Jérémie Nsengimana

A genome-wide association study of testicular germ cell tumor

We conducted a genome-wide association study for testicular germ cell tumor (TGCT), genotyping 307,666 SNPs in 730 cases and 1,435 controls from the UK and replicating associations in a further 571 cases and 1,806 controls. We found strong evidence for susceptibility loci on chromosome 5 (per allele OR = 1.37 (95% CI = 1.19–1.58), P = 3 × 10−13), chromosome 6 (OR = 1.50 (95% CI = 1.28–1.75), P = 10−13) and chromosome 12 (OR = 2.55 (95% CI = 2.05–3.19), P = 10−31). KITLG, encoding the ligand for the receptor tyrosine kinase KIT, which has previously been implicated in the pathogenesis of TGCT and the biology of germ cells, may explain the association on chromosome 12.

Variants near DMRT1, TERT and ATF7IP are associated with testicular germ cell cancer.

We conducted a genome-wide association study for testicular germ cell tumor, genotyping 298,782 SNPs in 979 affected individuals and 4,947 controls from the UK and replicating associations in a further 664 cases and 3,456 controls. We identified three new susceptibility loci, two of which include genes that are involved in telomere regulation. We identified two independent signals within the TERT-CLPTM1L locus on chromosome 5, which has previously been associated with multiple other cancers (rs4635969, OR = 1.54, P = 1.14 × 10−23; rs2736100, OR = 1.33, P = 7.55 × 10−15). We also identified a locus on chromosome 12 (rs2900333, OR = 1.27, P = 6.16 × 10−10) that contains ATF7IP, a regulator of TERT expression. Finally, we identified a locus on chromosome 9 (rs755383, OR = 1.37, P = 1.12 × 10−23), containing the sex determination gene DMRT1, which has been linked to teratoma susceptibility in mice.

Identification of nine new susceptibility loci for testicular cancer, including variants near DAZL and PRDM14

Testicular germ cell tumor (TGCT) is the most common cancer in young men and is notable for its high familial risks. So far, six loci associated with TGCT have been reported. From genome-wide association study (GWAS) analysis of 307,291 SNPs in 986 TGCT cases and 4,946 controls, we selected for follow-up 694 SNPs, which we genotyped in a further 1,064 TGCT cases and 10,082 controls from the UK. We identified SNPs at nine new loci (1q22, 1q24.1, 3p24.3, 4q24, 5q31.1, 8q13.3, 16q12.1, 17q22 and 21q22.3) showing association with TGCT (P < 5 × 10−8), which together account for an additional 4–6% of the familial risk of TGCT. The loci include genes plausibly related to TGCT development. PRDM14, at 8q13.3, is essential for early germ cell specification, and DAZL, at 3p24.3, is required for the regulation of germ cell development. Furthermore, PITX1, at 5q31.1, regulates TERT expression and is the third TGCT-associated locus implicated in telomerase regulation.

Gene Expression Profiling of Paraffin-Embedded Primary Melanoma Using the DASL Assay Identifies Increased Osteopontin Expression as Predictive of Reduced Relapse-Free Survival

Purpose: Gene expression studies in melanoma have been few because tumors are small and cryopreservation is rarely possible. The purpose of this study was to evaluate the Illumina DASL Array Human Cancer Panel for gene expression studies in formalin-fixed melanoma primary tumors and to identify prognostic biomarkers. Experimental Design: Primary tumors from two studies were sampled using a tissue microarray needle. Study 1: 254 tumors from a melanoma cohort recruited from 2000 to 2006. Study 2: 218 tumors from a case-control study of patients undergoing sentinel node biopsy. Results: RNA was obtained from 76 of blocks; 1.4 of samples failed analysis (transcripts from <250 of the 502 genes on the DASL chip detected). Increasing age of the block and increased melanin in the tumor were associated with reduced number of genes detected. The gene whose expression was most differentially expressed in association with relapse-free survival in study 1 was osteopontin (SPP1; P = 2.11 106) and supportive evidence for this was obtained in study 2 used as a validation set (P = 0.006; unadjusted data). Osteopontin level in study 1 remained a significant predictor of relapse-free survival when data were adjusted for age, sex, tumor site, and histologic predictors of relapse. Genes whose expression correlated most strongly with osteopontin were PBX1, BIRC5 (survivin), and HLF. Conclusion: Expression data were obtained from 74 of primary melanomas and provided confirmatory evidence that osteopontin expression is a prognostic biomarker. These results suggest that predictive biomarker studies may be possible using stored blocks from mature clinical trials. (Clin Cancer Res 2009;15(22):693946)

Linkage disequilibrium in the domesticated pig.

This study investigated the extent of linkage disequilibrium (LD) in two genomic regions (on chromosomes 4 and 7) in five populations of domesticated pigs. LD was measured with D′ and tested for significance with the Fisher exact test. Effects of genetic (linkage) distance, chromosome, population, and their interactions on D′ were tested both through a linear model analysis of covariance and by a theoretical nonlinear model. The overall result was that (1) the distance explained most of the variability of D′, (2) the effect of chromosome was significant, and (3) the effect of population was significant. The significance of the chromosome effect may have resulted from selection and the significance of the population effect illustrates the effects of population structures and effective population sizes on LD. These results suggest that mapping methods based on LD may be valuable even with only moderately dense marker spacing in pigs.

Patterns of Expression of DNA Repair Genes and Relapse From Melanoma

Purpose: To use gene expression profiling of formalin-fixed primary melanoma samples to detect expression patterns that are predictive of relapse and response to chemotherapy. Experimental Design: Gene expression profiles were identified in samples from two studies (472 tumors). Gene expression data for 502 cancer-related genes from these studies were combined for analysis. Results: Increased expression of DNA repair genes most strongly predicted relapse and was associated with thicker tumors. Increased expression of RAD51 was the most predictive of relapse-free survival in unadjusted analysis (hazard ratio, 2.98; P = 8.80 × 10−6). RAD52 (hazard ratio, 4.73; P = 0.0004) and TOP2A (hazard ratio, 3.06; P = 0.009) were independent predictors of relapse-free survival in multivariable analysis. These associations persisted when the analysis was further adjusted for demographic and histologic features of prognostic importance (RAD52 P = 0.01; TOP2A P = 0.02). Using principal component analysis, expression of DNA repair genes was summarized into one variable. Genes whose expression correlated with this variable were predominantly associated with the cell cycle and DNA repair. In 42 patients treated with chemotherapy, DNA repair gene expression was greater in tumors from patients who progressed on treatment. Further data supportive of a role for increased expression of DNA repair genes as predictive biomarkers are reported, which were generated using multiplex PCR. Conclusions: Overexpression of DNA repair genes (predominantly those involved in double-strand break repair) was associated with relapse. These data support the hypothesis that melanoma progression requires maintenance of genetic stability and give insight into mechanisms of melanoma drug resistance and potential therapies. Clin Cancer Res; 16(21); 5211–21. ©2010 AACR.

Melanoma sentinel node biopsy and prediction models for relapse and overall survival

Background:To optimise predictive models for sentinal node biopsy (SNB) positivity, relapse and survival, using clinico-pathological characteristics and osteopontin gene expression in primary melanomas.Methods:A comparison of the clinico-pathological characteristics of SNB positive and negative cases was carried out in 561 melanoma patients. In 199 patients, gene expression in formalin-fixed primary tumours was studied using Illuminas DASL assay. A cross validation approach was used to test prognostic predictive models and receiver operating characteristic curves were produced.Results:Independent predictors of SNB positivity were Breslow thickness, mitotic count and tumour site. Osteopontin expression best predicted SNB positivity (P=2.4 × 10−7), remaining significant in multivariable analysis. Osteopontin expression, combined with thickness, mitotic count and site, gave the best area under the curve (AUC) to predict SNB positivity (72.6%). Independent predictors of relapse-free survival were SNB status, thickness, site, ulceration and vessel invasion, whereas only SNB status and thickness predicted overall survival. Using clinico-pathological features (thickness, mitotic count, ulceration, vessel invasion, site, age and sex) gave a better AUC to predict relapse (71.0%) and survival (70.0%) than SNB status alone (57.0, 55.0%). In patients with gene expression data, the SNB status combined with the clinico-pathological features produced the best prediction of relapse (72.7%) and survival (69.0%), which was not increased further with osteopontin expression (72.7, 68.0%).Conclusion:Use of these models should be tested in other data sets in order to improve predictive and prognostic data for patients.

Identification of 19 new risk loci and potential regulatory mechanisms influencing susceptibility to testicular germ cell tumor

Genome-wide association studies (GWAS) have transformed understanding of susceptibility to testicular germ cell tumors (TGCTs), but much of the heritability remains unexplained. Here we report a new GWAS, a meta-analysis with previous GWAS and a replication series, totaling 7,319 TGCT cases and 23,082 controls. We identify 19 new TGCT risk loci, roughly doubling the number of known TGCT risk loci to 44. By performing in situ Hi-C in TGCT cells, we provide evidence for a network of physical interactions among all 44 TGCT risk SNPs and candidate causal genes. Our findings implicate widespread disruption of developmental transcriptional regulators as a basis of TGCT susceptibility, consistent with failed primordial germ cell differentiation as an initiating step in oncogenesis. Defective microtubule assembly and dysregulation of KIT–MAPK signaling also feature as recurrently disrupted pathways. Our findings support a polygenic model of risk and provide insight into the biological basis of TGCT.

Design Considerations for Genetic Linkage and Association Studies

This chapter describes the main issues that genetic epidemiologists usually consider in the design of linkage and association studies. For linkage, we briefly consider the situation of rare, highly penetrant alleles showing a disease pattern consistent with Mendelian inheritance investigated through parametric methods in large pedigrees or with autozygosity mapping in inbred families, and we then turn our focus to the most common design, affected sibling pairs, of more relevance for common, complex diseases. Theoretical and more practical power and sample size calculations are provided as a function of the strength of the genetic effect being investigated. We also discuss the impact of other determinants of statistical power such as disease heterogeneity, pedigree, and genotyping errors, as well as the effect of the type and density of genetic markers. Linkage studies should be as large as possible to have sufficient power in relation to the expected genetic effect size. Segregation analysis, a formal statistical technique to describe the underlying genetic susceptibility, may assist in the estimation of the relevant parameters to apply, for instance. However, segregation analyses estimate the total genetic component rather than a single-locus effect. Locus heterogeneity should be considered when power is estimated and at the analysis stage, i.e. assuming smaller locus effect than the total the genetic component from segregation studies. Disease heterogeneity should be minimised by considering subtypes if they are well defined or by otherwise collecting known sources of heterogeneity and adjusting for them as covariates; the power will depend upon the relationship between the disease subtype and the underlying genotypes. Ultimately, identifying susceptibility alleles of modest effects (e.g. RR≤1.5) requires a number of families that seem unfeasible in a single study. Meta-analysis and data pooling between different research groups can provide a sizeable study, but both approaches require even a higher level of vigilance about locus and disease heterogeneity when data come from different populations. All necessary steps should be taken to minimise pedigree and genotyping errors at the study design stage as they are, for the most part, due to human factors. A two-stage design is more cost-effective than one stage when using short tandem repeats (STRs). However, dense single-nucleotide polymorphism (SNP) arrays offer a more robust alternative, and due to their lower cost per unit, the total cost of studies using SNPs may in the future become comparable to that of studies using STRs in one or two stages. For association studies, we consider the popular case-control design for dichotomous phenotypes, and we provide power and sample size calculations for one-stage and multistage designs. For candidate genes, guidelines are given on the prioritisation of genetic variants, and for genome-wide association studies (GWAS), the issue of choosing an appropriate SNP array is discussed. A warning is issued regarding the danger of designing an underpowered replication study following an initial GWAS. The risk of finding spurious association due to population stratification, cryptic relatedness, and differential bias is underlined. GWAS have a high power to detect common variants of high or moderate effect. For weaker effects (e.g. relative risk<1.2), the power is greatly reduced, particularly for recessive loci. While sample sizes of 10,000 or 20,000 cases are not beyond reach for most common diseases, only meta-analyses and data pooling can allow attaining a study size of this magnitude for many other diseases. It is acknowledged that detecting the effects from rare alleles (i.e. frequency<5%) is not feasible in GWAS, and it is expected that novel methods and technology, such as next-generation resequencing, will fill this gap. At the current stage, the choice of which GWAS SNP array to use does not influence the power in populations of European ancestry. A multistage design reduces the study cost but has less power than the standard one-stage design. If one opts for a multistage design, the power can be improved by jointly analysing the data from different stages for the SNPs they share. The estimates of locus contribution to disease risk from genome-wide scans are often biased, and relying on them might result in an underpowered replication study. Population structure has so far caused less spurious associations than initially feared, thanks to systematic ethnicity matching and application of standard quality control measures. Differential bias could be a more serious threat and must be minimised by strictly controlling all the aspects of DNA acquisition, storage, and processing.

Multi-stage genome-wide association study identifies new susceptibility locus for testicular germ cell tumour on chromosome 3q25

Recent genome-wide association studies (GWAS) and subsequent meta-analyses have identified over 25 SNPs at 18 loci, together accounting for >15% of the genetic susceptibility to testicular germ cell tumour (TGCT). To identify further common SNPs associated with TGCT, here we report a three-stage experiment, involving 4098 cases and 18 972 controls. Stage 1 comprised previously published GWAS analysis of 307 291 SNPs in 986 cases and 4946 controls. In Stage 2, we used previously published customised Illumina iSelect genotyping array (iCOGs) data across 694 SNPs in 1064 cases and 10 082 controls. Here, we report new genotyping of eight SNPs showing some evidence of association in combined analysis of Stage 1 and Stage 2 in an additional 2048 cases of TGCT and 3944 controls (Stage 3). Through fixed-effects meta-analysis across three stages, we identified a novel locus at 3q25.31 (rs1510272) demonstrating association with TGCT [per-allele odds ratio (OR) = 1.16, 95% confidence interval (CI) = 1.06-1.27; P = 1.2 × 10(-9)].