1
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Feng B, Lai J, Fan X, Liu Y, Wang M, Wu P, Zhou Z, Yan Q, Sun L. Systematic comparison of variant calling pipelines of target genome sequencing cross multiple next-generation sequencers. Front Genet 2024; 14:1293974. [PMID: 38239851 PMCID: PMC10794554 DOI: 10.3389/fgene.2023.1293974] [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: 09/14/2023] [Accepted: 12/14/2023] [Indexed: 01/22/2024] Open
Abstract
Targeted genomic sequencing (TS) greatly benefits precision oncology by rapidly detecting genetic variations with better accuracy and sensitivity owing to its high sequencing depth. Multiple sequencing platforms and variant calling tools are available for TS, making it excruciating for researchers to choose. Therefore, benchmarking study across different platforms and pipelines available for TS is imperative. In this study, we performed a TS of Reference OncoSpan FFPE (HD832) sample enriched by TSO500 panel using four commercially available sequencers, and analyzed the output 50 datasets using five commonly-used bioinformatics pipelines. We systematically investigated the sequencing quality and variant detection sensitivity, expecting to provide optimal recommendations for future research. Four sequencing platforms returned highly concordant results in terms of base quality (Q20 > 94%), sequencing coverage (>97%) and depth (>2000×). Benchmarking revealed good concordance of variant calling across different platforms and pipelines, among which, FASTASeq 300 platform showed the highest sensitivity (100%) and precision (100%) in high-confidence variants calling when analyzed by SNVer and VarScan 2 algorithms. Furthermore, this sequencer demonstrated the shortest sequencing time (∼21 h) at the sequencing mode PE150. Through the intersection of 50 datasets generated in this study, we recommended a novel set of variant genes outside the truth set published by HD832, expecting to replenish HD832 for future research on tumor variant diagnosis. Besides, we applied these five tools to another panel (TargetSeq One) for Twist cfDNA Pan-cancer Reference Standard, comprehensive consideration of SNP and InDel sensitivity, SNVer and VarScan 2 performed best among them. Furthermore, SNVer and VarScan 2 also performed best for six cancer cell lines samples regarding SNP and InDel sensitivity. Considering the dissimilarity of variant calls across different pipelines for datasets from the same platform, we recommended an integration of multiple tools to improve variant calling sensitivity and accuracy for the cancer genome. Illumina and GeneMind technologies can be used independently or together by public health laboratories performing tumor TS. SNVer and VarScan 2 perform better regarding variant detection sensitivity for three typical tumor samples. Our study provides a standardized target sequencing resource to benchmark new bioinformatics protocols and sequencing platforms.
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Affiliation(s)
- Baosheng Feng
- GeneMind Biosciences Company Limited, Shenzhen, China
| | - Juan Lai
- GeneMind Biosciences Company Limited, Shenzhen, China
| | - Xue Fan
- Clinical Research Institute, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yongfeng Liu
- GeneMind Biosciences Company Limited, Shenzhen, China
| | - Miao Wang
- GeneMind Biosciences Company Limited, Shenzhen, China
| | - Ping Wu
- GeneMind Biosciences Company Limited, Shenzhen, China
| | - Zhiliang Zhou
- GeneMind Biosciences Company Limited, Shenzhen, China
| | - Qin Yan
- GeneMind Biosciences Company Limited, Shenzhen, China
| | - Lei Sun
- GeneMind Biosciences Company Limited, Shenzhen, China
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2
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Beeler JS, Bolton KL. How low can you go?: Methodologic considerations in clonal hematopoiesis variant calling. Leuk Res 2023; 135:107419. [PMID: 37956474 DOI: 10.1016/j.leukres.2023.107419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 10/25/2023] [Accepted: 10/30/2023] [Indexed: 11/15/2023]
Abstract
Clonal hematopoiesis (CH) is defined by the presence of an expanded clonal hematopoietic cell population due to an acquired mutation conferring a selective growth advantage and is known to predispose to hematologic malignancy. In this review, we discuss sequencing methods for CH detection in bulk sequencing data and corresponding bioinformatic approaches for variant calling, filtering, and curation. We detail practical recommendations for CH calling. Finally, we discuss how improvements in CH sequencing and bioinformatic approaches will enable the characterization of CH trajectories, its impact on human health, and therapeutic approaches to mitigate its adverse effects.
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Affiliation(s)
- J Scott Beeler
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Kelly L Bolton
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA.
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3
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Xiang X, Lu B, Song D, Li J, Shu K, Pu D. Evaluating the performance of low-frequency variant calling tools for the detection of variants from short-read deep sequencing data. Sci Rep 2023; 13:20444. [PMID: 37993475 PMCID: PMC10665316 DOI: 10.1038/s41598-023-47135-3] [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: 08/11/2023] [Accepted: 11/09/2023] [Indexed: 11/24/2023] Open
Abstract
Detection of low-frequency variants with high accuracy plays an important role in biomedical research and clinical practice. However, it is challenging to do so with next-generation sequencing (NGS) approaches due to the high error rates of NGS. To accurately distinguish low-level true variants from these errors, many statistical variants calling tools for calling low-frequency variants have been proposed, but a systematic performance comparison of these tools has not yet been performed. Here, we evaluated four raw-reads-based variant callers (SiNVICT, outLyzer, Pisces, and LoFreq) and four UMI-based variant callers (DeepSNVMiner, MAGERI, smCounter2, and UMI-VarCal) considering their capability to call single nucleotide variants (SNVs) with allelic frequency as low as 0.025% in deep sequencing data. We analyzed a total of 54 simulated data with various sequencing depths and variant allele frequencies (VAFs), two reference data, and Horizon Tru-Q sample data. The results showed that the UMI-based callers, except smCounter2, outperformed the raw-reads-based callers regarding detection limit. Sequencing depth had almost no effect on the UMI-based callers but significantly influenced on the raw-reads-based callers. Regardless of the sequencing depth, MAGERI showed the fastest analysis, while smCounter2 consistently took the longest to finish the variant calling process. Overall, DeepSNVMiner and UMI-VarCal performed the best with considerably good sensitivity and precision of 88%, 100%, and 84%, 100%, respectively. In conclusion, the UMI-based callers, except smCounter2, outperformed the raw-reads-based callers in terms of sensitivity and precision. We recommend using DeepSNVMiner and UMI-VarCal for low-frequency variant detection. The results provide important information regarding future directions for reliable low-frequency variant detection and algorithm development, which is critical in genetics-based medical research and clinical applications.
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Affiliation(s)
- Xudong Xiang
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
| | - Bowen Lu
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
| | - Dongyang Song
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
| | - Jie Li
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
| | - Kunxian Shu
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
| | - Dan Pu
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
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4
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Xie S, Isaacs K, Becker G, Murdoch BM. A computational framework for improving genetic variants identification from 5,061 sheep sequencing data. J Anim Sci Biotechnol 2023; 14:127. [PMID: 37779189 PMCID: PMC10544426 DOI: 10.1186/s40104-023-00923-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Accepted: 08/01/2023] [Indexed: 10/03/2023] Open
Abstract
BACKGROUND Pan-genomics is a recently emerging strategy that can be utilized to provide a more comprehensive characterization of genetic variation. Joint calling is routinely used to combine identified variants across multiple related samples. However, the improvement of variants identification using the mutual support information from multiple samples remains quite limited for population-scale genotyping. RESULTS In this study, we developed a computational framework for joint calling genetic variants from 5,061 sheep by incorporating the sequencing error and optimizing mutual support information from multiple samples' data. The variants were accurately identified from multiple samples by using four steps: (1) Probabilities of variants from two widely used algorithms, GATK and Freebayes, were calculated by Poisson model incorporating base sequencing error potential; (2) The variants with high mapping quality or consistently identified from at least two samples by GATK and Freebayes were used to construct the raw high-confidence identification (rHID) variants database; (3) The high confidence variants identified in single sample were ordered by probability value and controlled by false discovery rate (FDR) using rHID database; (4) To avoid the elimination of potentially true variants from rHID database, the variants that failed FDR were reexamined to rescued potential true variants and ensured high accurate identification variants. The results indicated that the percent of concordant SNPs and Indels from Freebayes and GATK after our new method were significantly improved 12%-32% compared with raw variants and advantageously found low frequency variants of individual sheep involved several traits including nipples number (GPC5), scrapie pathology (PAPSS2), seasonal reproduction and litter size (GRM1), coat color (RAB27A), and lentivirus susceptibility (TMEM154). CONCLUSION The new method used the computational strategy to reduce the number of false positives, and simultaneously improve the identification of genetic variants. This strategy did not incur any extra cost by using any additional samples or sequencing data information and advantageously identified rare variants which can be important for practical applications of animal breeding.
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Affiliation(s)
- Shangqian Xie
- Department of Animal, Veterinary & Food Sciences, University of Idaho, Moscow, ID, USA
| | | | - Gabrielle Becker
- Department of Animal, Veterinary & Food Sciences, University of Idaho, Moscow, ID, USA
| | - Brenda M Murdoch
- Department of Animal, Veterinary & Food Sciences, University of Idaho, Moscow, ID, USA.
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5
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Matsas A, Stefanoudakis D, Troupis T, Kontzoglou K, Eleftheriades M, Christopoulos P, Panoskaltsis T, Stamoula E, Iliopoulos DC. Tumor Markers and Their Diagnostic Significance in Ovarian Cancer. Life (Basel) 2023; 13:1689. [PMID: 37629546 PMCID: PMC10455076 DOI: 10.3390/life13081689] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 07/27/2023] [Accepted: 08/03/2023] [Indexed: 08/27/2023] Open
Abstract
Ovarian cancer (OC) is characterized by silent progression and late-stage diagnosis. It is critical to detect and accurately diagnose the disease early to improve survival rates. Tumor markers have emerged as valuable tools in the diagnosis and management of OC, offering non-invasive and cost-effective options for screening, monitoring, and prognosis. PURPOSE This paper explores the diagnostic importance of various tumor markers including CA-125, CA15-3, CA 19-9, HE4,hCG, inhibin, AFP, and LDH, and their impact on disease monitoring and treatment response assessment. METHODS Article searches were performed on PubMed, Scopus, and Google Scholar. Keywords used for the searching process were "Ovarian cancer", "Cancer biomarkers", "Early detection", "Cancer diagnosis", "CA-125","CA 15-3","CA 19-9", "HE4","hCG", "inhibin", "AFP", "LDH", and others. RESULTS HE4, when combined with CA-125, shows improved sensitivity and specificity, particularly in early-stage detection. Additionally, hCG holds promise as a prognostic marker, aiding treatment response prediction and outcome assessment. Novel markers like microRNAs, DNA methylation patterns, and circulating tumor cells offer potential for enhanced diagnostic accuracy and personalized management. Integrating these markers into a comprehensive panel may improve sensitivity and specificity in ovarian cancer diagnosis. However, careful interpretation of tumor marker results is necessary, considering factors such as age, menopausal status, and comorbidities. Further research is needed to validate and refine diagnostic algorithms, optimizing the clinical significance of tumor markers in ovarian cancer management. In conclusion, tumor markers such as CA-125, CA15-3, CA 19-9, HE4, and hCG provide valuable insights into ovarian cancer diagnosis, monitoring, and prognosis, with the potential to enhance early detection.
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Affiliation(s)
- Alkis Matsas
- Laboratory of Experimental Surgery and Surgical Research ‘N.S. Christeas’, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - Dimitrios Stefanoudakis
- Second Department of Obstetrics and Gynecology, Medical School, “Aretaieion” University Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - Theodore Troupis
- Department of Anatomy, Faculty of Health Sciences, Medical School, National and Kapodistrian University of Athens, MikrasAsias Str. 75, 11627 Athens, Greece
| | - Konstantinos Kontzoglou
- Laboratory of Experimental Surgery and Surgical Research ‘N.S. Christeas’, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - Makarios Eleftheriades
- Second Department of Obstetrics and Gynecology, Medical School, “Aretaieion” University Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - Panagiotis Christopoulos
- Second Department of Obstetrics and Gynecology, Medical School, “Aretaieion” University Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - Theodoros Panoskaltsis
- Second Department of Obstetrics and Gynecology, Medical School, “Aretaieion” University Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - Eleni Stamoula
- Department of Clinical Pharmacology, School of Medicine, Aristotle University of Thessaloniki, University Campus Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Dimitrios C. Iliopoulos
- Laboratory of Experimental Surgery and Surgical Research ‘N.S. Christeas’, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece
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6
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Brandt A, Thiele B, Schultheiß C, Daetwyler E, Binder M. Circulating Tumor DNA in Head and Neck Squamous Cell Carcinoma. Cancers (Basel) 2023; 15:cancers15072051. [PMID: 37046721 PMCID: PMC10093741 DOI: 10.3390/cancers15072051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 03/21/2023] [Accepted: 03/28/2023] [Indexed: 03/31/2023] Open
Abstract
Tumors shed cell-free DNA (cfDNA) into the plasma. “Liquid biopsies” are a diagnostic test to analyze cfDNA in order to detect minimal residual cancer, profile the genomic tumor landscape, and monitor cancers non-invasively over time. This technique may be useful in patients with head and neck squamous cell carcinoma (HNSCC) due to genetic tumor heterogeneity and limitations in imaging sensitivity. However, there are technical challenges that need to be overcome for the widespread use of liquid biopsy in the clinical management of these patients. In this review, we discuss our current understanding of HNSCC genetics and the role of cfDNA genomic analyses as an emerging precision diagnostic tool.
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Affiliation(s)
- Anna Brandt
- Department of Internal Medicine 5, Hematology and Oncology, University Hospital of Erlangen, 91054 Erlangen, Germany
| | - Benjamin Thiele
- Department of Oncology, Hematology and Bone Marrow Transplantation with Section of Pneumology, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Christoph Schultheiß
- Internal Medicine IV, Oncology/Hematology, Martin-Luther-University Halle-Wittenberg, Ernst-Grube-Straße 40, 06120 Halle (Saale), Germany
| | - Eveline Daetwyler
- Division of Medical Oncology, University Hospital Basel, 4031 Basel, Switzerland
| | - Mascha Binder
- Internal Medicine IV, Oncology/Hematology, Martin-Luther-University Halle-Wittenberg, Ernst-Grube-Straße 40, 06120 Halle (Saale), Germany
- Division of Medical Oncology, University Hospital Basel, 4031 Basel, Switzerland
- Correspondence: ; Tel.: +41-612-655-074; Fax: +41-612-655-316
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7
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Ahmad E, Ali A, Nimisha, Kumar Sharma A, Ahmed F, Mehdi Dar G, Mohan Singh A, Apurva, Kumar A, Athar A, Parveen F, Mahajan B, Singh Saluja S. Molecular approaches in cancer. Clin Chim Acta 2022; 537:60-73. [DOI: https:/doi.org/10.1016/j.cca.2022.09.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/28/2023]
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8
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Ahmad E, Ali A, Nimisha, Kumar Sharma A, Ahmed F, Mehdi Dar G, Mohan Singh A, Apurva, Kumar A, Athar A, Parveen F, Mahajan B, Singh Saluja S. Molecular approaches in cancer. Clin Chim Acta 2022; 537:60-73. [DOI: 10.1016/j.cca.2022.09.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 09/28/2022] [Accepted: 09/28/2022] [Indexed: 11/03/2022]
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9
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Zheng T. TLsub: A transfer learning based enhancement to accurately detect mutations with wide-spectrum sub-clonal proportion. Front Genet 2022; 13:981269. [DOI: 10.3389/fgene.2022.981269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 10/17/2022] [Indexed: 11/23/2022] Open
Abstract
Mutation detecting is a routine work for sequencing data analysis and the trading of existing tools often involves the combinations of signals on a set of overlapped sequencing reads. However, the subclonal mutations, which are reported to contribute to tumor recurrence and metastasis, are sometimes eliminated by existing signals. When the clonal proportion decreases, signals often present ambiguous, while complicated interactions among signals break the IID assumption for most of the machine learning models. Although the mutation callers could lower the thresholds, false positives are significantly introduced. The main aim here was to detect the subclonal mutations with high specificity from the scenario of ambiguous sample purities or clonal proportions. We proposed a novel machine learning approach for filtering false positive calls to accurately detect mutations with wide spectrum subclonal proportion. We have carried out a series of experiments on both simulated and real datasets, and compared to several state-of-art approaches, including freebayes, MuTect2, Sentieon and SiNVICT. The results demonstrated that the proposed method adapts well to different diluted sequencing signals and can significantly reduce the false positive when detecting subclonal mutations. The codes have been uploaded at https://github.com/TrinaZ/TL-fpFilter for academic usage only.
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10
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Shah UJ, Alsulimani A, Ahmad F, Mathkor DM, Alsaieedi A, Harakeh S, Nasiruddin M, Haque S. Bioplatforms in liquid biopsy: advances in the techniques for isolation, characterization and clinical applications. Biotechnol Genet Eng Rev 2022; 38:339-383. [PMID: 35968863 DOI: 10.1080/02648725.2022.2108994] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Tissue biopsy analysis has conventionally been the gold standard for cancer prognosis, diagnosis and prediction of responses/resistances to treatments. The existing biopsy procedures used in clinical practice are, however, invasive, painful and often associated with pitfalls like poor recovery of tumor cells and infeasibility for repetition in single patients. To circumvent these limitations, alternative non-invasive, rapid and economical, yet sturdy, consistent and dependable, biopsy techniques are required. Liquid biopsy is an emerging technology that fulfills these criteria and potentially much more in terms of subject-specific real-time monitoring of cancer progression, determination of tumor heterogeneity and treatment responses, and specific identification of the type and stages of cancers. The present review first briefly revisits the state-of-the-art technique of liquid biopsy and then proceeds to address in detail, the advances in the potential clinical applications of four major biological agencies present in liquid biopsy samples (circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), exosomes and tumor-educated platelets (TEPs)). Finally, the authors conclude with the limitations that need to be addressed in order for liquid biopsy to effectively replace the conventional invasive biopsy methods in the clinical settings.
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Affiliation(s)
- Ushma Jaykamal Shah
- MedGenome Labs Ltd, Kailash Cancer Hospital and Research Center, Vadodara, India
| | - Ahmad Alsulimani
- Medical Laboratory Technology Department, College of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia
| | - Faraz Ahmad
- Department of Biotechnology, School of Bio Sciences and Technology (SBST), Vellore Institute of Technology, Vellore, India
| | - Darin Mansor Mathkor
- Research and Scientific Studies Unit, College of Nursing and Allied Health Sciences, Jazan University, Jazan, Saudi Arabia
| | - Ahdab Alsaieedi
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia.,Vaccines and Immunotherapy Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Steve Harakeh
- King Fahd Medical Research Center, and Yousef Abdullatif Jameel Chair of Prophetic Medicine Application, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Mohammad Nasiruddin
- MedGenome Labs Ltd, Narayana Health City, Bangalore, India.,Genomics Lab, Orbito Asia Diagnostics, Coimbatore, India
| | - Shafiul Haque
- Research and Scientific Studies Unit, College of Nursing and Allied Health Sciences, Jazan University, Jazan, Saudi Arabia
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11
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Zheng T. DETexT: An SNV detection enhancement for low read depth by integrating mutational signatures into TextCNN. Front Genet 2022; 13:943972. [PMID: 36246660 PMCID: PMC9554618 DOI: 10.3389/fgene.2022.943972] [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: 05/14/2022] [Accepted: 09/06/2022] [Indexed: 12/01/2022] Open
Abstract
Detecting SNV at very low read depths helps to reduce sequencing requirements, lowers sequencing costs, and aids in the early screening, diagnosis, and treatment of cancer. However, the accuracy of SNV detection is significantly reduced at read depths below ×34 due to the lack of a sufficient number of read pairs to help filter out false positives. Many recent studies have revealed the potential of mutational signature (MS) in detecting true SNV, understanding the mutational processes that lead to the development of human cancers, and analyzing the endogenous and exogenous causes. Here, we present DETexT, an SNV detection method better suited to low read depths, which classifies false positive variants by combining MS with deep learning algorithms to mine correlation information around bases in individual reads without relying on the support of duplicate read pairs. We have validated the effectiveness of DETexT on simulated and real datasets and conducted comparative experiments. The source code has been uploaded to https://github.com/TrinaZ/extra-lowRD for academic use only.
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Affiliation(s)
- Tian Zheng
- Department of Computer Science and Technology, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, China
- Institute of Data Science and Information Quality, Shaanxi Engineering Research Center of Medical and Health Big Data, Xi’an Jiaotong University, Xi’an, China
- *Correspondence: Tian Zheng,
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12
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Sater V, Viailly PJ, Lecroq T, Prieur-Gaston É, Bohers É, Viennot M, Ruminy P, Dauchel H, Vera P, Jardin F. UMI-Varcal: A Low-Frequency Variant Caller for UMI-Tagged Paired-End Sequencing Data. Methods Mol Biol 2022; 2493:235-245. [PMID: 35751818 DOI: 10.1007/978-1-0716-2293-3_14] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The rapid transition from traditional sequencing methods to Next-Generation Sequencing (NGS) has allowed for a faster and more accurate detection of somatic variants (Single-Nucleotide Variant (SNV) and Copy Number Variation (CNV)) in tumor cells. NGS technologies require a succession of steps during which false variants can be silently added at low frequencies. Filtering these artifacts can be a rather difficult task especially when the experiments are designed to look for very low frequency variants. Recently, adding unique molecular barcodes called UMI (Unique Molecular Identifier) to the DNA fragments appears to be a very effective strategy to specifically filter out false variants from the variant calling results (Kukita et al. DNA Res 22(4):269-277, 2015; Newman et al. Nat Biotechnol 34(5):547-555, 2016; Schmitt et al. Proc Natl Acad Sci U S A 109(36):14508-14513). Here, we describe UMI-VarCal (Sater et al. Bioinformatics 36:2718-2724, 2020), which can use the UMI information from UMI-tagged reads to offer a faster and more accurate variant calling analysis.
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Affiliation(s)
- Vincent Sater
- Normandie Univ, UNIROUEN, LITIS EA 4108, Rouen, France.
| | - Pierre-Julien Viailly
- Department of Pathology, Centre Henri Becquerel, Rouen, France
- INSERM U1245, University of Normandie UNIROUEN, Rouen, France
| | | | | | - Élodie Bohers
- Department of Pathology, Centre Henri Becquerel, Rouen, France
- INSERM U1245, University of Normandie UNIROUEN, Rouen, France
| | - Mathieu Viennot
- Department of Pathology, Centre Henri Becquerel, Rouen, France
- INSERM U1245, University of Normandie UNIROUEN, Rouen, France
| | - Philippe Ruminy
- Department of Pathology, Centre Henri Becquerel, Rouen, France
- INSERM U1245, University of Normandie UNIROUEN, Rouen, France
| | | | - Pierre Vera
- Normandie Univ, UNIROUEN, LITIS EA 4108, Rouen, France
- Department of Pathology, Centre Henri Becquerel, Rouen, France
| | - Fabrice Jardin
- Department of Pathology, Centre Henri Becquerel, Rouen, France
- INSERM U1245, University of Normandie UNIROUEN, Rouen, France
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13
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Łukasiewicz M, Pastuszak K, Łapińska-Szumczyk S, Różański R, Veld SGJGI‘, Bieńkowski M, Stokowy T, Ratajska M, Best MG, Würdinger T, Żaczek AJ, Supernat A, Jassem J. Diagnostic Accuracy of Liquid Biopsy in Endometrial Cancer. Cancers (Basel) 2021; 13:5731. [PMID: 34830891 PMCID: PMC8616122 DOI: 10.3390/cancers13225731] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 11/09/2021] [Accepted: 11/11/2021] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Liquid biopsy is a minimally invasive collection of a patient body fluid sample. In oncology, they offer several advantages compared to traditional tissue biopsies. However, the potential of this method in endometrial cancer (EC) remains poorly explored. We studied the utility of tumor educated platelets (TEPs) and circulating tumor DNA (ctDNA) for preoperative EC diagnosis, including histology determination. METHODS TEPs from 295 subjects (53 EC patients, 38 patients with benign gynecologic conditions, and 204 healthy women) were RNA-sequenced. DNA sequencing data were obtained for 519 primary tumor tissues and 16 plasma samples. Artificial intelligence was applied to sample classification. RESULTS Platelet-dedicated classifier yielded AUC of 97.5% in the test set when discriminating between healthy subjects and cancer patients. However, the discrimination between endometrial cancer and benign gynecologic conditions was more challenging, with AUC of 84.1%. ctDNA-dedicated classifier discriminated primary tumor tissue samples with AUC of 96% and ctDNA blood samples with AUC of 69.8%. CONCLUSIONS Liquid biopsies show potential in EC diagnosis. Both TEPs and ctDNA profiles coupled with artificial intelligence constitute a source of useful information. Further work involving more cases is warranted.
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Affiliation(s)
- Marta Łukasiewicz
- Laboratory of Translational Oncology, Intercollegiate Faculty of Biotechnology, Medical University of Gdańsk, 80-211 Gdańsk, Poland; (M.Ł.); (K.P.); (A.J.Ż.)
| | - Krzysztof Pastuszak
- Laboratory of Translational Oncology, Intercollegiate Faculty of Biotechnology, Medical University of Gdańsk, 80-211 Gdańsk, Poland; (M.Ł.); (K.P.); (A.J.Ż.)
- Department of Algorithms and Systems Modelling, Faculty of Electronics, Telecommunication and Informatics, Gdańsk University of Technology, 80-233 Gdańsk, Poland
| | - Sylwia Łapińska-Szumczyk
- Department of Gynecology, Gyneacological Oncology and Gynecological Endocrinology, Medical University of Gdańsk, 80-211 Gdańsk, Poland; (S.Ł.-S.); (R.R.)
| | - Robert Różański
- Department of Gynecology, Gyneacological Oncology and Gynecological Endocrinology, Medical University of Gdańsk, 80-211 Gdańsk, Poland; (S.Ł.-S.); (R.R.)
| | - Sjors G. J. G. In ‘t Veld
- Department of Neurosurgery, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, 1081 HV Amsterdam, The Netherlands; (S.G.J.G.I.V.); (M.G.B.); (T.W.)
- Brain Tumor Center Amsterdam, Amsterdam University Medical Center, Vrije Universiteit Amsterdam Medical Center, Cancer Center Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Michał Bieńkowski
- Department of Pathomorphology, Medical University of Gdańsk, 80-211 Gdańsk, Poland;
| | - Tomasz Stokowy
- Department of Clinical Science, University of Bergen, 7800 Bergen, Norway;
- Centre of Biostatistics and Bioinformatics Analysis, Medical University of Gdańsk, 80-211 Gdańsk, Poland
| | - Magdalena Ratajska
- Department of Biology and Medical Genetics, Medical University of Gdańsk, 80-211 Gdańsk, Poland;
- Department of Pathology, University of Otago, Dunedin 9016, New Zealand
| | - Myron G. Best
- Department of Neurosurgery, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, 1081 HV Amsterdam, The Netherlands; (S.G.J.G.I.V.); (M.G.B.); (T.W.)
- Brain Tumor Center Amsterdam, Amsterdam University Medical Center, Vrije Universiteit Amsterdam Medical Center, Cancer Center Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Thomas Würdinger
- Department of Neurosurgery, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, 1081 HV Amsterdam, The Netherlands; (S.G.J.G.I.V.); (M.G.B.); (T.W.)
- Brain Tumor Center Amsterdam, Amsterdam University Medical Center, Vrije Universiteit Amsterdam Medical Center, Cancer Center Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Anna J. Żaczek
- Laboratory of Translational Oncology, Intercollegiate Faculty of Biotechnology, Medical University of Gdańsk, 80-211 Gdańsk, Poland; (M.Ł.); (K.P.); (A.J.Ż.)
| | - Anna Supernat
- Laboratory of Translational Oncology, Intercollegiate Faculty of Biotechnology, Medical University of Gdańsk, 80-211 Gdańsk, Poland; (M.Ł.); (K.P.); (A.J.Ż.)
| | - Jacek Jassem
- Department of Oncology and Radiotherapy, Medical University of Gdańsk, 80-211 Gdańsk, Poland;
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14
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Yin S, Xi R, Wu A, Wang S, Li Y, Wang C, Tang L, Xia Y, Yang D, Li J, Ye B, Yu Y, Wang J, Zhang H, Ren F, Zhang Y, Shen D, Wang L, Ying X, Li Z, Bu Z, Ji X, Gao X, Jia Y, Jia Z, Li N, Li Z, Ji JF, Xi JJ. Patient-derived tumor-like cell clusters for drug testing in cancer therapy. Sci Transl Med 2021; 12:12/549/eaaz1723. [PMID: 32581131 DOI: 10.1126/scitranslmed.aaz1723] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Revised: 12/17/2019] [Accepted: 03/17/2020] [Indexed: 02/06/2023]
Abstract
Several patient-derived tumor models emerged recently as robust preclinical drug-testing platforms. However, their potential to guide clinical therapy remained unclear. Here, we report a model called patient-derived tumor-like cell clusters (PTCs). PTCs result from the self-assembly and proliferation of primary epithelial, fibroblast, and immune cells, which structurally and functionally recapitulate original tumors. PTCs enabled us to accomplish personalized drug testing within 2 weeks after obtaining the tumor samples. The defined culture conditions and drug concentrations in the PTC model facilitate its clinical application in precision oncology. PTC tests of 59 patients with gastric, colorectal, or breast cancers revealed an overall accuracy of 93% in predicting their clinical outcomes. We implemented PTC to guide chemotherapy selection for a patient with mucinous rectal adenocarcinoma who experienced recurrence with metastases after conventional therapy. After three cycles of a nonconventional therapy identified by the PTC, the patient showed a positive response. These findings need to be validated in larger clinical trials, but they suggest that the PTC model could be prospectively implemented in clinical decision-making for therapy selection.
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Affiliation(s)
- Shenyi Yin
- State Key Laboratory of Natural and Biomimetic Drugs, Institute of Molecular Medicine, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing 100871, China
| | - Ruibin Xi
- School of Mathematical Sciences, Center for Statistical Science and Department of Biostatistics, Peking University, Beijing 100871, China
| | - Aiwen Wu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Gastrointestinal Surgery, Peking University Cancer Hospital and Institute, Beijing 100083, China
| | - Shu Wang
- Peking University People's Hospital, Beijing 100044, China
| | - Yingjie Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Gastrointestinal Surgery, Peking University Cancer Hospital and Institute, Beijing 100083, China
| | - Chaobin Wang
- Peking University People's Hospital, Beijing 100044, China
| | - Lei Tang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Gastrointestinal Surgery, Peking University Cancer Hospital and Institute, Beijing 100083, China
| | - Yuchao Xia
- School of Mathematical Sciences, Center for Statistical Science and Department of Biostatistics, Peking University, Beijing 100871, China
| | - Di Yang
- State Key Laboratory of Natural and Biomimetic Drugs, Institute of Molecular Medicine, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing 100871, China
| | - Juan Li
- State Key Laboratory of Natural and Biomimetic Drugs, Institute of Molecular Medicine, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing 100871, China
| | - Buqing Ye
- State Key Laboratory of Natural and Biomimetic Drugs, Institute of Molecular Medicine, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing 100871, China
| | - Ying Yu
- State Key Laboratory of Natural and Biomimetic Drugs, Institute of Molecular Medicine, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing 100871, China
| | - Junyi Wang
- State Key Laboratory of Natural and Biomimetic Drugs, Institute of Molecular Medicine, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing 100871, China
| | - Hanshuo Zhang
- State Key Laboratory of Natural and Biomimetic Drugs, Institute of Molecular Medicine, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing 100871, China.,GeneX Health Co. Ltd., Beijing 100195, China
| | - Fei Ren
- Peking University People's Hospital, Beijing 100044, China
| | - Yuanyuan Zhang
- Peking University People's Hospital, Beijing 100044, China
| | - Danhua Shen
- Peking University People's Hospital, Beijing 100044, China
| | - Lin Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Gastrointestinal Surgery, Peking University Cancer Hospital and Institute, Beijing 100083, China
| | - Xiangji Ying
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Gastrointestinal Surgery, Peking University Cancer Hospital and Institute, Beijing 100083, China
| | - Zhongwu Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Gastrointestinal Surgery, Peking University Cancer Hospital and Institute, Beijing 100083, China
| | - Zhaode Bu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Gastrointestinal Surgery, Peking University Cancer Hospital and Institute, Beijing 100083, China
| | - Xin Ji
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Gastrointestinal Surgery, Peking University Cancer Hospital and Institute, Beijing 100083, China
| | - Xiangyu Gao
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Gastrointestinal Surgery, Peking University Cancer Hospital and Institute, Beijing 100083, China
| | - Yongning Jia
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Gastrointestinal Surgery, Peking University Cancer Hospital and Institute, Beijing 100083, China
| | - Ziyu Jia
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Gastrointestinal Surgery, Peking University Cancer Hospital and Institute, Beijing 100083, China
| | - Nan Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Gastrointestinal Surgery, Peking University Cancer Hospital and Institute, Beijing 100083, China
| | - Ziyu Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Gastrointestinal Surgery, Peking University Cancer Hospital and Institute, Beijing 100083, China
| | - Jia-Fu Ji
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Gastrointestinal Surgery, Peking University Cancer Hospital and Institute, Beijing 100083, China.
| | - Jianzhong Jeff Xi
- State Key Laboratory of Natural and Biomimetic Drugs, Institute of Molecular Medicine, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing 100871, China.
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15
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Spatial Distribution of Private Gene Mutations in Clear Cell Renal Cell Carcinoma. Cancers (Basel) 2021; 13:cancers13092163. [PMID: 33946379 PMCID: PMC8124666 DOI: 10.3390/cancers13092163] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 04/02/2021] [Accepted: 04/27/2021] [Indexed: 12/15/2022] Open
Abstract
Simple Summary Tumours consist of multiple groups of similar cells resulting from differing evolutionary trajectories, i.e., subclones. These subclones are prevalent in clear cell renal cell carcinoma (ccRCC). The aim of this study is to determine how similar or dissimilar the subclones in 89 ccRCC tumours are from one another regarding their gene mutations and expression profiles, i.e., the extent of intra-tumour heterogeneity. The implications of these alterations with respect to signalling pathways is also assessed. Deep sequencing allows for the identification of mutations with low-allele frequencies, providing a more comprehensive view of the heterogeneity present in the tumours. With an average of 62% of mutations having been identified in only one of the two biopsies, some of which in turn are found to impact gene expression, the complex makeup of ccRCC tumours is evident, and this can drastically influence treatment outcome. Abstract Intra-tumour heterogeneity is the molecular hallmark of renal cancer, and the molecular tumour composition determines the treatment outcome of renal cancer patients. In renal cancer tumourigenesis, in general, different tumour clones evolve over time. We analysed intra-tumour heterogeneity and subclonal mutation patterns in 178 tumour samples obtained from 89 clear cell renal cell carcinoma patients. In an initial discovery phase, whole-exome and transcriptome sequencing data from paired tumour biopsies from 16 ccRCC patients were used to design a gene panel for follow-up analysis. In this second phase, 826 selected genes were targeted at deep coverage in an extended cohort of 89 patients for a detailed analysis of tumour heterogeneity. On average, we found 22 mutations per patient. Pairwise comparison of the two biopsies from the same tumour revealed that on average, 62% of the mutations in a patient were detected in one of the two samples. In addition to commonly mutated genes (VHL, PBRM1, SETD2 and BAP1), frequent subclonal mutations with low variant allele frequency (<10%) were observed in TP53 and in mucin coding genes MUC6, MUC16, and MUC3A. Of the 89 ccRCC tumours, 87 (~98%) harboured private mutations, occurring in only one of the paired tumour samples. Clonally exclusive pathway pairs were identified using the WES data set from 16 ccRCC patients. Our findings imply that shared and private mutations significantly contribute to the complexity of differential gene expression and pathway interaction and might explain the clonal evolution of different molecular renal cancer subgroups. Multi-regional sequencing is central for the identification of subclones within ccRCC.
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16
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Yang WY, Feng LF, Meng X, Chen R, Xu WH, Hou J, Xu T, Zhang L. Liquid biopsy in head and neck squamous cell carcinoma: circulating tumor cells, circulating tumor DNA, and exosomes. Expert Rev Mol Diagn 2020; 20:1213-1227. [PMID: 33232189 DOI: 10.1080/14737159.2020.1855977] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Introduction: Head and neck squamous cell carcinoma (HNSCC) is one of the most common cancers worldwide. Due to a lack of reliable markers, HNSCC patients are usually diagnosed at a late stage, which will lead to a worse outcome. Therefore, it is critical to improve the clinical management of cancer patients. Nowadays, the development of liquid biopsy enables a minimally invasive manner to extract molecular information from HNSCCs. Thus, this review aims to outline the clinical value of liquid biopsy in early detection, real-time monitoring, and prognostic evaluation of HNSCC. Areas covered: This comprehensive review focused on the characteristics as well as clinical applications of three liquid biopsy markers (CTCs, ctDNA, and exosomes) in HNSCC. What is more, it is promising to incorporate machine learning and 3D organoid models in the liquid biopsy of HNSCC. Expert opinion: Liquid biopsy provides a noninvasive technique to reflect the inter and intra-lesional heterogeneity through the detection of tumor cells or materials released from the primary and secondary tumors. Recently, some evolving technologies have the potential to combine with liquid biopsy to improve clinical management of HNSCC patients.
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Affiliation(s)
- Wen-Ying Yang
- College & Hospital of Stomatology, Anhui Medical University, Key Lab. Of Oral Diseases Research of Anhui Province , Hefei, 230032, China
| | - Lin-Fei Feng
- Department of Oral and Maxillofacial Surgery, The First Affiliated Hospital of Anhui Medical University , Hefei, 230032, China
| | - Xiang Meng
- College & Hospital of Stomatology, Anhui Medical University, Key Lab. Of Oral Diseases Research of Anhui Province , Hefei, 230032, China
| | - Ran Chen
- School of Stomatology, Anhui Medical University , Hefei, 230032, China
| | - Wen-Hua Xu
- College & Hospital of Stomatology, Anhui Medical University, Key Lab. Of Oral Diseases Research of Anhui Province , Hefei, 230032, China
| | - Jun Hou
- Department of Oral and Maxillofacial Surgery, The First Affiliated Hospital of Anhui Medical University , Hefei, 230032, China
| | - Tao Xu
- School of Pharmacy, Anhui Key Laboratory of Bioactivity of Natural Products, Anhui Medical University , Hefei, 230032, China.,Institute for Liver Diseases of Anhui Medical University, Anhui Medical University , Hefei, 230032, China
| | - Lei Zhang
- College & Hospital of Stomatology, Anhui Medical University, Key Lab. Of Oral Diseases Research of Anhui Province , Hefei, 230032, China.,Periodontal Department, Anhui Stomatology Hospital affiliated to Anhui Medical University , Hefei, 230032, China
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17
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Abelson S, Zeng AGX, Nofech-Mozes I, Wang TT, Ng SWK, Minden MD, Pugh TJ, Awadalla P, Shlush LI, Murphy T, Chan SM, Dick JE, Bratman SV. Integration of intra-sample contextual error modeling for improved detection of somatic mutations from deep sequencing. SCIENCE ADVANCES 2020; 6:6/50/eabe3722. [PMID: 33298453 PMCID: PMC7725472 DOI: 10.1126/sciadv.abe3722] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Accepted: 10/23/2020] [Indexed: 06/12/2023]
Abstract
Sensitive mutation detection by next-generation sequencing is critical for early cancer detection, monitoring minimal/measurable residual disease (MRD), and guiding precision oncology. Nevertheless, because of artifacts introduced during library preparation and sequencing, the detection of low-frequency variants at high specificity is problematic. Here, we present Espresso, an error suppression method that considers local sequence features to accurately detect single-nucleotide variants (SNVs). Compared to other advanced error suppression techniques, Espresso consistently demonstrated lower numbers of false-positive mutation calls and greater sensitivity. We demonstrated Espresso's superior performance in detecting MRD in the peripheral blood of patients with acute myeloid leukemia (AML) throughout their treatment course. Furthermore, we showed that accurate mutation calling in a small number of informative genomic loci might provide a cost-efficient strategy for pragmatic risk prediction of AML development in healthy individuals. More broadly, we aim for Espresso to aid with accurate mutation detection in many other research and clinical settings.
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Affiliation(s)
- Sagi Abelson
- Ontario Institute for Cancer Research, Toronto, ON, Canada.
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Andy G X Zeng
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Ido Nofech-Mozes
- Ontario Institute for Cancer Research, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Ting Ting Wang
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Stanley W K Ng
- Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, UK
| | - Mark D Minden
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Trevor J Pugh
- Ontario Institute for Cancer Research, Toronto, ON, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Philip Awadalla
- Ontario Institute for Cancer Research, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Liran I Shlush
- Division of Hematology, Rambam Healthcare Campus, Haifa, Israel
- Department of Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Tracy Murphy
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Steven M Chan
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - John E Dick
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Scott V Bratman
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
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18
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Sater V, Viailly PJ, Lecroq T, Prieur-Gaston É, Bohers É, Viennot M, Ruminy P, Dauchel H, Vera P, Jardin F. UMI-VarCal: a new UMI-based variant caller that efficiently improves low-frequency variant detection in paired-end sequencing NGS libraries. Bioinformatics 2020; 36:2718-2724. [PMID: 31985795 DOI: 10.1093/bioinformatics/btaa053] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Revised: 11/18/2019] [Accepted: 01/20/2020] [Indexed: 01/03/2023] Open
Abstract
MOTIVATION Next-generation sequencing has become the go-to standard method for the detection of single-nucleotide variants in tumor cells. The use of such technologies requires a PCR amplification step and a sequencing step, steps in which artifacts are introduced at very low frequencies. These artifacts are often confused with true low-frequency variants that can be found in tumor cells and cell-free DNA. The recent use of unique molecular identifiers (UMI) in targeted sequencing protocols has offered a trustworthy approach to filter out artefactual variants and accurately call low-frequency variants. However, the integration of UMI analysis in the variant calling process led to developing tools that are significantly slower and more memory consuming than raw-reads-based variant callers. RESULTS We present UMI-VarCal, a UMI-based variant caller for targeted sequencing data with better sensitivity compared to other variant callers. Being developed with performance in mind, UMI-VarCal stands out from the crowd by being one of the few variant callers that do not rely on SAMtools to do their pileup. Instead, at its core runs an innovative homemade pileup algorithm specifically designed to treat the UMI tags in the reads. After the pileup, a Poisson statistical test is applied at every position to determine if the frequency of the variant is significantly higher than the background error noise. Finally, an analysis of UMI tags is performed, a strand bias and a homopolymer length filter are applied to achieve better accuracy. We illustrate the results obtained using UMI-VarCal through the sequencing of tumor samples and we show how UMI-VarCal is both faster and more sensitive than other publicly available solutions. AVAILABILITY AND IMPLEMENTATION The entire pipeline is available at https://gitlab.com/vincent-sater/umi-varcal-master under MIT license. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Pierre-Julien Viailly
- Department of Pathology, Centre Henri Becquerel.,INSERM U1245, University of Normandie UNIROUEN, Rouen 76000, France
| | | | | | - Élodie Bohers
- Department of Pathology, Centre Henri Becquerel.,INSERM U1245, University of Normandie UNIROUEN, Rouen 76000, France
| | - Mathieu Viennot
- Department of Pathology, Centre Henri Becquerel.,INSERM U1245, University of Normandie UNIROUEN, Rouen 76000, France
| | - Philippe Ruminy
- Department of Pathology, Centre Henri Becquerel.,INSERM U1245, University of Normandie UNIROUEN, Rouen 76000, France
| | - Hélène Dauchel
- Department of Pathology, Centre Henri Becquerel.,INSERM U1245, University of Normandie UNIROUEN, Rouen 76000, France
| | - Pierre Vera
- University of Normandie UNIROUEN, LITIS EA 4108.,Department of Pathology, Centre Henri Becquerel
| | - Fabrice Jardin
- Department of Pathology, Centre Henri Becquerel.,INSERM U1245, University of Normandie UNIROUEN, Rouen 76000, France
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19
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Detection of genomic alterations in breast cancer with circulating tumour DNA sequencing. Sci Rep 2020; 10:16774. [PMID: 33033274 PMCID: PMC7544894 DOI: 10.1038/s41598-020-72818-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Accepted: 07/07/2020] [Indexed: 01/18/2023] Open
Abstract
Analysis of circulating cell-free DNA (cfDNA) has opened new opportunities for characterizing tumour mutational landscapes with many applications in genomic-driven oncology. We developed a customized targeted cfDNA sequencing approach for breast cancer (BC) using unique molecular identifiers (UMIs) for error correction. Our assay, spanning a 284.5 kb target region, is combined with a novel freely-licensed bioinformatics pipeline that provides detection of low-frequency variants, and reliable identification of copy number variations (CNVs) directly from plasma DNA. We first evaluated our pipeline on reference samples. Then in a cohort of 35 BC patients our approach detected actionable driver and clonal variants at low variant frequency levels in cfDNA that were concordant (77%) with sequencing of primary and/or metastatic solid tumour sites. We also detected ERRB2 gene CNVs used for HER2 subtype classification with 80% precision compared to immunohistochemistry. Further, we evaluated fragmentation profiles of cfDNA in BC and observed distinct differences compared to data from healthy individuals. Our results show that the developed assay addresses the majority of tumour associated aberrations directly from plasma DNA, and thus may be used to elucidate genomic alterations in liquid biopsy studies.
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20
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Sater V, Viailly PJ, Lecroq T, Ruminy P, Bérard C, Prieur-Gaston É, Jardin F. UMI-Gen: A UMI-based read simulator for variant calling evaluation in paired-end sequencing NGS libraries. Comput Struct Biotechnol J 2020; 18:2270-2280. [PMID: 32952940 PMCID: PMC7484502 DOI: 10.1016/j.csbj.2020.08.011] [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: 05/05/2020] [Revised: 08/03/2020] [Accepted: 08/05/2020] [Indexed: 11/02/2022] Open
Abstract
Motivation With Next Generation Sequencing becoming more affordable every year, NGS technologies asserted themselves as the fastest and most reliable way to detect Single Nucleotide Variants (SNV) and Copy Number Variations (CNV) in cancer patients. These technologies can be used to sequence DNA at very high depths thus allowing to detect abnormalities in tumor cells with very low frequencies. Multiple variant callers are publicly available and are usually efficient at calling out variants. However, when frequencies begin to drop under 1%, the specificity of these tools suffers greatly as true variants at very low frequencies can be easily confused with sequencing or PCR artifacts. The recent use of Unique Molecular Identifiers (UMI) in NGS experiments has offered a way to accurately separate true variants from artifacts. UMI-based variant callers are slowly replacing raw-read based variant callers as the standard method for an accurate detection of variants at very low frequencies. However, benchmarking done in the tools publication are usually realized on real biological data in which real variants are not known, making it difficult to assess their accuracy. Results We present UMI-Gen, a UMI-based read simulator for targeted sequencing paired-end data. UMI-Gen generates reference reads covering the targeted regions at a user customizable depth. After that, using a number of control files, it estimates the background error rate at each position and then modifies the generated reads to mimic real biological data. Finally, it will insert real variants in the reads from a list provided by the user. Availability The entire pipeline is available at https://gitlab.com/vincent-sater/umigen under MIT license.
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Affiliation(s)
- Vincent Sater
- University of Rouen Normandy UNIROUEN, LITIS EA 4108, 76000 Rouen, France.,INSERM U1245, University of Rouen Normandy UNIROUEN, 76000 Rouen, France
| | - Pierre-Julien Viailly
- Department of Pathology, Centre Henri Becquerel, 76000 Rouen, France.,INSERM U1245, University of Rouen Normandy UNIROUEN, 76000 Rouen, France
| | - Thierry Lecroq
- University of Rouen Normandy UNIROUEN, LITIS EA 4108, 76000 Rouen, France
| | - Philippe Ruminy
- Department of Pathology, Centre Henri Becquerel, 76000 Rouen, France.,INSERM U1245, University of Rouen Normandy UNIROUEN, 76000 Rouen, France
| | - Caroline Bérard
- University of Rouen Normandy UNIROUEN, LITIS EA 4108, 76000 Rouen, France
| | | | - Fabrice Jardin
- Department of Pathology, Centre Henri Becquerel, 76000 Rouen, France.,INSERM U1245, University of Rouen Normandy UNIROUEN, 76000 Rouen, France
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21
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Orabi B, Erhan E, McConeghy B, Volik SV, Le Bihan S, Bell R, Collins CC, Chauve C, Hach F. Alignment-free clustering of UMI tagged DNA molecules. Bioinformatics 2020; 35:1829-1836. [PMID: 30351359 DOI: 10.1093/bioinformatics/bty888] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Revised: 09/27/2018] [Accepted: 10/22/2018] [Indexed: 02/07/2023] Open
Abstract
MOTIVATION Next-Generation Sequencing has led to the availability of massive genomic datasets whose processing raises many challenges, including the handling of sequencing errors. This is especially pertinent in cancer genomics, e.g. for detecting low allele frequency variations from circulating tumor DNA. Barcode tagging of DNA molecules with unique molecular identifiers (UMI) attempts to mitigate sequencing errors; UMI tagged molecules are polymerase chain reaction (PCR) amplified, and the PCR copies of UMI tagged molecules are sequenced independently. However, the PCR and sequencing steps can generate errors in the sequenced reads that can be located in the barcode and/or the DNA sequence. Analyzing UMI tagged sequencing data requires an initial clustering step, with the aim of grouping reads sequenced from PCR duplicates of the same UMI tagged molecule into a single cluster, and the size of the current datasets requires this clustering process to be resource-efficient. RESULTS We introduce Calib, a computational tool that clusters paired-end reads from UMI tagged sequencing experiments generated by substitution-error-dominant sequencing platforms such as Illumina. Calib clusters are defined as connected components of a graph whose edges are defined in terms of both barcode similarity and read sequence similarity. The graph is constructed efficiently using locality sensitive hashing and MinHashing techniques. Calib's default clustering parameters are optimized empirically, for different UMI and read lengths, using a simulation module that is packaged with Calib. Compared to other tools, Calib has the best accuracy on simulated data, while maintaining reasonable runtime and memory footprint. On a real dataset, Calib runs with far less resources than alignment-based methods, and its clusters reduce the number of tentative false positive in downstream variation calling. AVAILABILITY AND IMPLEMENTATION Calib is implemented in C++ and its simulation module is implemented in Python. Calib is available at https://github.com/vpc-ccg/calib. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Baraa Orabi
- School of Computing Science, Faculty of Applied Sciences, Simon Fraser University, Burnaby BC, Canada
| | - Emre Erhan
- School of Computing Science, Faculty of Applied Sciences, Simon Fraser University, Burnaby BC, Canada
| | | | | | | | - Robert Bell
- Vancouver Prostate Centre, Vancouver BC, Canada
| | - Colin C Collins
- Vancouver Prostate Centre, Vancouver BC, Canada.,Department of Urologic Sciences, University of British Columbia, Vancouver BC, Canada
| | - Cedric Chauve
- Department of Mathematics, Simon Fraser University, Burnaby BC, Canada
| | - Faraz Hach
- Vancouver Prostate Centre, Vancouver BC, Canada.,Department of Urologic Sciences, University of British Columbia, Vancouver BC, Canada
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22
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Casiraghi N, Orlando F, Ciani Y, Xiang J, Sboner A, Elemento O, Attard G, Beltran H, Demichelis F, Romanel A. ABEMUS: platform-specific and data-informed detection of somatic SNVs in cfDNA. Bioinformatics 2020; 36:2665-2674. [PMID: 31922552 PMCID: PMC7203757 DOI: 10.1093/bioinformatics/btaa016] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 12/04/2019] [Accepted: 01/07/2020] [Indexed: 12/11/2022] Open
Abstract
MOTIVATION The use of liquid biopsies for cancer patients enables the non-invasive tracking of treatment response and tumor dynamics through single or serial blood drawn tests. Next-generation sequencing assays allow for the simultaneous interrogation of extended sets of somatic single-nucleotide variants (SNVs) in circulating cell-free DNA (cfDNA), a mixture of DNA molecules originating both from normal and tumor tissue cells. However, low circulating tumor DNA (ctDNA) fractions together with sequencing background noise and potential tumor heterogeneity challenge the ability to confidently call SNVs. RESULTS We present a computational methodology, called Adaptive Base Error Model in Ultra-deep Sequencing data (ABEMUS), which combines platform-specific genetic knowledge and empirical signal to readily detect and quantify somatic SNVs in cfDNA. We tested the capability of our method to analyze data generated using different platforms with distinct sequencing error properties and we compared ABEMUS performances with other popular SNV callers on both synthetic and real cancer patients sequencing data. Results show that ABEMUS performs better in most of the tested conditions proving its reliability in calling low variant allele frequencies somatic SNVs in low ctDNA levels plasma samples. AVAILABILITY AND IMPLEMENTATION ABEMUS is cross-platform and can be installed as R package. The source code is maintained on Github at http://github.com/cibiobcg/abemus, and it is also available at CRAN official R repository. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Nicola Casiraghi
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento 38123, Italy
| | - Francesco Orlando
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento 38123, Italy
| | - Yari Ciani
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento 38123, Italy
| | - Jenny Xiang
- Caryl and Israel Englander Institute for Precision Medicine, New York Presbyterian Hospital-Weill Cornell Medicine
- Genomics and Epigenomics Core Facility
| | - Andrea Sboner
- Caryl and Israel Englander Institute for Precision Medicine, New York Presbyterian Hospital-Weill Cornell Medicine
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Olivier Elemento
- Caryl and Israel Englander Institute for Precision Medicine, New York Presbyterian Hospital-Weill Cornell Medicine
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Gerhardt Attard
- UCL Cancer Institute, University College London, London WC1E 6BT, UK
| | - Himisha Beltran
- Caryl and Israel Englander Institute for Precision Medicine, New York Presbyterian Hospital-Weill Cornell Medicine
- Department of Medical Oncology, Dana Farber Cancer Institute, Boston, MA 02215, USA
- Department of Medicine, Division of Hematology and Medical Oncology, Weill Cornell Medicine, New York, NY 10021, USA
| | - Francesca Demichelis
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento 38123, Italy
- Caryl and Israel Englander Institute for Precision Medicine, New York Presbyterian Hospital-Weill Cornell Medicine
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Alessandro Romanel
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento 38123, Italy
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23
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Wu L, Deng Q, Xu Z, Zhou S, Li C, Li YX. A novel virtual barcode strategy for accurate panel-wide variant calling in circulating tumor DNA. BMC Bioinformatics 2020; 21:127. [PMID: 32245364 PMCID: PMC7118954 DOI: 10.1186/s12859-020-3412-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Accepted: 02/12/2020] [Indexed: 01/19/2023] Open
Abstract
Background Hybrid capture-based next-generation sequencing of DNA has been widely applied in the detection of circulating tumor DNA (ctDNA). Various methods have been proposed for ctDNA detection, but low-allelic-fraction (AF) variants are still a great challenge. In addition, no panel-wide calling algorithm is available, which hiders the full usage of ctDNA based ‘liquid biopsy’. Thus, we developed the VBCALAVD (Virtual Barcode-based Calling Algorithm for Low Allelic Variant Detection) in silico to overcome these limitations. Results Based on the understanding of the nature of ctDNA fragmentation, a novel platform-independent virtual barcode strategy was established to eliminate random sequencing errors by clustering sequencing reads into virtual families. Stereotypical mutant-family-level background artifacts were polished by constructing AF distributions. Three additional robust fine-tuning filters were obtained to eliminate stochastic mutant-family-level noises. The performance of our algorithm was validated using cell-free DNA reference standard samples (cfDNA RSDs) and normal healthy cfDNA samples (cfDNA controls). For the RSDs with AFs of 0.1, 0.2, 0.5, 1 and 5%, the mean F1 scores were 0.43 (0.25~0.56), 0.77, 0.92, 0.926 (0.86~1.0) and 0.89 (0.75~1.0), respectively, which indicates that the proposed approach significantly outperforms the published algorithms. Among controls, no false positives were detected. Meanwhile, characteristics of mutant-family-level noise and quantitative determinants of divergence between mutant-family-level noises from controls and RSDs were clearly depicted. Conclusions Due to its good performance in the detection of low-AF variants, our algorithm will greatly facilitate the noninvasive panel-wide detection of ctDNA in research and clinical settings. The whole pipeline is available at https://github.com/zhaodalv/VBCALAVD.
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Affiliation(s)
- Leilei Wu
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Qinfang Deng
- Department of Oncology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, 200433, China
| | - Ze Xu
- Smartquerier Biomedicine, Shanghai, 201203, China
| | - Songwen Zhou
- Department of Oncology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, 200433, China.
| | - Chao Li
- Smartquerier Biomedicine, Shanghai, 201203, China. .,Shanghai Center for Bioinformation Technology, Shanghai, 201203, China.
| | - Yi-Xue Li
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China. .,Shanghai Center for Bioinformation Technology, Shanghai, 201203, China. .,CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
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24
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Clonal Evolution and Heterogeneity of Osimertinib Acquired Resistance Mechanisms in EGFR Mutant Lung Cancer. CELL REPORTS MEDICINE 2020; 1. [PMID: 32483558 PMCID: PMC7263628 DOI: 10.1016/j.xcrm.2020.100007] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Clonal evolution of osimertinib-resistance mechanisms in EGFR mutant lung adenocarcinoma is poorly understood. Using multi-region whole-exome and RNA sequencing of prospectively collected pre- and post-osimertinib-resistant tumors, including at rapid autopsies, we identify a likely mechanism driving osimertinib resistance in all patients analyzed. The majority of patients acquire two or more resistance mechanisms either concurrently or in temporal sequence. Focal copy-number amplifications occur subclonally and are spatially and temporally separated from common resistance mutations such as EGFR C797S. MET amplification occurs in 66% (n = 6/9) of first-line osimertinib-treated patients, albeit spatially heterogeneous, often co-occurs with additional acquired focal copy-number amplifications and is associated with early progression. Noteworthy osimertinib-resistance mechanisms discovered include neuroendocrine differentiation without histologic transformation, PD-L1, KRAS amplification, and ESR1-AKAP12, MKRN1-BRAF fusions. The subclonal co-occurrence of acquired genomic alterations upon osimertinib resistance will likely require targeting multiple resistance mechanisms by combination therapies. Two or more subclonal genomic alterations are acquired upon osimertinib resistance 66% of first-line osimertinib-treated patients acquire MET amplification Acquired focal copy-number alterations are associated with early progression Neuroendocrine differentiation with NSCLC histology is revealed by RNA-seq analysis
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25
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Friedlaender A, Nouspikel T, Christinat Y, Ho L, McKee T, Addeo A. Tissue-Plasma TMB Comparison and Plasma TMB Monitoring in Patients With Metastatic Non-small Cell Lung Cancer Receiving Immune Checkpoint Inhibitors. Front Oncol 2020; 10:142. [PMID: 32117779 PMCID: PMC7028749 DOI: 10.3389/fonc.2020.00142] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Accepted: 01/27/2020] [Indexed: 12/13/2022] Open
Abstract
Immuno-oncology is an ever growing field that has seen important progress across the spectrum of cancers. Responses can be deep and durable. However, as only a minority of patients respond to checkpoint inhibition, predictive biomarkers are needed. Cancer is a genetic disease arising from the accumulation of somatic mutations in the DNA of affected cells. Tumor mutational burden (TMB), represents the number of somatic mutations in a tumor that form neoantigens, responsible for the immunogenicity of tumors. Randomized controlled trials have so far failed to show a survival benefit when stratifying patients by tissue TMB. TMB has also been evaluated in plasma (PTMB). PTMB is anticipated to represent the biology of the entire cancer, whereas obtaining tissue of an amenable primary or a metastatic lesion may be prone to sampling bias because of tumor heterogeneity. For this reason, we are evaluating the correlation between TMB and PTMB, and prospectively evaluating the impact of these biomarkers on clinical outcomes. We also discuss the technical difficulties inherent to performing and comparing these analyses. Furthermore, we evaluate the correlation between the evolution of PTMB during an immunotherapy treatment and response at 3 and 6 months, as we believe PTMB may be a dynamic biomarker. In this paper, we present results from the first 4 patients in this project.
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Affiliation(s)
- Alex Friedlaender
- Department of Oncology, University Hospital of Geneva (HUG), Geneva, Switzerland
| | - Thierry Nouspikel
- Service of Medical Genetics, Diagnostics Department, University Hospital of Geneva, Geneva, Switzerland
| | - Yann Christinat
- Department of Pathology, University Hospital of Geneva, Geneva, Switzerland
| | - Liza Ho
- Department of Pathology, University Hospital of Geneva, Geneva, Switzerland
| | - Thomas McKee
- Department of Pathology, University Hospital of Geneva, Geneva, Switzerland
| | - Alfredo Addeo
- Department of Oncology, University Hospital of Geneva (HUG), Geneva, Switzerland
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26
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Mizuno K, Akamatsu S, Sumiyoshi T, Wong JH, Fujita M, Maejima K, Nakano K, Ono A, Aikata H, Ueno M, Hayami S, Yamaue H, Chayama K, Inoue T, Ogawa O, Nakagawa H, Fujimoto A. eVIDENCE: a practical variant filtering for low-frequency variants detection in cell-free DNA. Sci Rep 2019; 9:15017. [PMID: 31641155 PMCID: PMC6805874 DOI: 10.1038/s41598-019-51459-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Accepted: 09/28/2019] [Indexed: 01/06/2023] Open
Abstract
Plasma cell-free DNA (cfDNA) testing plays an increasingly important role in precision medicine for cancer. However, circulating cell-free tumor DNA (ctDNA) is highly diluted by cfDNA from non-cancer cells, complicating ctDNA detection and analysis. To identify low-frequency variants, we developed a program, eVIDENCE, which is a workflow for filtering candidate variants detected by using the ThruPLEX tag-seq (Takara Bio), a commercially-available molecular barcoding kit. We analyzed 27 cfDNA samples from hepatocellular carcinoma patients. Sequencing libraries were constructed and hybridized to our custom panel targeting about 80 genes. An initial variant calling identified 36,500 single nucleotide variants (SNVs) and 9,300 insertions and deletions (indels) across the 27 samples, but the number was much greater than expected when compared with previous cancer genome studies. eVIDENCE was applied to the candidate variants and finally 70 SNVs and 7 indels remained. Of the 77 variants, 49 (63.6%) showed VAF of < 1% (0.20–0.98%). Twenty-five variants were selected in an unbiased manner and all were successfully validated, suggesting that eVIDENCE can identify variants with VAF of ≥ 0.2%. Additionally, this study is the first to detect hepatitis B virus integration sites and genomic rearrangements in the TERT region from cfDNA of HCC patients. We consider that our method can be applied in the examination of cfDNA from other types of malignancies using specific custom gene panels and will contribute to comprehensive ctDNA analysis.
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Affiliation(s)
- Kei Mizuno
- Department of Urology, Kyoto University Graduate School of Medicine, Kyoto, Japan.,Department of Drug Discovery Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Shusuke Akamatsu
- Department of Urology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Takayuki Sumiyoshi
- Department of Urology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Jing Hao Wong
- Department of Drug Discovery Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.,Department of Human Genetics, The University of Tokyo, Graduate School of Medicine, Tokyo, Japan
| | - Masashi Fujita
- Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Kazuaki Maejima
- Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Kaoru Nakano
- Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Atushi Ono
- Department of Gastroenterology and Metabolism, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Hiroshi Aikata
- Department of Gastroenterology and Metabolism, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Masaki Ueno
- Second Department of Surgery, Wakayama Medical University, Wakayama, Japan
| | - Shinya Hayami
- Second Department of Surgery, Wakayama Medical University, Wakayama, Japan
| | - Hiroki Yamaue
- Second Department of Surgery, Wakayama Medical University, Wakayama, Japan
| | - Kazuaki Chayama
- Department of Gastroenterology and Metabolism, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Takahiro Inoue
- Department of Urology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Osamu Ogawa
- Department of Urology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Hidewaki Nakagawa
- Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
| | - Akihiro Fujimoto
- Department of Drug Discovery Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan. .,Department of Human Genetics, The University of Tokyo, Graduate School of Medicine, Tokyo, Japan.
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27
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Sandmann S, Karimi M, de Graaf AO, Rohde C, Göllner S, Varghese J, Ernsting J, Walldin G, van der Reijden BA, Müller-Tidow C, Malcovati L, Hellström-Lindberg E, Jansen JH, Dugas M. appreci8: a pipeline for precise variant calling integrating 8 tools. Bioinformatics 2019; 34:4205-4212. [PMID: 29945233 PMCID: PMC6289140 DOI: 10.1093/bioinformatics/bty518] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Accepted: 06/25/2018] [Indexed: 12/14/2022] Open
Abstract
Motivation The application of next-generation sequencing in research and particularly in clinical routine requires valid variant calling results. However, evaluation of several commonly used tools has pointed out that not a single tool meets this requirement. False positive as well as false negative calls necessitate additional experiments and extensive manual work. Intelligent combination and output filtration of different tools could significantly improve the current situation. Results We developed appreci8, an automatic variant calling pipeline for calling single nucleotide variants and short indels by combining and filtering the output of eight open-source variant calling tools, based on a novel artifact- and polymorphism score. Appreci8 was trained on two data sets from patients with myelodysplastic syndrome, covering 165 Illumina samples. Subsequently, appreci8’s performance was tested on five independent data sets, covering 513 samples. Variation in sequencing platform, target region and disease entity was considered. All calls were validated by re-sequencing on the same platform, a different platform or expert-based review. Sensitivity of appreci8 ranged between 0.93 and 1.00, while positive predictive value ranged between 0.65 and 1.00. In all cases, appreci8 showed superior performance compared to any evaluated alternative approach. Availability and implementation Appreci8 is freely available at https://hub.docker.com/r/wwuimi/appreci8/. Sequencing data (BAM files) of the 678 patients analyzed with appreci8 have been deposited into the NCBI Sequence Read Archive (BioProjectID: 388411; https://www.ncbi.nlm.nih.gov/bioproject/PRJNA388411). Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sarah Sandmann
- Institute of Medical Informatics, University of Münster, Münster, Germany
| | - Mohsen Karimi
- Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
| | | | - Christian Rohde
- Department of Hematology, Oncology, and Rheumatology, Heidelberg University Hospital, Heidelberg, Germany
| | - Stefanie Göllner
- Department of Hematology, Oncology, and Rheumatology, Heidelberg University Hospital, Heidelberg, Germany
| | - Julian Varghese
- Institute of Medical Informatics, University of Münster, Münster, Germany
| | - Jan Ernsting
- Institute of Medical Informatics, University of Münster, Münster, Germany
| | - Gunilla Walldin
- Department of Medicine Huddinge, Karolinska Institutet, Stockholm, Sweden
| | | | - Carsten Müller-Tidow
- Department of Hematology, Oncology, and Rheumatology, Heidelberg University Hospital, Heidelberg, Germany
| | - Luca Malcovati
- Departments of Hematology Oncology & Molecular Medicine, Fondazione IRCCS Policlinico San Matteo & University of Pavia, Pavia, Italy
| | | | - Joop H Jansen
- Laboratory Hematology, RadboudUMC, Nijmegen GA, The Netherlands
| | - Martin Dugas
- Institute of Medical Informatics, University of Münster, Münster, Germany
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28
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Kleftogiannis D, Punta M, Jayaram A, Sandhu S, Wong SQ, Gasi Tandefelt D, Conteduca V, Wetterskog D, Attard G, Lise S. Identification of single nucleotide variants using position-specific error estimation in deep sequencing data. BMC Med Genomics 2019; 12:115. [PMID: 31375105 PMCID: PMC6679440 DOI: 10.1186/s12920-019-0557-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Accepted: 07/15/2019] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND Targeted deep sequencing is a highly effective technology to identify known and novel single nucleotide variants (SNVs) with many applications in translational medicine, disease monitoring and cancer profiling. However, identification of SNVs using deep sequencing data is a challenging computational problem as different sequencing artifacts limit the analytical sensitivity of SNV detection, especially at low variant allele frequencies (VAFs). METHODS To address the problem of relatively high noise levels in amplicon-based deep sequencing data (e.g. with the Ion AmpliSeq technology) in the context of SNV calling, we have developed a new bioinformatics tool called AmpliSolve. AmpliSolve uses a set of normal samples to model position-specific, strand-specific and nucleotide-specific background artifacts (noise), and deploys a Poisson model-based statistical framework for SNV detection. RESULTS Our tests on both synthetic and real data indicate that AmpliSolve achieves a good trade-off between precision and sensitivity, even at VAF below 5% and as low as 1%. We further validate AmpliSolve by applying it to the detection of SNVs in 96 circulating tumor DNA samples at three clinically relevant genomic positions and compare the results to digital droplet PCR experiments. CONCLUSIONS AmpliSolve is a new tool for in-silico estimation of background noise and for detection of low frequency SNVs in targeted deep sequencing data. Although AmpliSolve has been specifically designed for and tested on amplicon-based libraries sequenced with the Ion Torrent platform it can, in principle, be applied to other sequencing platforms as well. AmpliSolve is freely available at https://github.com/dkleftogi/AmpliSolve .
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Affiliation(s)
- Dimitrios Kleftogiannis
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- Present address: Genome Institute of Singapore (GIS), Agency of Science Research and Technology (A*STAR), Singapore, 138672, Singapore
| | - Marco Punta
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | | | - Shahneen Sandhu
- Peter MacCallum Cancer Centre and University of Melbourne, Melbourne, Victoria, Australia
| | - Stephen Q Wong
- Peter MacCallum Cancer Centre and University of Melbourne, Melbourne, Victoria, Australia
| | - Delila Gasi Tandefelt
- Department of Urology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Vincenza Conteduca
- Department of Medical Oncology, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, 47014, Meldola, Italy
| | | | - Gerhardt Attard
- UCL Cancer Institute, University College London, London, UK.
| | - Stefano Lise
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK.
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29
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Singer J, Irmisch A, Ruscheweyh HJ, Singer F, Toussaint NC, Levesque MP, Stekhoven DJ, Beerenwinkel N. Bioinformatics for precision oncology. Brief Bioinform 2019; 20:778-788. [PMID: 29272324 PMCID: PMC6585151 DOI: 10.1093/bib/bbx143] [Citation(s) in RCA: 35] [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: 06/06/2017] [Revised: 09/29/2017] [Indexed: 12/13/2022] Open
Abstract
Molecular profiling of tumor biopsies plays an increasingly important role not only in cancer research, but also in the clinical management of cancer patients. Multi-omics approaches hold the promise of improving diagnostics, prognostics and personalized treatment. To deliver on this promise of precision oncology, appropriate bioinformatics methods for managing, integrating and analyzing large and complex data are necessary. Here, we discuss the specific requirements of bioinformatics methods and software that arise in the setting of clinical oncology, owing to a stricter regulatory environment and the need for rapid, highly reproducible and robust procedures. We describe the workflow of a molecular tumor board and the specific bioinformatics support that it requires, from the primary analysis of raw molecular profiling data to the automatic generation of a clinical report and its delivery to decision-making clinical oncologists. Such workflows have to various degrees been implemented in many clinical trials, as well as in molecular tumor boards at specialized cancer centers and university hospitals worldwide. We review these and more recent efforts to include other high-dimensional multi-omics patient profiles into the tumor board, as well as the state of clinical decision support software to translate molecular findings into treatment recommendations.
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Affiliation(s)
- Jochen Singer
- Department of Biosystems Science and Engineering of ETH Zurich in Basel, Switzerland
| | - Anja Irmisch
- Department of Dermatology at the University of Zurich Hospital in Zurich, Switzerland
| | | | | | | | | | | | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering of ETH Zurich in Basel, Switzerland
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Calling Variants in the Clinic: Informed Variant Calling Decisions Based on Biological, Clinical, and Laboratory Variables. Comput Struct Biotechnol J 2019; 17:561-569. [PMID: 31049166 PMCID: PMC6482431 DOI: 10.1016/j.csbj.2019.04.002] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Revised: 03/12/2019] [Accepted: 04/03/2019] [Indexed: 01/10/2023] Open
Abstract
Deep sequencing genomic analysis is becoming increasingly common in clinical research and practice, enabling accurate identification of diagnostic, prognostic, and predictive determinants. Variant calling, distinguishing between true mutations and experimental errors, is a central task of genomic analysis and often requires sophisticated statistical, computational, and/or heuristic techniques. Although variant callers seek to overcome noise inherent in biological experiments, variant calling can be significantly affected by outside factors including those used to prepare, store, and analyze samples. The goal of this review is to discuss known experimental features, such as sample preparation, library preparation, and sequencing, alongside diverse biological and clinical variables, and evaluate their effect on variant caller selection and optimization.
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31
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Future of Liquid Biopsies With Growing Technological and Bioinformatics Studies: Opportunities and Challenges in Discovering Tumor Heterogeneity With Single-Cell Level Analysis. ACTA ACUST UNITED AC 2019; 24:104-108. [PMID: 29601337 DOI: 10.1097/ppo.0000000000000308] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Liquid biopsy provides minimally invasive and readily obtainable access to tumor-associated biological material in blood or other body fluids. These samples provide important insights into cancer biology, such as primary tumor heterogeneity; real-time tumor evolution; response to therapy, including immunotherapy; and mechanisms of cancer metastasis. Initial biological materials studied were circulating tumor cells and circulating nucleic acids, including circulating tumor DNA and microRNAs; more recently, studies have expanded to investigate extracellular vesicles, such as exosomes, microvesicles, and large oncosomes; tumor-derived circulating endothelial cells; and tumor-educated platelets. Even with an ongoing ambitious investment effort to develop liquid biopsy as an early cancer detection test in asymptomatic individuals, current challenges remain regarding how to access and analyze rare cells and tumor-derived nucleic acids in cancer patients. Technologies and associated bioinformatics tools are continuously evolving to capture these rare materials in an unbiased manner and to analyze them with high confidence. After first presenting recent applications of liquid biopsy, this review discusses aspects affecting the field, including tumor heterogeneity, single-cell analyses, and associated computational tools that will shape the future of liquid biopsy, with resultant opportunities and challenges.
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32
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Jiménez-Otero N, de Uña-Álvarez J, Pardo-Fernández JC. Goodness-of-fit tests for disorder detection in NGS experiments. Biom J 2018; 61:424-441. [PMID: 30589104 DOI: 10.1002/bimj.201700284] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2017] [Revised: 09/28/2018] [Accepted: 10/07/2018] [Indexed: 11/11/2022]
Abstract
Next-generation sequencing (NGS) experiments are often performed in biomedical research nowadays, leading to methodological challenges related to the high-dimensional and complex nature of the recorded data. In this work we review some of the issues that arise in disorder detection from NGS experiments, that is, when the focus is the detection of deletion and duplication disorders for homozygosity and heterozygosity in DNA sequencing. A statistical model to cope with guanine/cytosine bias and phasing and prephasing phenomena at base level is proposed, and a goodness-of-fit procedure for disorder detection is derived. The method combines the proper evaluation of local p-values (one for each DNA base) with suitable corrections for multiple comparisons and the discrete nature of the p-values. A global test for the detection of disorders in the whole DNA region is proposed too. The performance of the introduced procedures is investigated through simulations. A real data illustration is provided.
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Affiliation(s)
| | - Jacobo de Uña-Álvarez
- Department of Statistics and Operations Research, SiDOR Research Group & CINBIO, University of Vigo, Vigo, Pontevedra, Spain
| | - Juan Carlos Pardo-Fernández
- Department of Statistics and Operations Research, SiDOR Research Group & CINBIO, University of Vigo, Vigo, Pontevedra, Spain
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33
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Elazezy M, Joosse SA. Techniques of using circulating tumor DNA as a liquid biopsy component in cancer management. Comput Struct Biotechnol J 2018; 16:370-378. [PMID: 30364656 PMCID: PMC6197739 DOI: 10.1016/j.csbj.2018.10.002] [Citation(s) in RCA: 216] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Accepted: 10/04/2018] [Indexed: 12/12/2022] Open
Abstract
Precision medicine in the clinical management of cancer may be achieved through the diagnostic platform called “liquid biopsy”. This method utilizes the detection of biomarkers in blood for prognostic and predictive purposes. One of the latest blood born markers under investigation in the field of liquid biopsy in cancer patients is circulating tumor DNA (ctDNA). ctDNA is released by tumor cells through different mechanisms and can therefore provide information about the genomic make-up of the tumor currently present in the patient. Through longitudinal ctDNA-based liquid biopsies, tumor dynamics may be monitored to predict and assess drug response and/or resistance. However, because ctDNA is highly fragmented and because its concentration can be extremely low in a high background of normal circulating DNA, screening for clinical relevant mutations is challenging. Although significant progress has been made in advancing the detection and analysis of ctDNA in the last few years, the current challenges include standardization and increasing current techniques to single molecule sensitivity in combination with perfect specificity. This review focuses on the potential role of ctDNA in the clinical management of cancer patients, the current technologies that are being employed, and the hurdles that still need to be taken to achieve ctDNA-based liquid biopsy towards precision medicine.
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Affiliation(s)
- Maha Elazezy
- University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246 Hamburg, Germany
| | - Simon A Joosse
- University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246 Hamburg, Germany
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Abstract
Early detection of ovarian cancer could reduce mortality by 10% to 30%. Effective screening requires high sensitivity (>75%) and extremely high specificity (99.7%). Clinical trials suggest the best specificity is achieved with 2-stage strategies in which increasing serum CA125 level triggers transvaginal sonography to detect a malignant pelvic mass, although evidence for such approaches improving overall survival has been limited. Screening may be improved by combining CA125 with novel biomarkers, such as autoantibodies, circulating tumor DNA, or microRNAs. In order to detect premetastatic ovarian cancers originating in the distal fallopian tube, more sensitive approaches to diagnostic imaging are required.
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Affiliation(s)
- Kevin M Elias
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA.
| | - Jing Guo
- Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA; Department of Gynecology and Obstetrics, Shanghai Tenth People's Hospital, Tongji University, 301 Yanchang Road, Jingan, Shanghai 200072, China
| | - Robert C Bast
- Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA
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Mariani S, Bertero L, Coppola V, Saracco G, Arezzo A, Francia Di Celle P, Metovic J, Marchiò C, Cassoni P. Awareness of mutational artefacts in suboptimal DNA samples: possible risk for therapeutic choices. Expert Rev Mol Diagn 2018; 18:467-475. [PMID: 29676606 DOI: 10.1080/14737159.2018.1468254] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
BACKGROUND Technical biases due to PCR artefacts could represent an insidious obstacle for mutational analysis and precision medicine. METHODS The authors report a retrospective analysis by fast COLD-PCR and sequencing of 31 suboptimal tumor DNA samples obtained from FFPE tissues and liquid biopsies. RESULTS In FFPE tumor tissues and plasma liquid biopsies of patients with lung and colorectal adenocarcinoma, we observed a significant rate of artefactual KRAS mutations, unveiled by repeated analysis following UDG pretreatment as well as by simple repetition without UDG pretreatment step, thus suggesting a DNA damage different from cytosine deamination. UDG pretreatment was not only unnecessary to contrast artefacts occurrence, but also hampered the efficiency of mutational screening, reducing the analytical sensitivity. Taken individually or considered together, the reduced DNA input per reaction and UDG pretreatment limited the detection of 'real' mutated alleles, decreasing PCR sensitivity enough to hamper distinction between artefactual and true subclonal mutations of KRAS. CONCLUSIONS Careful validation of analytical sensitivities should always be carried out through standard controls, and strategies other than UDG pretreatment need to be identified to avoid both amplification of artefactual mutations and failure to identify real subclonal mutations.
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Affiliation(s)
- Sara Mariani
- a Department of Medical Sciences , University of Turin and Pathology Unit, Città della Salute e della Scienza Hospital , Torino , Italy
| | - Luca Bertero
- a Department of Medical Sciences , University of Turin and Pathology Unit, Città della Salute e della Scienza Hospital , Torino , Italy
| | - Vittoria Coppola
- a Department of Medical Sciences , University of Turin and Pathology Unit, Città della Salute e della Scienza Hospital , Torino , Italy
| | - Giorgio Saracco
- b Department of Medical Sciences , University of Turin and Gastroenterology Unit, Città della Salute e della Scienza Hospital , Torino , Italy
| | - Alberto Arezzo
- c Department of Surgical Sciences , University of Turin and Surgical Unit, Città della Salute e della Scienza Hospital , Torino , Italy
| | | | - Jasna Metovic
- a Department of Medical Sciences , University of Turin and Pathology Unit, Città della Salute e della Scienza Hospital , Torino , Italy
| | - Caterina Marchiò
- a Department of Medical Sciences , University of Turin and Pathology Unit, Città della Salute e della Scienza Hospital , Torino , Italy
| | - Paola Cassoni
- a Department of Medical Sciences , University of Turin and Pathology Unit, Città della Salute e della Scienza Hospital , Torino , Italy
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36
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What Does This Mutation Mean? The Tools and Pitfalls of Variant Interpretation in Lymphoid Malignancies. Int J Mol Sci 2018; 19:ijms19041251. [PMID: 29677173 PMCID: PMC5979354 DOI: 10.3390/ijms19041251] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2018] [Revised: 04/09/2018] [Accepted: 04/14/2018] [Indexed: 01/21/2023] Open
Abstract
High throughput sequencing (HTS) is increasingly important in determining cancer diagnoses, with subsequent prognostic and therapeutic implications. The biology of cancer is becoming increasingly deciphered and it is clear that therapy needs to be individually tailored. Whilst translational research plays an important role in lymphoid malignancies, few guidelines exist to guide biologists and routine laboratories through this constantly evolving field. In this article, we review the challenges of interpreting HTS in lymphoid malignancies and provide a toolkit to interpret single nucleotide variants obtained from HTS. We define the pre-analytical issues such as sequencing DNA obtained from formalin-fixed and paraffin-embedded tissue (FFPE), the acquisition of germline DNA, or the bioinformatic pitfalls, the analytical issues encountered and how to manage them. We describe the main constitutional and cancer databases, their characteristics and limitations, with an emphasis on variant interpretation in lymphoid malignancies. Finally, we discuss the challenges of predictions that one can make using in silico or in vitro modelling, pharmacogenomic screening, and the limits of those prediction tools. This description of the current status in genomic interpretation highlights the need for new large databases and international collaboration in the lymphoma field.
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37
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Xu C. A review of somatic single nucleotide variant calling algorithms for next-generation sequencing data. Comput Struct Biotechnol J 2018; 16:15-24. [PMID: 29552334 PMCID: PMC5852328 DOI: 10.1016/j.csbj.2018.01.003] [Citation(s) in RCA: 145] [Impact Index Per Article: 24.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2017] [Revised: 01/20/2018] [Accepted: 01/28/2018] [Indexed: 02/06/2023] Open
Abstract
Detection of somatic mutations holds great potential in cancer treatment and has been a very active research field in the past few years, especially since the breakthrough of the next-generation sequencing technology. A collection of variant calling pipelines have been developed with different underlying models, filters, input data requirements, and targeted applications. This review aims to enumerate these unique features of the state-of-the-art variant callers, in the hope to provide a practical guide for selecting the appropriate pipeline for specific applications. We will focus on the detection of somatic single nucleotide variants, ranging from traditional variant callers based on whole genome or exome sequencing of paired tumor-normal samples to recent low-frequency variant callers designed for targeted sequencing protocols with unique molecular identifiers. The variant callers have been extensively benchmarked with inconsistent performances across these studies. We will review the reference materials, datasets, and performance metrics that have been used in the benchmarking studies. In the end, we will discuss emerging trends and future directions of the variant calling algorithms.
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Affiliation(s)
- Chang Xu
- Life Science Research and Foundation, Qiagen Sciences, Inc., 6951 Executive Way, Frederick, Maryland 21703, USA
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MacConaill LE, Burns RT, Nag A, Coleman HA, Slevin MK, Giorda K, Light M, Lai K, Jarosz M, McNeill MS, Ducar MD, Meyerson M, Thorner AR. Unique, dual-indexed sequencing adapters with UMIs effectively eliminate index cross-talk and significantly improve sensitivity of massively parallel sequencing. BMC Genomics 2018; 19:30. [PMID: 29310587 PMCID: PMC5759201 DOI: 10.1186/s12864-017-4428-5] [Citation(s) in RCA: 114] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2017] [Accepted: 12/29/2017] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Sample index cross-talk can result in false positive calls when massively parallel sequencing (MPS) is used for sensitive applications such as low-frequency somatic variant discovery, ancient DNA investigations, microbial detection in human samples, or circulating cell-free tumor DNA (ctDNA) variant detection. Therefore, the limit-of-detection of an MPS assay is directly related to the degree of index cross-talk. RESULTS Cross-talk rates up to 0.29% were observed when using standard, combinatorial adapters, resulting in 110,180 (0.1% cross-talk rate) or 1,121,074 (0.29% cross-talk rate) misassigned reads per lane in non-patterned and patterned Illumina flow cells, respectively. Here, we demonstrate that using unique, dual-matched indexed adapters dramatically reduces index cross-talk to ≤1 misassigned reads per flow cell lane. While the current study was performed using dual-matched indices, using unique, dual-unrelated indices would also be an effective alternative. CONCLUSIONS For sensitive downstream analyses, the use of combinatorial indices for multiplexed hybrid capture and sequencing is inappropriate, as it results in an unacceptable number of misassigned reads. Cross-talk can be virtually eliminated using dual-matched indexed adapters. These results suggest that use of such adapters is critical to reduce false positive rates in assays that aim to identify low allele frequency events, and strongly indicate that dual-matched adapters be implemented for all sensitive MPS applications.
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Affiliation(s)
- Laura E MacConaill
- Center for Cancer Genome Discovery, DFCI, 450 Brookline Avenue, Dana840b, Boston, MA, 02215, USA.,Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
| | - Robert T Burns
- Center for Cancer Genome Discovery, DFCI, 450 Brookline Avenue, Dana840b, Boston, MA, 02215, USA
| | - Anwesha Nag
- Center for Cancer Genome Discovery, DFCI, 450 Brookline Avenue, Dana840b, Boston, MA, 02215, USA
| | - Haley A Coleman
- Center for Cancer Genome Discovery, DFCI, 450 Brookline Avenue, Dana840b, Boston, MA, 02215, USA
| | - Michael K Slevin
- Center for Cancer Genome Discovery, DFCI, 450 Brookline Avenue, Dana840b, Boston, MA, 02215, USA
| | | | - Madelyn Light
- Integrated DNA Technologies, Inc., Redwood City, CA, USA
| | - Kevin Lai
- Integrated DNA Technologies, Inc., Redwood City, CA, USA
| | - Mirna Jarosz
- Integrated DNA Technologies, Inc., Redwood City, CA, USA
| | | | - Matthew D Ducar
- Center for Cancer Genome Discovery, DFCI, 450 Brookline Avenue, Dana840b, Boston, MA, 02215, USA
| | - Matthew Meyerson
- Center for Cancer Genome Discovery, DFCI, 450 Brookline Avenue, Dana840b, Boston, MA, 02215, USA.,Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA.,Department of Medical Oncology, Dana-Farber Cancer Institute (DFCI), Boston, MA, USA.,Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Aaron R Thorner
- Center for Cancer Genome Discovery, DFCI, 450 Brookline Avenue, Dana840b, Boston, MA, 02215, USA.
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Wang R, Li X, Zhang H, Wang K, He J. Cell-free circulating tumor DNA analysis for breast cancer and its clinical utilization as a biomarker. Oncotarget 2017; 8:75742-75755. [PMID: 29088906 PMCID: PMC5650461 DOI: 10.18632/oncotarget.20608] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Accepted: 08/17/2017] [Indexed: 01/05/2023] Open
Abstract
Circulating tumor DNA (ctDNA) in the blood of cancer patients contains much information on genetic and epigenetic profiles associated with cancer development, progression, and response to therapy. Analysis of ctDNA provides an opportunity for non-invasive sampling of tumor DNA repetitiously and therefore advance precision medicine. Recent development in massively parallel sequencing and digital genomic techniques support the analytical and clinical validity of ctDNA as a promising 'liquid biopsy' in human cancer. In this review, we discussed the current status of cell-free ctDNA including ctDNA biology, recently developed techniques for ctDNA detection, breast cancer specific detecting strategies, with a focus on clinical applications of ctDNA-based biomarkers in breast oncology.
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Affiliation(s)
- Ru Wang
- Department of Breast Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, P.R. China
| | - Xiao Li
- Department of Breast Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, P.R. China
| | - Huimin Zhang
- Department of Breast Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, P.R. China
| | - Ke Wang
- Department of Breast Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, P.R. China
| | - Jianjun He
- Department of Breast Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, P.R. China
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VarScan2 analysis of de novo variants in monozygotic twins discordant for schizophrenia. Psychiatr Genet 2017; 27:62-70. [DOI: 10.1097/ypg.0000000000000162] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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41
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Hofmann AL, Behr J, Singer J, Kuipers J, Beisel C, Schraml P, Moch H, Beerenwinkel N. Detailed simulation of cancer exome sequencing data reveals differences and common limitations of variant callers. BMC Bioinformatics 2017; 18:8. [PMID: 28049408 PMCID: PMC5209852 DOI: 10.1186/s12859-016-1417-7] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2016] [Accepted: 12/10/2016] [Indexed: 12/30/2022] Open
Abstract
Background Next-generation sequencing of matched tumor and normal biopsy pairs has become a technology of paramount importance for precision cancer treatment. Sequencing costs have dropped tremendously, allowing the sequencing of the whole exome of tumors for just a fraction of the total treatment costs. However, clinicians and scientists cannot take full advantage of the generated data because the accuracy of analysis pipelines is limited. This particularly concerns the reliable identification of subclonal mutations in a cancer tissue sample with very low frequencies, which may be clinically relevant. Results Using simulations based on kidney tumor data, we compared the performance of nine state-of-the-art variant callers, namely deepSNV, GATK HaplotypeCaller, GATK UnifiedGenotyper, JointSNVMix2, MuTect, SAMtools, SiNVICT, SomaticSniper, and VarScan2. The comparison was done as a function of variant allele frequencies and coverage. Our analysis revealed that deepSNV and JointSNVMix2 perform very well, especially in the low-frequency range. We attributed false positive and false negative calls of the nine tools to specific error sources and assigned them to processing steps of the pipeline. All of these errors can be expected to occur in real data sets. We found that modifying certain steps of the pipeline or parameters of the tools can lead to substantial improvements in performance. Furthermore, a novel integration strategy that combines the ranks of the variants yielded the best performance. More precisely, the rank-combination of deepSNV, JointSNVMix2, MuTect, SiNVICT and VarScan2 reached a sensitivity of 78% when fixing the precision at 90%, and outperformed all individual tools, where the maximum sensitivity was 71% with the same precision. Conclusions The choice of well-performing tools for alignment and variant calling is crucial for the correct interpretation of exome sequencing data obtained from mixed samples, and common pipelines are suboptimal. We were able to relate observed substantial differences in performance to the underlying statistical models of the tools, and to pinpoint the error sources of false positive and false negative calls. These findings might inspire new software developments that improve exome sequencing pipelines and further the field of precision cancer treatment. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1417-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ariane L Hofmann
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstr, Basel, 26, 4058, Switzerland.,Swiss Institute of Bioinformatics, Mattenstr, Basel, 26, 4058, Switzerland
| | - Jonas Behr
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstr, Basel, 26, 4058, Switzerland.,Swiss Institute of Bioinformatics, Mattenstr, Basel, 26, 4058, Switzerland
| | - Jochen Singer
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstr, Basel, 26, 4058, Switzerland.,Swiss Institute of Bioinformatics, Mattenstr, Basel, 26, 4058, Switzerland
| | - Jack Kuipers
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstr, Basel, 26, 4058, Switzerland.,Swiss Institute of Bioinformatics, Mattenstr, Basel, 26, 4058, Switzerland
| | - Christian Beisel
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstr, Basel, 26, 4058, Switzerland
| | - Peter Schraml
- Institute for Surgical Pathology, University Hospital Zurich, Schmelzbergstrasse 12, Zurich, 8091, Switzerland
| | - Holger Moch
- Institute for Surgical Pathology, University Hospital Zurich, Schmelzbergstrasse 12, Zurich, 8091, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstr, Basel, 26, 4058, Switzerland. .,Swiss Institute of Bioinformatics, Mattenstr, Basel, 26, 4058, Switzerland.
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