Reliable analysis of clinical tumor-only whole exome sequencing data

AbstractBackgroundAllele-specific copy number alteration (CNA) analysis is essential to study the functional impact of single nucleotide variants (SNV) and the process of tumorigenesis. Most commonly used tools in the field rely on high quality genome-wide data with matched normal profiles, limiting their applicability in clinical settings.MethodsWe propose a workflow, based on the open-source PureCN R/Bioconductor package in conjunction with widely used variant-calling and copy number segmentation algorithms, for allele-specific CNA analysis from whole exome sequencing (WES) without matched normals. We use The Cancer Genome Atlas (TCGA) ovarian carcinoma (OV) and lung adenocarcinoma (LUAD) datasets to benchmark its performance against gold standard SNP6 microarray and WES datasets with matched normal samples. Our workflow further classifies SNVs by somatic status and then uses this information to infer somatic mutational signatures and tumor mutational burden (TMB).ResultsApplication of our workflow to tumor-only WES data produces tumor purity and ploidy estimates that are highly concordant with estimates from SNP6 microarray data and matched-normal WES data. The presence of cancer type-specific somatic mutational signatures was inferred with high accuracy. We also demonstrate high concordance of TMB between our tumor-only workflow and matched normal pipelines.ConclusionThe proposed workflow provides, to our knowledge, the only open-source option for comprehensive allele-specific CNA analysis and SNV classification of tumor-only WES with demonstrated high accuracy.

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Reliable Analysis of Clinical Tumor-Only Whole-Exome Sequencing Data

JCO Clinical Cancer Informatics ◽

10.1200/cci.19.00130 ◽

2020 ◽

pp. 321-335 ◽

Cited By ~ 1

Author(s):

Sehyun Oh ◽

Ludwig Geistlinger ◽

Marcel Ramos ◽

Martin Morgan ◽

Levi Waldron ◽

...

Keyword(s):

Open Source ◽

Exome Sequencing ◽

Whole Exome Sequencing ◽

Microarray Data ◽

Copy Number ◽

High Accuracy ◽

Cancer Type ◽

Mutational Signatures ◽

Whole Exome ◽

Allele Specific

PURPOSE Allele-specific copy number alteration (CNA) analysis is essential to study the functional impact of single-nucleotide variants (SNVs) and the process of tumorigenesis. However, controversy over whether it can be performed with sufficient accuracy in data without matched normal profiles and a lack of open-source implementations have limited its application in clinical research and diagnosis. METHODS We benchmark allele-specific CNA analysis performance of whole-exome sequencing (WES) data against gold standard whole-genome SNP6 microarray data and against WES data sets with matched normal samples. We provide a workflow based on the open-source PureCN R/Bioconductor package in conjunction with widely used variant-calling and copy number segmentation algorithms for allele-specific CNA analysis from WES without matched normals. This workflow further classifies SNVs by somatic status and then uses this information to infer somatic mutational signatures and tumor mutational burden (TMB). RESULTS Application of our workflow to tumor-only WES data produces tumor purity and ploidy estimates that are highly concordant with estimates from SNP6 microarray data and matched normal WES data. The presence of cancer type–specific somatic mutational signatures was inferred with high accuracy. We also demonstrate high concordance of TMB between our tumor-only workflow and matched normal pipelines. CONCLUSION The proposed workflow provides, to our knowledge, the only open-source option with demonstrated high accuracy for comprehensive allele-specific CNA analysis and SNV classification of tumor-only WES. An implementation of the workflow is available on the Terra Cloud platform of the Broad Institute (Cambridge, MA).

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Allele-specific copy-number discovery from whole-genome and whole-exome sequencing

Nucleic Acids Research ◽

10.1093/nar/gkv319 ◽

2015 ◽

Vol 43 (14) ◽

pp. e90-e90 ◽

Cited By ~ 11

Author(s):

WeiBo Wang ◽

Wei Wang ◽

Wei Sun ◽

James J. Crowley ◽

Jin P. Szatkiewicz

Keyword(s):

Exome Sequencing ◽

Whole Exome Sequencing ◽

Copy Number ◽

Whole Genome ◽

Whole Exome ◽

Allele Specific

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Case Report: Novel RPGRIP1L Gene Mutations Identified by Whole Exome Sequencing in a Patient With Multiple Primary Tumors

Frontiers in Genetics ◽

10.3389/fgene.2021.620472 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jiani Guo ◽

Yu Yang ◽

Zhuqing Ji ◽

Mengchu Yao ◽

Xiaotian Xia ◽

...

Keyword(s):

Exome Sequencing ◽

Pancreatic Adenocarcinoma ◽

Whole Exome Sequencing ◽

The Cancer Genome Atlas ◽

Cancer Type ◽

Primary Tumors ◽

Multiple Primary Tumors ◽

Whole Exome ◽

Multiple Primary ◽

Cancer Types

A 78 years old Chinese woman with five different cancer types and a family history of malignancy was the subject of this study. Pancreatic adenocarcinoma and gingival squamous cell carcinoma tissues were obtained from the patient and sequenced using Whole Exome Sequencing. Whole exome sequencing identified 20 mutation sites in six candidate genes. Sanger Sequencing was used for further validation. The results verified six mutations in three genes, OBSCN, TTN, and RPGRIP1L, in at least one cancer type. Immunohistochemistry was used to verify protein expression. mRNA expression analysis using The Cancer Genome Atlas database revealed that RPGRIP1L was highly expressed in several cancer types, especially in pancreatic adenocarcinoma, and correlated with patient survival and sensitivity to paclitaxel, probably through the TGF-β signaling pathway. The newly identified somatic mutations in RPGRIP1L might contribute to pathogenesis in the patients. Protein conformation simulation demonstrated that the alterations had caused the binding pocket at position 708 to change from concave to convex, which could restrict contraction and extension, and interfere with the physiological function of the protein. Further studies are required to determine the implication of RPGRIP1L in this family and in multiple primary tumors.

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Allele-specific copy number estimation by whole exome sequencing

The Annals of Applied Statistics ◽

10.1214/17-aoas1043 ◽

2017 ◽

Vol 11 (2) ◽

pp. 1169-1192 ◽

Cited By ~ 6

Author(s):

Hao Chen ◽

Yuchao Jiang ◽

Kara N. Maxwell ◽

Katherine L. Nathanson ◽

Nancy Zhang

Keyword(s):

Exome Sequencing ◽

Whole Exome Sequencing ◽

Copy Number ◽

Copy Number Estimation ◽

Whole Exome ◽

Allele Specific

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Whole-exome sequencing reveals potential germline and somatic mutations in 60 malignant ovarian germ cell tumors

Biology of Reproduction ◽

10.1093/biolre/ioab052 ◽

2021 ◽

Author(s):

Juan Chen ◽

Yan Li ◽

Jianlei Wu ◽

Yakun Liu ◽

Shan Kang

Keyword(s):

Germ Cell ◽

Exome Sequencing ◽

Whole Exome Sequencing ◽

Peripheral Blood ◽

Copy Number ◽

Somatic Mutations ◽

Germ Cell Tumors ◽

Germline Mutations ◽

Deletion Region ◽

Whole Exome

Abstract Background Malignant ovarian germ cell tumors (MOGCTs) are rare and heterogeneous ovary tumors. We aimed to identify potential germline mutations and somatic mutations in MOGCTs by whole-exome sequencing. Methods The peripheral blood and tumor samples from these patients were used to identify germline mutations and somatic mutations, respectively. For those genes corresponding to copy number alterations (CNA) deletion and duplication region, functional annotation of was performed. Immunohistochemistry was performed to evaluate the expression of mutated genes corresponding to CNA deletion region. Results In peripheral blood, copy number loss and gain were mostly found in yolk sac tumors (YST). Moreover, POU5F1 was the most significant mutated gene with mutation frequency > 10% in both CNA deletion and duplication region. In addition, strong cytoplasm staining of POU5F1 (corresponding to CNA deletion region) was found in 2 YST and nuclear staining in 2 dysgerminomas (DG) tumor samples. Genes corresponding to CNA deletion region were significantly enriched in the signaling pathway of regulating pluripotency of stem cells. In addition, genes corresponding to CNA duplication region were significantly enriched in the signaling pathways of RIG-I-like receptor, Toll-like receptor, NF-kappa B and Jak–STAT. KRT4, RPL14, PCSK6, PABPC3 and SARM1 mutations were detected in both peripheral blood and tumor samples. Conclusions Identification of potential germline mutations and somatic mutations in MOGCTs may provide a new field in understanding the genetic feature of the rare biological tumor type in the ovary.

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Whole Exome Sequencing of Simultaneous Primary Multifocal Lung Cancer (SMPLC): Case Report and Bioinformatics Analysis

10.21203/rs.3.rs-96042/v1 ◽

2020 ◽

Author(s):

Donglin Zhu ◽

Minghong Shen ◽

Jinghuan Lv

Keyword(s):

Lung Cancer ◽

Exome Sequencing ◽

Whole Exome Sequencing ◽

Bioinformatics Analysis ◽

Clonal Evolution ◽

Evolutionary Relationship ◽

Primary Lung Cancer ◽

The Cancer Genome Atlas ◽

Control Group ◽

Whole Exome

Abstract Background: To understand the molecular mechanism of synchronous multifocal lung cancer (SMLC) is of great significance for the differential diagnosis of intrapulmonary metastasis (IM) and synchronous multiple primary lung cancer (SMPLC). Recently, next-generation sequencing (NGS) has become a useful tool for understanding SMLC. Case presentation: In this study, two lesions of a 61-year-old man with lung cancer were detected by whole exome sequencing (WES) and the correlation between different lesions was analyzed at the molecular level. Lesion 1 was adenocarcinoma and lesion 2 was squamous cell carcinoma. Gene mutation and copy number variation (CNV) are different in the two lesions. The genome of lesion 2 is more unstable. The clonal evolution analysis showed that there was no obvious evolutionary relationship between the two lesions, and both lesions were independent double primary lesions. Bioinformatics analysis revealed that the alternate genes of the two lesions were inconsistent in function and pathway. PCA analysis was performed using the Cancer Genome Atlas (TCGA) database and the GTEx database, and it was found that the changed genes in these two lesions were significantly separated from the control group, and the changes of TP53 and EGFR genes in the TCGA database were further described. Conclusions: These results indicate that NGS may provide new ideas for SMLC classification.

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RefCNV: Identification of Gene-Based Copy Number Variants Using Whole Exome Sequencing

Cancer Informatics ◽

10.4137/cin.s36612 ◽

2016 ◽

Vol 15 ◽

pp. CIN.S36612 ◽

Cited By ~ 3

Author(s):

Lun-Ching Chang ◽

Biswajit Das ◽

Chih-Jian Lih ◽

Han Si ◽

Corinne E. Camalier ◽

...

Keyword(s):

Exome Sequencing ◽

Whole Exome Sequencing ◽

Copy Number ◽

Copy Number Variants ◽

Estimation Methods ◽

Single Nucleotide Variants ◽

False Positive Error ◽

Reference Set ◽

Genome Wide ◽

Whole Exome

With rapid advances in DNA sequencing technologies, whole exome sequencing (WES) has become a popular approach for detecting somatic mutations in oncology studies. The initial intent of WES was to characterize single nucleotide variants, but it was observed that the number of sequencing reads that mapped to a genomic region correlated with the DNA copy number variants (CNVs). We propose a method RefCNV that uses a reference set to estimate the distribution of the coverage for each exon. The construction of the reference set includes an evaluation of the sources of variability in the coverage distribution. We observed that the processing steps had an impact on the coverage distribution. For each exon, we compared the observed coverage with the expected normal coverage. Thresholds for determining CNVs were selected to control the false-positive error rate. RefCNV prediction correlated significantly ( r = 0.96–0.86) with CNV measured by digital polymerase chain reaction for MET (7q31), EGFR (7p12), or ERBB2 (17q12) in 13 tumor cell lines. The genome-wide CNV analysis showed a good overall correlation (Spearman's coefficient = 0.82) between RefCNV estimation and publicly available CNV data in Cancer Cell Line Encyclopedia. RefCNV also showed better performance than three other CNV estimation methods in genome-wide CNV analysis.

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