Latchezar Dimitrov

Evidence for two independent prostate cancer risk-associated loci in the HNF1B gene at 17q12.

We carried out a fine-mapping study in the HNF1B gene at 17q12 in two study populations and identified a second locus associated with prostate cancer risk, ∼26 kb centromeric to the first known locus (rs4430796); these loci are separated by a recombination hot spot. We confirmed the association with a SNP in the second locus (rs11649743) in five additional populations, with P = 1.7 × 10−9 for an allelic test of the seven studies combined. The association at each SNP remained significant after adjustment for the other SNP.

The Interaction of Four Genes in the Inflammation Pathway Significantly Predicts Prostate Cancer Risk

It is widely hypothesized that the interactions of multiple genes influence individual risk to prostate cancer. However, current efforts at identifying prostate cancer risk genes primarily rely on single-gene approaches. In an attempt to fill this gap, we carried out a study to explore the joint effect of multiple genes in the inflammation pathway on prostate cancer risk. We studied 20 genes in the Toll-like receptor signaling pathway as well as several cytokines. For each of these genes, we selected and genotyped haplotype-tagging single nucleotide polymorphisms (SNP) among 1,383 cases and 780 controls from the CAPS (CAncer Prostate in Sweden) study population. A total of 57 SNPs were included in the final analysis. A data mining method, multifactor dimensionality reduction, was used to explore the interaction effects of SNPs on prostate cancer risk. Interaction effects were assessed for all possible n SNP combinations, where n = 2, 3, or 4. For each n SNP combination, the model providing lowest prediction error among 100 cross-validations was chosen. The statistical significance levels of the best models in each n SNP combination were determined using permutation tests. A four-SNP interaction (one SNP each from IL-10, IL-1RN, TIRAP, and TLR5) had the lowest prediction error (43.28%, P = 0.019). Our ability to analyze a large number of SNPs in a large sample size is one of the first efforts in exploring the effect of high-order gene-gene interactions on prostate cancer risk, and this is an important contribution to this new and quickly evolving field.

Comprehensive assessment of DNA copy number alterations in human prostate cancers using Affymetrix 100K SNP mapping array

Although multiple recurrent chromosomal alterations have been identified in prostate cancer cells, the specific genes driving the apparent selection of these changes remain largely unknown. In part, this uncertainty is due to the limited resolution of the techniques used to detect these alterations. In this study, we applied a high‐resolution genome‐wide method, Affymetrix 100K SNP mapping array, to screen for somatic DNA copy number (CN) alterations among 22 pairs of samples from primary prostate cancers and matched nonmalignant tissues. We detected 355 recurrent deletions and 223 recurrent gains, many of which were novel. As expected, the sizes of novel alterations tend to be smaller. Importantly, among tumors with increasing grade, Gleason sum 6, 7, and 8, we found a significant trend of larger number of alterations in the tumors with higher grade. Overall, gains are significantly more likely to occur within genes (74%) than are deletions (49%). However, when we looked at the most frequent CN alterations, defined as those in ≥4 subjects, we observed that both gains (85%) and deletions (57%) occur preferentially within genes. An example of a novel, recurrent alteration observed in this study was a deletion between the ERG and TMPRSS2 genes on chromosome 21, presumably related to the recently identified fusion transcripts from these two genes. Results from this study provide a basis for a systematic and comprehensive cataloging of CN alterations associated with grades of prostate cancer, and the subsequent identification of specific genes that associated with initiation and progression of the disease. This article contains supplementary material available via the Internet at http://www.interscience.wiley.com/jpages/1045‐2257/suppmat.

Multiple genomic alterations on 21q22 predict various TMPRSS2/ERG fusion transcripts in human prostate cancers.

A number of TMPRSS2/ERG fusion transcripts have been reported since the discovery that recurrent genomic rearrangements result in the fusion of TMPRSS2 and ETS family member genes. In this article we present evidence demonstrating that multiple genomic alterations contribute to the formation of various TMPRSS2/ERG transcripts. Using allele‐specific analysis of the data generated from the GeneChip 500K SNP array we observed both hemizygous and homozygous deletions occurring at different locations between and within TMPRSS2 and ERG in prostate cancers. The 500K SNP array enabled us to fine map the start and end of each deletion to specific introns of these two genes, and to predict a variety of fusion transcripts, including a new form which was confirmed by sequence analysis of the fusion transcripts in various tumors. We also inferred that translocation is an additional mechanism of fusion for these two genes in some tumors, based on largely diploid genomic DNA between TMPRSS and ERG, and different fusion transcripts produced in these tumors. Using a bioinformatics approach, we then uncovered the consensus sequences in the regions harboring the breakpoints of the deletions. These consensus sequences were homologous to the human Alu‐Sq and Alu‐Sp subfamily consensus sequences, with more than 80% homology. The presence/absence of Alu family consensus sequence in the introns of TMPRSS2 and ERG correlates with the presence/absence of fusion transcripts of theses two genes, indicating that these consensus sequences may contribute to genomic deletions and the fusion of TMPRSS2 and ERG in prostate cancer.

Integration of Somatic Deletion Analysis of Prostate Cancers and Germline Linkage Analysis of Prostate Cancer Families Reveals Two Small Consensus Regions for Prostate Cancer Genes at 8p

The evidence for tumor suppressor genes at 8p is well supported by many somatic deletion studies and genetic linkage studies. However, it remains a challenge to pinpoint the tumor suppressor genes at 8p primarily because the implicated regions are broad. In this study, we attempted to narrow down the implicated regions by incorporating evidence from both somatic and germline studies. Using high-resolution Affymetrix arrays, we identified two small common deleted regions among 55 prostate tumors at 8p23.1 (9.8-11.5 Mb) and 8p21.3 (20.6-23.7 Mb). Interestingly, our fine mapping linkage analysis at 8p among 206 hereditary prostate cancer families also provided evidence for linkage at these two regions at 8p23.1 (5.8-11.2 Mb) and at 8p21.3 (19.6-23.9 Mb). More importantly, by combining the results from the somatic deletion analysis and genetic linkage analysis, we were able to further narrow the regions to approximately 1.4 Mb at 8p23.1 and approximately 3.1 Mb at 8p21.3. These smaller consensus regions may facilitate a more effective search for prostate cancer genes at 8p.

Transferability and Fine Mapping of Type 2 Diabetes Loci in African Americans The Candidate Gene Association Resource Plus Study

Type 2 diabetes (T2D) disproportionally affects African Americans (AfA) but, to date, genetic variants identified from genome-wide association studies (GWAS) are primarily from European and Asian populations. We examined the single nucleotide polymorphism (SNP) and locus transferability of 40 reported T2D loci in six AfA GWAS consisting of 2,806 T2D case subjects with or without end-stage renal disease and 4,265 control subjects from the Candidate Gene Association Resource Plus Study. Our results revealed that seven index SNPs at the TCF7L2, KLF14, KCNQ1, ADCY5, CDKAL1, JAZF1, and GCKR loci were significantly associated with T2D (P < 0.05). The strongest association was observed at TCF7L2 rs7903146 (odds ratio [OR] 1.30; P = 6.86 × 10−8). Locus-wide analysis demonstrated significant associations (Pemp < 0.05) at regional best SNPs in the TCF7L2, KLF14, and HMGA2 loci as well as suggestive signals in KCNQ1 after correction for the effective number of SNPs at each locus. Of these loci, the regional best SNPs were in differential linkage disequilibrium (LD) with the index and adjacent SNPs. Our findings suggest that some loci discovered in prior reports affect T2D susceptibility in AfA with similar effect sizes. The reduced and differential LD pattern in AfA compared with European and Asian populations may facilitate fine mapping of causal variants at loci shared across populations.

Interaction effect of PTEN and CDKN1B chromosomal regions on prostate cancer linkage

The tumor suppressor functions of PTEN and CDKN1B have been extensively characterized. Recent data from mouse models suggest that, for some organs, the combined action of both PTEN and CDKN1B has a stronger tumor suppressor function than each alone; for the prostate, heterozygous knockout of both genes leads to 100% penetrance for prostate cancer. To assess whether such an interaction contributes to an increased risk of prostate cancer in humans, we performed a series of epistatic PTEN and CDKN1B interaction analyses in a collection of 188 high-risk hereditary prostate cancer families. Two different analytical approaches were performed; a nonparametric linkage (NPL) regression analysis that simultaneously models allele sharing at these two regions in all families, and an ordered subset analysis (OSA) that assesses linkage evidence at a target region in a subset of families based on the magnitude of allele sharing at the reference region. The strongest evidence of interaction effect was observed at 10q23-24 and 12p11-13 from both the NPL regression analysis (P=0.0002) in all families and the OSA analyses in subsets of families. A LOD-delta of 3.15 (P=0.01) was observed at 10q23-24 among 54 families with the highest NPL scores at 12p11-13, and a LOD-delta of 2.63 (P=0.02) was observed at 12p11-13 among 34 families with the highest NPL scores at 10q23-24. The evidence for the interaction was stronger when using additional fine-mapping markers in the PTEN (10q23) and CDKN1B (12p13) regions. Our data are consistent with epistatic interactions between the PTEN and CDKN1B genes affecting risk for prostate cancer and demonstrate the utility of modeling epistatic effects in linkage analysis to detect susceptibility genes of complex diseases.

Genome-wide linkage analysis of 1,233 prostate cancer pedigrees from the International Consortium for prostate cancer Genetics using novel sumLINK and sumLOD analyses

Prostate cancer (PC) is generally believed to have a strong inherited component, but the search for susceptibility genes has been hindered by the effects of genetic heterogeneity. The recently developed sumLINK and sumLOD statistics are powerful tools for linkage analysis in the presence of heterogeneity.

Two-locus genome-wide linkage scan for prostate cancer susceptibility genes with an interaction effect

Prostate cancer represents a significant worldwide public health burden. Epidemiological and genetic epidemiological studies have consistently provided data supporting the existence of inherited prostate cancer susceptibility genes. Segregation analyses of prostate cancer suggest that a multigene model may best explain familial clustering of this disease. Therefore, modeling gene–gene interactions in linkage analysis may improve the power to detect chromosomal regions harboring these disease susceptibility genes. In this study, we systematically screened for prostate cancer linkage by modeling two-locus gene–gene interactions for all possible pairs of loci across the genome in 426 prostate cancer families from Johns Hopkins Hospital, University of Michigan, University of Umeå, and University of Tampere. We found suggestive evidence for an epistatic interaction for six sets of loci (target chromosome-wide/reference marker-specific P≤0.0001). Evidence for these interactions was found in two independent subsets from within the 426 families. While the validity of these results requires confirmation from independent studies and the identification of the specific genes underlying this linkage evidence, our approach of systematically assessing gene–gene interactions across the entire genome represents a promising alternative approach for gene identification for prostate cancer.

Association of prostate cancer risk with SNPs in regions containing androgen receptor binding sites captured by ChIP-on-chip analyses

Genome‐wide association studies (GWAS) have identified approximately three dozen single nucleotide polymorphisms (SNPs) consistently associated with prostate cancer (PCa) risk. Despite the reproducibility of these associations, the molecular mechanism for most of these SNPs has not been well elaborated as most lie within non‐coding regions of the genome. Androgens play a key role in prostate carcinogenesis. Recently, using ChIP‐on‐chip technology, 22,447 androgen receptor (AR) binding sites have been mapped throughout the genome, greatly expanding the genomic regions potentially involved in androgen‐mediated activity.