Abstract B13: Whole-genome mutation landscape in pancreatic ductal adenocarcinoma.

Abstract

Pancreatic ductal adenocarcinoma (PDAC) is a rare cancer with a very high mortality rate. Because it is extremely difficult to detect at an early stage; PDAC tumors often spread to regional lymph nodes or distant metastases by the time they are diagnosed. Published reports have already identified a number of chromosomal alterations at many genomic levels; however PDAC still lacks a comprehensive catalogue for the whole genome mutation spectrum. The goal of our study is to annotate all types of identifiable genomic aberrations based on whole genome sequencing of 5 PDAC tumors. It is a common knowledge that surgical primary tissues of PDAC have very low tumor content. Hence, for our study all five primary tumors have been modeled as xenografts using NOD-SCID mice to enrich for tumor cells. Here, we report on the cancer-specific genome alterations in 5 PDAC tumors and show that xenograft models do represent genomic landscape of primary tumors. All samples were whole-genome sequenced using Illumina HiSeq to give a minimum coverage of 30X. We have developed an analysis pipeline to identify somatic single nucleotide variations (SNVs) using The Genome Analysis Tool Kit (GATK), copy number alterations (CNAs) using KSseg (in-house CNV algorithm) and structural variations using Geometric Analysis of Structural Variants (GASV). A number of filters have been implemented to separate germline variants and mouse derived contamination from the cancer specific somatic variation. Our analysis has identified an average of 1527 SNVs, 1555 INDELs, and 53 CNAs per PDAC genome (combined for primary and xenograft). All somatic SNVs were verified using Ion Torrent based sequencing technology with a verification rate of 93%. CNAs were verified using Nimblegen 2.1M Array-based Comparative Genome Hybridization technology, and produced a verification rate of greater than 98% for losses and 60-97% for gains. We also observed a high level of overlap between primary tumor and xenograft samples, with 84% of total primary tumor SNVs and 61% of INDELS (called across all samples) being found in the correlating xenograft genome. After verification of SNVs by deep sequencing, we observe an additional 50% of SNVs that were called only in the xenograft samples validate in the primary sample. Our results show that the somatic single nucleotide mutation rate is in the range of 1 - 4 SNVs/Mb and there is a statistically significant increase in the G>T transversions. It is well known that methylated CpG dinucleotides are the preferred sites for G > T transversions and we are investigating the role played by DNA methylation alterations. All somatic variants were annotated using an in-house software package based on Sequence Ontology classification of variant effects to integrate different types of variations and provide a functional interpretation. Our analysis has identified 290 genes that are functionally impacted in 4 or more genomes by any type of mutation. They include 6 known oncogenes, 10 protein kinases, 9 cell differentiation markers, 17 transcription factors and 6 cytokines and growth factors. Functional enrichment analysis on this gene set using MSigDB v3.0 database shows important cancer-related pathways including the NK cells pathway, the Adherens junctions interactions pathway, and the axon guidance signaling pathway. Citation Format: Carson Holt, Fouad Yousif, Lee Timms, Michelle Sam, Kimberly Begley, Thomas Hudson, John D. McPherson, Lincoln D. Stein, Lakshmi B. Muthuswamy, Christina Yung, Tim Beck, Bojan Losic, Niloofar Arshadi, Christine Ouelltt, Irinia Kalatskaya, Richard de Borja, Robert Denroche. Whole-genome mutation landscape in pancreatic ductal adenocarcinoma. [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Progress and Challenges; Jun 18-21, 2012; Lake Tahoe, NV. Philadelphia (PA): AACR; Cancer Res 2012;72(12 Suppl):Abstract nr B13.