Yusuke Matsui

NECAB3 Promotes Activation of Hypoxia-inducible factor-1 during Normoxia and Enhances Tumourigenicity of Cancer Cells.

Unlike most cells, cancer cells activate hypoxia inducible factor-1 (HIF-1) to use glycolysis even at normal oxygen levels, or normoxia. Therefore, HIF-1 is an attractive target in cancer therapy. However, the regulation of HIF-1 during normoxia is not well characterised, although Mint3 was recently found to activate HIF-1 in cancer cells and macrophages by suppressing the HIF-1 inhibitor, factor inhibiting HIF-1 (FIH-1). In this study, we analysed Mint3-binding proteins to investigate the mechanism by which Mint3 regulates HIF-1. Yeast two-hybrid screening using Mint3 as bait identified N-terminal EF-hand calcium binding protein 3 (NECAB3) as a novel factor regulating HIF-1 activity via Mint3. NECAB3 bound to the phosphotyrosine-binding domain of Mint3, formed a ternary complex with Mint3 and FIH-1, and co-localised with Mint3 at the Golgi apparatus. Depletion of NECAB3 decreased the expression of HIF-1 target genes and reduced glycolysis in normoxic cancer cells. NECAB3 mutants that binds Mint3 but lacks an intact monooxygenase domain also inhibited HIF-1 activation. Inhibition of NECAB3 in cancer cells by either expressing shRNAs or generating a dominant negative mutant reduced tumourigenicity. Taken together, the data indicate that NECAB3 is a promising new target for cancer therapy.

ER stress signaling promotes the survival of cancer 'persister cells' tolerant to EGFR tyrosine kinase inhibitors

An increasingly recognized component of resistance to tyrosine kinase inhibitors (TKI) involves persistence of a drug-tolerant subpopulation of cancer cells that survive despite effective eradication of the majority of the cell population. Multiple groups have demonstrated that these drug-tolerant persister cells undergo transcriptional adaptation via an epigenetic state change that promotes cell survival. Because this mode of TKI drug tolerance appears to involve transcriptional addiction to specific genes and pathways, we hypothesized that systematic functional screening of EGFR TKI/transcriptional inhibitor combination therapy would yield important mechanistic insights and alternative drug escape pathways. We therefore performed a genome-wide CRISPR/Cas9 enhancer/suppressor screen in EGFR-dependent lung cancer PC9 cells treated with erlotinib + THZ1 (CDK7/12 inhibitor) combination therapy, a combination previously shown to suppress drug-tolerant cells in this setting. As expected, suppression of multiple genes associated with transcriptional complexes (EP300, CREBBP, and MED1) enhanced erlotinib/THZ1 synergy. Unexpectedly, we uncovered nearly every component of the recently described ufmylation pathway in the synergy suppressor group. Loss of ufmylation did not affect canonical downstream EGFR signaling. Instead, absence of this pathway triggered a protective unfolded protein response associated with STING upregulation, promoting protumorigenic inflammatory signaling but also unique dependence on Bcl-xL. These data reveal that dysregulation of ufmylation and ER stress comprise a previously unrecognized TKI drug tolerance pathway that engages survival signaling, with potentially important therapeutic implications.Significance: These findings reveal a novel function of the recently described ufmylation pathway, an ER stress survival signaling in drug-tolerant persister cells, which has important biological and therapeutic implications. Cancer Res; 78(4); 1044-57. ©2017 AACR.

D3M: detection of differential distributions of methylation levels

MOTIVATION DNA methylation is an important epigenetic modification related to a variety of diseases including cancers. We focus on the methylation data from Illuminas Infinium HumanMethylation450 BeadChip. One of the key issues of methylation analysis is to detect the differential methylation sites between case and control groups. Previous approaches describe data with simple summary statistics or kernel function, and then use statistical tests to determine the difference. However, a summary statistics-based approach cannot capture complicated underlying structure, and a kernel function-based approach lacks interpretability of results. RESULTS We propose a novel method D(3)M, for detection of differential distribution of methylation, based on distribution-valued data. Our method can detect the differences in high-order moments, such as shapes of underlying distributions in methylation profiles, based on the Wasserstein metric. We test the significance of the difference between case and control groups and provide an interpretable summary of the results. The simulation results show that the proposed method achieves promising accuracy and shows favorable results compared with previous methods. Glioblastoma multiforme and lower grade glioma data from The Cancer Genome Atlas show that our method supports recent biological advances and suggests new insights. AVAILABILITY AND IMPLEMENTATION R implemented code is freely available from https://github.com/ymatts/D3M/ CONTACT: ymatsui@med.nagoya-u.ac.jp or shimamura@med.nagoya-u.ac.jp SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

GIMLET: Identifying Biological Modulators in Context-Specific Gene Regulation Using Local Energy Statistics

The regulation of transcription factor activity dynamically changes across cellular conditions and disease subtypes. The identification of biological modulators contributing to context-specific gene regulation is one of the challenging tasks in systems biology, which is necessary to understand and control cellular responses across different genetic backgrounds and environmental conditions. Previous approaches for identifying biological modulators from gene expression data were restricted to the capturing of a particular type of a three-way dependency among a regulator, its target gene, and a modulator; these methods cannot describe the complex regulation structure, such as when multiple regulators, their target genes, and modulators are functionally related. Here, we propose a statistical method for identifying biological modulators by capturing multivariate local dependencies, based on energy statistics, which is a class of statistics based on distances. Subsequently, our method assigns a measure of statistical significance to each candidate modulator through a permutation test. We compared our approach with that of a leading competitor for identifying modulators, and illustrated its performance through both simulations and real data analysis. Our method, entitled genome-wide identification of modulators using local energy statistical test (GIMLET), is implemented with R (≥ 3.2.2) and is available from github (https://github.com/tshimam/GIMLET).

A Network of Networks Approach for Modeling Interconnected Brain Tissue-Specific Networks

Motivation Recent sequence-based analyses have identified a lot of gene variants that may contribute to neurogenetic disorders such as autism spectrum disorder and schizophrenia. Several state-of-the-art network-based analyses have been proposed for mechanical understanding of genetic variants in neurogenetic disorders. However, these methods were mainly designed for modeling and analyzing single networks that do not interact with or depend on other networks, and thus cannot capture the properties between interdependent systems in brain-specific tissues, circuits, and regions which are connected each other and affect behavior and cognitive processes. Results We introduce a novel and efficient framework, called a “Network of Networks” (NoN) approach, to infer the interconnectivity structure between multiple networks where the response and the predictor variables are topological information matrices of given networks. We also propose Graph-Oriented SParsE Learning (GOSPEL), a new sparse structural learning algorithm for network graph data to identify a subset of the topological information matrices of the predictors related to the response. We demonstrate on simulated data that GOSPEL outperforms existing kernel-based algorithms in terms of F-measure. On real data from human brain region-specific functional networks associated with the autism risk genes, we show that the NoN model provides insights on the autism-associated interconnectivity structure between functional interaction networks and a comprehensive understanding of the genetic basis of autism across diverse regions of the brain. Availability Our software is available from https://github.com/infinite-point/GOSPEL. Contact kawakubo@med.nagoya-u.ac.jp, shimamura@med.nagoya-u.ac.jp Supplementary information Supplementary data are available at Bioinformatics online.

A temporal shift of the evolutionary principle shaping intratumor heterogeneity in colorectal cancer

Advanced colorectal cancer harbors extensive intratumor heterogeneity shaped by neutral evolution; however, intratumor heterogeneity in colorectal precancerous lesions has been poorly studied. We perform multiregion whole-exome sequencing on ten early colorectal tumors, which contained adenoma and carcinoma in situ. By comparing with sequencing data from advanced colorectal tumors, we show that the early tumors accumulate a higher proportion of subclonal driver mutations than the advanced tumors, which is highlighted by subclonal mutations in KRAS and APC. We also demonstrate that variant allele frequencies of subclonal mutations tend to be higher in early tumors, suggesting that the subclonal mutations are subject to selective sweep in early tumorigenesis while neutral evolution is dominant in advanced ones. This study establishes that the evolutionary principle underlying intratumor heterogeneity shifts from Darwinian to neutral evolution during colorectal tumor progression.Advanced colorectal cancers are characterised by intra-tumour heterogeneity dictated by neutral evolution. Here the authors analyse early colorectal tumours by whole-exome sequencing and find that Darwinian evolution determines the fate of early lesions in colorectal adenoma and carcinoma in situ.

Tumor subclonal progression model for cancer hallmark acquisition

Recent advances in the methodologies of reconstructing cancer evolutionary trajectories opened the horizon for deciphering the subclonal populations and their evolutionary architectures under the cancer ecosystems. An important challenge of the cancer evolution studies is connecting genetic aberrations in subclones to clinically interpretable and actionable target of subclones for individual patients. In this paper, we present a novel method for constructing tumor subclonal progression model for cancer hallmark acquisition using multi-regional sequencing data. We parepare a subclonal evolutionary tree inferred from variant allele frequencies and estimate the pathway alternation probabilities from large scale cohort genomic data. We then construct an evolutionary tree of pathway alternation that takes account of selectivity of pathway alternations by the notion of probabilistic causality. We show the effectiveness of our method using a dataset of clear cell renal cell carcinomas.

phyC: Clustering cancer evolutionary trees

Multi-regional sequencing provides new opportunities to investigate genetic heterogeneity within or between common tumors from an evolutionary perspective. Several state-of-the-art methods have been proposed for reconstructing cancer evolutionary trees based on multi-regional sequencing data to develop models of cancer evolution. However, there have been few studies on comparisons of a set of cancer evolutionary trees. We propose a clustering method (phyC) for cancer evolutionary trees, in which sub-groups of the trees are identified based on topology and edge length attributes. For interpretation, we also propose a method for evaluating the sub-clonal diversity of trees in the clusters, which provides insight into the acceleration of sub-clonal expansion. Simulation showed that the proposed method can detect true clusters with sufficient accuracy. Application of the method to actual multi-regional sequencing data of clear cell renal carcinoma and non-small cell lung cancer allowed for the detection of clusters related to cancer type or phenotype. phyC is implemented with R(≥3.2.2) and is available from https://github.com/ymatts/phyC.