Sir Peter MacCallum Department of Oncology - Research Publications
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ItemA simple consensus approach improves somatic mutation prediction accuracy(BMC, 2013-09-30)Differentiating true somatic mutations from artifacts in massively parallel sequencing data is an immense challenge. To develop methods for optimal somatic mutation detection and to identify factors influencing somatic mutation prediction accuracy, we validated predictions from three somatic mutation detection algorithms, MuTect, JointSNVMix2 and SomaticSniper, by Sanger sequencing. Full consensus predictions had a validation rate of >98%, but some partial consensus predictions validated too. In cases of partial consensus, read depth and mapping quality data, along with additional prediction methods, aided in removing inaccurate predictions. Our consensus approach is fast, flexible and provides a high-confidence list of putative somatic mutations.
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ItemExome Sequencing Identifies Rare Deleterious Mutations in DNA Repair Genes FANCC and BLM as Potential Breast Cancer Susceptibility Alleles(PUBLIC LIBRARY SCIENCE, 2012-09)Despite intensive efforts using linkage and candidate gene approaches, the genetic etiology for the majority of families with a multi-generational breast cancer predisposition is unknown. In this study, we used whole-exome sequencing of thirty-three individuals from 15 breast cancer families to identify potential predisposing genes. Our analysis identified families with heterozygous, deleterious mutations in the DNA repair genes FANCC and BLM, which are responsible for the autosomal recessive disorders Fanconi Anemia and Bloom syndrome. In total, screening of all exons in these genes in 438 breast cancer families identified three with truncating mutations in FANCC and two with truncating mutations in BLM. Additional screening of FANCC mutation hotspot exons identified one pathogenic mutation among an additional 957 breast cancer families. Importantly, none of the deleterious mutations were identified among 464 healthy controls and are not reported in the 1,000 Genomes data. Given the rarity of Fanconi Anemia and Bloom syndrome disorders among Caucasian populations, the finding of multiple deleterious mutations in these critical DNA repair genes among high-risk breast cancer families is intriguing and suggestive of a predisposing role. Our data demonstrate the utility of intra-family exome-sequencing approaches to uncover cancer predisposition genes, but highlight the major challenge of definitively validating candidates where the incidence of sporadic disease is high, germline mutations are not fully penetrant, and individual predisposition genes may only account for a tiny proportion of breast cancer families.
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ItemMicroRNA Genes and Their Target 3′-Untranslated Regions Are Infrequently Somatically Mutated in Ovarian Cancers(PUBLIC LIBRARY SCIENCE, 2012-04-20)MicroRNAs are key regulators of gene expression and have been shown to have altered expression in a variety of cancer types, including epithelial ovarian cancer. MiRNA function is most often achieved through binding to the 3'-untranslated region of the target protein coding gene. Mutation screening using massively-parallel sequencing of 712 miRNA genes in 86 ovarian cancer cases identified only 5 mutated miRNA genes, each in a different case. One mutation was located in the mature miRNA, and three mutations were predicted to alter the secondary structure of the miRNA transcript. Screening of the 3'-untranslated region of 18 candidate cancer genes identified one mutation in each of AKT2, EGFR, ERRB2 and CTNNB1. The functional effect of these mutations is unclear, as expression data available for AKT2 and EGFR showed no increase in gene transcript. Mutations in miRNA genes and 3'-untranslated regions are thus uncommon in ovarian cancer.
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ItemCONTRA: copy number analysis for targeted resequencing(OXFORD UNIV PRESS, 2012-05-15)MOTIVATION: In light of the increasing adoption of targeted resequencing (TR) as a cost-effective strategy to identify disease-causing variants, a robust method for copy number variation (CNV) analysis is needed to maximize the value of this promising technology. RESULTS: We present a method for CNV detection for TR data, including whole-exome capture data. Our method calls copy number gains and losses for each target region based on normalized depth of coverage. Our key strategies include the use of base-level log-ratios to remove GC-content bias, correction for an imbalanced library size effect on log-ratios, and the estimation of log-ratio variations via binning and interpolation. Our methods are made available via CONTRA (COpy Number Targeted Resequencing Analysis), a software package that takes standard alignment formats (BAM/SAM) and outputs in variant call format (VCF4.0), for easy integration with other next-generation sequencing analysis packages. We assessed our methods using samples from seven different target enrichment assays, and evaluated our results using simulated data and real germline data with known CNV genotypes.