Abstract 3899: Integrating RNA/DNA analysis with a comprehensive cancer panel to improve interpretations of stage four metastatic renal cell carcinoma

Abstract

Next-generation sequencing is now commonly applied to measure genomic modifications, providing the opportunity for a more accurate view of cancer progression. That being said, several challenges including tumor purity, heterogeneity and the broad set of cancer associated cellular functions, increase the difficulty of interpreting cancer genomes and make analyzing them distinct from analysis of germline diseases. The ability to detect low allelic mutations in the broad set of cancer-associated genes is critically important. To address these issues, we developed an extended cancer panel, a targeted enrichment sequencing platform that includes over 1,300 cancer genes and 200 miRNAs that improves coverage in traditionally difficult to sequence NGS regions. In this study, we apply an integrated RNA/DNA analysis approach as well as our extended cancer panel to explore mutations in metastatic renal cell carcinoma (RCC) tissue samples before and after treatment.

Our goal with this study was to test the importance of integrating DNA and RNA analysis, which allows for detection of mutational events that cannot be observed by either method alone. With RNA analysis it is possible to detect gene fusion events, expression of small variants (SNVs/indels), and gene expression levels while DNA analysis allows for detection of loss of heterozygosity (LOH), copy number variants (CNVs), and small variants. By integrating both the RNA and DNA analysis, it is possible to identify unique variant classes, such as unexpressed variants, allele specific expression (ASE), effects of CNVs on gene expression levels, and how fusion gene integration sites affect CNVs. In our study, each variant class was interpreted pre- and post-treatment, allowing for optimal analysis of treatment effectiveness.

By applying both our RNA/DNA integration approach and our extended cancer panel to investigate metastatic RCC samples, we were able to classify many genomic alterations that drive cancer progression. For example, we found that anywhere from 5% to 30% of small variant mutations in driver genes called in the DNA reside in genes that are unexpressed in the RNA. Likewise, we noted multiple cases of ASE, where high profile variants observed in DNA were entirely absent in the RNA despite high expression levels. Strong correlations between CNVs observed in DNA and gene expression changes found in RNA were also detected. We also observed that about half of the RCC samples we tested were biallelic for VHL mutations, suggesting inherited predisposition combined with second hit somatic mutations, facilitating cancer progression.

This analysis demonstrates how combining an integrated RNA/DNA approach with a cancer focused augmented enrichment panel allows for detection of both low allelic representation variants and unique variant classes, both of which are critical for accurate interpretation of cancer samples.

Citation Format: Sean M. Boyle, Michael J. Clark, Elena Helman, Alexander S. Parker, Thai Ho, Shujun Luo, Scott Kirk, Parin Sripakdeevong, Mirian Karbelashvili, Deanna M. Church, Michael Snyder, John West, Rich Chen. Integrating RNA/DNA analysis with a comprehensive cancer panel to improve interpretations of stage four metastatic renal cell carcinoma. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3899. doi:10.1158/1538-7445.AM2015-3899