Clinical cancer genomic profiling by three-platform sequencing of whole genome, whole exome and transcriptome

Rusch, Michael; Nakitandwe, Joy; Shurtleff, Sheila; Newman, Scott; Zhang, Zhaojie; Edmonson, Michael N.; Parker, Matthew; Jiao, Yuannian; Ma, Xiaotu; Liu, Yanling; Gu, Jiali; Walsh, Michael F.; Becksfort, Jared; Thrasher, Andrew; Li, Yongjin; McMurry, James; Hedlund, Erin; Patel, Aman; Easton, John; Yergeau, Donald; Vadodaria, Bhavin; Tatevossian, Ruth; Raimondi, Susana C.; Hedges, Dale J.; Chen, Xiang; Hagiwara, Kohei; McGee, Rose B.; Robinson, Giles; Klco, Jeffery M.; Grüber, Tanja A.; Ellison, David W.; Downing, James R.; Zhang, Jinghui

doi:10.1038/s41467-018-06485-7

Cited by 163 publications

(161 citation statements)

References 63 publications

(60 reference statements)

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“…Previously, we have shown that this approach achieved a positive predictive rate of 98.8% and a sensitivity of 94.3% for exonic indels, assuring the quality of the training set (Rusch et al, 2018). A possible limitation of the training set is that indels in the set were predominantly small indels with the maximum length being 23-nt.…”

Section: Discussionmentioning

confidence: 77%

“…In our initial assessment, nearly one third of RNA-Seq indels supported by WES were not validated by further investigations, suggesting that they were PCR artifacts common to both platforms. To construct a high-quality training set, we employed three-platform clinical sequencing of WGS, WES and RNA-Seq with WGS data generated from a PCR-free library protocol (Rusch et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…Importantly, the WGS libraries were prepared by a PCR-free protocol to minimize PCR-artifacts. The details of nucleic acid extraction, library preparation, sequencing and variant detection were previously described (Rusch et al, 2018).…”

Section: Datasetsmentioning

confidence: 99%

“…Two public datasets were used as test sets. The first set (TestSet1) was comprised of 77 RNA-Seq samples of 20 tumor types ( Supplementary Table S2) previously used for developing a clinical pipeline (Rusch et al, 2018)…”

Section: Datasetsmentioning

confidence: 99%

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RNAIndel: discovering somatic coding indels from tumor RNA-Seq data

Hagiwara

Ding

Edmonson

et al. 2019

Preprint

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Reliable identification of expressed somatic insertion/deletion (indels) is an unmet demand due to artifacts generated in PCR-based RNA-Seq library preparation and the lack of normal RNA-Seq data, presenting analytical challenges for discovery of somatic indels in tumor trasncriptome.By implementing features characterized by PCR-free whole-genome and whole-exome sequencing into a machine-learning framework, we present RNAIndel, a tool for predicting somatic, germline and artifact indels from tumor RNA-Seq data alone. RNAIndel robustly predicts 87 93% of somatic indels from 235 samples with heterogeneous conditions, even recovering subclonal (VAF range 0.01-0.15) driver indels missed by targeted deep-sequencing, outperforming the current best-practice for RNA-Seq variant calling which had 57% sensitivity but with 12 times more false positives.RNAIndel is freely available at https://github.com/stjude/RNAIndel Contact: jinghui.zhang@stjude.org IntroductionTranscriptome sequencing (RNA-Seq) is a versatile platform for performing a multitude of cancer genomic analyses such as gene expression profiling, allele specific expression, alternative splicing and fusion transcript detection. However, variant identification in RNA-Seq is not a common practice due to the presence of artifacts introduced in library preparation, the intrinsic complexity of splicing, and RNA editing (Piskol et al., 2013). RNA-Seq data are predominantly generated from tumor-only samples as acquisition of a normal tissue with a comparable transcriptome is a rare practice. This lack of matching normal data further complicates somatic variant discovery in RNA-Seq. Despite these challenges, there are compelling reasons to explore RNA-Seq data for variant detection: 1) RNA variants are expressed, therefore more interpretable to cancer phenotype and clinical actionability than DNA variants; and 2) Some studies only analyze tumor specimen by RNA-Seq, and employing variant detection will make full use of the available data resources. Thus, successful development of RNA-Seq variant calling tools will make this platform an interpretable and cost-effective alternative to DNA-based whole-genome or whole-exome sequencing (DNA-Seq), the current standard platform for somatic variant detection. Various single nucleotide variants (SNV) detection tools dedicated to RNA-Seq have been developed. SNPiR (Piskol et al., 2013) calls true RNA-Seq SNVs by hard-filtering calls in repetitive and low-quality regions, around splice sites, and at known RNA-editing sites. RVboost (Wang et al., 2014) is a machine-learning method to prioritize true SNVs trained on common SNPs in the input RNA-Seq data. eSNV-Detect (Tang et al., 2016) incorporates results generated from two mappers to confidently call expressed SNVs by removing mapping artifacts from individual mappers. Opossum (Oikkonen and Lise 2017) preprocesses RNA-Seq reads for SNV calling by splitting intron-spanning reads and removing spurious reads. By contrast, inde detection in transcriptome is more challengingand has been ...

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Section: Discussionmentioning

confidence: 77%

Section: Discussionmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

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RNAIndel: discovering somatic coding indels from tumor RNA-Seq data

Hagiwara

Ding

Edmonson

et al. 2019

Preprint

Self Cite

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“…WES and RNAseq were performed on the samples at the McGill University and Genome Quebec Innovation Center, according to current standards in precision oncology [55]. Sequencing was done using an Illumina HiSeq 2500 using the 'rapid-run' mode with 100 bp paired-end reads.…”

Section: Wes and Rnaseq Data Generationmentioning

confidence: 99%

Proteogenomics of Colorectal Cancer Liver Metastases: Complementing Precision Oncology with Phenotypic Data

Blank-Landeshammer

Richard

Mitsa

et al. 2019

Cancers

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Hotspot testing for activating KRAS mutations is used in precision oncology to select colorectal cancer (CRC) patients who are eligible for anti-EGFR treatment. However, even for KRAS wildtype tumors anti-EGFR response rates are <30%, while mutated-KRAS does not entirely rule out response, indicating the need for improved patient stratification. We performed proteogenomic phenotyping of KRAS wildtype and KRAS G12V CRC liver metastases (mCRC). Among >9000 proteins we detected considerable expression changes including numerous proteins involved in progression and resistance in CRC. We identified peptides representing a number of predicted somatic mutations, including KRAS G12V . For eight of these, we developed a multiplexed parallel reaction monitoring (PRM) mass spectrometry assay to precisely quantify the mutated and canonical protein variants. This allowed phenotyping of eight mCRC tumors and six paired healthy tissues, by determining mutation rates on the protein level. Total KRAS expression varied between tumors (0.47-1.01 fmol/µg total protein) and healthy tissues (0.13-0.64 fmol/µg). In KRAS G12V -mCRC, G12V-mutation levels were 42-100%, while one patient had only 10% KRAS G12V but 90% KRAS wildtype . This might represent a missed therapeutic opportunity: based on hotspot sequencing, the patient was excluded from anti-EGFR treatment and instead received chemotherapy, while PRM-based tumor-phenotyping indicates the patient might have benefitted from anti-EGFR therapy.

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JWES: a new pipeline for whole genome/exome sequence data processing, management, and gene‐variant discovery, annotation, prediction, and genotyping

et al. 2021

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Whole genome and exome sequencing (WGS/WES) are the most popular next‐generation sequencing (NGS) methodologies and are at present often used to detect rare and common genetic variants of clinical significance. We emphasize that automated sequence data processing, management, and visualization should be an indispensable component of modern WGS and WES data analysis for sequence assembly, variant detection (SNPs, SVs), imputation, and resolution of haplotypes. In this manuscript, we present a newly developed findable, accessible, interoperable, and reusable (FAIR) bioinformatics‐genomics pipeline Java based Whole Genome/Exome Sequence Data Processing Pipeline (JWES) for efficient variant discovery and interpretation, and big data modeling and visualization. JWES is a cross‐platform, user‐friendly, product line application, that entails three modules: (a) data processing, (b) storage, and (c) visualization. The data processing module performs a series of different tasks for variant calling, the data storage module efficiently manages high‐volume gene‐variant data, and the data visualization module supports variant data interpretation with Circos graphs. The performance of JWES was tested and validated in‐house with different experiments, using Microsoft Windows, macOS Big Sur, and UNIX operating systems. JWES is an open‐source and freely available pipeline, allowing scientists to take full advantage of all the computing resources available, without requiring much computer science knowledge. We have successfully applied JWES for processing, management, and gene‐variant discovery, annotation, prediction, and genotyping of WGS and WES data to analyze variable complex disorders. In summary, we report the performance of JWES with some reproducible case studies, using open access and in‐house generated, high‐quality datasets.

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Clinical cancer genomic profiling by three-platform sequencing of whole genome, whole exome and transcriptome

Cited by 163 publications

References 63 publications

RNAIndel: discovering somatic coding indels from tumor RNA-Seq data

RNAIndel: discovering somatic coding indels from tumor RNA-Seq data

Proteogenomics of Colorectal Cancer Liver Metastases: Complementing Precision Oncology with Phenotypic Data

JWES: a new pipeline for whole genome/exome sequence data processing, management, and gene‐variant discovery, annotation, prediction, and genotyping

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