Continuous exposure to chrysotile asbestos can cause transformation of human mesothelial cells via HMGB1 and TNF-α signaling.

Malignant mesothelioma is strongly associated with asbestos exposure. Among asbestos fibers, crocidolite is considered the most and chrysotile the least oncogenic. Chrysotile accounts for more than 90% of the asbestos used worldwide, but its capacity to induce malignant mesothelioma is still debated. We found that chrysotile and crocidolite exposures have similar effects on human mesothelial cells. Morphological and molecular alterations suggestive of epithelial-mesenchymal transition, such as E-cadherin down-regulation and ß-catenin phosphorylation followed by nuclear translocation, were induced by both chrysotile and crocidolite. Gene expression profiling revealed high-mobility group box-1 protein (HMGB1) as a key regulator of the transcriptional alterations induced by both types of asbestos. Crocidolite and chrysotile induced differential expression of 438 out of 28,869 genes interrogated by oligonucleotide microarrays. Out of these 438 genes, 57 were associated with inflammatory and immune response and cancer, and 14 were HMGB1 targeted genes. Crocidolite-induced gene alterations were sustained, whereas chrysotile-induced gene alterations returned to background levels within 5 weeks. Similarly, HMGB1 release in vivo progressively increased for 10 or more weeks after crocidolite exposure, but returned to background levels within 8 weeks after chrysotile exposure. Continuous administration of chrysotile was required for sustained high serum levels of HMGB1. These data support the hypothesis that differences in biopersistence influence the biological activities of these two asbestos fibers.

Metabolic characteristics distinguishing intrahepatic cholangiocarcinoma: a negative pilot study of (18)F-fluorocholine PET/CT clarified by transcriptomic analysis.

PET using fluorine-18 fluorocholine ((18)F-fluorocholine) may detect malignancies that involve altered choline metabolism. While (18)F-fluorocholine PET/CT has shown greater sensitivity for detecting hepatocellular carcinoma (HCC) than (18)F-fluoro-D-deoxyglucose (FDG) PET/CT, it is not known whether it can also detect intrahepatic cholangiocarcinoma (ICC), a less common form of primary liver cancer. Clinical, radiographic, and histopathologic data from 5 patients with ICC and 23 patients with HCC from a diagnostic trial of liver (18)F-fluorocholine PET/CT imaging were analyzed to preliminarily evaluate (18)F-fluorocholine PET/CT for ICC. Imaging was correlated with whole-genome expression profiling to identify molecular pathways associated with tumor phenotypes. On PET/CT, all ICC tumors demonstrated low (18)F-fluorocholine uptake with a significantly lower tumor to mean background uptake ratio than HCC tumors (0.69 vs. 1.64, p < 0.0001), but no corresponding significant difference in liver parenchyma uptake of (18)F-fluorocholine between ICC and HCC patients (8.0 vs. 7.7, p = 0.74). Two ICC patients demonstrated increased tumor metabolism on FDG PET/CT, while immunohistochemical analysis of ICC tumors revealed overexpression of glucose transporter 1 (GLUT-1) and hexokinase indicating a hyper-glycolytic phenotype. Gene expression analysis revealed down-regulation of farnesoid-X-receptor and other lipid pathways in ICC relative to HCC, and up-regulation of glycolytic pathways and GLUT-1 by HIF1α. These results imply limited utility of (18)F-fluorocholine in ICC, however, significant metabolic differences between ICC, HCC, and parenchymal liver tissue may still provide clues about the underlying liver pathology. Gene and protein expression analysis support hyperglycolysis as a more dominant metabolic trait of ICC.

Comparison of genome-scale DNA methylation profiles in hepatocellular carcinoma by viral status.

Hepatocellular carcinoma (HCC) incidence has steadily increased in the US over the past 30 years. Our understanding of epigenetic regulation in HCC is still limited, especially the impact of hepatitis B virus (HBV) or hepatitis C virus (HCV) infection on aberrant DNA methylation. We performed genome-wide DNA methylation profiling in 33 fresh frozen tumor samples, including 10 HBV-HCC, 13 HCV-HCC, and 10 non-infected (NIV-HCC) using the Illumina HumanMethylation450 BeadChip. Gene expression profiling was also performed using the Illumina whole-genome DASL HT Assay. Biological influences and gene networks of the differentially-methylated (DM) CpG loci were predicted using the Ingenuity Pathway Analysis. Genome-wide methylation analysis identified 7, 26, and 98 DM loci between HBV-HCC vs. HCV-HCC, HBV-HCC vs. NIV-HCC, and HCV-HCC vs. NIV-HCC, respectively, at P < 5 × 10(-5) for each. Overall, the DM loci were highly enriched for enhancers (48%), promoters (37%), or CpG islands and surrounding regions (37%). Most DM loci were hypermethylated in HCV-HCC compared to HBV-HCC or NIV-HCC. The DM loci were associated with a variety of biological functions including Cell Morphology (HBV-HCC vs. NIV-HCC), Cell Death/ Survival (HBV-HCC vs. NIV-HCC), or Cellular Growth and Proliferation (HCV-HCC vs. NIV-HCC). A subset of the DM loci were correlated (either positively or negatively) with their gene expression or associated with alcohol consumption, BMI, cirrhosis, diabetes, and cigarette smoking. Our findings of differential methylation by viral infection lend insights into the potential effects of viral infection on the epigenetic regulation and further the development and progression of HCC.

Joint analysis of multiple high-dimensional data types using sparse matrix approximations of rank-1 with applications to ovarian and liver cancer.

BACKGROUND: Technological advances enable the cost-effective acquisition of Multi-Modal Data Sets (MMDS) composed of measurements for multiple, high-dimensional data types obtained from a common set of bio-samples. The joint analysis of the data matrices associated with the different data types of a MMDS should provide a more focused view of the biology underlying complex diseases such as cancer that would not be apparent from the analysis of a single data type alone. As multi-modal data rapidly accumulate in research laboratories and public databases such as The Cancer Genome Atlas (TCGA), the translation of such data into clinically actionable knowledge has been slowed by the lack of computational tools capable of analyzing MMDSs. Here, we describe the Joint Analysis of Many Matrices by ITeration (JAMMIT) algorithm that jointly analyzes the data matrices of a MMDS using sparse matrix approximations of rank-1. METHODS: The JAMMIT algorithm jointly approximates an arbitrary number of data matrices by rank-1 outer-products composed of "sparse" left-singular vectors (eigen-arrays) that are unique to each matrix and a right-singular vector (eigen-signal) that is common to all the matrices. The non-zero coefficients of the eigen-arrays identify small subsets of variables for each data type (i.e., signatures) that in aggregate, or individually, best explain a dominant eigen-signal defined on the columns of the data matrices. The approximation is specified by a single "sparsity" parameter that is selected based on false discovery rate estimated by permutation testing. Multiple signals of interest in a given MDDS are sequentially detected and modeled by iterating JAMMIT on "residual" data matrices that result from a given sparse approximation. RESULTS: We show that JAMMIT outperforms other joint analysis algorithms in the detection of multiple signatures embedded in simulated MDDS. On real multimodal data for ovarian and liver cancer we show that JAMMIT identified multi-modal signatures that were clinically informative and enriched for cancer-related biology. CONCLUSIONS: Sparse matrix approximations of rank-1 provide a simple yet effective means of jointly reducing multiple, big data types to a small subset of variables that characterize important clinical and/or biological attributes of the bio-samples from which the data were acquired.

Elucidating the landscape of aberrant DNA methylation in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most common cancers and frequently presents with an advanced disease at diagnosis. There is only limited knowledge of genome-scale methylation changes in HCC. METHODS AND FINDINGS: We performed genome-wide methylation profiling in a total of 47 samples including 27 HCC and 20 adjacent normal liver tissues using the Illumina HumanMethylation450 BeadChip. We focused on differential methylation patterns in the promoter CpG islands as well as in various less studied genomic regions such as those surrounding the CpG islands, i.e. shores and shelves. Of the 485,577 loci studied, significant differential methylation (DM) was observed between HCC and adjacent normal tissues at 62,692 loci or 13% (p<1.03e-07). Of them, 61,058 loci (97%) were hypomethylated and most of these loci were located in the intergenic regions (43%) or gene bodies (33%). Our analysis also identified 10,775 differentially methylated (DM) loci (17% out of 62,692 loci) located in or surrounding the gene promoters, 4% of which reside in known Differentially Methylated Regions (DMRs) including reprogramming specific DMRs and cancer specific DMRs, while the rest (10,315) involving 4,106 genes could be potential new HCC DMR loci. Interestingly, the promoter-related DM loci occurred twice as frequently in the shores than in the actual CpG islands. We further characterized 982 DM loci in the promoter CpG islands to evaluate their potential biological function and found that the methylation changes could have effect on the signaling networks of Cellular development, Gene expression and Cell death (p=1.0e-38), with BMP4, CDKN2A, GSTP1, and NFATC1 on the top of the gene list. CONCLUSION: Substantial changes of DNA methylation at a genome-wide level were observed in HCC. Understanding epigenetic changes in HCC will help to elucidate the pathogenesis and may eventually lead to identification of molecular markers for liver cancer diagnosis, treatment and prognosis.

Viral hepatitis markers in liver tissue in relation to serostatus in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) incidence is increasing in the United States. Hepatitis B virus (HBV) and hepatitis C virus (HCV) are major causes of HCC. Hepatitis infection in patients with HCC is generally diagnosed by serology, which is not always consistent with the presence of HBV and HCV in the liver. The relationship of liver viral status to serostatus in hepatocarcinogenesis is not fully understood. METHODS: HBV and HCV were evaluated in formalin-fixed, paraffin-embedded liver tissue specimens in a retrospective study of 61 U.S. HCC cases of known serologic status. HBV DNA and HCV RNA were detected by PCR, reverse transcription PCR (RT-PCR), and pyrosequencing, and HBsAg and HBcAg were evaluated by immunohistochemistry. RESULTS: Viral markers were detected in the liver tissue of 25 of 61 (41%) HCC cases. Tissue viral and serologic status were discordant in 27 (44%) cases, including those with apparent "occult" infection. Specifically, HBV DNA was detected in tissue of 4 of 39 (10%) serum HBsAg (-) cases, including 1 anti-HCV(+) case; and HCV RNA was detected in tissue of 3 of 42 (7%) anti-HCV seronegative cases, including two with serologic evidence of HBV. CONCLUSIONS: Viral hepatitis, including HBV-HCV coinfection, may be unrecognized in up to 17% of patients with HCC when based on serology alone. Further research is needed to understand the clinical significance of viral makers in liver tissue of patients with HCC in the absence of serologic indices. IMPACT: The contribution of HBV and HCV to the increasing incidence of HCC in the United States may be underestimated.

Healthcare disparities in Asians and Pacific Islanders with hepatocellular cancer.

BACKGROUND: Hawaii has the highest incidence of hepatocellular cancer (HCC) in the United States and the largest proportion of Asians and Pacific Islanders. HCC studies generally combine these groups into 1 ethnicity, and we sought to examine differences between Asian and Pacific Islander subpopulations. METHODS: Demographic, clinical, and treatment data for 617 patients with HCC (420 Asians, 114 whites, and 83 Pacific Islanders) were reviewed. Main outcome measures included HCC screening and liver transplantation. RESULTS: Asian and Pacific Islander subgroups had significantly more immigrants, and age was different between groups. Compared with whites, Pacific Islanders and Filipinos had less HCC screening and liver transplantation procedures, fewer met Milan criteria, and a smaller proportion of those with Milan criteria actually underwent transplantation. CONCLUSIONS: There were significant differences in risk factors, clinical presentation, treatment, and access to care among Asian, Pacific Islander, and white patients with HCC. Future HCC studies may benefit from differentiating subgroups within Asian and Pacific Islander populations to better focus these efforts.

Initial neural net construction for the detection of cervical intraepithelial neoplasia by fluorescence imaging.

OBJECTIVE: The aim of this study was to initiate neural net construction for the detection of cervical intraepithelial neoplasia by fluorescence imaging. STUDY DESIGN: Thirty-three women with abnormal Papanicolaou smears underwent fluorescence imaging during colposcopy. With the use of >4000 training pixels and >1000 test pixels, intrapatient nets were constructed from the spectral data of 17 women. An interpatient net that discriminated between cervical intraepithelial neoplasia 1 and normal tissue classes among patients was constructed with the use of >2300 training pixels and >2000 test pixels from 12 women. Average correct classification rates were determined. Sensitivities, specificities, and positive and negative predictive values for cervical intraepithelial neoplasia grade 1 and normal tissue classes were calculated. Extrapolated false-color cervical images were generated. RESULTS: Average correct classification rates were 96.5% for the intrapatient nets and 97.5% for the interpatient net. The sensitivity, specificity, and positive and negative predictive values for cervical intraepithelial neoplasia grade 1 were 98.2%, 98.9%, 71.4%, and 99.9%, respectively. CONCLUSION: Initial results suggest that neural nets that are constructed from fluorescence imaging spectra may offer a potential method for the detection of cervical intraepithelial neoplasia.