Daisuke Komura

Global variation in copy number in the human genome

Copy number variation (CNV) of DNA sequences is functionally significant but has yet to be fully ascertained. We have constructed a first-generation CNV map of the human genome through the study of 270 individuals from four populations with ancestry in Europe, Africa or Asia (the HapMap collection). DNA from these individuals was screened for CNV using two complementary technologies: single-nucleotide polymorphism (SNP) genotyping arrays, and clone-based comparative genomic hybridization. A total of 1,447 copy number variable regions (CNVRs), which can encompass overlapping or adjacent gains or losses, covering 360 megabases (12% of the genome) were identified in these populations. These CNVRs contained hundreds of genes, disease loci, functional elements and segmental duplications. Notably, the CNVRs encompassed more nucleotide content per genome than SNPs, underscoring the importance of CNV in genetic diversity and evolution. The data obtained delineate linkage disequilibrium patterns for many CNVs, and reveal marked variation in copy number among populations. We also demonstrate the utility of this resource for genetic disease studies.

A wave of nascent transcription on activated human genes.

Genome-wide studies reveal that transcription by RNA polymerase II (Pol II) is dynamically regulated. To obtain a comprehensive view of a single transcription cycle, we switched on transcription of five long human genes (>100 kbp) with tumor necrosis factor-α (TNFα) and monitored (using microarrays, RNA fluorescence in situ hybridization, and chromatin immunoprecipitation) the appearance of nascent RNA, changes in binding of Pol II and two insulators (the cohesin subunit RAD21 and the CCCTC-binding factor CTCF), and modifications of histone H3. Activation triggers a wave of transcription that sweeps along the genes at ≈3.1 kbp/min; splicing occurs cotranscriptionally, a major checkpoint acts several kilobases downstream of the transcription start site to regulate polymerase transit, and Pol II tends to stall at cohesin/CTCF binding sites.

The Niche Component Periostin Is Produced by Cancer-Associated Fibroblasts, Supporting Growth of Gastric Cancer through ERK Activation

Overexpression of periostin (POSTN), an extracellular matrix protein, has been observed in several cancers. We investigated the importance of POSTN in gastric cancer. Genome-wide gene expression analysis using publicly available microarray data sets revealed significantly high POSTN expression in cancer tissues from stage II-IV gastric cancer, compared with background normal tissues. The POSTN/vimentin mRNA expression ratio was highly associated with gene groups that regulate the cell cycle and cell proliferation. IHC showed that periglandular POSTN deposition, comprising linear deposition abutting the glandular epithelial cells in normal mucosa, disappeared during intestinal gastric cancer progression. Stromal POSTN deposition was also detected at the invasive front of intestinal-type and diffuse-type cancers. In situ hybridization confirmed POSTN mRNA in cancer-associated fibroblasts, but not in tumor cells themselves. POSTN enhanced the in vitro growth of OCUM-2MLN and OCUM-12 diffuse-type gastric cancer cell lines, accompanied by the activation of ERK. Furthermore, coinoculation of gastric cancer cells with POSTN-expressing NIH3T3 mouse fibroblast cells facilitated tumor formation. The OCUM-2MLN orthotopic inoculation model demonstrated that tumors of the gastric wall in Postn(-/-) mice were significantly smaller than those in wild-type mice. Ki-67 and p-ERK positive rates were both lower in Postn(-/-) mice. These findings suggest that POSTN produced by cancer-associated fibroblasts constitutes a growth-supportive microenvironment for gastric cancer.

Tracking control of nonlinear direct drive arm via just-in-time method

This paper is concerned with the tracking control of nonlinear two-links direct drive (DD) arm based on just-in-time method, also known as JIT control. In order to validate JIT control, we apply it to numerical DD arm simulator. In this paper, we show the results of the experiments and investigate the possibility of applying JIT control to an actual DD arm.

Loss of YAP1 defines neuroendocrine differentiation of lung tumors

YAP1, the main Hippo pathway effector, is a potent oncogene and is overexpressed in non‐small‐cell lung cancer (NSCLC); however, the YAP1 expression pattern in small‐cell lung cancer (SCLC) has not yet been elucidated in detail. We report that the loss of YAP1 is a special feature of high‐grade neuroendocrine lung tumors. A hierarchical cluster analysis of 15 high‐grade neuroendocrine tumor cell lines containing 14 SCLC cell lines that depended on the genes of Hippo pathway molecules and neuroendocrine markers clearly classified these lines into two groups: the YAP1‐negative and neuroendocrine marker‐positive group (n = 11), and the YAP1‐positive and neuroendocrine marker‐negative group (n = 4). Among the 41 NSCLC cell lines examined, the loss of YAP1 was only observed in one cell line showing the strong expression of neuroendocrine markers. Immunostaining for YAP1, using the sections of 189 NSCLC, 41 SCLC, and 30 large cell neuroendocrine carcinoma (LCNEC) cases, revealed that the loss of YAP1 was common in SCLC (40/41, 98%) and LCNEC (18/30, 60%), but was rare in NSCLC (6/189, 3%). Among the SCLC and LCNEC cases tested, the loss of YAP1 correlated with the expression of neuroendocrine markers, and a survival analysis revealed that YAP1‐negative cases were more chemosensitive than YAP1‐positive cases. Chemosensitivity test for cisplatin using YAP1‐positive/YAP1‐negative SCLC cell lines also showed compatible results. YAP1‐sh‐mediated knockdown induced the neuroendocrine marker RAB3a, which suggested the possible involvement of YAP1 in the regulation of neuroendocrine differentiation. Thus, we showed that the loss of YAP1 has potential as a clinical marker for predicting neuroendocrine features and chemosensitivity.

Machine Learning Methods for Histopathological Image Analysis

Abundant accumulation of digital histopathological images has led to the increased demand for their analysis, such as computer-aided diagnosis using machine learning techniques. However, digital pathological images and related tasks have some issues to be considered. In this mini-review, we introduce the application of digital pathological image analysis using machine learning algorithms, address some problems specific to such analysis, and propose possible solutions.

Capturing the Difference in Humoral Immunity between Normal and Tumor Environments from RNA Sequences of B-Cell Receptors Using Supervised Machine Learning

The recent success of immunotherapy in treating tumors has attracted increasing interest in research related to the adaptive immune system in the tumor microenvironment. Recent advances in next-generation sequencing technology enabled the sequencing of whole T-cell receptors (TCRs) and B-cell receptors (BCRs)/immunoglobulins (Igs) in the tumor microenvironment. Since BCRs/Igs in tumor tissues have high affinities for tumor-specific antigens, the patterns of their amino acid sequences and other sequence-independent features such as the number of somatic hypermutations (SHMs) may differ between the normal and tumor microenvironments. However, given the high diversity of BCRs/Igs and the rarity of recurrent sequences among individuals, it is far more difficult to capture such differences in BCR/Ig sequences than in TCR sequences. The aim of this study was to explore the possibility of discriminating BCRs/Igs in tumor and in normal tissues, by capturing these differences using supervised machine learning methods applied to RNA sequences of BCRs/Igs. RNA sequences of BCRs/Igs were obtained from matched normal and tumor specimens from 90 gastric cancer patients. BCR/Ig-features obtained in Rep-Seq were used to classify individual BCR/Ig sequences into normal or tumor classes. Different machine learning models using various features were constructed as well as gradient boosting machine (GBM) classifier combining these models. The results demonstrated that BCR/Ig sequences between normal and tumor microenvironments exhibit their differences. Next, by using a GBM trained to classify individual BCR/Ig sequences, we tried to classify sets of BCR/Ig sequences into normal or tumor classes. As a result, an area under the curve (AUC) value of 0.826 was achieved, suggesting that BCR/Ig repertoires have distinct sequence-level features in normal and tumor tissues. To the best of our knowledge, this is the first study to show that BCR/Ig sequences derived from tumor and normal tissues have globally distinct patterns, and that these tissues can be effectively differentiated using BCR/Ig repertoires.

CASTIN: a system for comprehensive analysis of cancer-stromal interactome

BackgroundCancer microenvironment plays a vital role in cancer development and progression, and cancer-stromal interactions have been recognized as important targets for cancer therapy. However, identifying relevant and druggable cancer-stromal interactions is challenging due to the lack of quantitative methods to analyze whole cancer-stromal interactome.ResultsWe present CASTIN (CAncer-STromal INteractome analysis), a novel framework for the evaluation of cancer-stromal interactome from RNA-Seq data using cancer xenograft models. For each ligand-receptor interaction which is derived from curated protein-protein interaction database, CASTIN summarizes gene expression profiles of cancer and stroma into three evaluation indices. These indices provide quantitative evaluation and comprehensive visualization of interactome, and thus enable to identify critical cancer-microenvironment interactions, which would be potential drug targets.We applied CASTIN to the dataset of pancreas ductal adenocarcinoma, and successfully characterized the individual cancer in terms of cancer-stromal relationships, and identified both well-known and less-characterized druggable interactions.ConclusionsCASTIN provides comprehensive view of cancer-stromal interactome and is useful to identify critical interactions which may serve as potential drug targets in cancer-microenvironment. CASTIN is available at: http://github.com/tmd-gpat/CASTIN.

Luigi: Large-scale histopathological image retrieval system using deep texture representations

Background As a large number of digital histopathological images have been accumulated, there is a growing demand of content-based image retrieval (CBIR) in pathology for educational, diagnostic, or research purposes. However, no CBIR systems in digital pathology are publicly available. Results We developed a web application, the Luigi system, which retrieves similar histopathological images from various cancer cases. Using deep texture representations computed with a pre-trained convolutional neural network as an image feature in conjunction with an approximate nearest neighbor search method, the Luigi system provides fast and accurate results for any type of tissue or cell without the need for further training. In addition, users can easily submit query images of an appropriate scale into the Luigi system and view the retrieved results using our smartphone application. The cases stored in the Luigi database are obtained from The Cancer Genome Atlas with rich clinical, pathological, and molecular information. We tested the Luigi system by querying typical cancerous regions from four cancer types, and confirmed successful retrieval of relevant images. Conclusions The Luigi system will help students, pathologists, and researchers easily retrieve histopathological images of various cancers similar to those of the query image.

Difference in morphology and interactome profiles between orthotopic and subcutaneous gastric cancer xenograft models

In xenograft models, orthotopic (ORT) engraftment is thought to provide a different tumor microenvironment compared with subcutaneous (SC) engraftment. We attempted to characterize the biological difference between OE19 (adenocarcinoma of the gastroesophageal junction) SC and ORT models by pathological analysis and CASTIN (CAncer-STromal INteractome) analysis, which is a novel method developed to analyze the tumor-stroma interactome framework. In SC models, SCID mice were inoculated subcutaneously with OE19 cells, and tumor tissues were sampled at 3 weeks. In ORT models, SCID mice were inoculated under the serosal membrane of the stomach wall, and tumor tissues were sampled at 3 and 6 weeks after engraftment. Results from the two models were then compared. Histopathologically, the SC tumors were well circumscribed from the adjacent tissue, with scant stroma and the formation of large ductal structures. In contrast, the ORT tumors were less circumscribed, with small ductal structures invading into abundant stroma. Then we compared the transcriptome profiles of human tumor cells with the mouse stromal cells of each model by species-specific RNA sequencing. With CASTIN analysis, we successfully identified several interactions that are known to affect the tumor microenvironment as being selectively enhanced in the ORT model. In conclusion, pathological analysis and CASTIN analysis revealed that ORT models of OE19 cells have a more invasive character and enhanced interaction with stromal cells compared with SC models.