Comparison of the Data of a Next-Generation Sequencing Panel from K-MASTER Project with That of Orthogonal Methods for Detecting Targetable Genetic Alterations.

PURPOSE: K-MASTER project is a Korean national precision medicine platform that screened actionable mutations by analyzing next-generation sequencing (NGS) of solid tumor patients. We compared gene analyses between NGS panel from the K-MASTER project and orthogonal methods. MATERIALS AND METHODS: Colorectal, breast, non-small cell lung, and gastric cancer patients were included. We compared NGS results from K-MASTER projects with those of non-NGS orthogonal methods (KRAS, NRAS, and BRAF mutations in colorectal cancer [CRC]; epidermal growth factor receptor [EGFR], anaplastic lymphoma kinase [ALK] fusion, and reactive oxygen species 1 [ROS1] fusion in non-small cell lung cancer [NSCLC], and Erb-B2 receptor tyrosine kinase 2 (ERBB2) positivity in breast and gastric cancers). RESULTS: In the CRC cohort (n=225), the sensitivity and specificity of NGS were 87.4% and 79.3% (KRAS); 88.9% and 98.9% (NRAS); and 77.8% and 100.0% (BRAF), respectively. In the NSCLC cohort (n=109), the sensitivity and specificity of NGS for EGFR were 86.2% and 97.5%, respectively. The concordance rate for ALK fusion was 100%, but ROS1 fusion was positive in only one of three cases that were positive in orthogonal tests. In the breast cancer cohort (n=260), ERBB2 amplification was detected in 45 by NGS. Compared with orthogonal methods that integrated immunohistochemistry and in situ hybridization, sensitivity and specificity were 53.7% and 99.4%, respectively. In the gastric cancer cohort (n=64), ERBB2 amplification was detected in six by NGS. Compared with orthogonal methods, sensitivity and specificity were 62.5% and 98.2%, respectively. CONCLUSION: The results of the K-MASTER NGS panel and orthogonal methods showed a different degree of agreement for each genetic alteration, but generally showed a high agreement rate.

Genomic Landscape and Clinical Utility in Korean Advanced Pan-Cancer Patients from Prospective Clinical Sequencing: K-MASTER Program.

The fundamental principle of precision oncology is centralized on the identification of therapeutically exploitable targets that provides individual patients with cancer an opportunity to make informed decisions on a personalized level. To facilitate and adopt such concepts within clinical practice, we have initiated a nationwide, multi-institutional precision oncology screening program to examine and enroll patients into the most appropriate clinical trial based on their tumor's unique molecular properties. To determine the prevalence of essential major driver mutations and to explore their dynamic associations at both molecular and pathway levels, we present a comprehensive overview on the genomic properties of East Asian patients with cancer. We further delineate the extent of genomic diversity as well as clinical actionability in patients from Western and Eastern cultures at the pan-cancer and single-tumor entity levels. To support fellow oncology communities in future investigations involving large-scale analysis, all data have been made accessible to the public (https://kmportal.or.kr). SIGNIFICANCE: We present a comprehensive overview of molecular properties of East Asian pan-cancer patients and demonstrate significant diversity in terms of genomic characteristics as well as clinical utility compared with patients with European ancestry. The results of this study will lay the groundwork for designing personalized treatments in the clinical setting. See related commentary by Moyers and Subbiah, p. 886. This article is highlighted in the In This Issue feature, p. 873.

Clinical Application of Targeted Deep Sequencing in Metastatic Colorectal Cancer Patients: Actionable Genomic Alteration in K-MASTER Project.

PURPOSE: Next-generation sequencing (NGS) can facilitate precision medicine approaches in metastatic colorectal cancer (mCRC) patients. We investigated the molecular profiling of Korean mCRC patients under the K-MASTER project which was initiated in June 2017 as a nationwide precision medicine oncology clinical trial platform which used NGS assay to screen actionable mutations. MATERIALS AND METHODS: As of 22 January 2020, total of 994 mCRC patients were registered in K-MASTER project. Targeted sequencing was performed using three platforms which were composed of the K-MASTER cancer panel v1.1 and the SNUH FIRST Cancer Panel v3.01. If tumor tissue was not available, cell-free DNA was extracted and the targeted sequencing was performed by Axen Cancer Panel as a liquid biopsy. RESULTS: In 994 mCRC patients, we found 1,564 clinically meaningful pathogenic variants which mutated in 71 genes. Anti-EGFR therapy candidates were 467 patients (47.0%) and BRAF V600E mutation (n=47, 4.7%), deficient mismatch repair/microsatellite instability-high (n=15, 1.5%), HER2 amplifications (n=10, 1.0%) could be incorporated with recently approved drugs. The patients with high tumor mutation burden (n=101, 12.7%) and DNA damaging response and repair defect pathway alteration (n=42, 4.2%) could be enrolled clinical trials with immune checkpoint inhibitors. There were more colorectal cancer molecular alterations such as PIK3CA, KRAS G12C, atypical BRAF, and HER2 mutations and even rarer but actionable genes that approved or ongoing clinical trials in other solid tumors. CONCLUSION: K-MASTER project provides an intriguing background to investigate new clinical trials with biomarkers and give therapeutic opportunity for mCRC patients.

ITF2 prevents activation of the ß-catenin-TCF4 complex in colon cancer cells and levels decrease with tumor progression.

BACKGROUND & AIMS: Immunoglobulin transcription factor 2 (ITF2) was believed to promote neoplastic transformation via activation of ß-catenin. However, ITF2 recently was reported to suppress colon carcinogenesis. We investigated the roles of ITF2 in colorectal cancer cell lines and tumor formation and growth in mice. METHODS: Levels of ITF2, ß-catenin, and c-Myc were measured in 12 human colorectal tumor samples and by immunohistochemistry. ITF2 regulation of ß-catenin and T-cell factor (TCF) were analyzed using luciferase reporter, reverse-transcription quantitative polymerase chain reaction, flow cytometry, and immunoblot analyses. Mice were given subcutaneous injections of human colorectal cancer cell lines that stably express ITF2, small hairpin RNAs to reduce levels of ITF2, or control plasmids; xenograft tumor growth was assessed. Human colorectal carcinoma tissue arrays were used to associate levels of ITF2 expression and clinical outcomes. RESULTS: Levels of ß-catenin, cMyc, and ITF2 were increased in areas of human colon adenomas and carcinomas, compared with nontumor areas of the same tissues. ITF2 levels were reduced and cMyc levels were increased in areas of carcinoma, compared with adenoma. In human colorectal cancer cell lines, activation of the ß-catenin-TCF4 complex and expression of its target genes were regulated negatively by ITF2. ITF2 inhibited formation of the ß-catenin-TCF4 complex by competing with TCF4 for ß-catenin binding. Stable transgenic expression of ITF2 in human colorectal cancer cell lines reduced their proliferation and tumorigenic potential in mice, whereas small hairpin RNA knockdown of ITF2 promoted growth of xenograft tumors in mice. In an analysis of colorectal tumor tissue arrays, loss of ITF2 from colorectal tumor tissues was associated with poor outcomes of patients. A gene set enrichment analysis supported the negative correlation between the level of ITF2 and activity of the ß-catenin-TCF4 complex. CONCLUSIONS: In human colorectal cancer cell lines and tissue samples, ITF2 appears to prevent activation of the ß-catenin-TCF4 complex and transcription of its gene targets. Loss of ITF2 promotes the ability of colorectal cancer cells to form xenograft tumors, and is associated with tumor progression and shorter survival times of patients.

DialysisNet: Application for Integrating and Management Data Sources of Hemodialysis Information by Continuity of Care Record.

OBJECTIVES: Health Avatar Beans was for the management of chronic kidney disease and end-stage renal disease (ESRD). This article is about the DialysisNet system in Health Avatar Beans for the seamless management of ESRD based on the personal health record. METHODS: For hemodialysis data modeling, we identified common data elements for hemodialysis information (CDEHI). We used ASTM continuity of care record (CCR) and ISO/IEC 11179 for the compliance method with a standard model for the CDEHI. According to the contents of the ASTM CCR, we mapped the CDHEI to the contents and created the metadata from that. It was transformed and parsed into the database and verified according to the ASTM CCR/XML schema definition (XSD). DialysisNet was created as an iPad application. The contents of the CDEHI were categorized for effective management. For the evaluation of information transfer, we used CarePlatform, which was developed for data access. The metadata of CDEHI in DialysisNet was exchanged by the CarePlatform with semantic interoperability. RESULTS: The CDEHI was separated into a content list for individual patient data, a contents list for hemodialysis center data, consultation and transfer form, and clinical decision support data. After matching to the CCR, the CDEHI was transformed to metadata, and it was transformed to XML and proven according to the ASTM CCR/XSD. DialysisNet has specific consideration of visualization, graphics, images, statistics, and database. CONCLUSIONS: We created the DialysisNet application, which can integrate and manage data sources for hemodialysis information based on CCR standards.

Lifelog agent for human activity pattern analysis on health avatar platform.

OBJECTIVES: To provide accurate personalized medical care, it is necessary to gather individual-related data or contextual information regarding the target person. Nowadays a large number of people possess smartphones, which enables sensors in the smartphones to be used for lifelogging. The objective of the study is to analyze human activity pattern by using lifelog agent cooperating with the Health Avatar platform. METHODS: Using the lifelog measured by accelerometer and gyroscope in a smartphone at a 50 Hz rate, the agent reveals how long the user walks, runs, sits, stands, and lies down, and this information is summarized by hours. The summaries are sent to the Health Avatar platform and finally are written in the Continuity of Care Record (CCR) format. RESULTS: The lifelog agent is successfully operated with the Health Avatar platform. In addition, we implement an application that displays the user's activity patterns in a graph and calculates the metabolic equivalent of task based calorie burned by hour or by day using the lifelog of the CCR form to show that the lifelog can be used as medical records. CONCLUSIONS: The agent shows how lifelogs are analyzed and summarized to help activity recognition. We believe that our agent demonstrates a way of incorporating lifelogs into medical care and a way of exploiting lifelogs in a medical format.

Semantically enabled and statistically supported biological hypothesis testing with tissue microarray databases.

BACKGROUND: Although many biological databases are applying semantic web technologies, meaningful biological hypothesis testing cannot be easily achieved. Database-driven high throughput genomic hypothesis testing requires both of the capabilities of obtaining semantically relevant experimental data and of performing relevant statistical testing for the retrieved data. Tissue Microarray (TMA) data are semantically rich and contains many biologically important hypotheses waiting for high throughput conclusions. METHODS: An application-specific ontology was developed for managing TMA and DNA microarray databases by semantic web technologies. Data were represented as Resource Description Framework (RDF) according to the framework of the ontology. Applications for hypothesis testing (Xperanto-RDF) for TMA data were designed and implemented by (1) formulating the syntactic and semantic structures of the hypotheses derived from TMA experiments, (2) formulating SPARQLs to reflect the semantic structures of the hypotheses, and (3) performing statistical test with the result sets returned by the SPARQLs. RESULTS: When a user designs a hypothesis in Xperanto-RDF and submits it, the hypothesis can be tested against TMA experimental data stored in Xperanto-RDF. When we evaluated four previously validated hypotheses as an illustration, all the hypotheses were supported by Xperanto-RDF. CONCLUSIONS: We demonstrated the utility of high throughput biological hypothesis testing. We believe that preliminary investigation before performing highly controlled experiment can be benefited.

BioLattice: a framework for the biological interpretation of microarray gene expression data using concept lattice analysis.

MOTIVATION: A challenge in microarray data analysis is to interpret observed changes in terms of biological properties and relationships. One powerful approach is to make associations of gene expression clusters with biomedical ontologies and/or biological pathways. However, this approach evaluates only one cluster at a time, returning long unordered lists of annotations for clusters without considering the overall context of the experiment under investigation. RESULTS: BioLattice is a mathematical framework based on concept lattice analysis for the biological interpretation of gene expression data. By considering gene expression clusters as objects and associated annotations as attributes and by using set inclusion relationships BioLattice orders them to create a lattice of concepts, providing an 'executive' summary of the experimental context. External knowledge resources such as Gene Ontology trees and pathway graphs can be added incrementally. We propose two quantitative structural analysis methods, 'prominent sub-lattice' and 'core-periphery' analyses, enabling systematic comparison of experimental concepts and contexts. BioLattice is implemented as a web-based utility using Scalable Vector Graphics for interactive visualization. We applied it to real microarray datasets with improved biological interpretations of the experimental contexts.

ArrayXPath II: mapping and visualizing microarray gene-expression data with biomedical ontologies and integrated biological pathway resources using Scalable Vector Graphics.

SUMMARY: ArrayXPath (http://www.snubi.org/software/ArrayXPath/) is a web-based service for mapping and visualizing microarray gene-expression data with integrated biological pathway resources using Scalable Vector Graphics (SVG). Deciphering the crosstalk among pathways and integrating biomedical ontologies and knowledge bases may help biological interpretation of microarray data. ArrayXPath is empowered by integrating gene-pathway, disease-pathway, drug-pathway and pathway-pathway correlations with integrated Gene Ontology, Medical Subject Headings and OMIM Morbid Map-based annotations. We applied Fisher's exact test and relative risk to evaluate the statistical significance of the correlations. ArrayXPath produces Javascript-enabled SVGs for web-enabled interactive visualization of gene-expression profiles integrated with gene-pathway-disease interactions enriched by biomedical ontologies.

ArrayXPath: mapping and visualizing microarray gene-expression data with integrated biological pathway resources using Scalable Vector Graphics.

Biological pathways can provide key information on the organization of biological systems. ArrayXPath (http://www.snubi.org/software/ArrayXPath/) is a web-based service for mapping and visualizing microarray gene-expression data for integrated biological pathway resources using Scalable Vector Graphics (SVG). By integrating major bio-databases and searching pathway resources, ArrayXPath automatically maps different types of identifiers from microarray probes and pathway elements. When one inputs gene-expression clusters, ArrayXPath produces a list of the best matching pathways for each cluster. We applied Fisher's exact test and the false discovery rate (FDR) to evaluate the statistical significance of the association between a cluster and a pathway while correcting the multiple-comparison problem. ArrayXPath produces Javascript-enabled SVGs for web-enabled interactive visualization of pathways integrated with gene-expression profiles.

ChromoViz: multimodal visualization of gene expression data onto chromosomes using scalable vector graphics.

SUMMARY: ChromoViz is an R package for the visualization of microarray gene expression data, cross-species and cross-platform comparisons, as well as non-expression genomic data obtained from public databases onto chromosomes. Chromosomal visualization format is proposed for the clear decoupling of the data layer from the procedure layer and the combined visualization of genomic data from heterogeneous data sources. Visualization with Javascript-enabled scalable vector graphics enables interactive visualization and navigation of data objects on the Web. AVAILABILITY: http://www.snubi.org/software/ChromoViz/