Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Whole Genome Sequencing Informs Therapeutic Selection for Pancreatic Cancer

ABSTRACT: Pancreatic cancer remains one of the most lethal of malignancies and a major health burden. We performed whole genome sequencing and CNV analysis of 100 pancreatic ductal adenocarcinomas. Chromosomal rearrangements leading to gene disruption were frequent, affecting genes known to be important in pancreatic cancer (TP53, SMAD4, CDKN2A, ARID1A, ROBO2) and novel candidate drivers of pancreatic carcinogenesis (KDM6A and PREX2). Patterns of structural variation classified PDAC into 4 subtypes with potential clinical utility: stable, locally rearranged, scattered and unstable. A significant proportion harboured focal amplifications, many of which contained druggable oncogenes (ERBB2, MET, FGFR1, CDK6, PIK3R3, and PIK3CA), but at low individual frequency. Genomic instability co-segregated with inactivation of DNA maintenance genes (BRCA1, BRCA2, PALB2 or RPA1), and a mutational signature of DNA damage repair deficiency. This subgroup was associated with response to platinum-based therapy and defines candidate biomarkers of therapeutic responsiveness. DNA was assayed using Illumina SNP BeadChips as per manufacturerM-bM-^@M-^Ys instructions (Illumina, San Diego CA) (HumanOmni1-Quad or HumanOmni2.5-8 BeadChips). SNP arrays were scanned and data was processed using the Genotyping module (v1.8.4) in Genomestudio v2010.3 (Illumina, San Diego CA) to calculate B-allele frequencies (BAF) and logR values. GenoCN4 and GAP5 were used to call somatic regions of copy number change M-bM-^@M-^S gain, loss or copy neutral LOH. Recurrent regions of copy number change were determined and genes within these regions were extracted using ENSEMBL v70 annotations. Significant regions of gain and loss were identified by GISTIC6.

ORGANISM(S): Homo sapiens

SUBMITTER: Katia Nones

PROVIDER: E-GEOD-61502 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Whole genomes redefine the mutational landscape of pancreatic cancer.

Waddell Nicola N Pajic Marina M Patch Ann-Marie AM Chang David K DK Kassahn Karin S KS Bailey Peter P Johns Amber L AL Miller David D Nones Katia K Quek Kelly K Quinn Michael C J MC Robertson Alan J AJ Fadlullah Muhammad Z H MZ Bruxner Tim J C TJ Christ Angelika N AN Harliwong Ivon I Idrisoglu Senel S Manning Suzanne S Nourse Craig C Nourbakhsh Ehsan E Wani Shivangi S Wilson Peter J PJ Markham Emma E Cloonan Nicole N Anderson Matthew J MJ Fink J Lynn JL Holmes Oliver O Kazakoff Stephen H SH Leonard Conrad C Newell Felicity F Poudel Barsha B Song Sarah S Taylor Darrin D Waddell Nick N Wood Scott S Xu Qinying Q Wu Jianmin J Pinese Mark M Cowley Mark J MJ Lee Hong C HC Jones Marc D MD Nagrial Adnan M AM Humphris Jeremy J Chantrill Lorraine A LA Chin Venessa V Steinmann Angela M AM Mawson Amanda A Humphrey Emily S ES Colvin Emily K EK Chou Angela A Scarlett Christopher J CJ Pinho Andreia V AV Giry-Laterriere Marc M Rooman Ilse I Samra Jaswinder S JS Kench James G JG Pettitt Jessica A JA Merrett Neil D ND Toon Christopher C Epari Krishna K Nguyen Nam Q NQ Barbour Andrew A Zeps Nikolajs N Jamieson Nigel B NB Graham Janet S JS Niclou Simone P SP Bjerkvig Rolf R Grützmann Robert R Aust Daniela D Hruban Ralph H RH Maitra Anirban A Iacobuzio-Donahue Christine A CA Wolfgang Christopher L CL Morgan Richard A RA Lawlor Rita T RT Corbo Vincenzo V Bassi Claudio C Falconi Massimo M Zamboni Giuseppe G Tortora Giampaolo G Tempero Margaret A MA Gill Anthony J AJ Eshleman James R JR Pilarsky Christian C Scarpa Aldo A Musgrove Elizabeth A EA Pearson John V JV Biankin Andrew V AV Grimmond Sean M SM

Nature 20150201 7540

Pancreatic cancer remains one of the most lethal of malignancies and a major health burden. We performed whole-genome sequencing and copy number variation (CNV) analysis of 100 pancreatic ductal adenocarcinomas (PDACs). Chromosomal rearrangements leading to gene disruption were prevalent, affecting genes known to be important in pancreatic cancer (TP53, SMAD4, CDKN2A, ARID1A and ROBO2) and new candidate drivers of pancreatic carcinogenesis (KDM6A and PREX2). Patterns of structural variation (var ...[more]

PMID: 25719666

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Project description:Illumina Infinium whole genome genotyping (WGG) arrays are increasingly being applied in cancer genomics to study gene copy number alterations and allele-specific aberrations such as loss-of-heterozygosity (LOH). Methods developed for normalization of WGG arrays have mostly focused on diploid, normal samples. However, for cancer samples genomic aberrations may confound normalization and data interpretation. Therefore, we examined the effects of the conventionally used normalization method for Illumina Infinium arrays when applied to cancer samples. We demonstrate an asymmetry in the detection of the two alleles for each SNP, which deleteriously influences both allelic proportions and copy number estimates. The asymmetry is caused by a remaining bias between the two dyes used in the Infinium II assay after using the normalization method in Illumina’s proprietary software (BeadStudio). We propose a quantile normalization strategy for correction of this dye bias. We tested the normalization strategy using 535 individual hybridizations from 10 data sets from the analysis of cancer genomes and normal blood samples generated on Illumina Infinium II 300k version 1 and 2, 370k and 550k BeadChips. We show that the proposed normalization strategy successfully removes asymmetry in estimates of both allelic proportions and copy numbers. Additionally, the normalization strategy reduces the technical variation for copy number estimates while retaining the response to copy number alterations. The proposed normalization strategy represents a valuable low-level analysis tool that improves the quality of data obtained from Illumina Infinium arrays, in particular when used for LOH and copy number variation studies. To investigate the effects of a quantile normalization of Illumina Infinium data, compared to conventional normalization using BeadStudio (www.illumina.com), we renormalized 535 individual hybridizations conducted on Illumina 300K, 370K and 550K BeadChips. Sample types included breast cancer, colon cancer, urothelial carcinoma, leukemia as well as normal blood and HapMap samples. This series includes the 6 breast cancers hybridized on Illumina HumanHap 550K BeadChips.

Dataset Information

Whole Genome Sequencing Informs Therapeutic Selection for Pancreatic Cancer

Publications

Whole genomes redefine the mutational landscape of pancreatic cancer.

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