Multi-biomarker panel prediction model for diagnosis of pancreatic cancer.

BACKGROUND/PURPOSE: The current study aimed to develop a prediction model using a multi-marker panel as a diagnostic screening tool for pancreatic ductal adenocarcinoma. METHODS: Multi-center cohort of 1991 blood samples were collected from January 2011 to September 2019, of which 609 were normal, 145 were other cancer (colorectal, thyroid, and breast cancer), 314 were pancreatic benign disease, and 923 were pancreatic ductal adenocarcinoma. The automated multi-biomarker Enzyme-Linked Immunosorbent Assay kit was developed using three potential biomarkers: LRG1, TTR, and CA 19-9. Using a logistic regression model on a training data set, the predicted values for pancreatic ductal adenocarcinoma were obtained, and the result was classification into one of the three risk groups: low, intermediate, and high. The five covariates used to create the model were sex, age, and three biomarkers. RESULTS: Participants were categorized into four groups as normal (n = 609), other cancer (n = 145), pancreatic benign disease (n = 314), and pancreatic ductal adenocarcinoma (n = 923). The normal, other cancer, and pancreatic benign disease groups were clubbed into the non-pancreatic ductal adenocarcinoma group (n = 1068). The positive and negative predictive value, sensitivity, and specificity were 94.12, 90.40, 93.81, and 90.86, respectively. CONCLUSIONS: This study demonstrates a significant diagnostic performance of the multi-marker panel in distinguishing pancreatic ductal adenocarcinoma from normal and benign pancreatic disease states, as well as patients with other cancers.

CORRIGENDUM: Addition of data source: Diagnostic model for pancreatic cancer using a multi-biomarker panel.

[This corrects the article on p. 144 in vol. 100, PMID: 33748028.].

Diagnostic model for pancreatic cancer using a multi-biomarker panel.

PURPOSE: Diagnostic biomarkers of pancreatic ductal adenocarcinoma (PDAC) have been used for early detection to reduce its dismal survival rate. However, clinically feasible biomarkers are still rare. Therefore, in this study, we developed an automated multi-marker enzyme-linked immunosorbent assay (ELISA) kit using 3 biomarkers (leucine-rich alpha-2-glycoprotein [LRG1], transthyretin [TTR], and CA 19-9) that were previously discovered and proposed a diagnostic model for PDAC based on this kit for clinical usage. METHODS: Individual LRG1, TTR, and CA 19-9 panels were combined into a single automated ELISA panel and tested on 728 plasma samples, including PDAC (n = 381) and normal samples (n = 347). The consistency between individual panels of 3 biomarkers and the automated multi-panel ELISA kit were accessed by correlation. The diagnostic model was developed using logistic regression according to the automated ELISA kit to predict the risk of pancreatic cancer (high-, intermediate-, and low-risk groups). RESULTS: The Pearson correlation coefficient of predicted values between the triple-marker automated ELISA panel and the former individual ELISA was 0.865. The proposed model provided reliable prediction results with a positive predictive value of 92.05%, negative predictive value of 90.69%, specificity of 90.69%, and sensitivity of 92.05%, which all simultaneously exceed 90% cutoff value. CONCLUSION: This diagnostic model based on the triple ELISA kit showed better diagnostic performance than previous markers for PDAC. In the future, it needs external validation to be used in the clinic.

Machine learning methods for microbiome studies.

Researches on the microbiome have been actively conducted worldwide and the results have shown human gut bacterial environment significantly impacts on immune system, psychological conditions, cancers, obesity, and metabolic diseases. Thanks to the development of sequencing technology, microbiome studies with large number of samples are eligible on an acceptable cost nowadays. Large samples allow analysis of more sophisticated modeling using machine learning approaches to study relationships between microbiome and various traits. This article provides an overview of machine learning methods for non-data scientists interested in the association analysis of microbiomes and host phenotypes. Once genomic feature of microbiome is determined, various analysis methods can be used to explore the relationship between microbiome and host phenotypes that include penalized regression, support vector machine (SVM), random forest, and artificial neural network (ANN). Deep neural network methods are also touched. Analysis procedure from environment setup to extract analysis results are presented with Python programming language.

Statistical Methods for Identifying Biomarkers from miRNA Profiles of Cancers.

Biomarkers play important roles in early diagnosis and treatment plan for cancer patients and the importance is growing. With advances in high-throughput molecular profiling technology for various types of molecules such as DNA, RNA, proteins, or metabolites, it is now possible to perform massive profiling analysis that allows accelerating discovery of novel biomolecules. Because no single marker is sufficiently accurate for clinical use, the cancer biomarker is developed in the form of multiple biomarker panels. No single marker is sufficiently accurate for clinical use, and thus cancer biomarkers are developed in the form of multiple biomarker panels. Of various types of molecular biomarkers, microRNA (miRNA) has emerged as a class of promising cancer biomarker recently. MiRNAs are small noncoding RNAs that regulate gene expression. The chapter overviews the process of identification of biomarker panels from miRNA profiles focusing on statistical methods. Introduction to molecular cancer biomarkers is touched first. From sample design to miRNA profiling process is reviewed in the method section.Statistical methods for biomarker development are introduced according to three typical purposes of molecular biomarkers: tumor subtype classification, early detection, and prediction of treatment response or prognosis of patients. Example codes for R program are provided as well for selected methods.

Correction: Diagnostic performance enhancement of pancreatic cancer using proteomic multimarker panel.

[This corrects the article DOI: 10.18632/oncotarget.21861.].

Diagnostic performance enhancement of pancreatic cancer using proteomic multimarker panel.

Due to its high mortality rate and asymptomatic nature, early detection rates of pancreatic ductal adenocarcinoma (PDAC) remain poor. We measured 1000 biomarker candidates in 134 clinical plasma samples by multiple reaction monitoring-mass spectrometry (MRM-MS). Differentially abundant proteins were assembled into a multimarker panel from a training set (n=684) and validated in independent set (n=318) from five centers. The level of panel proteins was also confirmed by immunoassays. The panel including leucine-rich alpha-2 glycoprotein (LRG1), transthyretin (TTR), and CA19-9 had a sensitivity of 82.5% and a specificity of 92.1%. The triple-marker panel exceeded the diagnostic performance of CA19-9 by more than 10% (AUCCA19-9 = 0.826, AUCpanel= 0.931, P < 0.01) in all PDAC samples and by more than 30% (AUCCA19-9 = 0.520, AUCpanel = 0.830, P < 0.001) in patients with normal range of CA19-9 (<37U/mL). Further, it differentiated PDAC from benign pancreatic disease (AUCCA19-9 = 0.812, AUCpanel = 0.892, P < 0.01) and other cancers (AUCCA19-9 = 0.796, AUCpanel = 0.899, P < 0.001). Overall, the multimarker panel that we have developed and validated in large-scale samples by MRM-MS and immunoassay has clinical applicability in the early detection of PDAC.

Single Marker Family-Based Association Analysis Not Conditional on Parental Information.

Family-based association analysis unconditional on parental genotypes models the effects of observed genotypes. This approach has been shown to have greater power than conditional methods. In this chapter, we review popular association analysis methods accounting for familial correlations: the marginal model using generalized estimating equations (GEE), the mixed model with a polygenic random component, and genome-wide association analyses. The marginal approach does not explicitly model familial correlations but uses the information to improve the efficiency of parameter estimates. This model, using GEE, is useful when the correlation structure is not of interest; the correlations are treated as nuisance parameters. In the mixed model, familial correlations are modeled as random effects, e.g., the polygenic inheritance model accounts for correlations originating from shared genomic components within a family. These unconditional methods provide a flexible modeling framework for general pedigree data to accommodate traits with various distributions and many types of covariate effects. Genome-wide association studies usually test more than 10,000 SNPs and thus traditional statistical methods accounting for the familial correlations often suffer from a computational burden. Multiple approaches that have been recently proposed to avoid this computational issue are reviewed. The single-marker analysis procedures are demonstrated using the R package gee and the ASSOC program in the S.A.G.E. package, including how to prepare input data, conduct the analysis, and interpret the output. ASSOC allows models to include random components of additional familial correlations that may be not sufficiently explained by a polygenic effect and addresses nonnormality of response variables by transformation methods. With its ease of use, ASSOC provides a useful tool for association analysis of large pedigree data.

Molecular subtypes of pancreatic cancer based on miRNA expression profiles have independent prognostic value.

BACKGROUND AND AIM: Altered microRNAs (miRNA) expression, a typical feature of many cancers, is reportedly associated with prognosis according to several studies. Although numerous studies on miRNAs in pancreatic ductal adenocarcinoma have also attempted to identify prognostic biomarkers, more large-scale clinical studies are needed to establish the clinical significance of the results. Present study aimed to identify prognosis-related molecular subtypes of primary pancreas tumors using miRNA expression profiling. METHODS: Expression profiles of 1733 miRNAs were obtained by using microarray analysis of 104 pancreatic tumors of Korean patients. To detect subgroups informative in predicting the patient's prognosis, we applied unsupervised clustering methods and then analyzed the association of the molecular subgroups with survival time. Then, we constructed a classifier to predict the subgroup using penalized regression models. RESULTS: We have determined three pancreatic ductal adenocarcinoma tumor subtypes associated with prognosis based on miRNA expression profiles. These subtypes showed significantly different survival time for patients with the same clinical conditions. This demonstrates that our prognostic molecular subgroup has independent prognostic utility. The molecular subtypes can be predicted with a classifier of 19 miRNAs. Of the 19 signature miRNAs, miR-106b-star, miR-324-3p, and miR-615 were related to a p53 canonical pathway, and miR-324, miR-145-5p, miR-26b-5p, and miR-574-3p were related to a Cox-2 centered pathway. CONCLUSIONS: Our prognostic molecular subtypes demonstrated that miRNA profiles could be used as prognostic markers. Additionally, we have constructed a classifier that may be used to determine the molecular subgroup of new patient sample data. Further studies are needed for validation.

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

BACKGROUND: 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. METHODS: 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) data depository. For validation, miRNA activity was estimated by miRNA-target gene interaction and mRNA expression datasets in pancreatic cancer. RESULTS: 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. CONCLUSIONS: Our prediction models have strong potential for the diagnosis of pancreatic cancer.

Are Podoplanin Gene Polymorphisms Associated with Atopic Dermatitis in Koreans?

BACKGROUND: The histologic characteristics of atopic dermatitis (AD) include perivascular edema and dilated tortuous vessels in the papillary dermis. A single nucleotide polymorphism (SNP) of the fms-related tyrosine kinase 4 (FLT4) gene is associated with AD. OBJECTIVE: To investigate the associations between podoplanin (PDPN) gene SNPs and AD. METHODS: We genotyped 9 SNPs from 5 genes of 1,119 subjects (646 AD patients and 473 controls). We determined the promoter activity of 1 SNP (rs355022) by luciferase assay; this SNP was further investigated using 1,133 independent samples (441 AD patients and 692 controls). RESULTS: The rs355022 and rs425187 SNPs and the C-A haplotype in the PDPN gene were significantly associated with intrinsic AD in the initial experiment. The rs355022 SNP significantly affected promoter activity in the luciferase assay. However, these results were not replicated in the replication study. CONCLUSION: Two SNPs and the C-A haplotype in the PDPN gene are significantly associated with intrinsic AD; although, the results were confirmed by luciferase assay, they could not be replicated with independent samples. Nevertheless, further replication experiments should be performed in future studies.

Noninvasive prenatal diagnosis of duchenne muscular dystrophy: comprehensive genetic diagnosis in carrier, proband, and fetus.

BACKGROUND: Noninvasive prenatal diagnosis of monogenic disorders using maternal plasma and targeted massively parallel sequencing is being investigated actively. We previously demonstrated that comprehensive genetic diagnosis of a Duchenne muscular dystrophy (DMD) patient is feasible using a single targeted sequencing platform. Here we demonstrate the applicability of this approach to carrier detection and noninvasive prenatal diagnosis. METHODS: Custom solution-based target enrichment was designed to cover the entire dystrophin (DMD) gene region. Targeted massively parallel sequencing was performed using genomic DNA from 4 mother and proband pairs to test whether carrier status could be detected reliably. Maternal plasma DNA at varying gestational weeks was collected from the same families and sequenced using the same targeted platform to predict the inheritance of the DMD mutation by their fetus. Overrepresentation of an inherited allele was determined by comparing the allele fraction of 2 phased haplotypes after examining and correcting for the recombination event. RESULTS: The carrier status of deletion/duplication and point mutations was detected reliably through using a single targeted massively parallel sequencing platform. Whether the fetus had inherited the DMD mutation was predicted correctly in all 4 families as early as 6 weeks and 5 days of gestation. In one of these, detection of the recombination event and reconstruction of the phased haplotype produced a correct diagnosis. CONCLUSIONS: Noninvasive prenatal diagnosis of DMD is feasible using a single targeted massively parallel sequencing platform with tiling design.

Single nucleotide polymorphism in the FLT4 gene is associated with atopic dermatitis in Koreans.

BACKGROUND: The histology of atopic dermatitis includes dilated, tortuous vessels within the papillary dermis and perivascular edema. METHOD: This study included 1120 case-control samples (646 AD patients and 474 normal controls), for which we genotyped 34 SNPs from four VEGF family genes and the FLT4 gene. For the rs11607007 SNP in the VEGFB gene and three SNPs (rs10085109, rs3736062, and rs11949194) in the FLT4 gene, which had significant p-values in the initial stage, were further investigated using 1132 independent samples (440 AD patients and 692 normal controls). RESULT: Of the four SNPs, rs10085109 in the FLT4 gene was only significantly associated with the AD phenotype in both initial and replication samples. Although no SNPs in the VEGFA gene were significantly associated with AD, the rs2010963 SNP had a marginally significant effect on log-eosinophil counts. CONCLUSION: The rs10085109 SNP in the FLT4 gene were associated with susceptibility to AD.

Single marker family-based association analysis not conditional on parental information.

Family-based association analysis unconditional on parental genotypes models the effects of observed genotypes. This approach has been shown to have greater power than conditional methods. In this chapter, I review two popular association analysis methods accounting for familial correlations: the marginal model using generalized estimating equations (GEE) and the mixed model with a polygenic random component. The marginal approach does not explicitly model familial correlations but uses the information to improve the efficiency of parameter estimates. This model, using GEE, is useful when the correlation structure is not of interest; the correlations are treated as nuisance parameters. In the mixed model, familial correlations are modeled as random effects, e.g., the polygenic inheritance model accounts for correlations originating from shared genomic components within a family. These unconditional methods provide a flexible modeling framework for general pedigree data to accommodate traits with various distributions and many types of covariate effects. The analysis procedures are demonstrated using the ASSOC program in the S.A.G.E. package and the R package gee, including how to prepare input data, conduct the analysis, and interpret the output. ASSOC allows models to include random components of additional familial correlations that may be not sufficiently explained by a polygenic effect and addresses nonnormality of response variables by transformation methods. With its ease of use, ASSOC provides a useful tool for association analysis of large pedigree data.

Multiple pathway-based genetic variations associated with tobacco related multiple primary neoplasms.

BACKGROUND: In order to elucidate a combination of genetic alterations that drive tobacco carcinogenesis we have explored a unique model system and analytical method for an unbiased qualitative and quantitative assessment of gene-gene and gene-environment interactions. The objective of this case control study was to assess genetic predisposition in a biologically enriched clinical model system of tobacco related cancers (TRC), occurring as Multiple Primary Neoplasms (MPN). METHODS: Genotyping of 21 candidate Single Nucleotide Polymorphisms (SNP) from major metabolic pathways was performed in a cohort of 151 MPN cases and 210 cancer-free controls. Statistical analysis using logistic regression and Multifactor Dimensionality Reduction (MDR) analysis was performed for studying higher order interactions among various SNPs and tobacco habit. RESULTS: Increased risk association was observed for patients with at least one TRC in the upper aero digestive tract (UADT) for variations in SULT1A1 Arg²¹³His, mEH Tyr¹¹³His, hOGG1 Ser³²6Cys, XRCC1 Arg²8°His and BRCA2 Asn³7²His. Gene-environment interactions were assessed using MDR analysis. The overall best model by MDR was tobacco habit/p53(Arg/Arg)/XRCC1(Arg³99His)/mEH(Tyr¹¹³His) that had highest Cross Validation Consistency (8.3) and test accuracy (0.69). This model also showed significant association using logistic regression analysis. CONCLUSION: This is the first Indian study on a multipathway based approach to study genetic susceptibility to cancer in tobacco associated MPN. This approach could assist in planning additional studies for comprehensive understanding of tobacco carcinogenesis.

Analysis of exome sequences with and without incorporating prior biological knowledge.

Next-generation sequencing technology provides new opportunities and challenges in the search for genetic variants that underlie complex traits. It will also presumably uncover many new rare variants, but exactly how these variants should be incorporated into the data analysis remains a question. Several papers in our group from Genetic Analysis Workshop 17 evaluated different methods of rare variant analysis, including single-variant, gene-based, and pathway-based analyses and analyses that incorporated biological information. Although the performance of some of these methods strongly depends on the underlying disease model, integration of known biological information is helpful in detecting causal genes. Two work groups demonstrated that use of a Bayesian network and a collapsing receiver operating characteristic curve approach improves risk prediction when a disease is caused by many rare variants. Another work group suggested that modeling local rather than global ancestry may be beneficial when controlling the effect of population structure in rare variant association analysis.

Association of polymorphisms in genes encoding IL-4, IL-13 and their receptors with atopic dermatitis in a Korean population.

Th2-dominated immune responses are believed to contribute to the pathogenesis of atopic dermatitis (AD). IL-4 and IL-13 are typical pleiotropic Th2 cytokines that play a central role in IgE-dependent inflammatory reactions. Single-nucleotide polymorphisms (SNPs) in IL-4 and IL-13 have been reported in patients with allergic disease from numerous countries. Gene-gene interactions among genes have been identified in patients with asthma, although negative results have been reported. To investigate the associations of SNPs in these genes and the interactions between these genes in AD, we genotyped 23 SNPs of the IL-4, IL-13, IL-4R, IL-13Rα1 and IL-13Rα2 genes for 1089 case-control samples (631 AD patients and 458 controls) and analysed the SNPs and haplotypes in these genes. We also searched for gene-gene interactions among these five genes. Our data identified an association between rs3091307 and rs20541 in the IL-13 gene and between rs2265753 and rs2254672 in the IL-13Rα1 gene and the AD phenotype. In particular, three of the four SNPs were especially predictive of the allergic type of AD (ADe), and the haplotype TCGG in the IL-13Rα1 gene showed significant association with AD, especially ADe. Furthermore, the combination of rs3091307 GG/ rs2265753 GG (IL-13/IL-13Rα1) conveyed a significantly higher risk for developing ADe. However, we did not identify any SNPs in the IL-4, IL-4R and IL-13Rα2 genes that were associated with AD. As IL-13Rα1 is most likely expressed in Th17 cells rather than in Th2 cells, these data suggest diversity in the classification of Th cells that needs to be verified in future studies.

An association between IL-9 and IL-9 receptor gene polymorphisms and atopic dermatitis in a Korean population.

BACKGROUND: The genes encoding IL-9 and IL-9R have recently been implicated in the genetic basis of asthma and allergy. Although studies performed on transgenic and knockout mice have shown conflicting results, no evidence of skin changes has ever been reported in these animals. OBJECTIVE: To find association of the SNPs in IL-9 and IL-9R genes and interaction of these genes in atopic dermatitis. METHOD: We genotyped 5 SNPs from the IL-9 and IL-9R genes of 1090 subject samples (631 AD patients and 459 normal controls). A luciferase assay was then performed for the rs31563 (-4091G/A) SNP located in the IL-9 gene promoter region. RESULT: The rs31563 (-4091G/A) SNP in the IL-9 gene was significantly associated with the AD phenotype, especially allergic-type AD. In the luciferase assay, the rs31563 G construct was observed to have 1.54 times higher activity than the rs31563 A construct. Although no association was found between SNPs in IL-9R gene and AD, the rs3093467 SNP showed association with non-allergic AD. In the gene-gene interaction analysis, we found that IL-9/IL-9R genotype rs31563 GG/rs3093467 TT conveyed a greater risk for AD phenotype development. CONCLUSION: Significant evidence exists to suggest that the rs31563 SNP (-4091G/A) located in the IL-9 gene is associated with an increased susceptibility to AD. Similarly, the rs3093467 SNP in IL-9R gene seems to be associated with an increased risk for developing non-allergic AD. In a subsequent gene-gene interaction analysis, the rs31563 GG/rs3093467 TT genotype combination (IL-9/IL-9R) was found to exert a synergistic effect in the development of the AD phenotype. As the classes of helper T cells are diverse and the function of IL-9 cytokine has not been fully described, the cutaneous function of IL-9 needs to be further explored in future studies.

Estimating heritability using family and unrelated individuals data.

For the family data from Genetic Analysis Workshop 17, we obtained heritability estimates of quantitative traits Q1 and Q4 using the ASSOC program in the S.A.G.E. software package. ASSOC is a family-based method that estimates heritability through the estimation of variance components. The covariate-adjusted mean heritability was 0.650 for Q1 and 0.745 for Q4. For the unrelated individuals data, we estimated the heritability of Q1 as the proportion of total variance that can be accounted for by all single-nucleotide polymorphisms under an additive model. We examined a novel ordinary least-squares method, a naïve restricted maximum-likelihood method, and a calibrated restricted maximum-likelihood method. We applied the different methods to all 200 replicates for Q1. We observed that the ordinary least-squares method yielded many estimates outside the interval [0, 1]. The restricted maximum-likelihood estimates were more stable than the ordinary least-squares estimates. The naïve restricted maximum-likelihood method yielded an average estimate of 0.462 ± 0.1, and the calibrated restricted maximum-likelihood method yielded an average of 0.535 ± 0.121. Our results demonstrate discrepancies in heritability estimates using the family data and the unrelated individuals data.