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Ong SS, Ho PJ, Khng AJ, Tan BKT, Tan QT, Tan EY, Tan SM, Putti TC, Lim SH, Tang ELS, Li J, Hartman M. Genomic Insights into Idiopathic Granulomatous Mastitis through Whole-Exome Sequencing: A Case Report of Eight Patients. Int J Mol Sci 2024; 25:9058. [PMID: 39201744 PMCID: PMC11354296 DOI: 10.3390/ijms25169058] [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: 07/30/2024] [Revised: 08/17/2024] [Accepted: 08/19/2024] [Indexed: 09/03/2024] Open
Abstract
Idiopathic granulomatous mastitis (IGM) is a rare condition characterised by chronic inflammation and granuloma formation in the breast. The aetiology of IGM is unclear. By focusing on the protein-coding regions of the genome, where most disease-related mutations often occur, whole-exome sequencing (WES) is a powerful approach for investigating rare and complex conditions, like IGM. We report WES results on paired blood and tissue samples from eight IGM patients. Samples were processed using standard genomic protocols. Somatic variants were called with two analytical pipelines: nf-core/sarek with Strelka2 and GATK4 with Mutect2. Our WES study of eight patients did not find evidence supporting a clear genetic component. The discrepancies between variant calling algorithms, along with the considerable genetic heterogeneity observed amongst the eight IGM cases, indicate that common genetic drivers are not readily identifiable. With only three genes, CHIT1, CEP170, and CTR9, recurrently altering in multiple cases, the genetic basis of IGM remains uncertain. The absence of validation for somatic variants by Sanger sequencing raises further questions about the role of genetic mutations in the disease. Other potential contributors to the disease should be explored.
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Affiliation(s)
- Seeu Si Ong
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore 138672, Singapore; (S.S.O.)
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore
| | - Peh Joo Ho
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore 138672, Singapore; (S.S.O.)
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore 117597, Singapore
| | - Alexis Jiaying Khng
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore 138672, Singapore; (S.S.O.)
| | - Benita Kiat Tee Tan
- Department of General Surgery, Sengkang General Hospital, Singapore 544886, Singapore
- Department of Breast Surgery, Singapore General Hospital, Singapore 169608, Singapore
- Division of Surgical Oncology, National Cancer Centre, Singapore 169610, Singapore
| | - Qing Ting Tan
- Breast Department, KK Women’s and Children’s Hospital, Singapore 229899, Singapore
| | - Ern Yu Tan
- Department of General Surgery, Tan Tock Seng Hospital, Singapore 308433, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore
- Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), Singapore 138673, Singapore
| | - Su-Ming Tan
- Division of Breast Surgery, Changi General Hospital, Singapore 529889, Singapore
| | - Thomas Choudary Putti
- Department of Pathology, National University Health System, Singapore 119228, Singapore
| | - Swee Ho Lim
- Breast Department, KK Women’s and Children’s Hospital, Singapore 229899, Singapore
| | | | - Jingmei Li
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore 138672, Singapore; (S.S.O.)
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore
| | - Mikael Hartman
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore 117597, Singapore
- Department of Surgery, University Surgical Cluster, National University Health System, Singapore 119228, Singapore
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Rocha LGDN, Guimarães PAS, Carvalho MGR, Ruiz JC. Tumor Neoepitope-Based Vaccines: A Scoping Review on Current Predictive Computational Strategies. Vaccines (Basel) 2024; 12:836. [PMID: 39203962 PMCID: PMC11360805 DOI: 10.3390/vaccines12080836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Revised: 07/09/2024] [Accepted: 07/11/2024] [Indexed: 09/03/2024] Open
Abstract
Therapeutic cancer vaccines have been considered in recent decades as important immunotherapeutic strategies capable of leading to tumor regression. In the development of these vaccines, the identification of neoepitopes plays a critical role, and different computational methods have been proposed and employed to direct and accelerate this process. In this context, this review identified and systematically analyzed the most recent studies published in the literature on the computational prediction of epitopes for the development of therapeutic vaccines, outlining critical steps, along with the associated program's strengths and limitations. A scoping review was conducted following the PRISMA extension (PRISMA-ScR). Searches were performed in databases (Scopus, PubMed, Web of Science, Science Direct) using the keywords: neoepitope, epitope, vaccine, prediction, algorithm, cancer, and tumor. Forty-nine articles published from 2012 to 2024 were synthesized and analyzed. Most of the identified studies focus on the prediction of epitopes with an affinity for MHC I molecules in solid tumors, such as lung carcinoma. Predicting epitopes with class II MHC affinity has been relatively underexplored. Besides neoepitope prediction from high-throughput sequencing data, additional steps were identified, such as the prioritization of neoepitopes and validation. Mutect2 is the most used tool for variant calling, while NetMHCpan is favored for neoepitope prediction. Artificial/convolutional neural networks are the preferred methods for neoepitope prediction. For prioritizing immunogenic epitopes, the random forest algorithm is the most used for classification. The performance values related to the computational models for the prediction and prioritization of neoepitopes are high; however, a large part of the studies still use microbiome databases for training. The in vitro/in vivo validations of the predicted neoepitopes were verified in 55% of the analyzed studies. Clinical trials that led to successful tumor remission were identified, highlighting that this immunotherapeutic approach can benefit these patients. Integrating high-throughput sequencing, sophisticated bioinformatics tools, and rigorous validation methods through in vitro/in vivo assays as well as clinical trials, the tumor neoepitope-based vaccine approach holds promise for developing personalized therapeutic vaccines that target specific tumor cancers.
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Affiliation(s)
- Luiz Gustavo do Nascimento Rocha
- Biologia Computacional e Sistemas (BCS), Instituto Oswaldo Cruz (IOC), Fundação Oswaldo Cruz, Rio de Janeiro 21040-900, Brazil; (L.G.d.N.R.); (P.A.S.G.)
- Grupo Informática de Biossistemas e Genômica, Instituto René Rachou, Fundação Oswaldo Cruz, Belo Horizonte 30190-002, Brazil
| | - Paul Anderson Souza Guimarães
- Biologia Computacional e Sistemas (BCS), Instituto Oswaldo Cruz (IOC), Fundação Oswaldo Cruz, Rio de Janeiro 21040-900, Brazil; (L.G.d.N.R.); (P.A.S.G.)
- Grupo Informática de Biossistemas e Genômica, Instituto René Rachou, Fundação Oswaldo Cruz, Belo Horizonte 30190-002, Brazil
| | - Maria Gabriela Reis Carvalho
- Biologia Computacional e Sistemas (BCS), Instituto Oswaldo Cruz (IOC), Fundação Oswaldo Cruz, Rio de Janeiro 21040-900, Brazil; (L.G.d.N.R.); (P.A.S.G.)
- Grupo Informática de Biossistemas e Genômica, Instituto René Rachou, Fundação Oswaldo Cruz, Belo Horizonte 30190-002, Brazil
| | - Jeronimo Conceição Ruiz
- Biologia Computacional e Sistemas (BCS), Instituto Oswaldo Cruz (IOC), Fundação Oswaldo Cruz, Rio de Janeiro 21040-900, Brazil; (L.G.d.N.R.); (P.A.S.G.)
- Grupo Informática de Biossistemas e Genômica, Instituto René Rachou, Fundação Oswaldo Cruz, Belo Horizonte 30190-002, Brazil
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Goel M, Campoy JA, Krause K, Baus LC, Sahu A, Sun H, Walkemeier B, Marek M, Beaudry R, Ruiz D, Huettel B, Schneeberger K. The vast majority of somatic mutations in plants are layer-specific. Genome Biol 2024; 25:194. [PMID: 39049052 PMCID: PMC11267851 DOI: 10.1186/s13059-024-03337-0] [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: 01/26/2024] [Accepted: 07/15/2024] [Indexed: 07/27/2024] Open
Abstract
BACKGROUND Plant meristems are structured organs consisting of distinct layers of stem cells, which differentiate into new plant tissue. Mutations in meristematic layers can propagate into large sectors of the plant. However, the characteristics of meristematic mutations remain unclear, limiting our understanding of the genetic basis of somaclonal phenotypic variation. RESULTS Here, we analyse the frequency and distribution of somatic mutations in an apricot tree. We separately sequence the epidermis (developing from meristem layer 1) and the flesh (developing from meristem layer 2) of several fruits sampled across the entire tree. We find that most somatic mutations (> 90%) are specific to individual layers. Interestingly, layer 1 shows a higher mutation load than layer 2, implying different mutational dynamics between the layers. The distribution of somatic mutations follows the branching of the tree. This suggests that somatic mutations are propagated to developing branches through axillary meristems. In turn, this leads us to the unexpected observation that the genomes of layer 1 of distant branches are more similar to each other than to the genomes of layer 2 of the same branches. Finally, using single-cell RNA sequencing, we demonstrate that layer-specific mutations were only transcribed in the cells of the respective layers and can form the genetic basis of somaclonal phenotypic variation. CONCLUSIONS Here, we analyse the frequency and distribution of somatic mutations with meristematic origin. Our observations on the layer specificity of somatic mutations outline how they are distributed, how they propagate, and how they can impact clonally propagated crops.
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Affiliation(s)
- Manish Goel
- Faculty of Biology, LMU Munich, Planegg-Martinsried, Germany
- Department of Chromosome Biology, Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | - José A Campoy
- Department of Chromosome Biology, Max Planck Institute for Plant Breeding Research, Cologne, Germany
- Department of Pomology, Estación Experimental de Aula Dei (EEAD), CSIC, Saragossa, 50059, Spain
| | - Kristin Krause
- Department of Chromosome Biology, Max Planck Institute for Plant Breeding Research, Cologne, Germany
- Present address: Illumina Solutions Center Berlin, Berlin, Germany
| | - Lisa C Baus
- Faculty of Biology, LMU Munich, Planegg-Martinsried, Germany
- Department of Chromosome Biology, Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | - Anshupa Sahu
- Institute for Medical Biometry, Informatics and Epidemiology, University Hospital Bonn, Bonn, Germany
- Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Bonn, Germany
| | - Hequan Sun
- Department of Chromosome Biology, Max Planck Institute for Plant Breeding Research, Cologne, Germany
- Present address: Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Birgit Walkemeier
- Department of Chromosome Biology, Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | | | - Randy Beaudry
- Department of Horticulture, Michigan State University, East Lansing, MI, 48824, USA
| | - David Ruiz
- Department of Plant Breeding, CEBAS-CSIC, P.O. Box 164, Espinardo, Murcia, 30100, Spain
| | | | - Korbinian Schneeberger
- Faculty of Biology, LMU Munich, Planegg-Martinsried, Germany.
- Department of Chromosome Biology, Max Planck Institute for Plant Breeding Research, Cologne, Germany.
- CEPLAS (Cluster of Excellence On Plant Sciences), Heinrich-Heine University, Düsseldorf, Germany.
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Sandran NG, Fornarino DL, Corbett MA, Kroes T, Gardner AE, MacLennan AH, Gécz J, van Eyk CL. Application of multiple mosaic callers improves post-zygotic mutation detection from exome sequencing data. Genet Med 2024; 26:101220. [PMID: 39041334 DOI: 10.1016/j.gim.2024.101220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 07/15/2024] [Accepted: 07/16/2024] [Indexed: 07/24/2024] Open
Abstract
PURPOSE The gold standard for identification of post-zygotic variants (PZVs) is droplet digital polymerase chain reaction or high-depth sequencing across multiple tissues types. These approaches are yet to be systematically implemented for monogenic disorders. We developed PZV detection pipelines for correct classification of de novo variants. METHOD Our pipelines detect PZV in parents (gonosomal mosaicism [pGoM]) and children (somatic mosaicism, "M3"). We applied them to research exome sequencing (ES) data from the Australian Cerebral Palsy Biobank (n = 145 trios) and Simons Simplex Collection (n = 405 families). Candidate mosaic variants were validated using deep amplicon sequencing or droplet digital polymerase chain reaction. RESULTS 69.2% (M3trio), 63.9% (M3single), and 92.7% (pGoM) of detected variants were validated, with 48.6%, 56.7%, and 26.2% of variants, respectively, meeting strict criteria for mosaicism. In the Australian Cerebral Palsy Biobank, 16.6% of probands and 20.7% of parents had at least 1 true-positive somatic or pGoM variant, respectively. A large proportion of PZVs detected in Simons Simplex Collection parents (79.8%) and child (94.5%) were not previously reported. We reclassified 3.7% to 8.0% of germline de novo variants as mosaic. CONCLUSION Many PZVs were incorrectly classified as germline variants or missed by previous approaches. Systematic application of our pipelines could increase genetic diagnostic rate, improve estimates of recurrence risk in families, and benefit novel disease gene identification.
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Affiliation(s)
- Nandini G Sandran
- Neurogenetics Research Program, Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia; Australian Collaborative Cerebral Palsy Research Group, Robinson Research Institute, University of Adelaide, Adelaide, SA, Australia
| | - Dani L Fornarino
- Neurogenetics Research Program, Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia; Australian Collaborative Cerebral Palsy Research Group, Robinson Research Institute, University of Adelaide, Adelaide, SA, Australia
| | - Mark A Corbett
- Neurogenetics Research Program, Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia; Australian Collaborative Cerebral Palsy Research Group, Robinson Research Institute, University of Adelaide, Adelaide, SA, Australia
| | - Thessa Kroes
- Neurogenetics Research Program, Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia
| | - Alison E Gardner
- Neurogenetics Research Program, Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia
| | - Alastair H MacLennan
- Australian Collaborative Cerebral Palsy Research Group, Robinson Research Institute, University of Adelaide, Adelaide, SA, Australia
| | - Jozef Gécz
- Neurogenetics Research Program, Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia; Australian Collaborative Cerebral Palsy Research Group, Robinson Research Institute, University of Adelaide, Adelaide, SA, Australia; South Australian Health and Medical Research Institute, Adelaide, SA, Australia.
| | - Clare L van Eyk
- Neurogenetics Research Program, Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia; Australian Collaborative Cerebral Palsy Research Group, Robinson Research Institute, University of Adelaide, Adelaide, SA, Australia
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5
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Sergi A, Beltrame L, Marchini S, Masseroli M. Integrated approach to generate artificial samples with low tumor fraction for somatic variant calling benchmarking. BMC Bioinformatics 2024; 25:180. [PMID: 38720249 PMCID: PMC11077792 DOI: 10.1186/s12859-024-05793-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 04/19/2024] [Indexed: 05/12/2024] Open
Abstract
BACKGROUND High-throughput sequencing (HTS) has become the gold standard approach for variant analysis in cancer research. However, somatic variants may occur at low fractions due to contamination from normal cells or tumor heterogeneity; this poses a significant challenge for standard HTS analysis pipelines. The problem is exacerbated in scenarios with minimal tumor DNA, such as circulating tumor DNA in plasma. Assessing sensitivity and detection of HTS approaches in such cases is paramount, but time-consuming and expensive: specialized experimental protocols and a sufficient quantity of samples are required for processing and analysis. To overcome these limitations, we propose a new computational approach specifically designed for the generation of artificial datasets suitable for this task, simulating ultra-deep targeted sequencing data with low-fraction variants and demonstrating their effectiveness in benchmarking low-fraction variant calling. RESULTS Our approach enables the generation of artificial raw reads that mimic real data without relying on pre-existing data by using NEAT, a fine-grained read simulator that generates artificial datasets using models learned from multiple different datasets. Then, it incorporates low-fraction variants to simulate somatic mutations in samples with minimal tumor DNA content. To prove the suitability of the created artificial datasets for low-fraction variant calling benchmarking, we used them as ground truth to evaluate the performance of widely-used variant calling algorithms: they allowed us to define tuned parameter values of major variant callers, considerably improving their detection of very low-fraction variants. CONCLUSIONS Our findings highlight both the pivotal role of our approach in creating adequate artificial datasets with low tumor fraction, facilitating rapid prototyping and benchmarking of algorithms for such dataset type, as well as the important need of advancing low-fraction variant calling techniques.
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Affiliation(s)
- Aldo Sergi
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Via Ponzio 34/5, 20133, Milan, Italy.
- IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089, Milan, Rozzano, Italy.
| | - Luca Beltrame
- IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089, Milan, Rozzano, Italy
| | - Sergio Marchini
- IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089, Milan, Rozzano, Italy
| | - Marco Masseroli
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Via Ponzio 34/5, 20133, Milan, Italy
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Borch A, Carri I, Reynisson B, Alvarez HMG, Munk KK, Montemurro A, Kristensen NP, Tvingsholm SA, Holm JS, Heeke C, Moss KH, Hansen UK, Schaap-Johansen AL, Bagger FO, de Lima VAB, Rohrberg KS, Funt SA, Donia M, Svane IM, Lassen U, Barra C, Nielsen M, Hadrup SR. IMPROVE: a feature model to predict neoepitope immunogenicity through broad-scale validation of T-cell recognition. Front Immunol 2024; 15:1360281. [PMID: 38633261 PMCID: PMC11021644 DOI: 10.3389/fimmu.2024.1360281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 03/07/2024] [Indexed: 04/19/2024] Open
Abstract
Background Mutation-derived neoantigens are critical targets for tumor rejection in cancer immunotherapy, and better tools for neoepitope identification and prediction are needed to improve neoepitope targeting strategies. Computational tools have enabled the identification of patient-specific neoantigen candidates from sequencing data, but limited data availability has hindered their capacity to predict which of the many neoepitopes will most likely give rise to T cell recognition. Method To address this, we make use of experimentally validated T cell recognition towards 17,500 neoepitope candidates, with 467 being T cell recognized, across 70 cancer patients undergoing immunotherapy. Results We evaluated 27 neoepitope characteristics, and created a random forest model, IMPROVE, to predict neoepitope immunogenicity. The presence of hydrophobic and aromatic residues in the peptide binding core were the most important features for predicting neoepitope immunogenicity. Conclusion Overall, IMPROVE was found to significantly advance the identification of neoepitopes compared to other current methods.
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Affiliation(s)
- Annie Borch
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Ibel Carri
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Birkir Reynisson
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Heli M. Garcia Alvarez
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Kamilla K. Munk
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | | | | | - Siri A. Tvingsholm
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Jeppe Sejerø Holm
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Christina Heeke
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Keith Henry Moss
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Ulla Kring Hansen
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | | | | | | | | | - Samuel A. Funt
- Department of Medicine, Weill Cornell Medical College, New York, NY, United States
| | - Marco Donia
- National Center for Cancer Immune Therapy, Copenhagen University Hospital, Herlev, Denmark
| | - Inge Marie Svane
- National Center for Cancer Immune Therapy, Copenhagen University Hospital, Herlev, Denmark
| | - Ulrik Lassen
- Department of Oncology, Phase 1 Unit, Rigshospitalet, Copenhagen, Denmark
| | - Carolina Barra
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Morten Nielsen
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Sine Reker Hadrup
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
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7
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Brlek P, Bulić L, Bračić M, Projić P, Škaro V, Shah N, Shah P, Primorac D. Implementing Whole Genome Sequencing (WGS) in Clinical Practice: Advantages, Challenges, and Future Perspectives. Cells 2024; 13:504. [PMID: 38534348 DOI: 10.3390/cells13060504] [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: 02/06/2024] [Revised: 03/04/2024] [Accepted: 03/11/2024] [Indexed: 03/28/2024] Open
Abstract
The integration of whole genome sequencing (WGS) into all aspects of modern medicine represents the next step in the evolution of healthcare. Using this technology, scientists and physicians can observe the entire human genome comprehensively, generating a plethora of new sequencing data. Modern computational analysis entails advanced algorithms for variant detection, as well as complex models for classification. Data science and machine learning play a crucial role in the processing and interpretation of results, using enormous databases and statistics to discover new and support current genotype-phenotype correlations. In clinical practice, this technology has greatly enabled the development of personalized medicine, approaching each patient individually and in accordance with their genetic and biochemical profile. The most propulsive areas include rare disease genomics, oncogenomics, pharmacogenomics, neonatal screening, and infectious disease genomics. Another crucial application of WGS lies in the field of multi-omics, working towards the complete integration of human biomolecular data. Further technological development of sequencing technologies has led to the birth of third and fourth-generation sequencing, which include long-read sequencing, single-cell genomics, and nanopore sequencing. These technologies, alongside their continued implementation into medical research and practice, show great promise for the future of the field of medicine.
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Affiliation(s)
- Petar Brlek
- St. Catherine Specialty Hospital, 10000 Zagreb, Croatia
- International Center for Applied Biological Research, 10000 Zagreb, Croatia
- School of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
| | - Luka Bulić
- St. Catherine Specialty Hospital, 10000 Zagreb, Croatia
| | - Matea Bračić
- St. Catherine Specialty Hospital, 10000 Zagreb, Croatia
| | - Petar Projić
- International Center for Applied Biological Research, 10000 Zagreb, Croatia
| | | | - Nidhi Shah
- Dartmouth Hitchcock Medical Center, Lebannon, NH 03766, USA
| | - Parth Shah
- Dartmouth Hitchcock Medical Center, Lebannon, NH 03766, USA
| | - Dragan Primorac
- St. Catherine Specialty Hospital, 10000 Zagreb, Croatia
- International Center for Applied Biological Research, 10000 Zagreb, Croatia
- School of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
- Medical School, University of Split, 21000 Split, Croatia
- Eberly College of Science, The Pennsylvania State University, State College, PA 16802, USA
- The Henry C. Lee College of Criminal Justice and Forensic Sciences, University of New Haven, West Haven, CT 06516, USA
- REGIOMED Kliniken, 96450 Coburg, Germany
- Medical School, University of Rijeka, 51000 Rijeka, Croatia
- Faculty of Dental Medicine and Health, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
- Medical School, University of Mostar, 88000 Mostar, Bosnia and Herzegovina
- National Forensic Sciences University, Gujarat 382007, India
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8
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Jambulingam D, Rathinakannan VS, Heron S, Schleutker J, Fey V. Kuura-An automated workflow for analyzing WES and WGS data. PLoS One 2024; 19:e0296785. [PMID: 38236904 PMCID: PMC10796025 DOI: 10.1371/journal.pone.0296785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 12/19/2023] [Indexed: 01/22/2024] Open
Abstract
The advent of high-throughput sequencing technologies has revolutionized the field of genomic sciences by cutting down the cost and time associated with standard sequencing methods. This advancement has not only provided the research community with an abundance of data but has also presented the challenge of analyzing it. The paramount challenge in analyzing the copious amount of data is in using the optimal resources in terms of available tools. To address this research gap, we propose "Kuura-An automated workflow for analyzing WES and WGS data", which is optimized for both whole exome and whole genome sequencing data. This workflow is based on the nextflow pipeline scripting language and uses docker to manage and deploy the workflow. The workflow consists of four analysis stages-quality control, mapping to reference genome & quality score recalibration, variant calling & variant recalibration and variant consensus & annotation. An important feature of the DNA-seq workflow is that it uses the combination of multiple variant callers (GATK Haplotypecaller, DeepVariant, VarScan2, Freebayes and Strelka2), generating a list of high-confidence variants in a consensus call file. The workflow is flexible as it integrates the fragmented tools and can be easily extended by adding or updating tools or amending the parameters list. The use of a single parameters file enhances reproducibility of the results. The ease of deployment and usage of the workflow further increases computational reproducibility providing researchers with a standardized tool for the variant calling step in different projects. The source code, instructions for installation and use of the tool are publicly available at our github repository https://github.com/dhanaprakashj/kuura_pipeline.
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Affiliation(s)
- Dhanaprakash Jambulingam
- Institute of Biomedicine, Cancer Research Unit and FICAN West Cancer Centre, University of Turku and Turku University Hospital, Turku, Finland
| | - Venkat Subramaniam Rathinakannan
- Institute of Biomedicine, Cancer Research Unit and FICAN West Cancer Centre, University of Turku and Turku University Hospital, Turku, Finland
| | - Samuel Heron
- Institute of Biomedicine, Cancer Research Unit and FICAN West Cancer Centre, University of Turku and Turku University Hospital, Turku, Finland
| | - Johanna Schleutker
- Institute of Biomedicine, Cancer Research Unit and FICAN West Cancer Centre, University of Turku and Turku University Hospital, Turku, Finland
- Department of Genomics, Laboratory Division, Turku University Hospital, Turku, Finland
| | - Vidal Fey
- Institute of Biomedicine, Cancer Research Unit and FICAN West Cancer Centre, University of Turku and Turku University Hospital, Turku, Finland
- Faculty of Medicine and Health Technology/BioMediTech, Tampere University, Tampere, Finland
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Dhanushkumar T, M E S, Selvam PK, Rambabu M, Dasegowda KR, Vasudevan K, George Priya Doss C. Advancements and hurdles in the development of a vaccine for triple-negative breast cancer: A comprehensive review of multi-omics and immunomics strategies. Life Sci 2024; 337:122360. [PMID: 38135117 DOI: 10.1016/j.lfs.2023.122360] [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: 10/12/2023] [Revised: 12/15/2023] [Accepted: 12/15/2023] [Indexed: 12/24/2023]
Abstract
Triple-Negative Breast Cancer (TNBC) presents a significant challenge in oncology due to its aggressive behavior and limited therapeutic options. This review explores the potential of immunotherapy, particularly vaccine-based approaches, in addressing TNBC. It delves into the role of immunoinformatics in creating effective vaccines against TNBC. The review first underscores the distinct attributes of TNBC and the importance of tumor antigens in vaccine development. It then elaborates on antigen detection techniques such as exome sequencing, HLA typing, and RNA sequencing, which are instrumental in identifying TNBC-specific antigens and selecting vaccine candidates. The discussion then shifts to the in-silico vaccine development process, encompassing antigen selection, epitope prediction, and rational vaccine design. This process merges computational simulations with immunological insights. The role of Artificial Intelligence (AI) in expediting the prediction of antigens and epitopes is also emphasized. The review concludes by encapsulating how Immunoinformatics can augment the design of TNBC vaccines, integrating tumor antigens, advanced detection methods, in-silico strategies, and AI-driven insights to advance TNBC immunotherapy. This could potentially pave the way for more targeted and efficacious treatments.
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Affiliation(s)
- T Dhanushkumar
- Department of Biotechnology, School of Applied Sciences, REVA University, Bengaluru 560064, India
| | - Santhosh M E
- Department of Biotechnology, School of Applied Sciences, REVA University, Bengaluru 560064, India
| | - Prasanna Kumar Selvam
- Department of Biotechnology, School of Applied Sciences, REVA University, Bengaluru 560064, India
| | - Majji Rambabu
- Department of Biotechnology, School of Applied Sciences, REVA University, Bengaluru 560064, India
| | - K R Dasegowda
- Department of Biotechnology, School of Applied Sciences, REVA University, Bengaluru 560064, India
| | - Karthick Vasudevan
- Department of Biotechnology, School of Applied Sciences, REVA University, Bengaluru 560064, India.
| | - C George Priya Doss
- Laboratory of Integrative Genomics, Department of Integrative Biology, School of BioSciences and Technology, Vellore Institute of Technology (VIT), Vellore, India.
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10
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Li X, You J, Hong L, Liu W, Guo P, Hao X. Neoantigen cancer vaccines: a new star on the horizon. Cancer Biol Med 2023; 21:j.issn.2095-3941.2023.0395. [PMID: 38164734 PMCID: PMC11033713 DOI: 10.20892/j.issn.2095-3941.2023.0395] [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: 10/27/2023] [Accepted: 11/22/2023] [Indexed: 01/03/2024] Open
Abstract
Immunotherapy represents a promising strategy for cancer treatment that utilizes immune cells or drugs to activate the patient's own immune system and eliminate cancer cells. One of the most exciting advances within this field is the targeting of neoantigens, which are peptides derived from non-synonymous somatic mutations that are found exclusively within cancer cells and absent in normal cells. Although neoantigen-based therapeutic vaccines have not received approval for standard cancer treatment, early clinical trials have yielded encouraging outcomes as standalone monotherapy or when combined with checkpoint inhibitors. Progress made in high-throughput sequencing and bioinformatics have greatly facilitated the precise and efficient identification of neoantigens. Consequently, personalized neoantigen-based vaccines tailored to each patient have been developed that are capable of eliciting a robust and long-lasting immune response which effectively eliminates tumors and prevents recurrences. This review provides a concise overview consolidating the latest clinical advances in neoantigen-based therapeutic vaccines, and also discusses challenges and future perspectives for this innovative approach, particularly emphasizing the potential of neoantigen-based therapeutic vaccines to enhance clinical efficacy against advanced solid tumors.
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Affiliation(s)
- Xiaoling Li
- Cell Biotechnology Laboratory, Tianjin Cancer Hospital Airport Hospital, Tianjin 300308, China
- National Clinical Research Center for Cancer, Tianjin 300060, China
- Haihe Laboratory of Synthetic Biology, Tianjin 300090, China
| | - Jian You
- Department of Thoracic Oncology, Tianjin Cancer Hospital Airport Hospital, Tianjin 300308, China
- Department of Thoracic Oncology Surgery, Tianjin Medical University Cancer Institute & Hospital, Tianjin 300060, China
| | - Liping Hong
- Cell Biotechnology Laboratory, Tianjin Cancer Hospital Airport Hospital, Tianjin 300308, China
- National Clinical Research Center for Cancer, Tianjin 300060, China
- Haihe Laboratory of Synthetic Biology, Tianjin 300090, China
| | - Weijiang Liu
- Cell Biotechnology Laboratory, Tianjin Cancer Hospital Airport Hospital, Tianjin 300308, China
- National Clinical Research Center for Cancer, Tianjin 300060, China
- Haihe Laboratory of Synthetic Biology, Tianjin 300090, China
| | - Peng Guo
- Cell Biotechnology Laboratory, Tianjin Cancer Hospital Airport Hospital, Tianjin 300308, China
- National Clinical Research Center for Cancer, Tianjin 300060, China
- Haihe Laboratory of Synthetic Biology, Tianjin 300090, China
| | - Xishan Hao
- Cell Biotechnology Laboratory, Tianjin Cancer Hospital Airport Hospital, Tianjin 300308, China
- National Clinical Research Center for Cancer, Tianjin 300060, China
- Haihe Laboratory of Synthetic Biology, Tianjin 300090, China
- Tianjin Medical University Cancer Institute & Hospital, Tianjin 300060, China
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11
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Ahmed J, Das B, Shin S, Chen A. Challenges and Future Directions in the Management of Tumor Mutational Burden-High (TMB-H) Advanced Solid Malignancies. Cancers (Basel) 2023; 15:5841. [PMID: 38136385 PMCID: PMC10741991 DOI: 10.3390/cancers15245841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 11/28/2023] [Accepted: 12/11/2023] [Indexed: 12/24/2023] Open
Abstract
A standardized assessment of Tumor Mutational Burden (TMB) poses challenges across diverse tumor histologies, treatment modalities, and testing platforms, requiring careful consideration to ensure consistency and reproducibility. Despite clinical trials demonstrating favorable responses to immune checkpoint inhibitors (ICIs), not all patients with elevated TMB exhibit benefits, and certain tumors with a normal TMB may respond to ICIs. Therefore, a comprehensive understanding of the intricate interplay between TMB and the tumor microenvironment, as well as genomic features, is crucial to refine its predictive value. Bioinformatics advancements hold potential to improve the precision and cost-effectiveness of TMB assessments, addressing existing challenges. Similarly, integrating TMB with other biomarkers and employing comprehensive, multiomics approaches could further enhance its predictive value. Ongoing collaborative endeavors in research, standardization, and clinical validation are pivotal in harnessing the full potential of TMB as a biomarker in the clinic settings.
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Affiliation(s)
- Jibran Ahmed
- Developmental Therapeutics Clinic (DTC), National Cancer Institute (NCI), National Institute of Health (NIH), Bethesda, MD 20892, USA
| | - Biswajit Das
- Molecular Characterization Laboratory, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
| | - Sarah Shin
- Developmental Therapeutics Clinic (DTC), National Cancer Institute (NCI), National Institute of Health (NIH), Bethesda, MD 20892, USA
| | - Alice Chen
- Developmental Therapeutics Clinic (DTC), National Cancer Institute (NCI), National Institute of Health (NIH), Bethesda, MD 20892, USA
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12
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Shah RK, Cygan E, Kozlik T, Colina A, Zamora AE. Utilizing immunogenomic approaches to prioritize targetable neoantigens for personalized cancer immunotherapy. Front Immunol 2023; 14:1301100. [PMID: 38149253 PMCID: PMC10749952 DOI: 10.3389/fimmu.2023.1301100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Accepted: 11/29/2023] [Indexed: 12/28/2023] Open
Abstract
Advancements in sequencing technologies and bioinformatics algorithms have expanded our ability to identify tumor-specific somatic mutation-derived antigens (neoantigens). While recent studies have shown neoantigens to be compelling targets for cancer immunotherapy due to their foreign nature and high immunogenicity, the need for increasingly accurate and cost-effective approaches to rapidly identify neoantigens remains a challenging task, but essential for successful cancer immunotherapy. Currently, gene expression analysis and algorithms for variant calling can be used to generate lists of mutational profiles across patients, but more care is needed to curate these lists and prioritize the candidate neoantigens most capable of inducing an immune response. A growing amount of evidence suggests that only a handful of somatic mutations predicted by mutational profiling approaches act as immunogenic neoantigens. Hence, unbiased screening of all candidate neoantigens predicted by Whole Genome Sequencing/Whole Exome Sequencing may be necessary to more comprehensively access the full spectrum of immunogenic neoepitopes. Once putative cancer neoantigens are identified, one of the largest bottlenecks in translating these neoantigens into actionable targets for cell-based therapies is identifying the cognate T cell receptors (TCRs) capable of recognizing these neoantigens. While many TCR-directed screening and validation assays have utilized bulk samples in the past, there has been a recent surge in the number of single-cell assays that provide a more granular understanding of the factors governing TCR-pMHC interactions. The goal of this review is to provide an overview of existing strategies to identify candidate neoantigens using genomics-based approaches and methods for assessing neoantigen immunogenicity. Additionally, applications, prospects, and limitations of some of the current single-cell technologies will be discussed. Finally, we will briefly summarize some of the recent models that have been used to predict TCR antigen specificity and analyze the TCR receptor repertoire.
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Affiliation(s)
- Ravi K. Shah
- Department of Medicine, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Erin Cygan
- Department of Microbiology and Immunology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Tanya Kozlik
- Department of Medicine, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Alfredo Colina
- Department of Microbiology and Immunology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Anthony E. Zamora
- Department of Medicine, Medical College of Wisconsin, Milwaukee, WI, United States
- Department of Microbiology and Immunology, Medical College of Wisconsin, Milwaukee, WI, United States
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13
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Huang KK, Ma H, Chong RHH, Uchihara T, Lian BSX, Zhu F, Sheng T, Srivastava S, Tay ST, Sundar R, Tan ALK, Ong X, Lee M, Ho SWT, Lesluyes T, Ashktorab H, Smoot D, Van Loo P, Chua JS, Ramnarayanan K, Lau LHS, Gotoda T, Kim HS, Ang TL, Khor C, Lee JWJ, Tsao SKK, Yang WL, Teh M, Chung H, So JBY, Yeoh KG, Tan P. Spatiotemporal genomic profiling of intestinal metaplasia reveals clonal dynamics of gastric cancer progression. Cancer Cell 2023; 41:2019-2037.e8. [PMID: 37890493 PMCID: PMC10729843 DOI: 10.1016/j.ccell.2023.10.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 08/08/2023] [Accepted: 10/06/2023] [Indexed: 10/29/2023]
Abstract
Intestinal metaplasia (IM) is a pre-malignant condition of the gastric mucosa associated with increased gastric cancer (GC) risk. Analyzing 1,256 gastric samples (1,152 IMs) across 692 subjects from a prospective 10-year study, we identify 26 IM driver genes in diverse pathways including chromatin regulation (ARID1A) and intestinal homeostasis (SOX9). Single-cell and spatial profiles highlight changes in tissue ecology and IM lineage heterogeneity, including an intestinal stem-cell dominant cellular compartment linked to early malignancy. Expanded transcriptome profiling reveals expression-based molecular subtypes of IM associated with incomplete histology, antral/intestinal cell types, ARID1A mutations, inflammation, and microbial communities normally associated with the healthy oral tract. We demonstrate that combined clinical-genomic models outperform clinical-only models in predicting IMs likely to transform to GC. By highlighting strategies for accurately identifying IM patients at high GC risk and a role for microbial dysbiosis in IM progression, our results raise opportunities for GC precision prevention and interception.
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Affiliation(s)
- Kie Kyon Huang
- Program in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Haoran Ma
- Program in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Roxanne Hui Heng Chong
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore
| | - Tomoyuki Uchihara
- Program in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Benedict Shi Xiang Lian
- Program in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Feng Zhu
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore
| | - Taotao Sheng
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
| | - Supriya Srivastava
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore
| | - Su Ting Tay
- Program in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Raghav Sundar
- Program in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore 169857, Singapore; Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore; Department of Haematology-Oncology, National University Health System, Singapore 119074, Singapore
| | - Angie Lay Keng Tan
- Program in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Xuewen Ong
- Program in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Minghui Lee
- Program in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Shamaine Wei Ting Ho
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
| | | | | | - Duane Smoot
- Department of Internal Medicine, Meharry Medical College, Nashville, TN, USA
| | - Peter Van Loo
- The Francis Crick Institute, London, UK; Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Joy Shijia Chua
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore
| | - Kalpana Ramnarayanan
- Program in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Louis Ho Shing Lau
- Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Takuji Gotoda
- Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, Tokyo, Japan
| | - Hyun Soo Kim
- Department of Internal Medicine, Yonsei University Wonju College of Medicine, Seoul, Korea
| | - Tiing Leong Ang
- Department of Gastroenterology & Hepatology, Changi General Hospital, Singapore 529889, Singapore
| | - Christopher Khor
- Department of Gastroenterology & Hepatology, Singapore General Hospital, Singapore 169854, Singapore
| | - Jonathan Wei Jie Lee
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore; iHealthtech, National University of Singapore, Singapore, Singapore; SynCTI, National University of Singapore, Singapore 117599, Singapore; Department of Gastroenterology & Hepatology, National University Hospital, Singapore 119074, Singapore
| | - Stephen Kin Kwok Tsao
- Department of Gastroenterology & Hepatology, Tan Tock Seng Hospital, Singapore 308433, Singapore
| | - Wei Lyn Yang
- Department of Gastroenterology & Hepatology, Tan Tock Seng Hospital, Singapore 308433, Singapore
| | - Ming Teh
- Department of Pathology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore
| | - Hyunsoo Chung
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea.
| | - Jimmy Bok Yan So
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore; NUS Centre for Cancer Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Division of Surgical Oncology, National University Cancer Institute of Singapore (NCIS), Singapore, Singapore.
| | - Khay Guan Yeoh
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore; Department of Gastroenterology & Hepatology, National University Hospital, Singapore 119074, Singapore.
| | - Patrick Tan
- Program in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore 169857, Singapore; Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore; Cancer Science Institute of Singapore, National University of Singapore, Singapore 117599, Singapore; Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117593, Singapore; Cellular and Molecular Research, National Cancer Centre, Singapore, Singapore; Singhealth/Duke-NUS Institute of Precision Medicine, National Heart Centre Singapore, Singapore 168752, Singapore.
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14
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Cabello-Aguilar S, Vendrell JA, Solassol J. A Bioinformatics Toolkit for Next-Generation Sequencing in Clinical Oncology. Curr Issues Mol Biol 2023; 45:9737-9752. [PMID: 38132454 PMCID: PMC10741970 DOI: 10.3390/cimb45120608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 11/28/2023] [Accepted: 12/02/2023] [Indexed: 12/23/2023] Open
Abstract
Next-generation sequencing (NGS) has taken on major importance in clinical oncology practice. With the advent of targeted therapies capable of effectively targeting specific genomic alterations in cancer patients, the development of bioinformatics processes has become crucial. Thus, bioinformatics pipelines play an essential role not only in the detection and in identification of molecular alterations obtained from NGS data but also in the analysis and interpretation of variants, making it possible to transform raw sequencing data into meaningful and clinically useful information. In this review, we aim to examine the multiple steps of a bioinformatics pipeline as used in current clinical practice, and we also provide an updated list of the necessary bioinformatics tools. This resource is intended to assist researchers and clinicians in their genetic data analyses, improving the precision and efficiency of these processes in clinical research and patient care.
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Affiliation(s)
- Simon Cabello-Aguilar
- Montpellier BioInformatics for Clinical Diagnosis (MOBIDIC), Molecular Medicine and Genomics Platform (PMMG), CHU Montpellier, 34295 Montpellier, France
- Laboratoire de Biologie des Tumeurs Solides, Département de Pathologie et Oncobiologie, CHU Montpellier, Université de Montpellier, 34295 Montpellier, France; (J.A.V.); (J.S.)
| | - Julie A. Vendrell
- Laboratoire de Biologie des Tumeurs Solides, Département de Pathologie et Oncobiologie, CHU Montpellier, Université de Montpellier, 34295 Montpellier, France; (J.A.V.); (J.S.)
| | - Jérôme Solassol
- Laboratoire de Biologie des Tumeurs Solides, Département de Pathologie et Oncobiologie, CHU Montpellier, Université de Montpellier, 34295 Montpellier, France; (J.A.V.); (J.S.)
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15
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Ha YJ, Kang S, Kim J, Kim J, Jo SY, Kim S. Comprehensive benchmarking and guidelines of mosaic variant calling strategies. Nat Methods 2023; 20:2058-2067. [PMID: 37828153 PMCID: PMC10703685 DOI: 10.1038/s41592-023-02043-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Accepted: 09/12/2023] [Indexed: 10/14/2023]
Abstract
Rapid advances in sequencing and analysis technologies have enabled the accurate detection of diverse forms of genomic variants represented as heterozygous, homozygous and mosaic mutations. However, the best practices for mosaic variant calling remain disorganized owing to the technical and conceptual difficulties faced in evaluation. Here we present our benchmark of 11 feasible mosaic variant detection approaches based on a systematically designed whole-exome-level reference standard that mimics mosaic samples, supported by 354,258 control positive mosaic single-nucleotide variants and insertion-deletion mutations and 33,111,725 control negatives. We identified not only the best practice for mosaic variant detection but also the condition-dependent strengths and weaknesses of the current methods. Furthermore, feature-level evaluation and their combinatorial usage across multiple algorithms direct the way for immediate to prolonged improvements in mosaic variant detection. Our results will guide researchers in selecting suitable calling algorithms and suggest future strategies for developers.
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Affiliation(s)
- Yoo-Jin Ha
- Translational Genome Informatics Laboratory, Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
- Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Seungseok Kang
- Translational Genome Informatics Laboratory, Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
- Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jisoo Kim
- Translational Genome Informatics Laboratory, Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Junhan Kim
- Translational Genome Informatics Laboratory, Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Se-Young Jo
- Translational Genome Informatics Laboratory, Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
- Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Sangwoo Kim
- Translational Genome Informatics Laboratory, Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea.
- Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea.
- POSTECH Biotechnology Center, Pohang University of Science and Technology, Pohang, Republic of Korea.
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16
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Feng J. Identification and validation of molecular subtypes and a 9-gene risk model for breast cancer. Medicine (Baltimore) 2023; 102:e35204. [PMID: 37747033 PMCID: PMC10519538 DOI: 10.1097/md.0000000000035204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 08/22/2023] [Accepted: 08/23/2023] [Indexed: 09/26/2023] Open
Abstract
The long-term efficacy of treatment, heterogeneity, and complexity in the tumor microenvironment remained a clinical challenge in breast cancer (BRCA). There is a need to classify and refine appropriate therapeutic intervention decisions. A stable subtype classification based on gene expression associated with neoadjuvant chemotherapy (NAC) prognosis and assessment on the clinical features, immune infiltration, and mutational characteristics of the different subcategories was performed using ConsensusClusterPlus. We constructed a prognostic model by the least absolute shrinkage and selection operator regression (LASSO) and univariate Cox regression method and further investigated the association between the risk model and clinical features, mutation and immune characteristics of BRCA. We constructed 3 molecular clusters associated with NAC. We found that cluster 1 had the best prognosis, while cluster 3 showed a poor prognosis. Cluster 3 were associated with the advance stage, higher mutation score, activated oncogenic, and lower tumor immune dysfunction and exclusion (TIDE) score. Subsequently, we constructed a prognosis-related risk model comprising 9 genes (RLN2, MSLN, SAPCD2, LY6D, CACNG4, TUBA3E, LAMP3, GNMT, KLHDC7B). The higher-risk group exhibited lower immune infiltration and demonstrated improved overall survival (OS) in both the independent validation cohort. Finally, by combining clinicopathological features with the NAC-related prognostic risk model, we enhanced the accuracy of survival prediction and model performance. Here, we revealed 3 new molecular subtypes based on prognosis-related genes for BRCA NAC and developed a prognostic risk model. It has the potential to aid in the selection of appropriate individualized treatment and the prediction of patient prognosis.
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Affiliation(s)
- Jiexin Feng
- Department of Breast Surgery, Zhangzhou Hospital Affiliated to Fujian Medical University, Fujian, China
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17
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O’Sullivan B, Seoighe C. Comprehensive and realistic simulation of tumour genomic sequencing data. NAR Cancer 2023; 5:zcad051. [PMID: 37746635 PMCID: PMC10516706 DOI: 10.1093/narcan/zcad051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 08/25/2023] [Accepted: 09/08/2023] [Indexed: 09/26/2023] Open
Abstract
Accurate identification of somatic mutations and allele frequencies in cancer has critical research and clinical applications. Several computational tools have been developed for this purpose but, in the absence of comprehensive 'ground truth' data, assessing the accuracy of these methods is challenging. We created a computational framework to simulate tumour and matched normal sequencing data for which the source of all loci that contain non-reference bases is known, based on a phased, personalized genome. Unlike existing methods, we account for sampling errors inherent in the sequencing process. Using this framework, we assess accuracy and biases in inferred mutations and their frequencies in an established somatic mutation calling pipeline. We demonstrate bias in existing methods of mutant allele frequency estimation and show, for the first time, the observed mutation frequency spectrum corresponding to a theoretical model of tumour evolution. We highlight the impact of quality filters on detection sensitivity of clinically actionable variants and provide definitive assessment of false positive and false negative mutation calls. Our simulation framework provides an improved means to assess the accuracy of somatic mutation calling pipelines and a detailed picture of the effects of technical parameters and experimental factors on somatic mutation calling in cancer samples.
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Affiliation(s)
- Brian O’Sullivan
- School of Mathematical and Statistical Sciences, University of Galway, University Road, Galway H91 TK33, Ireland
| | - Cathal Seoighe
- School of Mathematical and Statistical Sciences, University of Galway, University Road, Galway H91 TK33, Ireland
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18
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Nishimura T, Kakiuchi N, Yoshida K, Sakurai T, Kataoka TR, Kondoh E, Chigusa Y, Kawai M, Sawada M, Inoue T, Takeuchi Y, Maeda H, Baba S, Shiozawa Y, Saiki R, Nakagawa MM, Nannya Y, Ochi Y, Hirano T, Nakagawa T, Inagaki-Kawata Y, Aoki K, Hirata M, Nanki K, Matano M, Saito M, Suzuki E, Takada M, Kawashima M, Kawaguchi K, Chiba K, Shiraishi Y, Takita J, Miyano S, Mandai M, Sato T, Takeuchi K, Haga H, Toi M, Ogawa S. Evolutionary histories of breast cancer and related clones. Nature 2023; 620:607-614. [PMID: 37495687 PMCID: PMC10432280 DOI: 10.1038/s41586-023-06333-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 06/15/2023] [Indexed: 07/28/2023]
Abstract
Recent studies have documented frequent evolution of clones carrying common cancer mutations in apparently normal tissues, which are implicated in cancer development1-3. However, our knowledge is still missing with regard to what additional driver events take place in what order, before one or more of these clones in normal tissues ultimately evolve to cancer. Here, using phylogenetic analyses of multiple microdissected samples from both cancer and non-cancer lesions, we show unique evolutionary histories of breast cancers harbouring der(1;16), a common driver alteration found in roughly 20% of breast cancers. The approximate timing of early evolutionary events was estimated from the mutation rate measured in normal epithelial cells. In der(1;16)(+) cancers, the derivative chromosome was acquired from early puberty to late adolescence, followed by the emergence of a common ancestor by the patient's early 30s, from which both cancer and non-cancer clones evolved. Replacing the pre-existing mammary epithelium in the following years, these clones occupied a large area within the premenopausal breast tissues by the time of cancer diagnosis. Evolution of multiple independent cancer founders from the non-cancer ancestors was common, contributing to intratumour heterogeneity. The number of driver events did not correlate with histology, suggesting the role of local microenvironments and/or epigenetic driver events. A similar evolutionary pattern was also observed in another case evolving from an AKT1-mutated founder. Taken together, our findings provide new insight into how breast cancer evolves.
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Affiliation(s)
- Tomomi Nishimura
- Department of Pathology and Tumour Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Department of Next-generation Clinical Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Department of Breast Surgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Nobuyuki Kakiuchi
- Department of Pathology and Tumour Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- The Hakubi Center for Advanced Research, Kyoto University, Kyoto, Japan
- Department of Gastroenterology and Hepatology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Kenichi Yoshida
- Department of Pathology and Tumour Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Division of Cancer Evolution, National Cancer Center Research Institute, Tokyo, Japan
| | - Takaki Sakurai
- Department of Diagnostic Pathology, Kyoto University Hospital, Kyoto, Japan
- Department of Diagnostic Pathology, Osaka Red Cross Hospital, Osaka, Japan
| | - Tatsuki R Kataoka
- Department of Diagnostic Pathology, Kyoto University Hospital, Kyoto, Japan
- Department of Pathology, Iwate Medical University, Iwate, Japan
| | - Eiji Kondoh
- Department of Gynecology and Obstetrics, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Department of Obstetrics and Gynecology Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Yoshitsugu Chigusa
- Department of Gynecology and Obstetrics, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Masahiko Kawai
- Department of Pediatrics, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | | | | | - Yasuhide Takeuchi
- Department of Pathology and Tumour Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Department of Diagnostic Pathology, Kyoto University Hospital, Kyoto, Japan
| | - Hirona Maeda
- Department of Pathology and Tumour Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Department of Diagnostic Pathology, Kyoto University Hospital, Kyoto, Japan
| | - Satoko Baba
- Pathology Project for Molecular Targets, Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan
- Division of Pathology, Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan
- Department of Pathology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Yusuke Shiozawa
- Department of Pathology and Tumour Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Ryunosuke Saiki
- Department of Pathology and Tumour Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Masahiro M Nakagawa
- Department of Pathology and Tumour Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Department of Next-generation Clinical Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yasuhito Nannya
- Department of Pathology and Tumour Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Division of Hematopoietic Disease Control, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Yotaro Ochi
- Department of Pathology and Tumour Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Tomonori Hirano
- Department of Pathology and Tumour Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Department of Gastroenterology and Hepatology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan
| | - Tomoe Nakagawa
- Department of Pathology and Tumour Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan
| | - Yukiko Inagaki-Kawata
- Department of Pathology and Tumour Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Department of Breast Surgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Kosuke Aoki
- Department of Pathology and Tumour Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Masahiro Hirata
- Department of Diagnostic Pathology, Kyoto University Hospital, Kyoto, Japan
| | - Kosaku Nanki
- Department of Organoid Medicine, Sakaguchi Laboratory, Keio University School of Medicine, Tokyo, Japan
| | - Mami Matano
- Department of Organoid Medicine, Sakaguchi Laboratory, Keio University School of Medicine, Tokyo, Japan
| | - Megumu Saito
- Department of Organoid Medicine, Sakaguchi Laboratory, Keio University School of Medicine, Tokyo, Japan
- Osaka Research Center for Drug Discovery, Otsuka Pharmaceutical Company, Limited, Osaka, Japan
| | - Eiji Suzuki
- Department of Breast Surgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Breast Surgery Department, Kobe City Medical Center General Hospital, Hyogo, Japan
| | - Masahiro Takada
- Department of Breast Surgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Masahiro Kawashima
- Department of Breast Surgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Kosuke Kawaguchi
- Department of Breast Surgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Kenichi Chiba
- Division of Genome Analysis Platform Development, National Cancer Center Research Institute, Tokyo, Japan
| | - Yuichi Shiraishi
- Division of Genome Analysis Platform Development, National Cancer Center Research Institute, Tokyo, Japan
| | - Junko Takita
- Department of Pediatrics, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Satoru Miyano
- Department of Integrated Analytics, M&D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan
- Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Masaki Mandai
- Department of Gynecology and Obstetrics, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Toshiro Sato
- Department of Organoid Medicine, Sakaguchi Laboratory, Keio University School of Medicine, Tokyo, Japan
| | - Kengo Takeuchi
- Pathology Project for Molecular Targets, Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan
- Division of Pathology, Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan
- Department of Pathology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Hironori Haga
- Department of Diagnostic Pathology, Kyoto University Hospital, Kyoto, Japan
| | - Masakazu Toi
- Department of Breast Surgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Seishi Ogawa
- Department of Pathology and Tumour Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan.
- Department of Medicine, Centre for Haematology and Regenerative Medicine, Karolinska Institute, Stockholm, Sweden.
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19
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Lu S, Lu H, Zheng T, Yuan H, Du H, Gao Y, Liu Y, Pan X, Zhang W, Fu S, Sun Z, Jin J, He QY, Chen Y, Zhang G. A multi-omics dataset of human transcriptome and proteome stable reference. Sci Data 2023; 10:455. [PMID: 37443183 PMCID: PMC10344951 DOI: 10.1038/s41597-023-02359-w] [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: 01/31/2023] [Accepted: 07/03/2023] [Indexed: 07/15/2023] Open
Abstract
The development of high-throughput omics technology has greatly promoted the development of biomedicine. However, the poor reproducibility of omics techniques limits their application. It is necessary to use standard reference materials of complex RNAs or proteins to test and calibrate the accuracy and reproducibility of omics workflows. The transcriptome and proteome of most cell lines shift during culturing, which limits their applicability as standard samples. In this study, we demonstrated that the human hepatocellular cell line MHCC97H has a very stable transcriptome (r = 0.983~0.997) and proteome (r = 0.966~0.988 for data-dependent acquisition, r = 0.970~0.994 for data-independent acquisition) after 9 subculturing generations, which allows this steady standard sample to be consistently produced on an industrial scale in long term. Moreover, this stability was maintained across labs and platforms. In sum, our study provides omics standard reference material and reference datasets for transcriptomic and proteomics research. This helps to further standardize the workflow and data quality of omics techniques and thus promotes the application of omics technology in precision medicine.
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Affiliation(s)
- Shaohua Lu
- Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes and MOE Key Laboratory of Tumor Molecular Biology, Institute of Life and Health Engineering, Jinan University, Guangzhou, China.
- Sino-French Hoffmann Institute, School of Basic Medical Sciences, State Key Laboratory of Respiratory Disease, Guangzhou Medical University, Guangzhou, China.
| | - Hong Lu
- Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes and MOE Key Laboratory of Tumor Molecular Biology, Institute of Life and Health Engineering, Jinan University, Guangzhou, China
| | - Tingkai Zheng
- Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes and MOE Key Laboratory of Tumor Molecular Biology, Institute of Life and Health Engineering, Jinan University, Guangzhou, China
| | - Huiming Yuan
- CAS Key Laboratory of Separation Science for Analytical Chemistry, National Chromatographic Research and Analysis Center, Dalian Institute of Chemical Physics, Chinese Academy of Science, Dalian, China
| | - Hongli Du
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Youhe Gao
- Department of Biochemistry and Molecular Biology, Beijing Key Laboratory of Gene Engineering Drug and Biotechnology, Beijing Normal University, Beijing, China
| | - Yongtao Liu
- Department of Biochemistry and Molecular Biology, Beijing Key Laboratory of Gene Engineering Drug and Biotechnology, Beijing Normal University, Beijing, China
| | - Xuanzhen Pan
- Department of Biochemistry and Molecular Biology, Beijing Key Laboratory of Gene Engineering Drug and Biotechnology, Beijing Normal University, Beijing, China
| | - Wenlu Zhang
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Shuying Fu
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Zhenghua Sun
- Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes and MOE Key Laboratory of Tumor Molecular Biology, Institute of Life and Health Engineering, Jinan University, Guangzhou, China
| | - Jingjie Jin
- Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes and MOE Key Laboratory of Tumor Molecular Biology, Institute of Life and Health Engineering, Jinan University, Guangzhou, China
| | - Qing-Yu He
- Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes and MOE Key Laboratory of Tumor Molecular Biology, Institute of Life and Health Engineering, Jinan University, Guangzhou, China
| | - Yang Chen
- Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes and MOE Key Laboratory of Tumor Molecular Biology, Institute of Life and Health Engineering, Jinan University, Guangzhou, China.
| | - Gong Zhang
- Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes and MOE Key Laboratory of Tumor Molecular Biology, Institute of Life and Health Engineering, Jinan University, Guangzhou, China.
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20
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Boßelmann CM, Leu C, Lal D. Technological and computational approaches to detect somatic mosaicism in epilepsy. Neurobiol Dis 2023:106208. [PMID: 37343892 DOI: 10.1016/j.nbd.2023.106208] [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: 03/05/2023] [Revised: 06/03/2023] [Accepted: 06/16/2023] [Indexed: 06/23/2023] Open
Abstract
Lesional epilepsy is a common and severe disease commonly associated with malformations of cortical development, including focal cortical dysplasia and hemimegalencephaly. Recent advances in sequencing and variant calling technologies have identified several genetic causes, including both short/single nucleotide and structural somatic variation. In this review, we aim to provide a comprehensive overview of the methodological advancements in this field while highlighting the unresolved technological and computational challenges that persist, including ultra-low variant allele fractions in bulk tissue, low availability of paired control samples, spatial variability of mutational burden within the lesion, and the issue of false-positive calls and validation procedures. Information from genetic testing in focal epilepsy may be integrated into clinical care to inform histopathological diagnosis, postoperative prognosis, and candidate precision therapies.
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Affiliation(s)
- Christian M Boßelmann
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA; Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Costin Leu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA; Department of Clinical and Experimental Epilepsy, Institute of Neurology, University College London, London, UK.
| | - Dennis Lal
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA; Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and M.I.T., Cambridge, MA, USA; Cologne Center for Genomics (CCG), University of Cologne, Cologne, DE, USA
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21
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Zhang S, Wu L, Li Z, Li Q, Zong Y, Zhu K, Chen L, Qin H, Meng R. An unusual ectopic thymoma clonal evolution analysis: A case report. Open Life Sci 2023; 18:20220600. [PMID: 37215501 PMCID: PMC10199323 DOI: 10.1515/biol-2022-0600] [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: 12/04/2022] [Revised: 03/13/2023] [Accepted: 03/22/2023] [Indexed: 05/24/2023] Open
Abstract
Thymomas and thymic carcinomas are rare and primary tumors of the mediastinum which is derived from the thymic epithelium. Thymomas are the most common primary anterior mediastinal tumor, while ectopic thymomas are rarer. Mutational profiles of ectopic thymomas may help expand our understanding of the occurrence and treatment options of these tumors. In this report, we sought to elucidate the mutational profiles of two ectopic thymoma nodules to gain deeper understanding of the molecular genetic information of this rare tumor and to provide guidance treatment options. We presented a case of 62-year-old male patient with a postoperative pathological diagnosis of type A mediastinal thymoma and ectopic pulmonary thymoma. After mediastinal lesion resection and thoracoscopic lung wedge resection, the mediastinal thymoma was completely removed, and the patient recovered from the surgery and no recurrence was found by examination until now. Whole exome sequencing was performed on both mediastinal thymoma and ectopic pulmonary thymoma tissue samples of the patient and clonal evolution analysis were further conducted to analyze the genetic characteristics. We identified eight gene mutations that were co-mutated in both lesions. Consistent with a previous exome sequencing analysis of thymic epithelial tumor, HRAS was also observed in both mediastinal lesion and lung lesion tissues. We also evaluated the intratumor heterogeneity of non-silent mutations. The results showed that the mediastinal lesion tissue has higher degree of heterogeneity and the lung lesion tissue has relatively low amount of variant heterogeneity in the detected variants. Through pathology and genomics sequencing detection, we initially revealed the genetic differences between mediastinal thymoma and ectopic thymoma, and clonal evolution analysis showed that these two lesions originated from multi-ancestral regions.
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Affiliation(s)
- Sijia Zhang
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 156 Wujiadun, Jianghan District, Wuhan, Hubei Province, 430022, China
| | - Lu Wu
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 156 Wujiadun, Jianghan District, Wuhan, Hubei Province, 430022, China
| | - Zhenyu Li
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 156 Wujiadun, Jianghan District, Wuhan, Hubei Province, 430022, China
| | - Qianwen Li
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 156 Wujiadun, Jianghan District, Wuhan, Hubei Province, 430022, China
| | - Yan Zong
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 156 Wujiadun, Jianghan District, Wuhan, Hubei Province, 430022, China
| | - Kuikui Zhu
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 156 Wujiadun, Jianghan District, Wuhan, Hubei Province, 430022, China
| | - Leichong Chen
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 156 Wujiadun, Jianghan District, Wuhan, Hubei Province, 430022, China
| | - Haifeng Qin
- Department of Pulmonary Neoplasm Internal Medicine, Fifth Medical Center of Chinese PLA General Hospital, Beijing, 100071, China
| | - Rui Meng
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 156 Wujiadun, Jianghan District, Wuhan, Hubei Province, 430022, China
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22
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Li S, Hu R, Small C, Kang TY, Liu CC, Zhou XJ, Li W. cfSNV: a software tool for the sensitive detection of somatic mutations from cell-free DNA. Nat Protoc 2023; 18:1563-1583. [PMID: 36849599 PMCID: PMC10411976 DOI: 10.1038/s41596-023-00807-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 11/24/2022] [Indexed: 03/01/2023]
Abstract
Cell-free DNA (cfDNA) in blood, viewed as a surrogate for tumor biopsy, has many clinical applications, including diagnosing cancer, guiding cancer treatment and monitoring treatment response. All these applications depend on an indispensable, yet underdeveloped task: detecting somatic mutations from cfDNA. The task is challenging because of the low tumor fraction in cfDNA. Recently, we developed the computational method cfSNV, the first method that comprehensively considers the properties of cfDNA for the sensitive detection of mutations from cfDNA. cfSNV vastly outperformed the conventional methods that were developed primarily for calling mutations from solid tumor tissues. cfSNV can accurately detect mutations in cfDNA even with medium-coverage (e.g., ≥200×) sequencing, which makes whole-exome sequencing (WES) of cfDNA a viable option for various clinical utilities. Here, we present a user-friendly cfSNV package that exhibits fast computation and convenient user options. We also built a Docker image of it, which is designed to enable researchers and clinicians with a limited computational background to easily carry out analyses on both high-performance computing platforms and local computers. Mutation calling from a standard preprocessed WES dataset (~250× and ~70 million base pair target size) can be carried out in 3 h on a server with eight virtual CPUs and 32 GB of random access memory.
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Affiliation(s)
- Shuo Li
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA
| | - Ran Hu
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA
- Bioinformatics Interdepartmental Graduate Program, University of California at Los Angeles, Los Angeles, CA, USA
- Institute for Quantitative & Computational Biosciences, University of California at Los Angeles, Los Angeles, CA, USA
| | - Colin Small
- Institute for Quantitative & Computational Biosciences, University of California at Los Angeles, Los Angeles, CA, USA
| | | | - Chun-Chi Liu
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA
- EarlyDiagnostics Inc., Los Angeles, CA, USA
| | - Xianghong Jasmine Zhou
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA.
- Institute for Quantitative & Computational Biosciences, University of California at Los Angeles, Los Angeles, CA, USA.
- EarlyDiagnostics Inc., Los Angeles, CA, USA.
| | - Wenyuan Li
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA.
- EarlyDiagnostics Inc., Los Angeles, CA, USA.
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23
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Furtado LV, Souers RJ, Vasalos P, Halley JG, Aisner DL, Nagarajan R, Voelkerding KV, Merker JD, Konnick EQ. Four-Year Laboratory Performance of the First College of American Pathologists In Silico Next-Generation Sequencing Bioinformatics Proficiency Testing Surveys. Arch Pathol Lab Med 2023; 147:137-142. [PMID: 35671151 DOI: 10.5858/arpa.2021-0384-cp] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/05/2022] [Indexed: 02/05/2023]
Abstract
CONTEXT.— In 2016, the College of American Pathologists (CAP) launched the first next-generation sequencing (NGS) in silico bioinformatics proficiency testing survey to evaluate the performance of clinical laboratory bioinformatics pipelines for the detection of oncology-associated variants at varying allele fractions. This survey focused on 2 commonly used oncology panels, the Illumina TruSeq Amplicon Cancer Panel and the Thermo Fisher Ion AmpliSeq Cancer Hotspot v2 Panel. OBJECTIVE.— To review the analytical performance of laboratories participating in the CAP NGS bioinformatics (NGSB) surveys, comprising NGSB1 for Illumina users and NGSB2 for Thermo Fisher Ion Torrent users, between 2016 and 2019. DESIGN.— Responses from 78 laboratories were analyzed for accuracy and associated performance characteristics. RESULTS.— The analytical sensitivity was 90.0% (1901 of 2112) for laboratories using the Illumina platform and 94.8% (2153 of 2272) for Thermo Fisher Ion Torrent users. Variant type and variant allele fraction were significantly associated with performance. False-negative results were seen mostly for multi-nucleotide variants and variants engineered at variant allele fractions of less than 25%. Analytical specificity for all participating laboratories was 99.8% (9303 of 9320). There was no statistically significant association between deletion-insertion length and detection rate. CONCLUSIONS.— These results demonstrated high analytical sensitivity and specificity, supporting the feasibility and utility of using in silico mutagenized NGS data sets as a supplemental challenge to CAP surveys for oncology-associated variants based on physical samples. This program demonstrates the opportunity and challenges that can guide future surveys inclusive of customized in silico programs.
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Affiliation(s)
- Larissa V Furtado
- From the Department of Pathology, St Jude Children's Research Hospital, Memphis, Tennessee (Furtado)
| | - Rhona J Souers
- From the Biostatistics Department (Souers), College of American Pathologists, Northfield, Illinois
| | - Patricia Vasalos
- From Proficiency Testing (Vasalos, Halley), College of American Pathologists, Northfield, Illinois
| | - Jaimie G Halley
- From Proficiency Testing (Vasalos, Halley), College of American Pathologists, Northfield, Illinois
| | - Dara L Aisner
- From the Department of Pathology, University of Colorado School of Medicine, Aurora (Aisner)
| | | | | | - Jason D Merker
- From Departments of Pathology and Laboratory Medicine & Genetics, Lineberger Comprehensive Cancer Center, University of North Carolina School of Medicine, Chapel Hill (Merker)
| | - Eric Q Konnick
- From the Department of Laboratory Medicine and Pathology, University of Washington, Seattle (Konnick)
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24
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Valori M, Lehikoinen J, Jansson L, Clancy J, Lundgren SA, Mustjoki S, Tienari P. High prevalence of low-allele-fraction somatic mutations in STAT3 in peripheral blood CD8+ cells in multiple sclerosis patients and controls. PLoS One 2022; 17:e0278245. [PMID: 36441748 PMCID: PMC9704626 DOI: 10.1371/journal.pone.0278245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 11/14/2022] [Indexed: 11/30/2022] Open
Abstract
Somatic mutations have a central role in cancer, but there are also a few rare autoimmune diseases in which somatic mutations play a major role. We have recently shown that nonsynonymous somatic mutations with low allele fractions are preferentially detectable in CD8+ cells and that the STAT3 gene is a promising target for screening. Here, we analyzed somatic mutations in the STAT3 SH2 domain in peripheral blood CD8+ cells in a set of 94 multiple sclerosis (MS) patients and 99 matched controls. PCR amplicons targeting the exons 20 and 21 of STAT3 were prepared and sequenced using the Illumina MiSeq instrument with 2x300bp reads. We designed a novel variant calling method, optimized for large number of samples, high sequencing depth (>25,000x) and small target genomic area. Overall, we discovered 64 STAT3 somatic mutations in the 193 donors, of which 63 were non-synonymous and 77% have been previously reported in cancer or lymphoproliferative disease. The overall median variant allele fraction was 0.065% (range 0.007-1.2%), without significant difference between MS and controls (p = 0.82). There were 26 (28%) MS patients vs. 24 (24%) controls with mutations (p = 0.62). Two or more mutations were found in 9 MS patients vs. 2 controls (p = 0.03, pcorr = 0.12). Carriership of mutations associated with older age and lower neutrophil counts. These results demonstrate that STAT3 SH2 domain is a hotspot for somatic mutations in CD8+ cells with a prevalence of 26% among the participants. There were no significant differences in the mutation prevalences between MS patients and controls. Further research is needed to elucidate the role of antigenic stimuli in the expansion of the mutant clones. Furthermore, the high discovered prevalence of STAT3 somatic mutations makes it feasible to analyze these mutations directly in tissue-infiltrating CD8+ cells in autoimmune diseases.
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Affiliation(s)
- Miko Valori
- Translational Immunology Research Program, University of Helsinki, Helsinki, Finland
- * E-mail:
| | - Joonas Lehikoinen
- Translational Immunology Research Program, University of Helsinki, Helsinki, Finland
- Neurocenter, Helsinki University Hospital, Helsinki, Finland
| | - Lilja Jansson
- Translational Immunology Research Program, University of Helsinki, Helsinki, Finland
- Neurocenter, Helsinki University Hospital, Helsinki, Finland
| | - Jonna Clancy
- Research and Development, Finnish Red Cross Blood Service, Helsinki, Finland
| | - Sofie A. Lundgren
- Translational Immunology Research Program, University of Helsinki, Helsinki, Finland
- Hematology Research Unit Helsinki, University of Helsinki and Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland
| | - Satu Mustjoki
- Translational Immunology Research Program, University of Helsinki, Helsinki, Finland
- Hematology Research Unit Helsinki, University of Helsinki and Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland
| | - Pentti Tienari
- Translational Immunology Research Program, University of Helsinki, Helsinki, Finland
- Neurocenter, Helsinki University Hospital, Helsinki, Finland
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25
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Jin J, Chen Z, Liu J, Du H, Zhang G. Towards an accurate and robust analysis pipeline for somatic mutation calling. Front Genet 2022; 13:979928. [PMID: 36457740 PMCID: PMC9705725 DOI: 10.3389/fgene.2022.979928] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Accepted: 11/01/2022] [Indexed: 12/24/2023] Open
Abstract
Accurate and robust somatic mutation detection is essential for cancer treatment, diagnostics and research. Various analysis pipelines give different results and thus should be systematically evaluated. In this study, we benchmarked 5 commonly-used somatic mutation calling pipelines (VarScan, VarDictJava, Mutect2, Strelka2 and FANSe) for their precision, recall and speed, using standard benchmarking datasets based on a series of real-world whole-exome sequencing datasets. All the 5 pipelines showed very high precision in all cases, and high recall rate in mutation rates higher than 10%. However, for the low frequency mutations, these pipelines showed large difference. FANSe showed the highest accuracy (especially the sensitivity) in all cases, and VarScan and VarDictJava outperformed Mutect2 and Strelka2 in low frequency mutations at all sequencing depths. The flaws in filter was the major cause of the low sensitivity of the four pipelines other than FANSe. Concerning the speed, FANSe pipeline was 8.8∼19x faster than the other pipelines. Our benchmarking results demonstrated performance of the somatic calling pipelines and provided a reference for a proper choice of such pipelines in cancer applications.
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Affiliation(s)
- Jingjie Jin
- Key Laboratory of Functional Protein Research, Guangdong Higher Education Institutes, Jinan University, Guangzhou, China
- MOE Key Laboratory of Tumor Molecular Biology, Institute of Life and Health Engineering, Jinan University, Guangzhou, China
| | - Zixi Chen
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | | | - Hongli Du
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Gong Zhang
- Key Laboratory of Functional Protein Research, Guangdong Higher Education Institutes, Jinan University, Guangzhou, China
- MOE Key Laboratory of Tumor Molecular Biology, Institute of Life and Health Engineering, Jinan University, Guangzhou, China
- Chi-Biotech Co. Ltd., Shenzhen, China
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26
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Makrooni MA, O'Sullivan B, Seoighe C. Bias and inconsistency in the estimation of tumour mutation burden. BMC Cancer 2022; 22:840. [PMID: 35918650 PMCID: PMC9347149 DOI: 10.1186/s12885-022-09897-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 07/11/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Tumour mutation burden (TMB), defined as the number of somatic mutations per megabase within the sequenced region in the tumour sample, has been used as a biomarker for predicting response to immune therapy. Several studies have been conducted to assess the utility of TMB for various cancer types; however, methods to measure TMB have not been adequately evaluated. In this study, we identified two sources of bias in current methods to calculate TMB. METHODS We used simulated data to quantify the two sources of bias and their effect on TMB calculation, we down-sampled sequencing reads from exome sequencing datasets from TCGA to evaluate the consistency in TMB estimation across different sequencing depths. We analyzed data from ten cancer cohorts to investigate the relationship between inferred TMB and sequencing depth. RESULTS We found that TMB, estimated by counting the number of somatic mutations above a threshold frequency (typically 0.05), is not robust to sequencing depth. Furthermore, we show that, because only mutations with an observed frequency greater than the threshold are considered, the observed mutant allele frequency provides a biased estimate of the true frequency. This can result in substantial over-estimation of the TMB, when the cancer sample includes a large number of somatic mutations at low frequencies, and exacerbates the lack of robustness of TMB to variation in sequencing depth and tumour purity. CONCLUSION Our results demonstrate that care needs to be taken in the estimation of TMB to ensure that results are unbiased and consistent across studies and we suggest that accurate and robust estimation of TMB could be achieved using statistical models that estimate the full mutant allele frequency spectrum.
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Affiliation(s)
- Mohammad A Makrooni
- School of Mathematical & Statistical Sciences, National University of Ireland, Galway, Ireland
| | - Brian O'Sullivan
- School of Mathematical & Statistical Sciences, National University of Ireland, Galway, Ireland
| | - Cathal Seoighe
- School of Mathematical & Statistical Sciences, National University of Ireland, Galway, Ireland.
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27
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Dodani DD, Nguyen MH, Morin RD, Marra MA, Corbett RD. Combinatorial and Machine Learning Approaches for Improved Somatic Variant Calling From Formalin-Fixed Paraffin-Embedded Genome Sequence Data. Front Genet 2022; 13:834764. [PMID: 35571031 PMCID: PMC9092826 DOI: 10.3389/fgene.2022.834764] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 03/18/2022] [Indexed: 11/13/2022] Open
Abstract
Formalin fixation of paraffin-embedded tissue samples is a well-established method for preserving tissue and is routinely used in clinical settings. Although formalin-fixed, paraffin-embedded (FFPE) tissues are deemed crucial for research and clinical applications, the fixation process results in molecular damage to nucleic acids, thus confounding their use in genome sequence analysis. Methods to improve genomic data quality from FFPE tissues have emerged, but there remains significant room for improvement. Here, we use whole-genome sequencing (WGS) data from matched Fresh Frozen (FF) and FFPE tissue samples to optimize a sensitive and precise FFPE single nucleotide variant (SNV) calling approach. We present methods to reduce the prevalence of false-positive SNVs by applying combinatorial techniques to five publicly available variant callers. We also introduce FFPolish, a novel variant classification method that efficiently classifies FFPE-specific false-positive variants. Our combinatorial and statistical techniques improve precision and F1 scores compared to the results of publicly available tools when tested individually.
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Affiliation(s)
- Dollina D Dodani
- The Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC, Canada
| | - Matthew H Nguyen
- The Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC, Canada
| | - Ryan D Morin
- Canada's Michael Smith Genome Sciences Centre, BC Cancer Research Institute, Provincial Health Services Authority, Vancouver, BC, Canada.,Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada
| | - Marco A Marra
- Canada's Michael Smith Genome Sciences Centre, BC Cancer Research Institute, Provincial Health Services Authority, Vancouver, BC, Canada.,Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Richard D Corbett
- Canada's Michael Smith Genome Sciences Centre, BC Cancer Research Institute, Provincial Health Services Authority, Vancouver, BC, Canada
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28
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Ip EKK, Troup M, Xu C, Winlaw DS, Dunwoodie SL, Giannoulatou E. Benchmarking the Effectiveness and Accuracy of Multiple Mitochondrial DNA Variant Callers: Practical Implications for Clinical Application. Front Genet 2022; 13:692257. [PMID: 35350246 PMCID: PMC8957813 DOI: 10.3389/fgene.2022.692257] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 01/27/2022] [Indexed: 12/30/2022] Open
Abstract
Mitochondrial DNA (mtDNA) mutations contribute to human disease across a range of severity, from rare, highly penetrant mutations causal for monogenic disorders to mutations with milder contributions to phenotypes. mtDNA variation can exist in all copies of mtDNA or in a percentage of mtDNA copies and can be detected with levels as low as 1%. The large number of copies of mtDNA and the possibility of multiple alternative alleles at the same DNA nucleotide position make the task of identifying allelic variation in mtDNA very challenging. In recent years, specialized variant calling algorithms have been developed that are tailored to identify mtDNA variation from whole-genome sequencing (WGS) data. However, very few studies have systematically evaluated and compared these methods for the detection of both homoplasmy and heteroplasmy. A publicly available synthetic gold standard dataset was used to assess four mtDNA variant callers (Mutserve, mitoCaller, MitoSeek, and MToolBox), and the commonly used Genome Analysis Toolkit “best practices” pipeline, which is included in most current WGS pipelines. We also used WGS data from 126 trios and calculated the percentage of maternally inherited variants as a metric of calling accuracy, especially for homoplasmic variants. We additionally compared multiple pathogenicity prediction resources for mtDNA variants. Although the accuracy of homoplasmic variant detection was high for the majority of the callers with high concordance across callers, we found a very low concordance rate between mtDNA variant callers for heteroplasmic variants ranging from 2.8% to 3.6%, for heteroplasmy thresholds of 5% and 1%. Overall, Mutserve showed the best performance using the synthetic benchmark dataset. The analysis of mtDNA pathogenicity resources also showed low concordance in prediction results. We have shown that while homoplasmic variant calling is consistent between callers, there remains a significant discrepancy in heteroplasmic variant calling. We found that resources like population frequency databases and pathogenicity predictors are now available for variant annotation but still need refinement and improvement. With its peculiarities, the mitochondria require special considerations, and we advocate that caution needs to be taken when analyzing mtDNA data from WGS data.
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Affiliation(s)
- Eddie K K Ip
- Victor Chang Cardiac Research Institute, Sydney, NSW, Australia.,St. Vincent's Clinical School, Sydney, NSW, Australia
| | - Michael Troup
- Victor Chang Cardiac Research Institute, Sydney, NSW, Australia
| | - Colin Xu
- School of Computer Science and Engineering, Sydney, NSW, Australia
| | - David S Winlaw
- Cardiothoracic Surgery, Cincinnati Children's Hospital Medical Centre, Heart Institute, Cincinnati, OH, United States
| | - Sally L Dunwoodie
- Victor Chang Cardiac Research Institute, Sydney, NSW, Australia.,St. Vincent's Clinical School, Sydney, NSW, Australia
| | - Eleni Giannoulatou
- Victor Chang Cardiac Research Institute, Sydney, NSW, Australia.,St. Vincent's Clinical School, Sydney, NSW, Australia
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29
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Endogenous ADAR-mediated RNA editing in non-human primates using stereopure chemically modified oligonucleotides. Nat Biotechnol 2022; 40:1093-1102. [DOI: 10.1038/s41587-022-01225-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 01/17/2022] [Indexed: 12/18/2022]
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30
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Manders F, Brandsma AM, de Kanter J, Verheul M, Oka R, van Roosmalen MJ, van der Roest B, van Hoeck A, Cuppen E, van Boxtel R. MutationalPatterns: the one stop shop for the analysis of mutational processes. BMC Genomics 2022; 23:134. [PMID: 35168570 PMCID: PMC8845394 DOI: 10.1186/s12864-022-08357-3] [Citation(s) in RCA: 62] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 02/01/2022] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND The collective of somatic mutations in a genome represents a record of mutational processes that have been operative in a cell. These processes can be investigated by extracting relevant mutational patterns from sequencing data. RESULTS Here, we present the next version of MutationalPatterns, an R/Bioconductor package, which allows in-depth mutational analysis of catalogues of single and double base substitutions as well as small insertions and deletions. Major features of the package include the possibility to perform regional mutation spectra analyses and the possibility to detect strand asymmetry phenomena, such as lesion segregation. On top of this, the package also contains functions to determine how likely it is that a signature can cause damaging mutations (i.e., mutations that affect protein function). This updated package supports stricter signature refitting on known signatures in order to prevent overfitting. Using simulated mutation matrices containing varied signature contributions, we showed that reliable refitting can be achieved even when only 50 mutations are present per signature. Additionally, we incorporated bootstrapped signature refitting to assess the robustness of the signature analyses. Finally, we applied the package on genome mutation data of cell lines in which we deleted specific DNA repair processes and on large cancer datasets, to show how the package can be used to generate novel biological insights. CONCLUSIONS This novel version of MutationalPatterns allows for more comprehensive analyses and visualization of mutational patterns in order to study the underlying processes. Ultimately, in-depth mutational analyses may contribute to improved biological insights in mechanisms of mutation accumulation as well as aid cancer diagnostics. MutationalPatterns is freely available at http://bioconductor.org/packages/MutationalPatterns .
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Affiliation(s)
- Freek Manders
- Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584CS, Utrecht, The Netherlands
- Oncode Institute, Jaarbeursplein 6, 3521 AL, Utrecht, The Netherlands
| | - Arianne M Brandsma
- Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584CS, Utrecht, The Netherlands
- Oncode Institute, Jaarbeursplein 6, 3521 AL, Utrecht, The Netherlands
| | - Jurrian de Kanter
- Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584CS, Utrecht, The Netherlands
- Oncode Institute, Jaarbeursplein 6, 3521 AL, Utrecht, The Netherlands
| | - Mark Verheul
- Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584CS, Utrecht, The Netherlands
- Oncode Institute, Jaarbeursplein 6, 3521 AL, Utrecht, The Netherlands
| | - Rurika Oka
- Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584CS, Utrecht, The Netherlands
- Oncode Institute, Jaarbeursplein 6, 3521 AL, Utrecht, The Netherlands
| | - Markus J van Roosmalen
- Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584CS, Utrecht, The Netherlands
- Oncode Institute, Jaarbeursplein 6, 3521 AL, Utrecht, The Netherlands
| | - Bastiaan van der Roest
- Oncode Institute, Jaarbeursplein 6, 3521 AL, Utrecht, The Netherlands
- Center for Molecular Medicine, University Medical Center Utrecht, Utrecht University, Universiteitsweg 100, 3584 CG, Utrecht, The Netherlands
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Universiteitsweg 100, 3584 CG, Utrecht, The Netherlands
| | - Arne van Hoeck
- Oncode Institute, Jaarbeursplein 6, 3521 AL, Utrecht, The Netherlands
- Center for Molecular Medicine, University Medical Center Utrecht, Utrecht University, Universiteitsweg 100, 3584 CG, Utrecht, The Netherlands
| | - Edwin Cuppen
- Oncode Institute, Jaarbeursplein 6, 3521 AL, Utrecht, The Netherlands
- Center for Molecular Medicine, University Medical Center Utrecht, Utrecht University, Universiteitsweg 100, 3584 CG, Utrecht, The Netherlands
| | - Ruben van Boxtel
- Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584CS, Utrecht, The Netherlands.
- Oncode Institute, Jaarbeursplein 6, 3521 AL, Utrecht, The Netherlands.
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31
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Establishment of reference standards for multifaceted mosaic variant analysis. Sci Data 2022; 9:35. [PMID: 35115554 PMCID: PMC8813952 DOI: 10.1038/s41597-022-01133-8] [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: 09/24/2021] [Accepted: 12/20/2021] [Indexed: 11/21/2022] Open
Abstract
Detection of somatic mosaicism in non-proliferative cells is a new challenge in genome research, however, the accuracy of current detection strategies remains uncertain due to the lack of a ground truth. Herein, we sought to present a set of ultra-deep sequenced WES data based on reference standards generated by cell line mixtures, providing a total of 386,613 mosaic single-nucleotide variants (SNVs) and insertion-deletion mutations (INDELs) with variant allele frequencies (VAFs) ranging from 0.5% to 56%, as well as 35,113,417 non-variant and 19,936 germline variant sites as a negative control. The whole reference standard set mimics the cumulative aspect of mosaic variant acquisition such as in the early developmental stage owing to the progressive mixing of cell lines with established genotypes, ultimately unveiling 741 possible inter-sample relationships with respect to variant sharing and asymmetry in VAFs. We expect that our reference data will be essential for optimizing the current use of mosaic variant detection strategies and for developing algorithms to enable future improvements. Measurement(s) | genotype | Technology Type(s) | DNA sequencing | Factor Type(s) | genotyping | Sample Characteristic - Organism | Homo sapiens | Sample Characteristic - Environment | cell line |
Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.16970041
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32
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Alburquerque-González B, López-Abellán MD, Luengo-Gil G, Montoro-García S, Conesa-Zamora P. Design of Personalized Neoantigen RNA Vaccines Against Cancer Based on Next-Generation Sequencing Data. Methods Mol Biol 2022; 2547:165-185. [PMID: 36068464 DOI: 10.1007/978-1-0716-2573-6_7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The good clinical results of immune checkpoint inhibitors (ICIs) in recent cancer therapy and the success of RNA vaccines against SARS-nCoV2 have provided important lessons to the scientific community. On the one hand, the efficacy of ICI depends on the number and immunogenicity of tumor neoantigens (TNAs) which unfortunately are not abundantly expressed in many cancer subtypes. On the other hand, novel RNA vaccines have significantly improved both the stability and immunogenicity of mRNA and its efficient delivery, this way overcoming past technique limitations and also allowing a quick vaccine development at the same time. These two facts together have triggered a resurgence of therapeutic cancer vaccines which can be designed to include individual TNAs and be synthesized in a timeframe short enough to be suitable for the tailored treatment of a given cancer patient.In this chapter, we explain the pipeline for the synthesis of TNA-carrying RNA vaccines which encompasses several steps such as individual tumor next-generation sequencing (NGS), selection of immunogenic TNAs, nucleic acid synthesis, drug delivery systems, and immunogenicity assessment, all of each step comprising different alternatives and variations which will be discussed.
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Affiliation(s)
- Begoña Alburquerque-González
- Pathology and Histology Department Facultad de Ciencias de la Salud, UCAM Universidad Católica San Antonio de Murcia, Murcia, Spain
| | - María Dolores López-Abellán
- Laboratory Medicine Department, Group of Molecular Pathology and Pharmacogenetics, Biomedical Research Institute from Murcia (IMIB), Hospital Universitario Santa Lucía, Cartagena, Spain
| | - Ginés Luengo-Gil
- Laboratory Medicine Department, Group of Molecular Pathology and Pharmacogenetics, Biomedical Research Institute from Murcia (IMIB), Hospital Universitario Santa Lucía, Cartagena, Spain
| | - Silvia Montoro-García
- Cell Culture Lab, Facultad de Ciencias de la Salud, UCAM Universidad Católica San Antonio de Murcia, Murcia, Spain
| | - Pablo Conesa-Zamora
- Pathology and Histology Department Facultad de Ciencias de la Salud, UCAM Universidad Católica San Antonio de Murcia, Murcia, Spain.
- Laboratory Medicine Department, Group of Molecular Pathology and Pharmacogenetics, Biomedical Research Institute from Murcia (IMIB), Hospital Universitario Santa Lucía, Cartagena, Spain.
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33
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Hollizeck S, Wong SQ, Solomon B, Chandrananda D, Dawson SJ. Custom workflows to improve joint variant calling from multiple related tumour samples: FreeBayesSomatic and Strelka2Pass. Bioinformatics 2021; 37:3916-3919. [PMID: 34469518 DOI: 10.1093/bioinformatics/btab606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 07/13/2021] [Accepted: 08/30/2021] [Indexed: 11/12/2022] Open
Abstract
SUMMARY This work describes two novel workflows for variant calling that extend the widely used algorithms of Strelka2 and FreeBayes to call somatic mutations from multiple related tumour samples and one matched normal sample. We show that these workflows offer higher precision and recall than their single tumour-normal pair equivalents in both simulated and clinical sequencing data. AVAILABILITY AND IMPLEMENTATION Source code freely available at the following link: https://atlassian.petermac.org.au/bitbucket/projects/DAW/repos/multisamplevariantcalling and executable through Janis (https://github.com/PMCC-BioinformaticsCore/janis) under the GPLv3 licence. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- S Hollizeck
- Peter MacCallum Cancer Centre, Melbourne, Victoria, 3000, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, 3000, Australia
| | - S Q Wong
- Peter MacCallum Cancer Centre, Melbourne, Victoria, 3000, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, 3000, Australia
| | - B Solomon
- Peter MacCallum Cancer Centre, Melbourne, Victoria, 3000, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, 3000, Australia
| | - D Chandrananda
- Peter MacCallum Cancer Centre, Melbourne, Victoria, 3000, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, 3000, Australia
| | - S-J Dawson
- Peter MacCallum Cancer Centre, Melbourne, Victoria, 3000, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, 3000, Australia.,Centre for Cancer Research, University of Melbourne, Melbourne, Victoria, 3000, Australia
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34
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Fang LT, Zhu B, Zhao Y, Chen W, Yang Z, Kerrigan L, Langenbach K, de Mars M, Lu C, Idler K, Jacob H, Zheng Y, Ren L, Yu Y, Jaeger E, Schroth GP, Abaan OD, Talsania K, Lack J, Shen TW, Chen Z, Stanbouly S, Tran B, Shetty J, Kriga Y, Meerzaman D, Nguyen C, Petitjean V, Sultan M, Cam M, Mehta M, Hung T, Peters E, Kalamegham R, Sahraeian SME, Mohiyuddin M, Guo Y, Yao L, Song L, Lam HYK, Drabek J, Vojta P, Maestro R, Gasparotto D, Kõks S, Reimann E, Scherer A, Nordlund J, Liljedahl U, Jensen RV, Pirooznia M, Li Z, Xiao C, Sherry ST, Kusko R, Moos M, Donaldson E, Tezak Z, Ning B, Tong W, Li J, Duerken-Hughes P, Catalanotti C, Maheshwari S, Shuga J, Liang WS, Keats J, Adkins J, Tassone E, Zismann V, McDaniel T, Trent J, Foox J, Butler D, Mason CE, Hong H, Shi L, Wang C, Xiao W. Establishing community reference samples, data and call sets for benchmarking cancer mutation detection using whole-genome sequencing. Nat Biotechnol 2021; 39:1151-1160. [PMID: 34504347 PMCID: PMC8532138 DOI: 10.1038/s41587-021-00993-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Accepted: 06/18/2021] [Indexed: 02/08/2023]
Abstract
The lack of samples for generating standardized DNA datasets for setting up a sequencing pipeline or benchmarking the performance of different algorithms limits the implementation and uptake of cancer genomics. Here, we describe reference call sets obtained from paired tumor-normal genomic DNA (gDNA) samples derived from a breast cancer cell line-which is highly heterogeneous, with an aneuploid genome, and enriched in somatic alterations-and a matched lymphoblastoid cell line. We partially validated both somatic mutations and germline variants in these call sets via whole-exome sequencing (WES) with different sequencing platforms and targeted sequencing with >2,000-fold coverage, spanning 82% of genomic regions with high confidence. Although the gDNA reference samples are not representative of primary cancer cells from a clinical sample, when setting up a sequencing pipeline, they not only minimize potential biases from technologies, assays and informatics but also provide a unique resource for benchmarking 'tumor-only' or 'matched tumor-normal' analyses.
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Affiliation(s)
- Li Tai Fang
- Bioinformatics Research & Early Development, Roche Sequencing Solutions Inc., Belmont, CA, USA
| | - Bin Zhu
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Yongmei Zhao
- Advanced Biomedical and Computational Sciences, Biomedical Informatics and Data Science Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Wanqiu Chen
- Center for Genomics, Loma Linda University School of Medicine, Loma Linda, CA, USA
| | - Zhaowei Yang
- Center for Genomics, Loma Linda University School of Medicine, Loma Linda, CA, USA
- Department of Allergy and Clinical Immunology, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Liz Kerrigan
- ATCC (American Type Culture Collection), Manassas, VA, USA
| | | | | | - Charles Lu
- Computational Genomics, Genomics Research Center (GRC), AbbVie, North Chicago, IL, USA
| | - Kenneth Idler
- Computational Genomics, Genomics Research Center (GRC), AbbVie, North Chicago, IL, USA
| | - Howard Jacob
- Computational Genomics, Genomics Research Center (GRC), AbbVie, North Chicago, IL, USA
| | - Yuanting Zheng
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Luyao Ren
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Ying Yu
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, China
| | | | | | | | - Keyur Talsania
- Advanced Biomedical and Computational Sciences, Biomedical Informatics and Data Science Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Justin Lack
- Advanced Biomedical and Computational Sciences, Biomedical Informatics and Data Science Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Tsai-Wei Shen
- Advanced Biomedical and Computational Sciences, Biomedical Informatics and Data Science Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Zhong Chen
- Center for Genomics, Loma Linda University School of Medicine, Loma Linda, CA, USA
| | - Seta Stanbouly
- Center for Genomics, Loma Linda University School of Medicine, Loma Linda, CA, USA
| | - Bao Tran
- Sequencing Facility, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Jyoti Shetty
- Sequencing Facility, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Yuliya Kriga
- Sequencing Facility, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Daoud Meerzaman
- Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology (CBIIT), National Cancer Institute, Rockville, MD, USA
| | - Cu Nguyen
- Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology (CBIIT), National Cancer Institute, Rockville, MD, USA
| | - Virginie Petitjean
- Biomarker Development, Novartis Institutes for Biomedical Research, Basel, Switzerland
| | - Marc Sultan
- Biomarker Development, Novartis Institutes for Biomedical Research, Basel, Switzerland
| | - Margaret Cam
- CCR Collaborative Bioinformatics Resource (CCBR), Office of Science and Technology Resources, Center for Cancer Research, Bethesda, MD, USA
| | - Monika Mehta
- Sequencing Facility, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Tiffany Hung
- Genentech, a member of the Roche group, South San Francisco, CA, USA
| | - Eric Peters
- Genentech, a member of the Roche group, South San Francisco, CA, USA
| | - Rasika Kalamegham
- Genentech, a member of the Roche group, South San Francisco, CA, USA
| | | | - Marghoob Mohiyuddin
- Bioinformatics Research & Early Development, Roche Sequencing Solutions Inc., Belmont, CA, USA
| | - Yunfei Guo
- Bioinformatics Research & Early Development, Roche Sequencing Solutions Inc., Belmont, CA, USA
| | - Lijing Yao
- Bioinformatics Research & Early Development, Roche Sequencing Solutions Inc., Belmont, CA, USA
| | - Lei Song
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Hugo Y K Lam
- Bioinformatics Research & Early Development, Roche Sequencing Solutions Inc., Belmont, CA, USA
| | - Jiri Drabek
- IMTM, Faculty of Medicine and Dentistry, Palacky University, Olomouc, Czech Republic
- European Infrastructure for Translational Medicine, Amsterdam, the Netherlands
| | - Petr Vojta
- IMTM, Faculty of Medicine and Dentistry, Palacky University, Olomouc, Czech Republic
- European Infrastructure for Translational Medicine, Amsterdam, the Netherlands
| | - Roberta Maestro
- European Infrastructure for Translational Medicine, Amsterdam, the Netherlands
- Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, National Cancer Institute, Unit of Oncogenetics and Functional Oncogenomics, Aviano, Italy
| | - Daniela Gasparotto
- European Infrastructure for Translational Medicine, Amsterdam, the Netherlands
- Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, National Cancer Institute, Unit of Oncogenetics and Functional Oncogenomics, Aviano, Italy
| | - Sulev Kõks
- European Infrastructure for Translational Medicine, Amsterdam, the Netherlands
- Perron Institute for Neurological and Translational Science, Nedlands, Western Australia, Australia
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Ene Reimann
- European Infrastructure for Translational Medicine, Amsterdam, the Netherlands
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Andreas Scherer
- European Infrastructure for Translational Medicine, Amsterdam, the Netherlands
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Jessica Nordlund
- European Infrastructure for Translational Medicine, Amsterdam, the Netherlands
- Department of Medical Sciences, Molecular Medicine and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Ulrika Liljedahl
- European Infrastructure for Translational Medicine, Amsterdam, the Netherlands
- Department of Medical Sciences, Molecular Medicine and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Roderick V Jensen
- Department of Biological Sciences, Virginia Tech, Blacksburg, VA, USA
| | - Mehdi Pirooznia
- Bioinformatics and Computational Biology Core, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Zhipan Li
- Sentieon Inc., Mountain View, CA, USA
| | - Chunlin Xiao
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Stephen T Sherry
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | | | - Malcolm Moos
- Center for Biologics Evaluation and Research, FDA, Silver Spring, MD, USA
| | - Eric Donaldson
- Center for Drug Evaluation and Research, FDA, Silver Spring, MD, USA
| | - Zivana Tezak
- Center for Devices and Radiological Health, FDA, Silver Spring, MD, USA
| | - Baitang Ning
- National Center for Toxicological Research, FDA, Jefferson, AR, USA
| | - Weida Tong
- National Center for Toxicological Research, FDA, Jefferson, AR, USA
| | - Jing Li
- Department of Allergy and Clinical Immunology, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | | | | | | | | | - Winnie S Liang
- Translational Genomics Research Institute, Phoenix, AZ, USA
| | - Jonathan Keats
- Translational Genomics Research Institute, Phoenix, AZ, USA
| | | | - Erica Tassone
- Translational Genomics Research Institute, Phoenix, AZ, USA
| | | | | | - Jeffrey Trent
- Translational Genomics Research Institute, Phoenix, AZ, USA
| | - Jonathan Foox
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Daniel Butler
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Christopher E Mason
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Huixiao Hong
- National Center for Toxicological Research, FDA, Jefferson, AR, USA.
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, China.
| | - Charles Wang
- Center for Genomics, Loma Linda University School of Medicine, Loma Linda, CA, USA.
- Department of Basic Science, Loma Linda University School of Medicine, Loma Linda, CA, USA.
| | - Wenming Xiao
- Center for Devices and Radiological Health, FDA, Silver Spring, MD, USA.
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35
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Karimi MR, Karimi AH, Abolmaali S, Sadeghi M, Schmitz U. Prospects and challenges of cancer systems medicine: from genes to disease networks. Brief Bioinform 2021; 23:6361045. [PMID: 34471925 PMCID: PMC8769701 DOI: 10.1093/bib/bbab343] [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: 05/04/2021] [Revised: 08/02/2021] [Accepted: 08/03/2021] [Indexed: 12/20/2022] Open
Abstract
It is becoming evident that holistic perspectives toward cancer are crucial in deciphering the overwhelming complexity of tumors. Single-layer analysis of genome-wide data has greatly contributed to our understanding of cellular systems and their perturbations. However, fundamental gaps in our knowledge persist and hamper the design of effective interventions. It is becoming more apparent than ever, that cancer should not only be viewed as a disease of the genome but as a disease of the cellular system. Integrative multilayer approaches are emerging as vigorous assets in our endeavors to achieve systemic views on cancer biology. Herein, we provide a comprehensive review of the approaches, methods and technologies that can serve to achieve systemic perspectives of cancer. We start with genome-wide single-layer approaches of omics analyses of cellular systems and move on to multilayer integrative approaches in which in-depth descriptions of proteogenomics and network-based data analysis are provided. Proteogenomics is a remarkable example of how the integration of multiple levels of information can reduce our blind spots and increase the accuracy and reliability of our interpretations and network-based data analysis is a major approach for data interpretation and a robust scaffold for data integration and modeling. Overall, this review aims to increase cross-field awareness of the approaches and challenges regarding the omics-based study of cancer and to facilitate the necessary shift toward holistic approaches.
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Affiliation(s)
| | | | | | - Mehdi Sadeghi
- Department of Cell & Molecular Biology, Semnan University, Semnan, Iran
| | - Ulf Schmitz
- Department of Molecular & Cell Biology, James Cook University, Townsville, QLD 4811, Australia
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Giles HH, Hegde MR, Lyon E, Stanley CM, Kerr ID, Garlapow ME, Eggington JM. The Science and Art of Clinical Genetic Variant Classification and Its Impact on Test Accuracy. Annu Rev Genomics Hum Genet 2021; 22:285-307. [PMID: 33900788 DOI: 10.1146/annurev-genom-121620-082709] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Clinical genetic variant classification science is a growing subspecialty of clinical genetics and genomics. The field's continued improvement is essential for the success of precision medicine in both germline (hereditary) and somatic (oncology) contexts. This review focuses on variant classification for DNA next-generation sequencing tests. We first summarize current limitations in variant discovery and definition, and then describe the current five- and four-tier classification systems outlined in dominant standards and guideline publications for germline and somatic tests, respectively. We then discuss measures of variant classification discordance and the field's bias for positive results, as well as considerations for panel size and population screening in the context of estimates of positive predictive value thatincorporate estimated variant classification imperfections. Finally, we share opinions on the current state of variant classification from some of the authors of the most widely used standards and guideline publications and from other domain experts.
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Affiliation(s)
- Hunter H Giles
- Center for Genomic Interpretation, Sandy, Utah 84092, USA; , ,
| | - Madhuri R Hegde
- PerkinElmer Genomics, Waltham, Massachusetts 02450, USA; .,Department of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
| | - Elaine Lyon
- HudsonAlpha Clinical Services Lab, Huntsville, Alabama 35806, USA;
| | - Christine M Stanley
- C2i Genomics, Cambridge, Massachusetts 02139, USA.,Variantyx, Framingham, Massachusetts 01701, USA;
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Kısakol B, Sarıhan Ş, Ergün MA, Baysan M. Detailed evaluation of cancer sequencing pipelines in different microenvironments and heterogeneity levels. ACTA ACUST UNITED AC 2021; 45:114-126. [PMID: 33907494 PMCID: PMC8068765 DOI: 10.3906/biy-2008-8] [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: 08/06/2020] [Accepted: 02/03/2021] [Indexed: 11/25/2022]
Abstract
The importance of next generation sequencing (NGS) rises in cancer research as accessing this key technology becomes easier for researchers. The sequence data created by NGS technologies must be processed by various bioinformatics algorithms within a pipeline in order to convert raw data to meaningful information. Mapping and variant calling are the two main steps of these analysis pipelines, and many algorithms are available for these steps. Therefore, detailed benchmarking of these algorithms in different scenarios is crucial for the efficient utilization of sequencing technologies. In this study, we compared the performance of twelve pipelines (three mapping and four variant discovery algorithms) with recommended settings to capture single nucleotide variants. We observed significant discrepancy in variant calls among tested pipelines for different heterogeneity levels in real and simulated samples with overall high specificity and low sensitivity. Additional to the individual evaluation of pipelines, we also constructed and tested the performance of pipeline combinations. In these analyses, we observed that certain pipelines complement each other much better than others and display superior performance than individual pipelines. This suggests that adhering to a single pipeline is not optimal for cancer sequencing analysis and sample heterogeneity should be considered in algorithm optimization.
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Affiliation(s)
- Batuhan Kısakol
- Department of Physiology and Medical Physics, Centre for Systems Medicine, Royal College of Surgeons in Ireland, Dublin Ireland
| | - Şahin Sarıhan
- Computer Engineering Department, Faculty of Engineering, Marmara University, İstanbul, Turkey Turkey
| | - Mehmet Arif Ergün
- Computer Engineering Department, Faculty of Computer and Informatics Engineering, İstanbul Technical University,İstanbul Turkey
| | - Mehmet Baysan
- Computer Engineering Department, Faculty of Computer and Informatics Engineering, İstanbul Technical University,İstanbul Turkey
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38
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Next Generation Sequencing Technology in the Clinic and Its Challenges. Cancers (Basel) 2021; 13:cancers13081751. [PMID: 33916923 PMCID: PMC8067551 DOI: 10.3390/cancers13081751] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 03/30/2021] [Accepted: 04/05/2021] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Precise identification and annotation of mutations are of utmost importance in clinical oncology. Insights of the DNA sequence can provide meaningful knowledge to unravel the underlying genetics of disease. Hence, tailoring of personalized medicine often relies on specific genomic alteration for treatment efficacy. The aim of this review is to highlight that sequencing harbors much more than just four nucleotides. Moreover, the gradual transition from first to second generation sequencing technologies has led to awareness for choosing the most appropriate bioinformatic analytic tools based on the aim, quality and demand for a specific purpose. Thus, the same raw data can lead to various results reflecting the intrinsic features of different datamining pipelines. Abstract Data analysis has become a crucial aspect in clinical oncology to interpret output from next-generation sequencing-based testing. NGS being able to resolve billions of sequencing reactions in a few days has consequently increased the demand for tools to handle and analyze such large data sets. Many tools have been developed since the advent of NGS, featuring their own peculiarities. Increased awareness when interpreting alterations in the genome is therefore of utmost importance, as the same data using different tools can provide diverse outcomes. Hence, it is crucial to evaluate and validate bioinformatic pipelines in clinical settings. Moreover, personalized medicine implies treatment targeting efficacy of biological drugs for specific genomic alterations. Here, we focused on different sequencing technologies, features underlying the genome complexity, and bioinformatic tools that can impact the final annotation. Additionally, we discuss the clinical demand and design for implementing NGS.
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Nachmanson D, Steward J, Yao H, Officer A, Jeong E, O'Keefe TJ, Hasteh F, Jepsen K, Hirst GL, Esserman LJ, Borowsky AD, Harismendy O. Mutational profiling of micro-dissected pre-malignant lesions from archived specimens. BMC Med Genomics 2020; 13:173. [PMID: 33208147 PMCID: PMC7672910 DOI: 10.1186/s12920-020-00820-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 11/09/2020] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Systematic cancer screening has led to the increased detection of pre-malignant lesions (PMLs). The absence of reliable prognostic markers has led mostly to over treatment resulting in potentially unnecessary stress, or insufficient treatment and avoidable progression. Importantly, most mutational profiling studies have relied on PML synchronous to invasive cancer, or performed in patients without outcome information, hence limiting their utility for biomarker discovery. The limitations in comprehensive mutational profiling of PMLs are in large part due to the significant technical and methodological challenges: most PML specimens are small, fixed in formalin and paraffin embedded (FFPE) and lack matching normal DNA. METHODS Using test DNA from a highly degraded FFPE specimen, multiple targeted sequencing approaches were evaluated, varying DNA input amount (3-200 ng), library preparation strategy (BE: Blunt-End, SS: Single-Strand, AT: A-Tailing) and target size (whole exome vs. cancer gene panel). Variants in high-input DNA from FFPE and mirrored frozen specimens were used for PML-specific variant calling training and testing, respectively. The resulting approach was applied to profile and compare multiple regions micro-dissected (mean area 5 mm2) from 3 breast ductal carcinoma in situ (DCIS). RESULTS Using low-input FFPE DNA, BE and SS libraries resulted in 4.9 and 3.7 increase over AT libraries in the fraction of whole exome covered at 20x (BE:87%, SS:63%, AT:17%). Compared to high-confidence somatic mutations from frozen specimens, PML-specific variant filtering increased recall (BE:85%, SS:80%, AT:75%) and precision (BE:93%, SS:91%, AT:84%) to levels expected from sampling variation. Copy number alterations were consistent across all tested approaches and only impacted by the design of the capture probe-set. Applied to DNA extracted from 9 micro-dissected regions (8 PML, 1 normal epithelium), the approach achieved comparable performance, illustrated the data adequacy to identify candidate driver events (GATA3 mutations, ERBB2 or FGFR1 gains, TP53 loss) and measure intra-lesion genetic heterogeneity. CONCLUSION Alternate experimental and analytical strategies increased the accuracy of DNA sequencing from archived micro-dissected PML regions, supporting the deeper molecular characterization of early cancer lesions and achieving a critical milestone in the development of biology-informed prognostic markers and precision chemo-prevention strategies.
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Affiliation(s)
- Daniela Nachmanson
- Bioinformatics and Systems Biology Graduate Program - UC San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, USA
| | - Joseph Steward
- Moores Cancer Center - UC San Diego Health - 3855 Health Sciences Dr., La Jolla, CA, 92093, USA
| | - Huazhen Yao
- Institute for Genomic Medicine - UC San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, USA
| | - Adam Officer
- Bioinformatics and Systems Biology Graduate Program - UC San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, USA.,Division of Biomedical Informatics, Department of Medicine - UC San Diego School of Medicine, 9500 Gilman Dr., La Jolla, CA, 92093, USA
| | - Eliza Jeong
- Moores Cancer Center - UC San Diego Health - 3855 Health Sciences Dr., La Jolla, CA, 92093, USA
| | - Thomas J O'Keefe
- Division of Breast Surgery and The Comprehensive Breast Health Center - UC San Diego School of Medicine, 3855 Health Sciences Dr., La Jolla, CA, 92093, USA
| | - Farnaz Hasteh
- Department of Pathology - UC San Diego School of Medicine, 9500 Gilman Dr., La Jolla, CA, 92093, USA
| | - Kristen Jepsen
- Institute for Genomic Medicine - UC San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, USA
| | - Gillian L Hirst
- Helen Diller Family Comprehensive Cancer Center - UC San Francisco School of Medicine, 1450 3rd St, San Francisco, CA, 94158, USA
| | - Laura J Esserman
- Helen Diller Family Comprehensive Cancer Center - UC San Francisco School of Medicine, 1450 3rd St, San Francisco, CA, 94158, USA
| | - Alexander D Borowsky
- Department of Pathology and Laboratory Medicine - UC Davis Comprehensive Cancer Center, UC Davis School of Medicine, 2279 45th Street, Sacramento, CA, 95817, USA
| | - Olivier Harismendy
- Moores Cancer Center - UC San Diego Health - 3855 Health Sciences Dr., La Jolla, CA, 92093, USA. .,Division of Biomedical Informatics, Department of Medicine - UC San Diego School of Medicine, 9500 Gilman Dr., La Jolla, CA, 92093, USA.
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Pös Z, Pös O, Styk J, Mocova A, Strieskova L, Budis J, Kadasi L, Radvanszky J, Szemes T. Technical and Methodological Aspects of Cell-Free Nucleic Acids Analyzes. Int J Mol Sci 2020; 21:ijms21228634. [PMID: 33207777 PMCID: PMC7697251 DOI: 10.3390/ijms21228634] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 11/12/2020] [Accepted: 11/13/2020] [Indexed: 02/07/2023] Open
Abstract
Analyzes of cell-free nucleic acids (cfNAs) have shown huge potential in many biomedical applications, gradually entering several fields of research and everyday clinical care. Many biological properties of cfNAs can be informative to gain deeper insights into the function of the organism, such as their different types (DNA, RNAs) and subtypes (gDNA, mtDNA, bacterial DNA, miRNAs, etc.), forms (naked or vesicle bound NAs), fragmentation profiles, sequence composition, epigenetic modifications, and many others. On the other hand, the workflows of their analyzes comprise many important steps, from sample collection, storage and transportation, through extraction and laboratory analysis, up to bioinformatic analyzes and statistical evaluations, where each of these steps has the potential to affect the outcome and informational value of the performed analyzes. There are, however, no universal or standard protocols on how to exactly proceed when analyzing different cfNAs for different applications, at least according to our best knowledge. We decided therefore to prepare an overview of the available literature and products commercialized for cfNAs processing, in an attempt to summarize the benefits and limitations of the currently available approaches, devices, consumables, and protocols, together with various factors influencing the workflow, its processes, and outcomes.
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Affiliation(s)
- Zuzana Pös
- Institute of Clinical and Translational Research, Biomedical Research Center, Slovak Academy of Sciences, 845 05 Bratislava, Slovakia; (Z.P.); (A.M.); (L.K.)
- Department of Molecular Biology, Faculty of Natural Sciences, Comenius University, 841 04 Bratislava, Slovakia;
- Geneton Ltd., 841 04 Bratislava, Slovakia; (L.S.); (J.B.)
| | - Ondrej Pös
- Department of Molecular Biology, Faculty of Natural Sciences, Comenius University, 841 04 Bratislava, Slovakia;
- Geneton Ltd., 841 04 Bratislava, Slovakia; (L.S.); (J.B.)
- Comenius University Science Park, Comenius University, 841 04 Bratislava, Slovakia;
| | - Jakub Styk
- Comenius University Science Park, Comenius University, 841 04 Bratislava, Slovakia;
- Faculty of Medicine, Institute of Medical Biology, Genetics and Clinical Genetics, 811 08 Bratislava, Slovakia
| | - Angelika Mocova
- Institute of Clinical and Translational Research, Biomedical Research Center, Slovak Academy of Sciences, 845 05 Bratislava, Slovakia; (Z.P.); (A.M.); (L.K.)
- Department of Molecular Biology, Faculty of Natural Sciences, Comenius University, 841 04 Bratislava, Slovakia;
| | | | - Jaroslav Budis
- Geneton Ltd., 841 04 Bratislava, Slovakia; (L.S.); (J.B.)
- Comenius University Science Park, Comenius University, 841 04 Bratislava, Slovakia;
- Slovak Center of Scientific and Technical Information, 811 04 Bratislava, Slovakia
| | - Ludevit Kadasi
- Institute of Clinical and Translational Research, Biomedical Research Center, Slovak Academy of Sciences, 845 05 Bratislava, Slovakia; (Z.P.); (A.M.); (L.K.)
- Department of Molecular Biology, Faculty of Natural Sciences, Comenius University, 841 04 Bratislava, Slovakia;
| | - Jan Radvanszky
- Institute of Clinical and Translational Research, Biomedical Research Center, Slovak Academy of Sciences, 845 05 Bratislava, Slovakia; (Z.P.); (A.M.); (L.K.)
- Department of Molecular Biology, Faculty of Natural Sciences, Comenius University, 841 04 Bratislava, Slovakia;
- Comenius University Science Park, Comenius University, 841 04 Bratislava, Slovakia;
- Correspondence: (J.R.); (T.S.); Tel.: +421-2-60296637 (J.R.); +421-2-9026-8807 (T.S.)
| | - Tomas Szemes
- Department of Molecular Biology, Faculty of Natural Sciences, Comenius University, 841 04 Bratislava, Slovakia;
- Geneton Ltd., 841 04 Bratislava, Slovakia; (L.S.); (J.B.)
- Comenius University Science Park, Comenius University, 841 04 Bratislava, Slovakia;
- Correspondence: (J.R.); (T.S.); Tel.: +421-2-60296637 (J.R.); +421-2-9026-8807 (T.S.)
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Gopanenko AV, Kosobokova EN, Kosorukov VS. Main Strategies for the Identification of Neoantigens. Cancers (Basel) 2020; 12:E2879. [PMID: 33036391 PMCID: PMC7600129 DOI: 10.3390/cancers12102879] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 10/01/2020] [Accepted: 10/05/2020] [Indexed: 12/24/2022] Open
Abstract
Genetic instability of tumors leads to the appearance of numerous tumor-specific somatic mutations that could potentially result in the production of mutated peptides that are presented on the cell surface by the MHC molecules. Peptides of this kind are commonly called neoantigens. Their presence on the cell surface specifically distinguishes tumors from healthy tissues. This feature makes neoantigens a promising target for immunotherapy. The rapid evolution of high-throughput genomics and proteomics makes it possible to implement these techniques in clinical practice. In particular, they provide useful tools for the investigation of neoantigens. The most valuable genomic approach to this problem is whole-exome sequencing coupled with RNA-seq. High-throughput mass-spectrometry is another option for direct identification of MHC-bound peptides, which is capable of revealing the entire MHC-bound peptidome. Finally, structure-based predictions could significantly improve the understanding of physicochemical and structural features that affect the immunogenicity of peptides. The development of pipelines combining such tools could improve the accuracy of the peptide selection process and decrease the required time. Here we present a review of the main existing approaches to investigating the neoantigens and suggest a possible ideal pipeline that takes into account all modern trends in the context of neoantigen discovery.
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Affiliation(s)
| | | | - Vyacheslav S. Kosorukov
- N.N. Blokhin National Medical Research Center of Oncology, Ministry of Health of the Russian Federation, 115478 Moscow, Russia; (A.V.G.); (E.N.K.)
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