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. 2018 Sep 27;9(1):3962.
doi: 10.1038/s41467-018-06485-7.

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

Michael Rusch  1   2 , Joy Nakitandwe  2   3 , Sheila Shurtleff  2   3 , Scott Newman  1   2 , Zhaojie Zhang  1   2 , Michael N Edmonson  1   2 , Matthew Parker  4 , Yuannian Jiao  1   2 , Xiaotu Ma  1   2 , Yanling Liu  1   2 , Jiali Gu  2   3 , Michael F Walsh  2   5 , Jared Becksfort  1   2 , Andrew Thrasher  1   2 , Yongjin Li  1   2 , James McMurry  2   6 , Erin Hedlund  1   2 , Aman Patel  1   2 , John Easton  1   2 , Donald Yergeau  1   2 , Bhavin Vadodaria  1   2 , Ruth G Tatevossian  2   3 , Susana Raimondi  2   3 , Dale Hedges  1   2 , Xiang Chen  1   2 , Kohei Hagiwara  1   2 , Rose McGee  2   5 , Giles W Robinson  2   5 , Jeffery M Klco  2   3 , Tanja A Gruber  2   3   5 , David W Ellison  7   8 , James R Downing  9   10 , Jinghui Zhang  11   12
Affiliations

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

Michael Rusch et al. Nat Commun. .

Abstract

To evaluate the potential of an integrated clinical test to detect diverse classes of somatic and germline mutations relevant to pediatric oncology, we performed three-platform whole-genome (WGS), whole exome (WES) and transcriptome (RNA-Seq) sequencing of tumors and normal tissue from 78 pediatric cancer patients in a CLIA-certified, CAP-accredited laboratory. Our analysis pipeline achieves high accuracy by cross-validating variants between sequencing types, thereby removing the need for confirmatory testing, and facilitates comprehensive reporting in a clinically-relevant timeframe. Three-platform sequencing has a positive predictive value of 97-99, 99, and 91% for somatic SNVs, indels and structural variations, respectively, based on independent experimental verification of 15,225 variants. We report 240 pathogenic variants across all cases, including 84 of 86 known from previous diagnostic testing (98% sensitivity). Combined WES and RNA-Seq, the current standard for precision oncology, achieved only 78% sensitivity. These results emphasize the critical need for incorporating WGS in pediatric oncology testing.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Design of clinical three-platform sequencing. a Overview of sequencing, variant detection, variant classification, panel review and report generation. Chromothripsis was used as an annotation for ploidy report and we followed guidelines by Korbel and Campbell. b Selection of 78 pediatric cancer patients with biomarkers identified by multiple molecular pathology assays
Fig. 2
Fig. 2
Integrative analysis of structural variation. a Multi-platform, multi-analysis approach for detecting SV by three-platform sequencing. SV detection is run using RNA-Seq and WES of the tumor sample, and integrated SV and CNA detection are run using WGS of the tumor and normal samples. b, c An example of linking three fusion transcripts of DIP2C-PDGFRA detected in RNA-Seq to DNA SVs detected in WGS in a high-grade glioma. The fusion transcripts are shown in protein view (b) with the domains marked in color and the vertical dotted lines marking the boundaries of each exon with chimeric RNA read counts indicated above the PDGFRA ideogram and wildtype exon 9–10 read counts below. The fusion transcript involving PDGFRA exon 10—marked by the gray line in the PDGFRA protein view (b)—is linked to a novel DNA SV involving chromosome 4, 11 and 10, shown using the gray line in (c), while the other two fusion transcripts, marked by the purple lines in (b), are linked to the same DNA SV involving chromosome 4 and 10, shown using the purple line in (c)
Fig. 3
Fig. 3
Accuracy of somatic variant detection based on capture validation of 18 cases with PCGP data. a Design of capture validation for measuring sensitivity and PPV of our analytical pipeline using somatic exonic SNV/indel detection as an example. For exonic SNV/indel, variants that passed the cross-validation filter and had adequate coverage from custom capture sequencing were considered “predicted positives”. Predicted positives that were validated by capture sequencing are considered true positives whereas all variants—including those that failed to be detected or those filtered by cross-validation filtering—that were validated by capture sequencing are considered “actual positives”. b Summary of PPV (true positives/predicted positives) for each variant type. The predicted positive variants for exonic indels and SNVs are the variants that pass the cross-validation filter, whereas other SNVs, which refers to high-confidence non-exonic SNVs, are based on WGS only; as such, they are reported separately. Our test does not report non-exonic indels, so they are omitted. c Summary of sensitivity (true positives/actual positives) for each variant type. For exonic SNV/indel, most of the variants are detected by our variant detection pipeline on both WGS and WES (WGS+WES) platforms; of those that are detected by our pipeline on one platform, results for WGS and WES were comparable, with slightly more detected by WGS. The “Missed” variants are the false negatives; they were removed by cross-validation filtering or only detected by PCGP
Fig. 4
Fig. 4
Comparison of clinical-PCGP concordance of two osteosarcoma cases. For each case, two venn diagrams show overlap between PCGP calls and clinical calls, and a scatter plot compares the SNV MAF obtained by capture validation performed during the present clinical study (x-axis) and previously by the PCGP validation lab (y-axis). The SNVs used for a MAF plot included both exonic and non-exonic variants. The tumor samples used for both cases were different than the tumor samples in PCGP, and both showed lower-than-average sensitivity. a MAFs from SJOS001 SNV calls in PCGP and Clinical were highly correlated, and the SVs and exonic SNVs showed higher correlation. b The lower tumor purity of the SJOS013 clinical sample is reflected in the MAF distribution. Differences between the PCGP and clinical samples in this case reduced the measured sensitivity
Fig. 5
Fig. 5
Detection of pathogenic or likely pathogenic variants by three-platform sequencing. a Number of variants detected by each platform either alone or in combination. b Comparison of copy number alterations detected by PCGP (outer circle) and three-platform sequencing (inner circle). The left CIRCOS plot depicts the CNAs detected on chromosome 13 showing near-perfect agreement between the samples analyzed by PCGP and three-platform sequencing. The right CIRCOS plot depicts chromosome 2, where a MYCN amplicon was detected only in the PCGP sample
Fig. 6
Fig. 6
Examples of novel findings due to inclusion of WGS. a Illustration of residual tumor detected in the normal sample of an AML. Associated read counts can be found in Table 1. b Germline TP53 deletion detected in a medulloblastoma. The tumor genome has a second somatic hit (i.e. a 13.7 Mb deletion) resulting in homozygous deletion of TP53. The tumor genome, shown in CIRCOS, underwent complex re-arrangement resulting in high amplification in GLI2, MYCN, and CCND2. c A variant of uncertain significance (VUS) in DNMT3A in a neuroblastoma. This variant deletes exons 3–6 of DNMT3A with aberrant transcription predicted to result in an in-frame deletion of the protein. The purple line linking exons 2–7 is annotated with the RNA-Seq read count of the aberrant splice junction caused by the deletion. The read count supporting each canonical splice junction is displayed underneath
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