Bowang Chen

Common variation at 3q26.2, 6p21.33, 17p11.2 and 22q13.1 influences multiple myeloma risk

To identify variants for multiple myeloma risk, we conducted a genome-wide association study with validation in additional series totaling 4,692 individuals with multiple myeloma (cases) and 10,990 controls. We identified four risk loci at 3q26.2 (rs10936599, P = 8.70 × 10−14), 6p21.33 (rs2285803, PSORS1C2, P = 9.67 × 10−11), 17p11.2 (rs4273077, TNFRSF13B, P = 7.67 × 10−9) and 22q13.1 (rs877529, CBX7, P = 7.63 × 10−16). These data provide further evidence for genetic susceptibility to this B-cell hematological malignancy, as well as insight into the biological basis of predisposition.

Somatic alterations in the melanoma genome: A high‐resolution array‐based comparative genomic hybridization study

We performed DNA microarray‐based comparative genomic hybridization to identify somatic alterations specific to melanoma genome in 60 human cell lines from metastasized melanoma and from 44 corresponding peripheral blood mononuclear cells. Our data showed gross but nonrandom somatic changes specific to the tumor genome. Although the CDKN2A (78%) and PTEN (70%) loci were the major targets of mono‐allelic and bi‐allelic deletions, amplifications affected loci with BRAF (53%) and NRAS (12%) as well as EGFR (52%), MITF (40%), NOTCH2 (35%), CCND1 (18%), MDM2 (18%), CCNE1 (10%), and CDK4 (8%). The amplified loci carried additional genes, many of which could potentially play a role in melanoma. Distinct patterns of copy number changes showed that alterations in CDKN2A tended to be more clustered in cell lines with mutations in the BRAF and NRAS genes; the PTEN locus was targeted mainly in conjunction with BRAF mutations. Amplification of CCND1, CDK4, and other loci was significantly increased in cell lines without BRAF‐NRAS mutations and so was the loss of chromosome arms 13q and 16q. Our data suggest involvement of distinct genetic pathways that are driven either through oncogenic BRAF and NRAS mutations complemented by aberrations in the CDKN2A and PTEN genes or involve amplification of oncogenic genomic loci and loss of 13q and 16q. It also emerges that each tumor besides being affected by major and most common somatic genetic alterations also acquires additional genetic alterations that could be crucial in determining response to small molecular inhibitors that are being currently pursued.

The CCND1 c.870G>A polymorphism is a risk factor for t(11;14)(q13;q32) multiple myeloma

A number of specific chromosomal abnormalities define the subgroups of multiple myeloma. In a meta-analysis of two genome-wide association studies of multiple myeloma including a total of 1,661 affected individuals, we investigated risk for developing a specific tumor karyotype. The t(11;14)(q13;q32) translocation in which CCND1 is placed under the control of the immunoglobulin heavy chain enhancer was strongly associated with the CCND1 c.870G>A polymorphism (P = 7.96 × 10−11). These results provide a model in which a constitutive genetic factor is associated with risk of a specific chromosomal translocation.

SNP_tools: A compact tool package for analysis and conversion of genotype data for MS-Excel

BackgroundSingle nucleotide polymorphism (SNP) genotyping is a major activity in biomedical research. Scientists prefer to have a facile access to the results which may require conversions between data formats. First hand SNP data is often entered in or saved in the MS-Excel format, but this software lacks genetic and epidemiological related functions. A general tool to do basic genetic and epidemiological analysis and data conversion for MS-Excel is needed.FindingsThe SNP_tools package is prepared as an add-in for MS-Excel. The code is written in Visual Basic for Application, embedded in the Microsoft Office package. This add-in is an easy to use tool for users with basic computer knowledge (and requirements for basic statistical analysis).ConclusionOur implementation for Microsoft Excel 2000-2007 in Microsoft Windows 2000, XP, Vista and Windows 7 beta can handle files in different formats and converts them into other formats. It is a free software.

Genetic epidemiology of cancer: From families to heritable genes

A reliable determination of familial risks for cancer is important for clinical counseling, prevention and understanding cancer etiology. Family‐based gene identification efforts may be targeted if the risks are well characterized and the mode of inheritance is identified. Medically verified data on familial risks have not been available for all types of cancer but they have become available through the use of the nationwide Swedish Family‐Cancer Database, which includes all Swedes born in 1932 and later with their parents, totaling over 10 million individuals. Over 150 publications have emanated from this source. The familial risks of cancer have been characterized for all main cancers and the contribution of environmental and heritable effects to the familial aggregation has been assessed. Furthermore, the mode of inheritance has been deduced by comparing risks from parental and sibling probands. Examples are shown on familial clustering of cancers, for which heritable susceptibility genes are yet unknown, such as squamous cell carcinoma of the skin, intestinal carcinoids, thyroid papillary tumors, brain astrocytomas and pituitary adenomas. Some common cancers, such as lung and kidney cancers, appear to show an early‐onset recessive component because familial risks among siblings are much higher than those in families where parents are probands. Many of the cancer sites showing high familial risks lack guidelines for clinical counseling or action level. In conclusion, we recommend that any future gene identification efforts, either using linkage or association designs, devise their strategies based on data from family studies. Clinical genetic counseling would benefit from reviewing established familial risks on all main types of cancer.

Familial Risks for Cervical Tumors in Full and Half Siblings: Etiologic Apportioning

Many studies have shown familial aggregation for cervical cancer, but they have been unable to distinguish between shared environmental and genetic effects. Full and half-siblings were identified from the nationwide Swedish Family-Cancer Database, including invasive and in situ cervical cancers in women up to age 70 years. Half-siblings were defined through a common father or mother. Standardized incidence ratios, adjusted for several variables, were calculated for proband-wise risks between full and half-siblings. The familial risk for full siblings was 1.84, compared with 1.40 for maternal and 1.27 for paternal half-siblings. These data were used to apportion familial risk for cervical tumors in full siblings into a heritable component, accounting for 64%, and an environmental component, accounting for 36% of the total risk. No evidence for gene-environment interactions was found. The intractable difficulty in separating cervical cancer causation will be an obstacle for a successful identification of susceptibility genes. (Cancer Epidemiol Biomarkers Prev 2006;15(7):1413-4)

Genome-wide association study on differentiated thyroid cancer.

CONTEXT Genome-wide association studies (GWASs) of differentiated thyroid cancer (DTC) have identified associations with polymorphisms at 2q35 (DIRC3), 8p12 (NRG1), 9q22.33 (FOXE1), and 14q13.2 (NKX2-1). However, most of the inherited genetic risk factors of DTC remain to be discovered. OBJECTIVE Our objective was to identify additional common DTC susceptibility loci. DESIGN We conducted a GWAS in a high-incidence Italian population of 690 cases and 497 controls and followed up the most significant polymorphisms in 2 additional Italian series and in 3 low-incidence populations totaling 2958 cases and 3727 controls. RESULTS After excluding the most robust previously identified locus (9q22.33), the strongest association was shown by rs6759952, confirming the recently published association in DIRC3 (odds ratio [OR] = 1.21, P = 6.4 × 10(-10), GWAS and all replications combined). Additionally, in the combined analysis of the Italian series, suggestive associations were attained with rs10238549 and rs7800391 in IMMP2L (OR = 1.27, P = 4.1 × 10(-6); and OR = 1.25, P = 5.7 × 10(-6)), rs7617304 in RARRES1 (OR = 1.25, P = 4.6 × 10(-5)) and rs10781500 in SNAPC4/CARD9 (OR = 1.23, P = 3.5 × 10(-5)). CONCLUSIONS Our findings provide additional insights into the genetic and biological basis of inherited genetic susceptibility to DTC. Additional studies are needed to determine the role of the identified polymorphisms in the development of DTC and their possible use in the clinical practice.

Genome-wide association study for colorectal cancer identifies risk polymorphisms in German familial cases and implicates MAPK signalling pathways in disease susceptibility

Genetic susceptibility accounts for approximately 35% of all colorectal cancer (CRC). Ten common low-risk variants contributing to CRC risk have been identified through genome-wide association studies (GWASs). In our GWAS, 610 664 genotyped single-nucleotide polymorphisms (SNPs) passed the quality control filtering in 371 German familial CRC patients and 1263 controls, and replication studies were conducted in four additional case-control sets (4915 cases and 5607 controls). Known risk loci at 8q24.21 and 11q23 were confirmed, and a previously unreported association, rs12701937, located between the genes GLI3 (GLI family zinc finger 3) and INHBA (inhibin, beta A) [P = 1.1 x 10(-3), odds ratio (OR) 1.14, 95% confidence interval (CI) 1.05-1.23, dominant model in the combined cohort], was identified. The association was stronger in familial cases compared with unselected cases (P = 2.0 x 10(-4), OR 1.36, 95% CI 1.16-1.60, dominant model). Two other unreported SNPs, rs6038071, 40 kb upstream of CSNK2A1 (casein kinase 2, alpha 1 polypeptide) and an intronic marker in MYO3A (myosin IIIA), rs11014993, associated with CRC only in the familial CRC cases (P = 2.5 x 10(-3), recessive model, and P = 2.7 x 10(-4), dominant model). Three software tools successfully pointed to the overrepresentation of genes related to the mitogen-activated protein kinase (MAPK) signalling pathways among the 1340 most strongly associated markers from the GWAS (allelic P value < 10(-3)). The risk of CRC increased significantly with an increasing number of risk alleles in seven genes involved in MAPK signalling events (P(trend) = 2.2 x 10(-16), OR(per allele) = 1.34, 95% CI 1.11-1.61).

Is there still a need for candidate gene approaches in the era of genome-wide association studies?

Most genetic variants associated with complex diseases in humans are believed to have a small impact on risk. With traditional candidate gene/pathway approaches several associations with disease risk could be identified. However, now that genome-wide association studies are feasible, the question arises if there is still a need for these approaches. By using HapMap data, we evaluated to which extent commercially available microarrays cover, through linkage disequilibrium, all currently known genes and biological processes in different populations. Furthermore, we estimated the power to detect an association with any specific SNP. Our study shows that coverage of individual genes and pathways by current commercial genotyping platforms is satisfactory for the vast majority of RefSeq gene regions. However, depending on the gene or the population, there may still be a need for candidate gene approaches, especially when looking at polymorphisms with low allele frequencies.

Consensus Pathways Implicated in Prognosis of Colorectal Cancer Identified Through Systematic Enrichment Analysis of Gene Expression Profiling Studies

Background A large number of gene expression profiling (GEP) studies on prognosis of colorectal cancer (CRC) has been performed, but no reliable gene signature for prediction of CRC prognosis has been found. Bioinformatic enrichment tools are a powerful approach to identify biological processes in high-throughput data analysis. Principal Findings We have for the first time collected the results from the 23 so far published independent GEP studies on CRC prognosis. In these 23 studies, 1475 unique, mapped genes were identified, from which 124 (8.4%) were reported in at least two studies, with 54 of them showing consisting direction in expression change between the single studies. Using these data, we attempted to overcome the lack of reproducibility observed in the genes reported in individual GEP studies by carrying out a pathway-based enrichment analysis. We used up to ten tools for overrepresentation analysis of Gene Ontology (GO) categories or Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in each of the three gene lists (1475, 124 and 54 genes). This strategy, based on testing multiple tools, allowed us to identify the oxidative phosphorylation chain and the extracellular matrix receptor interaction categories, as well as a general category related to cell proliferation and apoptosis, as the only significantly and consistently overrepresented pathways in the three gene lists, which were reported by several enrichment tools. Conclusions Our pathway-based enrichment analysis of 23 independent gene expression profiling studies on prognosis of CRC identified significantly and consistently overrepresented prognostic categories for CRC. These overrepresented categories have been functionally clearly related with cancer progression, and deserve further investigation.