Satoru Miyano

Frequent pathway mutations of splicing machinery in myelodysplasia.

Myelodysplastic syndromes and related disorders (myelodysplasia) are a heterogeneous group of myeloid neoplasms showing deregulated blood cell production with evidence of myeloid dysplasia and a predisposition to acute myeloid leukaemia, whose pathogenesis is only incompletely understood. Here we report whole-exome sequencing of 29 myelodysplasia specimens, which unexpectedly revealed novel pathway mutations involving multiple components of the RNA splicing machinery, including U2AF35, ZRSR2, SRSF2 and SF3B1. In a large series analysis, these splicing pathway mutations were frequent (∼45 to ∼85%) in, and highly specific to, myeloid neoplasms showing features of myelodysplasia. Conspicuously, most of the mutations, which occurred in a mutually exclusive manner, affected genes involved in the 3′-splice site recognition during pre-mRNA processing, inducing abnormal RNA splicing and compromised haematopoiesis. Our results provide the first evidence indicating that genetic alterations of the major splicing components could be involved in human pathogenesis, also implicating a novel therapeutic possibility for myelodysplasia.

Long Noncoding RNA HOTAIR Regulates Polycomb-Dependent Chromatin Modification and Is Associated with Poor Prognosis in Colorectal Cancers

The functional impact of recently discovered long noncoding RNAs (ncRNAs) in human cancer remains to be clarified. One long ncRNA which has attracted attention is the Hox transcript antisense intergenic RNA termed HOTAIR, a long ncRNA expressed from the developmental HOXC locus located on chromosome 12q13.13. In cooperation with Polycomb complex PRC2, the HOTAIR long ncRNA is reported to reprogram chromatin organization and promote breast cancer metastasis. In this study, we examined the status and function of HOTAIR in patients with stage IV colorectal cancer (CRC) who have liver metastases and a poor prognosis. HOTAIR expression levels were higher in cancerous tissues than in corresponding noncancerous tissues and high HOTAIR expression correlated tightly with the presence of liver metastasis. Moreover, patients with high HOTAIR expression had a relatively poorer prognosis. In a subset of 32 CRC specimens, gene set enrichment analysis using cDNA array data revealed a close correlation between expression of HOTAIR and members of the PRC2 complex (SUZ12, EZH2, and H3K27me3). Our findings suggest that HOTAIR expression is associated with a genome-wide reprogramming of PRC2 function not only in breast cancer but also in CRC, where upregulation of this long ncRNA may be a critical element in metastatic progression.

Identification of genetic networks from a small number of gene expression patterns under the Boolean network model.

Liang, Fuhrman and Somogyi (PSB98, 18-29, 1998) have described an algorithm for inferring genetic network architectures from state transition tables which correspond to time series of gene expression patterns, using the Boolean network model. Their results of computational experiments suggested that a small number of state transition (INPUT/OUTPUT) pairs are sufficient in order to infer the original Boolean network correctly. This paper gives a mathematical proof for their observation. Precisely, this paper devises a much simpler algorithm for the same problem and proves that, if the indegree of each node (i.e., the number of input nodes to each node) is bounded by a constant, only O(log n) state transition pairs (from 2n pairs) are necessary and sufficient to identify the original Boolean network of n nodes correctly with high probability. We made computational experiments in order to expose the constant factor involved in O(log n) notation. The computational results show that the Boolean network of size 100,000 can be identified by our algorithm from about 100 INPUT/OUTPUT pairs if the maximum indegree is bounded by 2. It is also a merit of our algorithm that the algorithm is conceptually so simple that it is extensible for more realistic network models.

Whole-genome sequencing of liver cancers identifies etiological influences on mutation patterns and recurrent mutations in chromatin regulators

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. We sequenced and analyzed the whole genomes of 27 HCCs, 25 of which were associated with hepatitis B or C virus infections, including two sets of multicentric tumors. Although no common somatic mutations were identified in the multicentric tumor pairs, their whole-genome substitution patterns were similar, suggesting that these tumors developed from independent mutations, although their shared etiological backgrounds may have strongly influenced their somatic mutation patterns. Statistical and functional analyses yielded a list of recurrently mutated genes. Multiple chromatin regulators, including ARID1A, ARID1B, ARID2, MLL and MLL3, were mutated in ∼50% of the tumors. Hepatitis B virus genome integration in the TERT locus was frequently observed in a high clonal proportion. Our whole-genome sequencing analysis of HCCs identified the influence of etiological background on somatic mutation patterns and subsequent carcinogenesis, as well as recurrent mutations in chromatin regulators in HCCs.

Landscape of genetic lesions in 944 patients with myelodysplastic syndromes

High-throughput DNA sequencing significantly contributed to diagnosis and prognostication in patients with myelodysplastic syndromes (MDS). We determined the biological and prognostic significance of genetic aberrations in MDS. In total, 944 patients with various MDS subtypes were screened for known/putative mutations/deletions in 104 genes using targeted deep sequencing and array-based genomic hybridization. In total, 845/944 patients (89.5%) harbored at least one mutation (median, 3 per patient; range, 0–12). Forty-seven genes were significantly mutated with TET2, SF3B1, ASXL1, SRSF2, DNMT3A, and RUNX1 mutated in >10% of cases. Many mutations were associated with higher risk groups and/or blast elevation. Survival was investigated in 875 patients. By univariate analysis, 25/48 genes (resulting from 47 genes tested significantly plus PRPF8) affected survival (P<0.05). The status of 14 genes combined with conventional factors revealed a novel prognostic model (‘Model-1’) separating patients into four risk groups (‘low’, ‘intermediate’, ‘high’, ‘very high risk’) with 3-year survival of 95.2, 69.3, 32.8, and 5.3% (P<0.001). Subsequently, a ‘gene-only model’ (‘Model-2’) was constructed based on 14 genes also yielding four significant risk groups (P<0.001). Both models were reproducible in the validation cohort (n=175 patients; P<0.001 each). Thus, large-scale genetic and molecular profiling of multiple target genes is invaluable for subclassification and prognostication in MDS patients.

Integrated molecular analysis of clear-cell renal cell carcinoma

Clear-cell renal cell carcinoma (ccRCC) is the most prevalent kidney cancer and its molecular pathogenesis is incompletely understood. Here we report an integrated molecular study of ccRCC in which ≥100 ccRCC cases were fully analyzed by whole-genome and/or whole-exome and RNA sequencing as well as by array-based gene expression, copy number and/or methylation analyses. We identified a full spectrum of genetic lesions and analyzed gene expression and DNA methylation signatures and determined their impact on tumor behavior. Defective VHL-mediated proteolysis was a common feature of ccRCC, which was caused not only by VHL inactivation but also by new hotspot TCEB1 mutations, which abolished Elongin C–VHL binding, leading to HIF accumulation. Other newly identified pathways and components recurrently mutated in ccRCC included PI3K-AKT-mTOR signaling, the KEAP1-NRF2-CUL3 apparatus, DNA methylation, p53-related pathways and mRNA processing. This integrated molecular analysis unmasked new correlations between DNA methylation, gene mutation and/or gene expression and copy number profiles, enabling the stratification of clinical risks for patients with ccRCC.

Hybrid Petri net representation of gene regulatory network.

It is important to provide a representation method of gene regulatory networks which realizes the intuitions of biologists while keeping the universality in its computational ability. In this paper, we propose a method to exploit hybrid Petri net (HPN) for representing gene regulatory networks. The HPN is an extension of Petri nets which have been used to represent many kinds of systems including stochastic ones in the field of computer sciences and engineerings. Since the HPN has continuous and discrete elements, it can easily handle biological factors such as protein and mRNA concentrations. We demonstrate that, by using HPNs, it is possible to translate biological facts into HPNs in a natural manner. It should be also emphasized that a hierarchical approach is taken for our construction of the genetic switch mechanism of lambda phage which is realized by using HPNs. This hierarchical approach with HPNs makes easier the arrangement of the components in the gene regulatory network based on the biological facts and provides us a prospective view of the network. We also show some computational results of the protein dynamics of the lambda phage mechanism that is simulated and observed by implementing the HPN on a currently available tool.

Estimation of genetic networks and functional structures between genes by using Bayesian networks and nonparametric regression.

We propose a new method for constructing genetic network from gene expression data by using Bayesian networks. We use nonparametric regression for capturing nonlinear relationships between genes and derive a new criterion for choosing the network in general situations. In a theoretical sense, our proposed theory and methodology include previous methods based on Bayes approach. We applied the proposed method to the S. cerevisiae cell cycle data and showed the effectiveness of our method by comparing with previous methods.

Somatic RHOA mutation in angioimmunoblastic T cell lymphoma

Angioimmunoblastic T cell lymphoma (AITL) is a distinct subtype of peripheral T cell lymphoma characterized by generalized lymphadenopathy and frequent autoimmune-like manifestations. Although frequent mutations in TET2, IDH2 and DNMT3A, which are common to various hematologic malignancies, have been identified in AITL, the molecular pathogenesis specific to this lymphoma subtype is unknown. Here we report somatic RHOA mutations encoding a p.Gly17Val alteration in 68% of AITL samples. Remarkably, all cases with the mutation encoding p.Gly17Val also had TET2 mutations. The RHOA mutation encoding p.Gly17Val was specifically identified in tumor cells, whereas TET2 mutations were found in both tumor cells and non-tumor hematopoietic cells. RHOA encodes a small GTPase that regulates diverse biological processes. We demonstrated that the Gly17Val RHOA mutant did not bind GTP and also inhibited wild-type RHOA function. Our findings suggest that impaired RHOA function in cooperation with preceding loss of TET2 function contributes to AITL-specific pathogenesis.

Identification of Genes Upregulated in ALK-Positive and EGFR/KRAS/ALK-Negative Lung Adenocarcinomas

Activation of the EGFR, KRAS, and ALK oncogenes defines 3 different pathways of molecular pathogenesis in lung adenocarcinoma. However, many tumors lack activation of any pathway (triple-negative lung adenocarcinomas) posing a challenge for prognosis and treatment. Here, we report an extensive genome-wide expression profiling of 226 primary human stage I-II lung adenocarcinomas that elucidates molecular characteristics of tumors that harbor ALK mutations or that lack EGFR, KRAS, and ALK mutations, that is, triple-negative adenocarcinomas. One hundred and seventy-four genes were selected as being upregulated specifically in 79 lung adenocarcinomas without EGFR and KRAS mutations. Unsupervised clustering using a 174-gene signature, including ALK itself, classified these 2 groups of tumors into ALK-positive cases and 2 distinct groups of triple-negative cases (groups A and B). Notably, group A triple-negative cases had a worse prognosis for relapse and death, compared with cases with EGFR, KRAS, or ALK mutations or group B triple-negative cases. In ALK-positive tumors, 30 genes, including ALK and GRIN2A, were commonly overexpressed, whereas in group A triple-negative cases, 9 genes were commonly overexpressed, including a candidate diagnostic/therapeutic target DEPDC1, that were determined to be critical for predicting a worse prognosis. Our findings are important because they provide a molecular basis of ALK-positive lung adenocarcinomas and triple-negative lung adenocarcinomas and further stratify more or less aggressive subgroups of triple-negative lung ADC, possibly helping identify patients who may gain the most benefit from adjuvant chemotherapy after surgical resection.