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Diagnosis of fusion genes using targeted RNA sequencing.

ABSTRACT: Fusion genes are a major cause of cancer. Their rapid and accurate diagnosis can inform clinical action, but current molecular diagnostic assays are restricted in resolution and throughput. Here, we show that targeted RNA sequencing (RNAseq) can overcome these limitations. First, we establish that fusion gene detection with targeted RNAseq is both sensitive and quantitative by optimising laboratory and bioinformatic variables using spike-in standards and cell lines. Next, we analyse a clinical patient cohort and improve the overall fusion gene diagnostic rate from 63% with conventional approaches to 76% with targeted RNAseq while demonstrating high concordance for patient samples with previous diagnoses. Finally, we show that targeted RNAseq offers additional advantages by simultaneously measuring gene expression levels and profiling the immune-receptor repertoire. We anticipate that targeted RNAseq will improve clinical fusion gene detection, and its increasing use will provide a deeper understanding of fusion gene biology.

SUBMITTER: Heyer EE

PROVIDER: S-EPMC6437215 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Diagnosis of fusion genes using targeted RNA sequencing.

Heyer Erin E EE Deveson Ira W IW Wooi Danson D Selinger Christina I CI Lyons Ruth J RJ Hayes Vanessa M VM O'Toole Sandra A SA Ballinger Mandy L ML Gill Devinder D Thomas David M DM Mercer Tim R TR Blackburn James J

Nature communications 20190327 1

Fusion genes are a major cause of cancer. Their rapid and accurate diagnosis can inform clinical action, but current molecular diagnostic assays are restricted in resolution and throughput. Here, we show that targeted RNA sequencing (RNAseq) can overcome these limitations. First, we establish that fusion gene detection with targeted RNAseq is both sensitive and quantitative by optimising laboratory and bioinformatic variables using spike-in standards and cell lines. Next, we analyse a clinical p ...[more]

PMID: 30918253

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Project description:MOTIVATION:Cancer is a complex disease that involves rapidly evolving cells, often forming multiple distinct clones. In order to effectively understand progression of a patient-specific tumor, one needs to comprehensively sample tumor DNA at multiple time points, ideally obtained through inexpensive and minimally invasive techniques. Current sequencing technologies make the 'liquid biopsy' possible, which involves sampling a patient's blood or urine and sequencing the circulating cell free DNA (cfDNA). A certain percentage of this DNA originates from the tumor, known as circulating tumor DNA (ctDNA). The ratio of ctDNA may be extremely low in the sample, and the ctDNA may originate from multiple tumors or clones. These factors present unique challenges for applying existing tools and workflows to the analysis of ctDNA, especially in the detection of structural variations which rely on sufficient read coverage to be detectable. RESULTS:Here we introduce SViCT?, a structural variation (SV) detection tool designed to handle the challenges associated with cfDNA analysis. SViCT?can detect breakpoints and sequences of various structural variations including deletions, insertions, inversions, duplications and translocations. SViCT?extracts discordant read pairs, one-end anchors and soft-clipped/split reads, assembles them into contigs, and re-maps contig intervals to a reference genome using an efficient k-mer indexing approach. The intervals are then joined using a combination of graph and greedy algorithms to identify specific structural variant signatures. We assessed the performance of SViCT?and compared it to state-of-the-art tools using simulated cfDNA datasets with properties matching those of real cfDNA samples. The positive predictive value and sensitivity of our tool was superior to all the tested tools and reasonable performance was maintained down to the lowest dilution of 0.01% tumor DNA in simulated datasets. Additionally, SViCT?was able to detect all known SVs in two real cfDNA reference datasets (at 0.6-5% ctDNA) and predict a novel structural variant in a prostate cancer cohort. AVAILABILITY:SViCT?is available at https://github.com/vpc-ccg/svict. Contact:faraz.hach@ubc.ca.

Dataset Information

Diagnosis of fusion genes using targeted RNA sequencing.

Publications

Diagnosis of fusion genes using targeted RNA sequencing.

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