is to understand the mechanisms of cell transformation. This is key to developing more efficient cancer detection methods and therapeutic approaches. One milestone towards this objective is the identification of all the genes with mutations capable of driving tumours. Since the 1970s, the list of cancer genes has been growing steadily. Because cancer driver genes are under positive selection in tumorigenesis, their observed patterns of somatic mutations across tumours in a cohort deviate from those expected from neutral mutagenesis. These deviations, which constitute signals of positive selection, may be detected by carefully designed bioinformatics methods, which have become the state of the art in the identification of driver genes. A systematic approach combining several of these signals could lead to a compendium of mutational cancer genes. In this Review, we present the Integrative OncoGenomics (IntOGen) pipeline, an implementation of such an approach to obtain the compendium of mutational cancer drivers. Its application to somatic mutations of more than 28,000 tumours of 66 cancer types reveals 568 cancer genes and points towards their mechanisms of tumorigenesis. The application of this approach to the ever-growing datasets of somatic tumour mutations will support the continuous refinement of our knowledge of the genetic basis of cancer.
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Affiliation(s)
- Francisco Martínez-Jiménez
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Ferran Muiños
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Inés Sentís
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Jordi Deu-Pons
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Iker Reyes-Salazar
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Claudia Arnedo-Pac
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Loris Mularoni
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Oriol Pich
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Jose Bonet
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Hanna Kranas
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Abel Gonzalez-Perez
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain.
- Research Program on Biomedical Informatics, Universitat Pompeu Fabra, Barcelona, Spain.
| | - Nuria Lopez-Bigas
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain.
- Research Program on Biomedical Informatics, Universitat Pompeu Fabra, Barcelona, Spain.
- Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain.
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30
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Trevino V. HotSpotAnnotations-a database for hotspot mutations and annotations in cancer. Database (Oxford) 2020; 2020:baaa025. [PMID: 32386297 PMCID: PMC7211031 DOI: 10.1093/database/baaa025] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 02/20/2020] [Accepted: 03/11/2020] [Indexed: 12/21/2022]
Abstract
Hotspots, recurrently mutated DNA positions in cancer, are thought to be oncogenic drivers because random chance is unlikely and the knowledge of clear examples of oncogenic hotspots in genes like BRAF, IDH1, KRAS and NRAS among many other genes. Hotspots are attractive because provide opportunities for biomedical research and novel treatments. Nevertheless, recent evidence, such as DNA hairpins for APOBEC3A, suggests that a considerable fraction of hotspots seem to be passengers rather than drivers. To document hotspots, the database HotSpotsAnnotations is proposed. For this, a statistical model was implemented to detect putative hotspots, which was applied to TCGA cancer datasets covering 33 cancer types, 10 182 patients and 3 175 929 mutations. Then, genes and hotspots were annotated by two published methods (APOBEC3A hairpins and dN/dS ratio) that may inform and warn researchers about possible false functional hotspots. Moreover, manual annotation from users can be added and shared. From the 23 198 detected as possible hotspots, 4435 were selected after false discovery rate correction and minimum mutation count. From these, 305 were annotated as likely for APOBEC3A whereas 442 were annotated as unlikely. To date, this is the first database dedicated to annotating hotspots for possible false functional hotspots.
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Affiliation(s)
- Victor Trevino
- Tecnologico de Monterrey, Escuela de Medicina, Cátedra de Bioinformática, Morones Prieto No. 3000, Colonia Los Doctores, Monterrey, Nuevo León 64710, Mexico
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