A graph-based multi-sample test for identifying pathways associated with cancer progression

Cancer is in general not a result of an abnormality of a single gene but a consequence of changes in many genes, it is therefore of great importance to understand the roles of different oncogenic and tumor suppressor pathways in tumorigenesis. In recent years, there have been many computational models developed to study the genetic alterations of different pathways in the evolutionary process of cancer. However, most of the methods are knowledge-based enrichment analyses and inflexible to analyze user-defined pathways or gene sets. In this paper, we develop a nonparametric and data-driven approach to testing for the dynamic changes of pathways over the cancer progression. Our method is based on an expansion and refinement of the pathway being studied, followed by a graph-based multivariate test, which is very easy to implement in practice. The new test is applied to the rich Cancer Genome Atlas data to study the (epi)genetic alterations of 186 KEGG pathways in the development of serous ovarian cancer. To make use of the comprehensive data, we incorporate three data types in the analysis representing gene expression level, copy number and DNA methylation level. Our analysis suggests a list of nine pathways that are closely associated with serous ovarian cancer progression, including cell cycle, ERBB, JAK-STAT signaling and p53 signaling pathways. By pairwise tests, we found that most of the identified pathways contribute only to a particular transition step. For instance, the cell cycle and ERBB pathways play key roles in the early-stage transition, while the ECM receptor and apoptosis pathways contribute to the progression from stage III to stage IV. The proposed computational pipeline is powerful in detecting important pathways and gene sets that drive cancers at certain stage(s). It offers new insights into the understanding of molecular mechanism of cancer initiation and progression.     

Synthesis and biological evaluation of a new series of histone deacetylases inhibitors

Histone deacetylases （HDACs） play an important role in tumorigenesis. Inhibition of HDACs is considered as a potent strategy for cancer therapy. Two lead compounds （ja and jb） were found to have activities against HDACs with IC50 at about 15 μmol/L. Then a new series of hydroximic acid derivatives were designed and synthesized based on them. The HDACs activity assay in vitro found that compounds J04 and ,109 are nearly as potent as the positive control drug Zolinza.     

Up a gear? The significance of an elevated mutation rate in tumorigenesis

Mutations involved in many cancers have been identified, but with some cancers requiring six or more mutations to take on their fully metastatic forms, the question remains whether all of these mutations can be acquired via a process of successive mutation, at a normal rate, and clonal expansion or whether heightened mutation rates are required. This issue has been debated for decades. Recently there has been much interest in forms of genomic instability such as chromosomal instability and microsatellite instability. It remains to definitively show whether or not these instabilities are very early causal events in tumorigenesis. This article reviews the evidence for and against genomic instability being an early causal event in tumorigenesis and surveys the mathematical modelling literature in this area. The focus is on chromosomal instability and microsatellite instability in colorectal cancer.     

Linking TPPII to the protein interaction and signalling networks

Tripeptidyl peptidase II (TPPII) is primarily considered a house-keeping exopeptidase, which contributes to the functions of the ubiquitin-proteasome system by the maintenance of the cellular amino acid homeostasis. Although functionally well-characterised in vitro and using the mammalian cell models, less is known about the molecular mechanisms of its involvement in the signalling and metabolic pathways, which mediate its cellular functions. The present protein-protein interaction network analysis identified these mechanisms involved in the adaptive and innate immunity, the metabolism of the glucose, cancer cell growth, apoptosis, cell cycle and DNA damage responses. The interaction network constructed based on the publicly available protein-protein interaction data was extended by the application GeneMania, which was further used for the pathway enrichment, the protein function prediction and the protein node prioritisation analysis. The analysis suggested that the molecular mechanisms linked to the adaptive and innate immunity (ID, Kit receptor, BCR, IL-2 and G-CSF signalling; the regulation of NFκB), the aerobic glycolysis (ID and IL-2 signalling), tumorigenesis (TGF-β and p53 signalling; the top priority nodes MAPKs, mTOR regulation), diabetes (Kit receptor signalling; the top priority node GSK3β) and neurodegeneration (the control of mTOR and Aβ peptide degradation) are controlling the resulting TPPII interaction network. The uncharacterized interactions with two lung cancer suppressors (DOK3, DENND2D), a protein involved in the increased risk of the lung cancer in smokers (CYP1A1) and a protein implicated in asthmatic reactions (CHIA) suggest potential roles of TPPII in the lung cancer pathology. The interactions with methyltransferase CARNMT1, which modifies di- and tripeptides and the xenobiotic processing enzyme CYP1A1, are additional candidates for the breakthrough in new functions discovery of TPPII.