Yongkang Kim

Proposed Nomogram Predicting the Individual Risk of Malignancy in the Patients With Branch Duct Type Intraductal Papillary Mucinous Neoplasms of the Pancreas.

Objectives: This study evaluated individual risks of malignancy and proposed a nomogram for predicting malignancy of branch duct type intraductal papillary mucinous neoplasms (BD-IPMNs) using the large database for IPMN. Background: Although consensus guidelines list several malignancy predicting factors in patients with BD-IPMN, those variables have different predictability and individual quantitative prediction of malignancy risk is limited. Methods: Clinicopathological factors predictive of malignancy were retrospectively analyzed in 2525 patients with biopsy proven BD-IPMN at 22 tertiary hospitals in Korea and Japan. The patients with main duct dilatation >10 mm and inaccurate information were excluded. Results: The study cohort consisted of 2258 patients. Malignant IPMNs were defined as those with high grade dysplasia and associated invasive carcinoma. Of 2258 patients, 986 (43.7%) had low, 443 (19.6%) had intermediate, 398 (17.6%) had high grade dysplasia, and 431 (19.1%) had invasive carcinoma. To construct and validate the nomogram, patients were randomly allocated into training and validation sets, with fixed ratios of benign and malignant lesions. Multiple logistic regression analysis resulted in five variables (cyst size, duct dilatation, mural nodule, serum CA19-9, and CEA) being selected to construct the nomogram. In the validation set, this nomogram showed excellent discrimination power through a 1000 times bootstrapped calibration test. Conclusion: A nomogram predicting malignancy in patients with BD-IPMN was constructed using a logistic regression model. This nomogram may be useful in identifying patients at risk of malignancy and for selecting optimal treatment methods. The nomogram is freely available at http://statgen.snu.ac.kr/software/nomogramIPMN.

Benzyl Isothiocyanate Suppresses High-Fat Diet- Stimulated Mammary Tumor Progression via the Alteration of Tumor Microenvironments in Obesity-Resistant BALB/c Mice

We previously reported that a high‐fat diet (HFD) and M2‐macrophages induce changes in tumor microenvironments and stimulate tumor growth and metastasis of 4T1 mammary cancer cells in BALB/c mice. In this study, we attempted to determine whether benzyl isothiocyanate (BITC) inhibits HFD‐induced changes in tumor progression and in tumor microenvironments. Four groups of female BALB/c mice (4‐week‐old) were fed on a control diet (CD, 10 kcal% fat) and HFD (60 kcal% fat) containing BITC (0, 25, or 100 mg/kg diet) for 20 weeks. Following 16 weeks of feeding, 4T1 cells (5 × 104 cells) were injected into the mammary fat pads, and animals were killed 30 d after the injection. HFD feeding increased solid tumor growth and the number of tumor nodules in the lung and liver, as compared to the CD group, and these increases were inhibited by BITC supplementation. The number of lipid vacuoles, CD45+ leukocytes and CD206+ M2‐macrophages, expression of Ki67, levels of cytokines/chemokines, including macrophage‐colony stimulating factor (M‐CSF) and monocyte chemoattractant protein‐1, and mRNA levels of F4/80, CD86, Ym1, CD163, CCR2, and M‐CSF receptor were increased in the tumor tissues of HFD‐fed mice, and these increases were inhibited by BITC supplementation. In vitro culture results demonstrated that BITC inhibited macrophage migration as well as lipid droplet accumulation in 3T3‐L1 cells. These results suggest that suppression of lipid accumulation and macrophage infiltration in tumor tissues may be one of the mechanisms by which BITC suppresses tumor progression in HFD‐fed mice.

β-Caryophyllene potently inhibits solid tumor growth and lymph node metastasis of B16F10 melanoma cells in high-fat diet-induced obese C57BL/6N mice

We reported previously that high-fat diet (HFD) feeding stimulated solid tumor growth and lymph node (LN) metastasis in C57BL/6N mice injected with B16F10 melanoma cells. β-caryophyllene (BCP) is a natural bicyclic sesquiterpene found in many essential oils and has been shown to exert anti-inflammatory activities. To examine whether BCP inhibits HFD-induced melanoma progression, 4-weeks old, male C57BL/6N mice were fed a control diet (CD, 10 kcal% fat) or HFD (60 kcal% fat + 0, 0.15 or 0.3% BCP) for the entire experimental period. After 16 weeks of feeding, B16F10s were subcutaneously injected into mice. Three weeks later, tumors were resected, and mice were killed 2 weeks post-resection. Although HFD feeding increased body weight gain, fasting blood glucose levels, solid tumor growth, LN metastasis, tumor cell proliferation, angiogenesis and lymphangiogenesis, it decreased apoptotic cells, all of which were suppressed by dietary BCP. HFD feeding increased the number of lipid vacuoles and F4/80+ macrophage (MΦ) and macrophage mannose receptor (MMR)+ M2-MΦs in tumor tissues and adipose tissues surrounding the LN, which was suppressed by BCP. HFD feeding increased the levels of CCL19 and CCL21 in the LN and the expression of CCR7 in the tumor; these changes were blocked by dietary BCP. In vitro culture results revealed that BCP inhibited lipid accumulation in 3T3-L1 preadipocytes; monocyte migration and monocyte chemoattractant protein-1 secretion by B16F10s, adipocytes and M2-MΦs; angiogenesis and lymphangiogenesis. The suppression of adipocyte and M2-cell accumulation and the inhibition of CCL19/21-CCR7 axis may be a part of mechanisms for the BCP suppression of HFD-stimulated melanoma progression.

Biomarker Development for Intraductal Papillary Mucinous Neoplasms Using Multiple Reaction Monitoring Mass Spectrometry.

Intraductal papillary mucinous neoplasm (IPMN) is a common precursor of pancreatic cancer (PC). Much clinical attention has been directed toward IPMNs due to the increase in the prevalence of PC. The diagnosis of IPMN depends primarily on a radiological examination, but the diagnostic accuracy of this tool is not satisfactory, necessitating the development of accurate diagnostic biomarkers for IPMN to prevent PC. Recently, high-throughput targeted proteomic quantification methods have accelerated the discovery of biomarkers, rendering them powerful platforms for the evolution of IPMN diagnostic biomarkers. In this study, a robust multiple reaction monitoring (MRM) pipeline was applied to discovery and verify IPMN biomarker candidates in a large cohort of plasma samples. Through highly reproducible MRM assays and a stringent statistical analysis, 11 proteins were selected as IPMN marker candidates with high confidence in 184 plasma samples, comprising a training (n = 84) and test set (n = 100). To improve the discriminatory power, we constructed a six-protein panel by combining marker candidates. The multimarker panel had high discriminatory power in distinguishing between IPMN and controls, including other benign diseases. Consequently, the diagnostic accuracy of IPMN can be improved dramatically with this novel plasma-based panel in combination with a radiological examination.

A Comparative Study on Multifactor Dimensionality Reduction Methods for Detecting Gene-Gene Interactions with the Survival Phenotype

Genome-wide association studies (GWAS) have extensively analyzed single SNP effects on a wide variety of common and complex diseases and found many genetic variants associated with diseases. However, there is still a large portion of the genetic variants left unexplained. This missing heritability problem might be due to the analytical strategy that limits analyses to only single SNPs. One of possible approaches to the missing heritability problem is to consider identifying multi-SNP effects or gene-gene interactions. The multifactor dimensionality reduction method has been widely used to detect gene-gene interactions based on the constructive induction by classifying high-dimensional genotype combinations into one-dimensional variable with two attributes of high risk and low risk for the case-control study. Many modifications of MDR have been proposed and also extended to the survival phenotype. In this study, we propose several extensions of MDR for the survival phenotype and compare the proposed extensions with earlier MDR through comprehensive simulation studies.

Integrative analysis of multi-omics data for identifying multi-markers for diagnosing pancreatic cancer

BackgroundmicroRNA (miRNA) expression plays an influential role in cancer classification and malignancy, and miRNAs are feasible as alternative diagnostic markers for pancreatic cancer, a highly aggressive neoplasm with silent early symptoms, high metastatic potential, and resistance to conventional therapies.MethodsIn this study, we evaluated the benefits of multi-omics data analysis by integrating miRNA and mRNA expression data in pancreatic cancer. Using support vector machine (SVM) modelling and leave-one-out cross validation (LOOCV), we evaluated the diagnostic performance of single- or multi-markers based on miRNA and mRNA expression profiles from 104 PDAC tissues and 17 benign pancreatic tissues. For selecting even more reliable and robust markers, we performed validation by independent datasets from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) data depositories. For validation, miRNA activity was estimated by miRNA-target gene interaction and mRNA expression datasets in pancreatic cancer.ResultsUsing a comprehensive identification approach, we successfully identified 705 multi-markers having powerful diagnostic performance for PDAC. In addition, these marker candidates annotated with cancer pathways using gene ontology analysis.ConclusionsOur prediction models have strong potential for the diagnosis of pancreatic cancer.microRNA (miRNA) expression plays an influential role in cancer classification and malignancy, and miRNAs are feasible as alternative diagnostic markers for pancreatic cancer, a highly aggressive neoplasm with silent early symptoms, high metastatic potential, and resistance to conventional therapies. In this study, we evaluated the benefits of multi-omics data analysis by integrating miRNA and mRNA expression data in pancreatic cancer. Using support vector machine (SVM) modelling and leave-one-out cross validation (LOOCV), we evaluated the diagnostic performance of single- or multi-markers based on miRNA and mRNA expression profiles from 104 PDAC tissues and 17 benign pancreatic tissues. For selecting even more reliable and robust markers, we performed validation by independent datasets from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) data depositories. For validation, miRNA activity was estimated by miRNA-target gene interaction and mRNA expression datasets in pancreatic cancer. Using a comprehensive identification approach, we successfully identified 705 multi-markers having powerful diagnostic performance for PDAC. In addition, these marker candidates annotated with cancer pathways using gene ontology analysis. Our prediction models have strong potential for the diagnosis of pancreatic cancer.

Rare variant association test with multiple phenotypes

Although genome‐wide association studies (GWAS) have now discovered thousands of genetic variants associated with common traits, such variants cannot explain the large degree of “missing heritability,” likely due to rare variants. The advent of next generation sequencing technology has allowed rare variant detection and association with common traits, often by investigating specific genomic regions for rare variant effects on a trait. Although multiple correlated phenotypes are often concurrently observed in GWAS, most studies analyze only single phenotypes, which may lessen statistical power. To increase power, multivariate analyses, which consider correlations between multiple phenotypes, can be used. However, few existing multivariant analyses can identify rare variants for assessing multiple phenotypes. Here, we propose Multivariate Association Analysis using Score Statistics (MAAUSS), to identify rare variants associated with multiple phenotypes, based on the widely used sequence kernel association test (SKAT) for a single phenotype. We applied MAAUSS to whole exome sequencing (WES) data from a Korean population of 1,058 subjects to discover genes associated with multiple traits of liver function. We then assessed validation of those genes by a replication study, using an independent dataset of 3,445 individuals. Notably, we detected the gene ZNF620 among five significant genes. We then performed a simulation study to compare MAAUSSs performance with existing methods. Overall, MAAUSS successfully conserved type 1 error rates and in many cases had a higher power than the existing methods. This study illustrates a feasible and straightforward approach for identifying rare variants correlated with multiple phenotypes, with likely relevance to missing heritability.

Serum fibronectin distinguishes the early stages of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death, necessitating the discovery of serum markers for its early detection. In this study, a total of 180 serum samples from liver cirrhosis (LC), hepatocellular carcinoma (HCC) patients and paired samples of HCC patients who recovered (Recovery) were analyzed by multiple reaction monitoring-mass spectrometry (MRM-MS) to verify biomarkers. The three-fold crossvalidation was repeated 100 times in the training and test sets to evaluate statistical significance of 124 candidate proteins. This step resulted in 2 proteins that had an area under the receiver operating curve (AUROC) values ≥0.800 in the training (n = 90) and test sets (n = 90). Specifically, fibronectin (FN1, WCGTTQNYDADQK), distinguished HCC from LC patients, with an AUROC value of 0.926 by logistic regression. A FN1 protein was selected for validation in an independent sample (n = 60) using enzyme-linked immunosorbent assay (ELISA). The combination of alpha-fetoprotein (AFP) and FN1 improved the diagnostic performance and differentiated HCC patients with normal AFP levels. Our study has examined candidate markers for the benign disease state and malignancy and has followed up on the consequent recovery. Thus, improvement in the early detection of HCC by a 2-marker panel (AFP + FN1) might benefit HCC patients.

Robust Gene-Gene Interaction Analysis in Genome Wide Association Studies.

Genome-wide association studies (GWAS) have successfully discovered hundreds of associations between genetic variants and complex traits. Most GWAS have focused on the identification of single variants. It has been shown that most of the variants that were discovered by GWAS could only partially explain disease heritability. The explanation for this missing heritability is generally believed to be gene-gene (GG) or gene-environment (GE) interactions and other structural variants. Generalized multifactor dimensionality reduction (GMDR) has been proven to be reasonably powerful in detecting GG and GE interactions; however, its performance has been found to decline when outlying quantitative traits are present. This paper proposes a robust GMDR estimation method (based on the L-estimator and M-estimator estimation methods) in an attempt to reduce the effects caused by outlying traits. A comparison of robust GMDR with the original MDR based on simulation studies showed the former method to outperform the latter. The performance of robust GMDR is illustrated through a real GWA example consisting of 8,577 samples from the Korean population using the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) level as a phenotype. Robust GMDR identified the KCNH1 gene to have strong interaction effects with other genes on the function of insulin secretion.

Practical Issues in Screening and Variable Selection in Genome-Wide Association Analysis

Variable selection methods play an important role in high-dimensional statistical modeling and analysis. Computational cost and estimation accuracy are the two main concerns for statistical inference from ultrahigh-dimensional data. In particular, genome-wide association studies (GWAS), which focus on identifying single nucleotide polymorphisms (SNPs) associated with a disease of interest, have produced ultrahigh-dimensional data. Numerous methods have been proposed to handle GWAS data. Most statistical methods have adopted a two-stage approach: pre-screening for dimensional reduction and variable selection to identify causal SNPs. The pre-screening step selects SNPs in terms of their P-values or the absolute values of the regression coefficients in single SNP analysis. Penalized regressions, such as the ridge, lasso, adaptive lasso, and elastic-net regressions, are commonly used for the variable selection step. In this paper, we investigate which combination of pre-screening method and penalized regression performs best on a quantitative phenotype using two real GWAS datasets.