the extended models and analytic strategies developed at LPG will be applied to gain insight into HCC etiology and susceptibility. HCC is a quintessential complex trait with well-documented environmental risk factors (65-69) and evidence of constitutional genetic differences in susceptibility (70-74). The Buetow laboratory hypothesizes that coherence will emerge from HCCs apparent molecular etiologic heterogeneity through analysis of networks. We tested the hypothesis that HCC arises through molecular alterations in a finite number of biologic processes. More specifically, a subset of networks will be observed to be common across the spectrum of liver disease with different network components discriminating the different disease states. Moreover, the common processes provide a unified underlying molecular etiology for the diverse environmental risk factors HBV, HCV, alcoholism, and obesity. Lastly, we hypothesize that T-cell mediated immunity may represent one of these modules. It is the primary goal to identify these network components using genetic and pathway methods, the laboratory will validate its current findings through analysis of additional, prospective, independent data sets and will use laboratory-based characterizations of the networks to assess their significance. The laboratory will synthesize the multidimensional molecular data, and add epigenetic analysis in partnership with the Lee lab. In partnership with Nodality, the nucleic acid findings will be translated into alterations occurring at the single cell protein level. Leveraging the resources of the NIDDK NASH Clinical Research Network, a broader definition of disease progression will be assessed. The laboratory will use the findings to develop predictors of HCC and susceptibility. The Buetow laboratory has identified pathways associated with the development and progression of HCC (85). Gene expression was measured for 48 HBV+ and HCV+ tumor and tumor-adjacent liver samples using the Affymetrix oligonucleotide microarrays U133A platform. Using PathOlogist, we measured alterations in molecular interaction in pathways in the PID (28) by calculating pathway activity and pathway consistency scores from the observed gene expression data. To investigate the role that SHH signaling pathway may play in the development and progression of HCC, SNU449 (human hepatocellular carcinoma cells) were transfected with small interfering RNAs (siRNAs) corresponding to PTCH, SHH, RASGRF1 and a negative control. Thus, siRNA mediated silencing of SHH gene expression resulted in a significant reduction of cell proliferation in the SNU449 cell line indicating that SHH plays a major role in promoting cell proliferation in HCC. Over and above the role of the SHH network, a combination of 4 pathways correctly classified tumor and normal tissue with 92% accuracy. Interestingly, one of the four pathways was visceral fat deposits and the metabolic syndrome. This pathway represents the integration of genes activated by insulin and steroids, inflammation, and lipid metabolism. This result suggests that the samples whose major risk factors are HBV and HCV may operate through a common etiologic pathway obesity/diabetes-associated pathway likely to be important in HCC in low-rate areas (88). To identify etiologic and susceptibility loci for HCC, the Buetow laboratory has conducted an association study analyzing single nucleotide polymorphisms (SNPs) and copy number variations (CNV) in DNA isolated from peripheral blood (89). This work used the Affymetrix SNP 6.0 microarray. This study involved unrelated HCC and liver cirrhosis (LC) patients seen at the Asan Medical Center, Seoul, South Korea. 89% of the HCC cases and 76% of the LC cases were chronically infected with either HBV or HCV. Two sources of controls were used. The first sets of controls were unrelated individuals from the Asan Medical Center, Seoul, South Korea. The viral infection status of controls was not ascertained. A second source of controls was HBV+ individuals of Chinese origin from Haimen City, China. We used a two stage discovery and replication design to control for over-fitting and to validate observed results. A total of 386 Korean HCC cases, 86 Korean cirrhosis cases 587 Korean controls, and 100 Chinese controls passed the quality control evaluations. Individuals from the Korean population set were assigned to the discovery (Stage 1) or validation (Stage 2) groups based on their order of enrollment in the study. Stage 1 included 271 controls, 180 HCC cases and 66 LC cases;Stage 2 included 316 controls, 206 HCC patients and 20 individuals with cirrhosis. Key findings from the two stage analysis were further validated using the Chinese control samples. Cases were randomly selected for each plate one-by-one using a random-number generator. For each case in the discovery phase, a matching control was selected by finding its best match in sex and age among the control samples. This strategy of processing each Stage 1 case along with a matched control was aimed at minimizing the possible effects of technical variation on experimental results. Controls in validation phase have limited clinical information and therefore were selected randomly. Samples were analyzed separately for copy number variation using the Affymetrix Genotyping Console program with the resulting copy number log2ratio data serving as input for the R DNA copy package for the circular binary segmentation (CBS) algorithm We identified a strong association with copy number variation at the T-cell receptor gamma and alpha loci (p-value <1x10-15) in HCC cases when contrasted to controls. TaqMan real-time PCR assays (Applied Biosystems, Foster City, CA) were used to confirm the SNP6.0 CNV results for TRA and TRG. TRA and TRG variation appears to be somatic in origin, reflecting differences between T-cell receptor processing in lymphocytes from individuals with liver disease and healthy individuals that is not attributable to chronic hepatitis virus infection. The pattern of T cell receptor variation suggests the presence of different T-lymphocyte populations in case and control populations that potentially reflects maturation or proliferation of a subpopulation of T cells. CNV patterns at TRA suggest that rearrangement events generate functional alpha chains more frequently than delta chains. Low copy number segments observed in individual samples frequently encompass the TCR delta constant region, but rarely include the TCR alpha constant region To establish that the SNP6.0 genotype calls were not experimental artifacts, we genotyped these markers using TaqMan assays. These TaqMan results were in complete agreement with the high-throughput Affymetrix 6.0 platform SNP array generated data. All three SNPs are independently associated with HCC, showing neither an additive nor multiplicative effect. Interestingly, in addition to their association with HCC, two of the three SNPs were associated with cirrhosis (p values of 0.0052 and 0.0007, respectively). In contrast, one SNP was only weakly associated with cirrhosis (p-value is 0.0408). Comparison of SNP allele frequencies in HCC and cirrhosis patients, however, identified two variants that distinguish HCC from cirrhosis. One SNP, is located within the TPTE2 gene;the second, lies in a gene-poor region of 2q14.1. Both polymorphisms are distinct from those identified in the comparison of HCC cases and controls. Combined analysis of copy number variation, individual SNPs, and pathways suggest that HCC susceptibility is mediated by germline factors affecting the immune response and differences in T-cell receptor processing. Our findings provide genomic evidence that genes involved in the immune response play a critical role in the development of HCC.
|Edmonson, Michael N; Zhang, Jinghui; Yan, Chunhua et al. (2011) Bambino: a variant detector and alignment viewer for next-generation sequencing data in the SAM/BAM format. Bioinformatics 27:865-6|
|Clifford, Robert J; Zhang, Jinghui; Meerzaman, Daoud M et al. (2010) Genetic variations at loci involved in the immune response are risk factors for hepatocellular carcinoma. Hepatology 52:2034-43|
|Braun, Rosemary; Rowe, William; Schaefer, Carl et al. (2009) Needles in the haystack: identifying individuals present in pooled genomic data. PLoS Genet 5:e1000668|