Abstract 1830: Answering the challenge of cancer genomic testing
Authors: Clark Michael J. 
Published: July 2016

Cancer genomes are a hodgepodge of mutations. Single nucleotide somatic variants, complex structural rearrangements, whole and partial gene deletions and amplifications, splice variants and expression changes—these are the potential mutation types in cancer. Tumor heterogeneity is common, tumor samples can be solid or liquid biopsies, and they are often contaminated with adjacent normal tissue. Cancer samples are treated with formalin for archival purposes causing additional genomic variations. All of these factors combine to make genomic analysis in cancer far more complicated than that of normal germline tissue.

Recent efforts to define gold standard human genomes lack many features characteristic of cancer genomes. A single cancer sample may contain a few distinct variant types, but typically not enough to confidently assess the accuracy of cancer variant detection algorithms. The Horizon Quantitative Multiplex, an engineered FFPE cell line mix with variants at allele frequencies (AFs) from 1.0 to 41.5%, and the Acrometrix Oncology Hotspot Control with &gt;500 SNVs and small indels in one sample are available standards. In the Horizon cell line, we detected 18/18 SNVs and 4/4 indels. Meanwhile, we found that the artificial nature of the Acrometrix sample made it incompatible with next generation sequencing. While samples of this type have limited utility, there is a clear need for samples containing the full spectrum of mutation types and frequences.

Since the commercially available standards are of limited utility, we aimed to create a cancer standard set that consists of many cancer cell lines in combination with some real primary cancer samples. We started with a collection of 28 cancer cell lines including 817 known SNVs, 62 small indels, 21 deletions, 23 amplifications, and 14 gene fusions. We simulated tumor heterogeneity by mixing the cell lines at various ratios, generating variant allele frequencies down to 1% and emulated reduced purity by mixing cell lines with paired normal samples at ratios down to 10%. This gave us our platform accuracy metrics for tumor-only somatic analysis.

These experiments required sequencing over 200 samples and mixes. Using augmented exome and cancer gene panel (targeting &gt;1,500 cancer genes), we were able to interrogate substantial quantities of variants. We detected 16136/16146 SNVs at 5% AF, 639/646 indels at 10% AF, 29/30 CNAs at 20% purity, and 14/14 gene fusions using the panel, for example. These high variant counts gave us tighter confidence intervals and accuracy metrics for detection of all major cancer variant types.

After validating our variant calling approaches via this set, we applied our algorithms to primary tumor samples both formalin treated and fresh frozen, and also tested them both with and without a paired normal tissue sample. These gave us the ability to understand the fundamental effects of formalin fixation, and the benefits of including the paired normal sample, which will be covered in this presentation.

Citation Format: Michael J. Clark. Answering the challenge of cancer genomic testing. [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 1830.