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. 2013 Nov;31(11):1023-31.
doi: 10.1038/nbt.2696. Epub 2013 Oct 20.

Development and Validation of a Clinical Cancer Genomic Profiling Test Based on Massively Parallel DNA Sequencing

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Free PMC article

Development and Validation of a Clinical Cancer Genomic Profiling Test Based on Massively Parallel DNA Sequencing

Garrett M Frampton et al. Nat Biotechnol. .
Free PMC article

Abstract

As more clinically relevant cancer genes are identified, comprehensive diagnostic approaches are needed to match patients to therapies, raising the challenge of optimization and analytical validation of assays that interrogate millions of bases of cancer genomes altered by multiple mechanisms. Here we describe a test based on massively parallel DNA sequencing to characterize base substitutions, short insertions and deletions (indels), copy number alterations and selected fusions across 287 cancer-related genes from routine formalin-fixed and paraffin-embedded (FFPE) clinical specimens. We implemented a practical validation strategy with reference samples of pooled cell lines that model key determinants of accuracy, including mutant allele frequency, indel length and amplitude of copy change. Test sensitivity achieved was 95-99% across alteration types, with high specificity (positive predictive value >99%). We confirmed accuracy using 249 FFPE cancer specimens characterized by established assays. Application of the test to 2,221 clinical cases revealed clinically actionable alterations in 76% of tumors, three times the number of actionable alterations detected by current diagnostic tests.

Conflict of interest statement

COMPETING FINANCIAL INTERESTS

The authors declare competing financial interests: details are available in the online version of the paper.

Figures

Figure 1
Figure 1
NGS-based cancer genomic profiling test workflow. (a) DNA is extracted from routine FFPE biopsy or surgical specimens. (b) 50–200 ng of DNA undergoes whole-genome shotgun library construction and hybridization-based capture of 4,557 exons of 287 cancer-related genes and 47 introns of 19 genes frequently rearranged in solid tumors. Hybrid-capture libraries are sequenced to high depth using the Illumina HiSeq2000 platform. (c) Sequence data are processed using a customized analysis pipeline designed to accurately detect multiple classes of genomic alterations: base substitutions, short insertions/deletions, copy-number alterations and selected gene fusions. (d) Detected mutations are annotated according to clinical significance and reported.
Figure 2
Figure 2
Base substitution and indel detection performance. (Base substitutions given in panels a–c, indels in panels d–f.) (a) Expected allele frequencies of base substitution alterations within the test set. (b) Detection sensitivity as a function of sample median exon coverage. Error bars, s.e.m. (c) Allele frequencies measured in pooled samples (y axis) match the frequencies expected based on the genotypes and mixing ratios of constituent cell lines (x axis). (d) Expected allele frequencies of indel alterations within the test set. (e) Detection sensitivity as a function of sample median exon coverage. Error bars, s.e.m. (f) Allele frequencies measured in pooled samples (y axis) match the frequencies expected based on the genotypes, ploidy and mixing ratios of constituent cell lines (x axis).
Figure 3
Figure 3
CNA detection performance. (a) Example of CNA data. HCC2218 cell line mixed with matched normal sample at 100% (a), 50% (b) and 20% tumor content (c). Y axes denote log-ratio measurements of coverage obtained in test samples versus a normal reference sample, with assessed copy numbers marked by dashed lines. Each point denotes a genomic region measured by the assay (blue exon, cyan SNP), and these are ordered by genomic position. Red lines indicate average log-ratio in a segment, whereas green lines illustrate the model prediction. Asterisks denote the detected CDH1 homozygous deletion (chr16) and ERBB2 amplification (chr17). (d) Summary of sensitivity results of CNA calling validation study for focal amplifications and homozygous deletions in samples with tumor fractions ≥30% and 20–30%. Error bars, s.e.m.
Figure 4
Figure 4
Concordance with clinical testing on FFPE specimens. Tumor specimens (N = 249) were assayed using the NGS-based test and by several other methods. (a) Overlap between positive alteration calls by NGS and Sequenom or gel sizing at 91 mutually tested sites in 118 FFPE clinical cancer specimens. (b) Specific alterations comprising the 97 concordant calls. (c) Histogram of NGS MAF in the 97 concordant calls. The high prevalence of low MAF in clinical cancer specimens is highlighted. (d) Examples of confirmation of NGS CNAs by IHC. (e) Summary of concordance between NGS, FISH and IHC findings across four genes (Supplementary Table 12).
Figure 5
Figure 5
Reproducibility of mutation detection in FFPE specimens. Two large colon cancer resections were repeatedly tested in separate process batches. DNA was first extracted from all of the tissue in each FFPE block in several batches, pooled, and then used to make 200 ng aliquots. The reproducibility of mutation detection and measured MAF were examined. The MAF measured for the known somatic alterations in these samples is shown, with samples ordered left to right from earliest to latest. (a) One specimen was tested 79 times between November 9, 2011 and June 17, 2012. Three known somatic mutations (COSMIC) were detected in this sample: APC c.4394-4395insAG p.S1465fs*9, KRAS c.35G>A p.G12D, and PTEN c.235G>A p.A79T. Each of these mutations was successfully detected in 79/79 tests. (b) A second specimen was tested 71 times between July 12, 2012 and October 21, 2012. Two known somatic mutations were detected in this sample: APC c.694C>T p.R232* and KRAS c.35G>T p.G12V. Each of these mutations was successfully detected in 71/71 tests.
Figure 6
Figure 6
Clinically actionable alterations in patient samples. (a) Distribution of tumor tissue of origin (type) for profiled specimens. (b) Frequency of all reported alterations in most commonly altered genes among the specimens. Alterations are colored according to alteration class, as depicted in panel c. Error bars, s.e.m. (c) Distribution of clinically actionable alteration classes detected. (d) Frequency of ERBB2 alterations detected among specimens of various tumor types. Alterations are colored according to class, as in panel c. Error bars, s.e.m. (e) Distribution of substitution and indel mutations, across the domain structure of the ERBB2 protein. Individual mutations are represented as triangles colored according to the tumor type.

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