| Literature DB >> 31119060 |
Xuchao Zhang1,2, Zhiyong Liang3, Shengyue Wang4, Shun Lu5, Yong Song6, Ying Cheng7, Jianming Ying8, Weiping Liu9, Yingyong Hou10, Yangqiu Li11, Yi Liu12, Jun Hou13, Xiufeng Liu14, Jianyong Shao15, Yanhong Tai16, Zheng Wang17, Li Fu18, Hui Li7, Xiaojun Zhou19, Hua Bai20, Mengzhao Wang21, You Lu22, Jinji Yang23, Wenzhao Zhong23, Qing Zhou23, Xuening Yang23, Jie Wang24, Cheng Huang25, Xiaoqing Liu26, Xiaoyan Zhou27, Shirong Zhang28, Hongxia Tian1,2, Yu Chen1,2, Ruibao Ren29, Ning Liao30, Chunyan Wu31, Zhongzheng Zhu32, Hongming Pan33, Yanhong Gu34, Liwei Wang35, Yunpeng Liu36, Suzhan Zhang37, Tianshu Liu38, Gong Chen39, Zhimin Shao40, Binghe Xu24, Qingyuan Zhang41, Ruihua Xu42, Lin Shen43, Yilong Wu1,2, On Behalf Of Chinese Society Of Clinical Oncology Csco Tumor Biomarker Committee1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43.
Abstract
Next-generation sequencing (NGS) technology is capable of sequencing millions or billions of DNA molecules simultaneously. Therefore, it represents a promising tool for the analysis of molecular targets for the initial diagnosis of disease, monitoring of disease progression, and identifying the mechanism of drug resistance. On behalf of the Tumor Biomarker Committee of the Chinese Society of Clinical Oncology (CSCO) and the China Actionable Genome Consortium (CAGC), the present expert group hereby proposes advisory guidelines on clinical applications of NGS technology for the analysis of cancer driver genes for precision cancer therapy. This group comprises an assembly of laboratory cancer geneticists, clinical oncologists, bioinformaticians, pathologists, and other professionals. After multiple rounds of discussions and revisions, the expert group has reached a preliminary consensus on the need of NGS in clinical diagnosis, its regulation, and compliance standards in clinical sample collection. Moreover, it has prepared NGS criteria, the sequencing standard operation procedure (SOP), data analysis, report, and NGS platform certification and validation.Entities:
Keywords: Next-generation sequencing technology; cancer; consensus
Year: 2019 PMID: 31119060 PMCID: PMC6528448 DOI: 10.20892/j.issn.2095-3941.2018.0142
Source DB: PubMed Journal: Cancer Biol Med ISSN: 2095-3941 Impact factor: 4.248
Original sequences (FASTQ) and aligned sequences (BAM) quality parameters
| Parameter | Description |
| Median base quality for each cycle | Base quality dropped at the end of reads. The average “median base quality” for a batch sequence should not be <20 (Phred quality score) |
| Duplication rate | Duplication rate reflects the library complexity |
| Adaptor removal ratio (if applicable) | The ratio of removed adaptor to the reads is an index of sequence quality |
| Mapping rate | The ratio of reads that are successfully mapped to reference genome |
| On target rate | The ratio of reads that are mapped to targeted regions |
| Average sequencing depth on target region | The average sequencing depth for the target regions meeting the clinical needs |
| Distribution of sequencing depth on target region | Either a distribution plot or a table to indicate the sequencing depth across the target regions meeting the clinical needs |
| Variants detecting quality parameters | |
| Parameter | Description |
| Total variant count | Total variant count in target regions meeting the clinical needs should be similar to the same patient population by using the same gene test with the same target regions |
| Known SNP ratio | In general, the ratio of known SNPs to the total variant count should be >90% |
| Insertion/deletion (Indel) ratio | The ratio of insertion/deletion to the total variant count |
| Homozygous variant ratio | The ratio of homozygous variants to the total variant count |
| Nonsense mutation ratio | The ratio of nonsense mutations to the total variant count |
| Transition to transversion ratio | The ratio of transition to transversion |
A brief description of the procedure for clinical tumor NGS testing
| Step | Description | Tools and database | Output |
| Base calling and duplicate removal | Base calling and duplicate removal, also known as initial analysis | Sequencing platform configuration software | FASTQ format |
| Primer removal | Primer sequences for amplicon sequencing must be removed from the reads | CutAdapt, BWA, etc. | FASTQ or BAM format |
| Adaptor removal | Remove the adaptor sequences from the end of reads. It may interfere with the alignment and cause false-positive/false-negative variant calling if not being trimmed | CutAdapt, BWA, Trimmomatic, SeqPrep, etc. | FASTQ or BAM format |
| Low-quality base removal | Low-quality bases may also interfere with the alignment and cause false results. These bases should usually be trimmed from the ends of read | CutAdapt, BWA, Trimmomatic, SeqPrep, etc. | FASTQ or BAM format |
| Alignment | In the alignment step, paired-/single-end reads are aligned to the reference genome. SNVs and small indels could be recognized in this step | BWA, Novalign, Stampy , SOAP2, LifeScope, Bowtie, etc. | BAM format |
| Duplicate removal (optional) | Duplicates can be introduced by PCR amplifications in the library construction and sequencing steps. Implausible duplicates in the original DNA decrease the accuracy of the calling and should be removed. Probe hybridization capture sequencing generates fewer duplicates, because DNA is randomly fragmented during library construction. Amplicon sequencing does not require deduplication if there are no allele barcodes, and requires if there are | Picard Mark Duplicates, SAMtools, etc. | BAM format |
| Indel realignment (optional) | Misalignment is usually seen around indels which can cause false results, especially at the beginning or end of the reads. Local realignment method can determine these locations, minimize this error, and increase accuracy | GATK RealignerTargetCreator and IndelRealigner, SRMA, etc. | BAM format |
| Base quality score recalibration (optional) | The base quality score could be recalibrated after the alignment/realignment to decrease the false-positive rate | GATK BaseRecalibrator and PrintReads, ReQON, etc. | BAM format |
| Variant calling | Variant calling refers to the detection and description of variations (including SNVs and small indels) based on differences between sequencing data and reference genomes | GATK UnifiedGenotyper, GATK HaplotypeCaller, SAMtools, MuTect, Varscan, Platypus, etc. | VCF format |
| Annotation | The variant interpretation relies on detailed annotation. The basic annotation includes gene name, gene structure areas (exon, splicing region, intron, intragenic region, etc.), and coding information. SNP information, pathogenicity, and other references could also be included | ANNOVAR, SnpEff, , Cartagenia Bench Lab NGS, dbSNP, 1000 Genomes, ESP6500, SIFT, PhyloP, MutationTaster, COSMIC, OMIM, ClinVar, HGMD, etc. | CSV, TSV, TXT, Excel, etc. |
| Filtering | Disease related variants could be identified by strict filtering large amount of annotated variant calling results. Typical filtering criteria removes low-quality variants, non-coding regions (eg, intron and intragenic region), synonymous SNVs, and known low-frequency SNPs in healthy populations. Labs should set up an internal database to analyze the false positives that often occur on their own platforms and perform rigorous filtering of these false positives | Cartagenia Bench Lab NGS, SnpSift, etc. | CSV, TSV, TXT, Excel, database, etc. |
| Name | XXX | Gender | Male | Pathological diagnosis | Lung adenocarcinoma | |
| Hospital number | XXX | Age | X yrs | |||
| Specimen number | XXX | |||||
| Specimen type | Fresh tissue specimen | Tumor cell content | Frozen section assessment 80% | DNA content and quality | XXX | |
| Specimen acceptance date | 2018-3-5 | Result report date | 2018-3-9 | |||
| Using NGS technology to detect 286 exon mutations in lung cancer (Appendices 1). Massively parallel sequencing exons and the sequence near the splicing site of these genes. Sample processing, library construction, sequencing, and analysis were all performed at the GLCI Central Laboratory. Testing platform is XXX, and analysis software is XXX. Reference genome is GRCh38. | ||||||
| Continued | ||||||
| Continued | ||||||
| 1: List of testing gene omitted. 2: NGS quality parameters omitted. 3: List of all mutations and variations in this test (generic descriptions of signaling pathways where major variant genes are located, other polymorphisms, or general descriptions of mutations with uncertain clinical targeting may be listed at the end of the table) omitted. | ||||||