Gordon Okimoto

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.

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 β-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.

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.

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. Cancer Epidemiol Biomarkers Prev; 22(11); 2016–23. ©2013 AACR.

Persistent Inflammatory Pathways Associated with Early Onset Myocardial Infarction in a Medicated Multiethnic Hawaiian Cohort

In spite of current standard therapies to target the major pathomechanisms in myocardial infarction (MI), inflammatory gene expression patterns have been consistently revealed in MI patients. In a multiethnic cohort, we aimed to identify MI-associated pathomechanisms that may be unresponsive to medical treatment to improve diagnosis and therapy. Gene expression profiles in whole blood were analyzed in medicated Asian, African American and Caucasian patients living in Hawaii with a history of early MI and age, ethnicity, risk factor and medication-matched controls. PANTHER ontological and Ingenuity Pathway analysis and functional evaluation of the consistently differentially expressed genes identified coordinated up-regulation of genes for inflammation (LGALS3, PTX3, ZBTB32, BCL2L1), T-cell activation (IL12RB1, VAV3, JAG1, CAMP), immune imbalance (IL-8, IL2RA, CCR7, AHNAK), and active atherosclerosis (NR1H4, BIN1, GSTT1, MARCO) that persist in MI patients in spite of concerted treatment efforts to control vascular pathology. Furthermore, significant ethnic differences appear to exist within the active disease mechanisms that need to be further investigated to identify key targets for effective medical intervention.

Integrated analysis of multiple high-dimensional data sets by joint rank-1 matrix approximations

In this work, we developed an algorithm for the integrated analysis of multiple high-dimensional data matrices based on sparse rank-one matrix approximations. The algorithm approximates multiple data matrices with rank one outer products composed of sparse left singular-vectors that are unique to each matrix and a right singular-vector that is shared by all of the data matrices. The right-singular vector represents a signal we wish to detect in the row-space of each matrix. The non-zero components of the resulting left-singular vectors identify rows of each matrix that in aggregate provide a sparse linear representation of the shared right-singular vector. This sparse representation facilitates downstream interpretation and validation of the resulting model based on the rows selected from each matrix. False discovery rate is used to select an appropriate ℓ1 penalty parameter that imposes sparsity on the left singular-vector but not the common right singular-vector of the joint approximation. Since a given multi-modal data set (MMDS) may contain multiple signals of interest the algorithm is iteratively applied to the residualized version of original data to sequentially capture and model each distinct signal in terms of rows from the different matrices. We show that the algorithm outperforms standard singular value decomposition over a wide range of simulation scenarios in terms of detection accuracy. Analysis of real data for ovarian and liver cancer resulted in compact gene expression signatures that were predictive of clinical outcomes and highly enriched for cancer related biology.

Measuring the Implications of the D-Basis in Analysis of Data in Biomedical Studies

We introduce the parameter of relevance of an attribute of a binary table to another attribute of the same table, computed with respect to an implicational basis of a closure system associated with the table. This enables a ranking of all attributes, by relevance parameter to the same fixed attribute, and, as a consequence, reveals the implications of the basis most relevant to this attribute. As an application of this new metric, we test the algorithm for D-basis extraction presented in Adaricheva and Nation [1] on biomedical data related to the survival groups of patients with particular types of cancer. Each test case requires a specialized approach in converting the real-valued data into binary data and careful analysis of the transformed data in a multi-disciplinary environment of cross-field collaboration.

A neural network model to classify liver cancer patients using data expansion and compression

We develop a neural network model to classify liver cancer patients into high-risk and low-risk groups using genomic data. Our approach provides a novel technique to classify big data sets using neural network models. We preprocess the data before training the neural network models. We first expand the data using wavelet analysis. We then compress the wavelet coefficients by mapping them onto a new scaled orthonormal coordinate system. Then the data is used to train a neural network model that enables us to classify cancer patients into two different classes of high-risk and low-risk patients. We use the leave-one-out approach to build a neural network model. This neural network model enables us to classify a patient using genomic data without any information about the survival time of the patient. The results from genomic data analysis are compared with survival time analysis. It is shown that the expansion and compression of data using wavelet analysis and singular value decomposition (SVD) is essential to train the neural network model.

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

BackgroundTechnological 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.MethodsThe 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.ResultsWe 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.ConclusionsSparse 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.

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

ABSTRACT 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.