Sicotte, Hugues

Whole exome sequencing (WES) provides an unprecedented opportunity to identify the potential aetiological role of rare functional variants in human complex diseases. Large-scale collaborations have generated germline WES data on patients with a number of diseases, especially cancer, but less often on healthy controls under the same sequencing procedures. These data can be a valuable resource for identifying new disease susceptibility loci if study designs are appropriately applied. This review describes suggested strategies and technical considerations when focusing on case-only study designs that use WES data in complex disease scenarios. These include variant filtering based on frequency and functionality, gene prioritisation, interrogation of different data types and targeted sequencing validation. We propose that if case-only WES designs were applied in an appropriate manner, new susceptibility genes containing rare variants for human complex diseases can be detected.

Abstract Motivation: Exome sequencing (exome-seq) data, which are typically used for calling exonic mutations, have also been utilized in detecting DNA copy number variations (CNVs). Despite the existence of several CNV detection tools, there is still a great need for a sensitive and an accurate CNV-calling algorithm with built-in QC steps, and does not require a paired reference for each sample. Results: We developed a novel method named PatternCNV, which (i) accounts for the read coverage variations between exons while leveraging the consistencies of this variability across different samples; (ii) reduces alignment BAM files to WIG format and therefore greatly accelerates computation; (iii) incorporates multiple QC measures designed to identify outlier samples and batch effects; and (iv) provides a variety of visualization options including chromosome, gene and exon-level views of CNVs, along with a tabular summarization of the exon-level CNVs. Compared with other CNV-calling algorithms using data from a lymphoma exome-seq study, PatternCNV has higher sensitivity and specificity. Availability and implementation: The software for PatternCNV is implemented using Perl and R, and can be used in Mac or Linux environments. Software and user manual are available at http://bioinformaticstools.mayo.edu/research/patterncnv/ , and R package at https://github.com/topsoil/patternCNV/ . Contact: Asmann.Yan@mayo.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Abstract Ovarian cancer is a clinically and molecularly heterogeneous disease. The driving forces behind this variability are unknown. Here, we report wide variation in the expression of the DNA cytosine deaminase APOBEC3B, with elevated expression in the majority of ovarian cancer cell lines (three SDs above the mean of normal ovarian surface epithelial cells) and high-grade primary ovarian cancers. APOBEC3B is active in the nucleus of several ovarian cancer cell lines and elicits a biochemical preference for deamination of cytosines in 5′-TC dinucleotides. Importantly, examination of whole-genome sequence from 16 ovarian cancers reveals that APOBEC3B expression correlates with total mutation load as well as elevated levels of transversion mutations. In particular, high APOBEC3B expression correlates with C-to-A and C-to-G transversion mutations within 5′-TC dinucleotide motifs in early-stage high-grade serous ovarian cancer genomes, suggesting that APOBEC3B-catalyzed genomic uracil lesions are further processed by downstream DNA "repair" enzymes including error-prone translesion polymerases. These data identify a potential role for APOBEC3B in serous ovarian cancer genomic instability. Cancer Res; 73(24); 7222–31. ©2013 AACR.

Genomewide association studies (GWAS) and candidate‐gene studies have implicated single‐nucleotide polymorphisms (SNPs) in at least 45 different genes as putative glioma risk factors. Attempts to validate these associations have yielded variable results and few genetic risk factors have been consistently replicated. We conducted a case‐control study of Caucasian glioma cases and controls from the University of California San Francisco (810 cases, 512 controls) and the Mayo Clinic (852 cases, 789 controls) in an attempt to replicate previously reported genetic risk factors for glioma. Sixty SNPs selected from the literature (eight from GWAS and 52 from candidate‐gene studies) were successfully genotyped on an Illumina custom genotyping panel. Eight SNPs in/near seven different genes (TERT, EGFR, CCDC26, CDKN2A, PHLDB1, RTEL1, TP53) were significantly associated with glioma risk in the combined dataset (P < 0.05), with all associations in the same direction as in previous reports. Several SNP associations showed considerable differences across histologic subtype. All eight successfully replicated associations were first identified by GWAS, although none of the putative risk SNPs from candidate‐gene studies was associated in the full case‐control sample (all P values > 0.05). Although several confirmed associations are located near genes long known to be involved in gliomagenesis (e.g., EGFR, CDKN2A, TP53), these associations were first discovered by the GWAS approach and are in noncoding regions. These results highlight that the deficiencies of the candidate‐gene approach lay in selecting both appropriate genes and relevant SNPs within these genes.

Abstract Background: Invasive ovarian cancer is a significant cause of gynecologic cancer mortality. Methods: We examined whether this mortality was associated with inherited variation in approximately 170 candidate genes/regions [993 single-nucleotide polymorphisms (SNPs)] in a multistage analysis based initially on 312 Mayo Clinic cases (172 deaths). Additional analyses used The Cancer Genome Atlas (TCGA; 127 cases, 62 deaths). For the most compelling gene, we immunostained Mayo Clinic tissue microarrays (TMA, 326 cases) and conducted consortium-based SNP replication analysis (2,560 cases, 1,046 deaths). Results: The strongest initial mortality association was in HGF (hepatocyte growth factor) at rs1800793 (HR = 1.7, 95% CI = 1.3–2.2, P = 2.0 × 10−5) and with overall variation in HGF (gene-level test, P = 3.7 × 10−4). Analysis of TCGA data revealed consistent associations [e.g., rs5745709 (r2 = 0.96 with rs1800793): TCGA HR = 2.4, CI = 1.4–4.1, P = 2.2 × 10−3; Mayo Clinic + TCGA HR = 1.6, CI = 1.3–1.9, P = 7.0 × 10−5] and suggested genotype correlation with reduced HGF mRNA levels (P = 0.01). In Mayo Clinic TMAs, protein levels of HGF, its receptor MET (C-MET), and phospho-MET were not associated with genotype and did not serve as an intermediate phenotype; however, phospho-MET was associated with reduced mortality (P = 0.01) likely due to higher expression in early-stage disease. In eight additional ovarian cancer case series, HGF rs5745709 was not associated with mortality (HR = 1.0, CI = 0.9–1.1, P = 0.87). Conclusions: We conclude that although HGF signaling is critical to migration, invasion, and apoptosis, it is unlikely that HGF genetic variation plays a major role in ovarian cancer mortality. Furthermore, any minor role is not related to genetically-determined expression. Impact: Our study shows the utility of multiple data types and multiple data sets in observational studies. Cancer Epidemiol Biomarkers Prev; 20(8); 1638–48. ©2011 AACR.

Abstract Using data from patients with invasive epithelial ovarian cancer, we sought to examine whether overall survival was associated with common inherited variation in candidate genes and genomic regions including those involved in angiogenesis, mitosis, and regions of interest from genome-wide association and expression studies. We conducted a two-stage analysis based on Mayo Clinic patients with replication of peak results in the Cancer Genome Atlas (TCGA). Ovarian cancer patients (N=312) seen at the Mayo Clinic diagnosed from 1999 to 2006 with median follow-up of 3.7 years (range, 0.1 − 9.5 years) and 172 observed deaths were analyzed at 993 single nucleotide polymorphisms (SNPs) in 179 genes. For all patients and for serous patients only (N=192 with 129 observed deaths), Cox regression was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for each SNP, adjusted for other prognostic factors. Twenty-nine SNPs in ten genes revealed p &lt; 0.01 among all cases or among serous cases only, including 21 independent SNP at r2=0.9; analyses within four genes (HGF, PLG, PRKACB, and DCTN5) yielded p &lt; 0.001. The strongest result was in HGF at rs1800793 (all cases: HR 1.7, 95% CI 1.3 − 2.2, p-trend 1.9 E-5; serous cases: HR 1.6, 95% CI 1.2 − 2.2, p-trend 1.8 E-3). A correlated SNP rs2214825 also showed association (r2=0.76; all cases: HR 1.4, 95% CI 1.1 − 1.8, p-trend 3.3 E-3; serous cases: HR 1.4, 95% CI 1.0 − 1.9, p-trend 0.03), and PCA gene-level tests of the HGF locus were also significantly associated with survival (all cases, p=3.7 E-4; serous cases, p=0.01). Among all cases, differential survival was also associated with DCTN5 rs12447304 (HR 1.8, 95% CI 1.3-2.6; p=7.1 E-4), and among serous cases, with PLG rs783173 (HR 1.6, 95% CI 1.2 − 2.0, p = 8.1 E-4) and PRKACB rs1402694 (HR 1.7, 95% CI 1.3 − 2.2, p=3.2 E-4). We then examined SNPs in the most suggestive gene, HGF, using data from TCGA which had genotyped four overlapping HGF SNPs in 350 invasive cases (188 deaths). Covariate-adjusted TCGA analysis at rs2214825 revealed consistent results (HR 1.7, 95% CI 1.3-2.2, p=1.4 E-4) as did analyses of three other HGF SNPs (HR range 1.3 to 1.5, p range 3.4 E-3 to 0.049); correlation between SNP genotypes and HGF expression was also observed (p=1.4 E-3, rs2214825, probe 210997_at). While this is the first report of SNPs in HGF, it has long been known that the HGF signaling pathway, activated by HGF ligation of c-MET, plays a key role ovarian cancer cell growth, migration, and invasion. c-MET is overexpressed in 40%-60% of ovarian tumors, and high overexpression is associated with lower overall survival. In addition, HGF has been shown to enhance the response of ovarian cancer cells to low doses of paclitaxel and cisplatin by activating the intrinsic apoptotic pathway, leading to cancer cell death. These results now suggest that inherited differences in HGF among women with invasive disease may play a role in survival, holding potential clues for tailored avenues of treatment. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 897.

AbstractMotivation: A central problem in biomarker discovery from large-scale gene expression or single nucleotide polymorphism (SNP) data is the computational challenge of taking into account the dependence among all the features. Methods that ignore the dependence usually identify non-reproducible biomarkers across independent datasets. We introduce a new graph-based semi-supervised feature classification algorithm to identify discriminative disease markers by learning on bipartite graphs. Our algorithm directly classifies the feature nodes in a bipartite graph as positive, negative or neutral with network propagation to capture the dependence among both samples and features (clinical and genetic variables) by exploring bi-cluster structures in a graph. Two features of our algorithm are: (1) our algorithm can find a global optimal labeling to capture the dependence among all the features and thus, generates highly reproducible results across independent microarray or other high-thoughput datasets, (2) our algorithm is capable of handling hundreds of thousands of features and thus, is particularly useful for biomarker identification from high-throughput gene expression and SNP data. In addition, although designed for classifying features, our algorithm can also simultaneously classify test samples for disease prognosis/diagnosis.Results: We applied the network propagation algorithm to study three large-scale breast cancer datasets. Our algorithm achieved competitive classification performance compared with SVMs and other baseline methods, and identified several markers with clinical or biological relevance with the disease. More importantly, our algorithm also identified highly reproducible marker genes and enriched functions from the independent datasets.Availability: Supplementary results and source code are available at http://compbio.cs.umn.edu/Feature_Class.Contact: kuang@cs.umn.eduSupplementary information: Supplementary data are available at Bioinformatics online.

We conducted a genome-wide association study to identify novel genes influencing diastolic blood pressure (BP) response to hydrochlorothiazide, a commonly prescribed thiazide diuretic preferred for the treatment of high BP. Affymetrix GeneChip Human Mapping 100K Arrays were used to measure single nucleotide polymorphisms across the 22 autosomes in 194 non-Hispanic black subjects and 195 non-Hispanic white subjects with essential hypertension selected from opposite tertiles of the race- and sex-specific distributions of age-adjusted diastolic BP response to hydrochlorothiazide (25 mg daily, PO, for 4 weeks). The black sample consisted of 97 “good” responders (diastolic BP response [mean±SD]=−18.3±4.2 mm Hg; age=47.1±6.1 years; 51.5% women) and 97 “poor” responders (diastolic BP response=−0.18±4.3; age=47.4±6.5 years; 51.5% women). Haplotype trend regression identified a region of chromosome 12q15 in which haplotypes constructed from 3 successive single nucleotide polymorphisms (rs317689, rs315135, and rs7297610) in proximity to lysozyme ( LYZ ), YEATS domain containing 4 ( YEATS4 ), and fibroblast growth receptor substrate 2 ( FRS2 ) were significantly associated with diastolic BP response (nominal P =2.39×10 −7 ; Bonferroni corrected P =0.024; simulated experiment-wise P =0.040). Genotyping of 35 additional single nucleotide polymorphisms selected to “tag” linkage disequilibrium blocks in these genes provided corroboration that variation in LYZ and YEATS4 was associated with diastolic BP response in a statistically independent data set of 291 black subjects and in the sample of 294 white subjects. These results support the use of genome-wide association analyses to identify novel genes influencing antihypertensive drug responses.