In:
Cancer Research, American Association for Cancer Research (AACR), Vol. 76, No. 14_Supplement ( 2016-07-15), p. 3624-3624
Kurzfassung:
Background: As our knowledge of how DNA alterations can drive cancer progression increases, assays that can simultaneously detect multiple types of variants in a simple and cost-effective manner are becoming increasingly crucial. This holds true of copy number variations (CNVs), where evaluation of this type of variant is an important and necessary feature of any solid tumor profiling assay. Conventional methods for detecting CNVs such as immunohistochemistry (IHC), fluorescence in situ hybridization (FISH), and quantitative PCR (qPCR) are limited to detecting only one gene amplification at a time. This can be a significant drawback with FFPE samples, where the DNA is of low abundance and often heavily degraded, while the number of gene amplifications known to be important in cancer continues to grow. Additionally, different tumor types may express amplifications at different rates and expression may be heterogeneous within the tumor, potentially with irregular staining patterns - all of which illustrate the need for new approaches to CNV detection. Methods: Next-generation sequencing (NGS) offers the ability to assess variants in multiple genes using one sample. To that end, Illumina is developing a comprehensive, hybrid capture-based NGS assay targeting 170 key cancer genes for sequencing on the NextSeq1 platform. The assay consists of a DNA workflow for the identification of single-nucleotide variants (SNVs), small insertions and deletions (indels), as well as an RNA workflow for the identification of splice variants and gene fusions. In addition, using the DNA workflow, a novel analysis pipeline, and CNV caller, CNVs from 57 different genes can be simultaneously assessed all by sequencing of a single sample. Results: Here we present data on both cell lines and FFPE samples of SNVs and indels down to 5% allele fraction and CNVs down to ∼2-fold amplification, all from 40 ng of DNA. To demonstrate the accuracy and precision of our CNV detection method, we tested 7 samples for CNVs using orthogonal CNV detection methods. The Illumina NGS assay detected ERBB2 amplifications in 4 out of 7 samples. Of the 4 Illumina NGS positive samples, 3 samples were positive by FISH and all 4 were positive by droplet digital PCR (ddPCR) and the Illumina TruSight Tumor 15 panel. The 3 samples that were negative for ERBB2 amplifications by the Illumina NGS assay were also negative by both FISH and ddPCR. Within these samples we also found a previously unknown FGFR1 amplification. Conclusions: The novel Illumina NGS library preparation method is an innovative and useful tool to find multiple CNVs, along with other variant types, within a single sample. The assay can detect multiple CNVs within a single FFPE sample and identify previously uncharacterized CNVs that could be important in finding the correct treatment for a cancer patient. Citation Format: Chia-Ling Hsieh, Clare Zlatkov, Byron Luo, Chen Zhao, Kathryn Stephens, Han-Yu Chuang, Lisa Kelly, Katherine Chang, Rachel Liang, Jianli Cao, Scott Lang, Ashley Adams, Naseem Ajili, Laurel Ball, Glorianna Caves, Danny Chou, Katie Clark, Brian Crain, Anthony Daulo, Sarah Dumm, Ridwana Ekram, Yonmee Han, Anne Jager, Suzanne Johansen, Li Teng, Jenn Lococo, Jaime McLean, Juli Parks, Jason Rostron, Jennifer Sayne, Jennifer Silhavy, June Snedecor, Mckenzi Toh, Stephanie Tong, Elizabeth Upsall, Paulina Walichiewicz, Xiao Chen, Amanda Young, Ali Kuraishy, Karen Gutekunst, Matt Friedenberg, Charles Lin. Development of a comprehensive and highly sensitive next-generation sequencing assay for detection of copy number variations. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3624.
Materialart:
Online-Ressource
ISSN:
0008-5472
,
1538-7445
DOI:
10.1158/1538-7445.AM2016-3624
Sprache:
Englisch
Verlag:
American Association for Cancer Research (AACR)
Publikationsdatum:
2016
ZDB Id:
2036785-5
ZDB Id:
1432-1
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