In:
Cancer Research, American Association for Cancer Research (AACR), Vol. 77, No. 13_Supplement ( 2017-07-01), p. 3732-3732
Abstract:
Expanding the paradigm of solid tumor profiling from single-gene testing to comprehensive panels presents many challenges. One such challenges is the ability of these panels to detect genetic alterations from FFPE samples, where the DNA is of low abundance and often heavily compromised. Despite these challenges, next-generation sequencing (NGS) offers the ability to assess multiple variants simultaneously in an ever-expanding list of relevant tumor genes. To that end, Illumina developed a comprehensive, hybrid capture-based NGS assay targeting 170 key cancer genes that is FFPE optimized. The assay consists of a DNA workflow for the identification of single and multiple nucleotide variants (SNVs, MNVs), small insertions and deletions (indels), gene amplifications, as well as a RNA workflow for the identification of splice variants and gene fusions. Following sequencing on the NextSeq® or HiSeq® instruments, the analytical pipeline initiates variant calling. The DNA aligner and variant callers were first optimized against the simulated read data from & gt;40,000 COSMIC[1] mutations reported in the exons of the 170 genes. To reduce false positive variant calling due to systematic errors, each variant call was evaluated against its locus specific background error distribution. This distribution was compiled from a panel of FFPE normal samples and was also used to normalize against systematic bias in read coverage to increase the accuracy of amplification calling. Furthermore, gene amplification calling was improved by the addition of enhancer probes to the hybrid capture pool. The analytical sensitivity and specificity of TruSight® Tumor 170* was assessed on a large collection of FFPE samples and reference material. A panel of 72 cancer samples, including multiple tissue types, reference standards, and cell line and FFPE mixes were used to evaluate the limit of detection. The samples contained 533 SNVs, 80 indels including deletions up to 30 base pairs and insertions up to 31 base pairs, 4 MNVs, and 31 gene amplifications, characterized by orthogonal testing methods. Using 40 ng DNA input, detection sensitivity of the & gt;1000 variants (including replicates) tested at variant allele frequencies down to ~5% was at 99.6%, while detection sensitivity of gene amplifications as low as 1.45x to 2.2x was at 98%. For limit of blank samples, a panel of 24 normal samples was used. Again using 40 ng DNA input, we show & gt;99% specificity for small variant calling and & gt;95% specificity for gene amplification calling. These data demonstrates the TruSight® Tumor 170 is able to detect multiple variant types within a single sample at low nucleic acid input, while exhibiting high analytical sensitivity and specificity for low allele fraction detection. [1] Forbes, et al. (2015) *For Research Use Only. Not for use in diagnostic procedures. Citation Format: Danny Chou, Xiao Chen, Austin Purdy, Li Teng, Byron Luo, Chen Zhao, Laurel Ball, Allan Castaneda, Katie Clark, Brian Crain, Anthony Daulo, Manh Do, Tingting Du, Sarah Dumm, Yonmee Han, Michael Havern, Chia-Ling Hsieh, Tingting Jiang, Suzanne Johansen, Scott Lang, Rachel Liang, Jennifer S. LoCoco, Jaime McLean, Yousef Nassiri, Jason Rostron, Jennifer Silhavy, June Snedecor, Natasha Talago, Kevin Wu, Clare Zlatkov, Ali Kuraishy, Karen Gutekunst, Sohela De Rozieres, Matthew Friedenberg, Han-Yu Chuang, Anne C. Jager. Analytical performance of TruSight® Tumor 170 on small nucleotide variations and gene amplifications using DNA from formalin-fixed, paraffin-embedded (FFPE) solid tumor samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3732. doi:10.1158/1538-7445.AM2017-3732
Type of Medium:
Online Resource
ISSN:
0008-5472
,
1538-7445
DOI:
10.1158/1538-7445.AM2017-3732
Language:
English
Publisher:
American Association for Cancer Research (AACR)
Publication Date:
2017
detail.hit.zdb_id:
2036785-5
detail.hit.zdb_id:
1432-1
detail.hit.zdb_id:
410466-3
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