Stochastic Modelling of Tumorigenesis in p53 Deficient Transgenic Mice

The aim of this project was to develop the stochastic models of tumorigenesis to investigate the implications of experimental data on tumour induction in wild type and p53 deficient mice for tumorigenesis mechanisms. These studies have focused on the development of stochastic process models for p53 mediated spontaneous and radiation- induced tumorigenesis in mice, in which up to 3 stages are assumed to be required for malignant transformation. The stages are conceived as the inactivation of one and both p53 alleles with a third, genetically unspecified stage which may be composite. The model has been used to explore the influence of mutation rate, stage number, and the number of stem cells at risk on the kinetics of spontaneous appearance of tumours and tumour multiplicity. As expected, tumours tended to occur earlier and the more tumours per mouse tend to be acquired with lesser stage number, higher mutation rate and higher stem cell number

Cancer evolution and individual susceptibility

El pdf del artículo es la versión de autor.Cancer susceptibility is due to interactions between inherited genetic factors and exposure to environmental carcinogens. The genetic component is constituted mainly by weakly acting low-penetrance genetic variants that interact among themselves, as well as with the environment. These low susceptibility genes can be categorized into two main groups: one includes those that control intrinsic tumor cell activities (i.e. apoptosis, proliferation or DNA repair), and the other contains those that modulate the function of extrinsic tumor cell compartments (i.e. stroma, angiogenesis, or endocrine and immune systems). Genome-Wide Association Studies (GWAS) of human populations have identified numerous genetic loci linked with cancer risk and behavior, but nevertheless the major component of cancer heritability remains to be explained. One reason may be that GWAS cannot readily capture gene–gene or gene–environment interactions. Mouse model approaches offer an alternative or complementary strategy, because of our ability to control both the genetic and environmental components of risk. Recently developed genetic tools, including high-throughput technologies such as SNP, CGH and gene expression microarrays, have led to more powerful strategies for refining quantitative trait loci (QTL) and identifying the critical genes. In particular, the cross-species approaches will help to refine locations of QTLs, and reveal their genetic and environmental interactions. The identification of human tumor susceptibility genes and discovery of their roles in carcinogenesis will ultimately be important for the development of methods for prediction of risk, diagnosis, prevention and therapy for human cancers.J. H. Mao is supported by Office of Biological & Environmental Research, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231, by Laboratory Downloaded on 20 February 2012 Published on 24 January 2011 on http://pubs.rsc.org | doi:10.1039/C0IB00094A View Online This journal is c The Royal Society of Chemistry 2011 Integr. Biol., 2011, 3, 316–328 325 Directed Research & Development Program (LDRD), and by the National Institutes of Health, National Cancer Institute grant R01 CA116481. J. Pe´rez-Losada is partially supported by FEDER and MICINN (PLE2009-119), FIS (PI070057; PI10/00328), CSIC (200920I137), Junta de Castilla y Leo´ n (SAN126/SA66/09; SA079A09). A. Castellanos-Martı´n is supported by FEDER and MICINN (PLE2009-119).Peer reviewe

PR-Set7 is Degraded in a Conditional Cul4A Transgenic Mouse Model of Lung Cancer.

BackgroundMaintenance of genomic integrity is essential to ensure normal organismal development and to prevent diseases such as cancer. PR-Set7 (also known as Set8) is a cell cycle regulated enzyme that catalyses monomethylation of histone 4 at Lys20 (H4K20me1) to promote chromosome condensation and prevent DNA damage. Recent studies show that CRL4CDT2-mediated ubiquitylation of PR-Set7 leads to its degradation during S phase and after DNA damage. This might occur to ensure appropriate changes in chromosome structure during the cell cycle or to preserve genome integrity after DNA damage.MethodsWe developed a new model of lung tumor development in mice harboring a conditionally expressed allele of Cul4A. We have therefore used a mouse model to demonstrate for the first time that Cul4A is oncogenic in vivo. With this model, staining of PR-Set7 in the preneoplastic and tumor lesions in AdenoCre-induced mouse lungs was performed. Meanwhile we identified higher protein level changes of γ-tubulin and pericentrin by IHC.ResultsThe level of PR-Set7 down-regulated in the preneoplastic and adenocarcinomous lesions following over-expression of Cul4A. We also identified higher levels of the proteins pericentrin and γ-tubulin in Cul4A mouse lungs induced by AdenoCre.ConclusionsPR-Set7 is a direct target of Cul4A for degradation and involved in the formation of lung tumors in the conditional Cul4A transgenic mouse model

Geometric bionics: Lotus effect helps polystyrene nanotube films get good blood compatibility

Various biomaterials have been widely used for manufacturing biomedical applications including artificial organs, medical devices and disposable clinical apparatus, such as vascular prostheses, blood pumps, artificial kidney, artificial hearts, dialyzers and plasma separators, which could be used in contact with blood^1^. However, the research tasks of improving hemocompatibility of biomaterials have been carrying out with the development of biomedical requirements^2^. Since the interactions that lead to surface-induced thrombosis occurring at the blood-biomaterial interface become a reason of familiar current complications with grafts therapy, improvement of the blood compatibility of artificial polymer surfaces is, therefore a major issue in biomaterials science^3^. After decades of focused research, various approaches of modifying biomaterial surfaces through chemical or biochemical methods to improve their hemocompatibility were obtained^1^. In this article, we report that polystyrene nanotube films with morphology similar to the papilla on lotus leaf can be used as blood-contacted biomaterials by virtue of Lotus effect^4^. Clearly, this idea, resulting from geometric bionics that mimicking the structure design of lotus leaf, is very novel technique for preparation of hemocompatible biomaterials

Pan-cancer evaluation of clinical value of mitotic network activity index (MNAI) and its predictive value for immunotherapy

Increased mitotic activity is associated with the genesis and aggressiveness of many cancers. To assess the clinical value of mitotic activity as prognostic biomarker, we performed a pan-cancer study on the mitotic network activity index (MNAI) constructed based on 54-gene mitotic apparatus network. Our pan-cancer assessment on TCGA (33 tumor types, 10,061 patients) and validation on other publicly available cohorts (23 tumor types, 9,209 patients) confirmed the significant association of MNAI with overall survival, progression-free survival, and other prognostic endpoints in multiple cancer types, including lower-grade gliomas (LGG), breast invasive carcinoma (BRCA), as well as many others. We also showed significant association between MNAI and genetic instability, which provides a biological explanation of its prognostic impact at pan-cancer landscape. Our association analysis revealed that patients with high MNAI benefitted more from anti-PD-1 and Anti-CTLA-4 treatment. In addition, we demonstrated that multimodal integration of MNAI and the AI-empowered Cellular Morphometric Subtypes (CMS) significantly improved the predictive power of prognosis compared to using MNAI and CMS alone. Our results suggest that MNAI can be used as a potential prognostic biomarker for different tumor types toward different clinical endpoints, and multimodal integration of MNAI and CMS exceeds individual biomarker for precision prognosis

Systematic Analysis of Impact of Sampling Regions and Storage Methods on Fecal Gut Microbiome and Metabolome Profiles.

The contribution of human gastrointestinal (GI) microbiota and metabolites to host health has recently become much clearer. However, many confounding factors can influence the accuracy of gut microbiome and metabolome studies, resulting in inconsistencies in published results. In this study, we systematically investigated the effects of fecal sampling regions and storage and retrieval conditions on gut microbiome and metabolite profiles from three healthy children. Our analysis indicated that compared to homogenized and snap-frozen samples (standard control [SC]), different sampling regions did not affect microbial community alpha diversity, while a total of 22 of 176 identified metabolites varied significantly across different sampling regions. In contrast, storage conditions significantly influenced the microbiome and metabolome. Short-term room temperature storage had a minimal effect on the microbiome and metabolome profiles. Sample storage in RNALater showed a significant level of variation in both microbiome and metabolome profiles, independent of the storage or retrieval conditions. The effect of RNALater on the metabolome was stronger than the effect on the microbiome, and individual variability between study participants outweighed the effect of RNALater on the microbiome. We conclude that homogenizing stool samples was critical for metabolomic analysis but not necessary for microbiome analysis. Short-term room temperature storage had a minimal effect on the microbiome and metabolome profiles and is recommended for short-term fecal sample storage. In addition, our study indicates that the use of RNALater as a storage medium of stool samples for microbial and metabolomic analyses is not recommended.IMPORTANCE The gastrointestinal microbiome and metabolome can provide a new angle to understand the development of health and disease. Stool samples are most frequently used for large-scale cohort studies. Standardized procedures for stool sample handling and storage can be a determining factor for performing microbiome or metabolome studies. In this study, we focused on the effects of stool sampling regions and stool sample storage conditions on variations in the gut microbiome composition and metabolome profile

A Robust Gene Expression Prognostic Signature for Overall Survival in High-Grade Serous Ovarian Cancer.

The objective of this research was to develop a robust gene expression-based prognostic signature and scoring system for predicting overall survival (OS) of patients with high-grade serous ovarian cancer (HGSOC). Transcriptomic data of HGSOC patients were obtained from six independent studies in the NCBI GEO database. Genes significantly deregulated and associated with OS in HGSOCs were selected using GEO2R and Kaplan-Meier analysis with log-rank testing, respectively. Enrichment analysis for biological processes and pathways was performed using Gene Ontology analysis. A resampling/cross-validation method with Cox regression analysis was used to identify a novel gene expression-based signature associated with OS, and a prognostic scoring system was developed and further validated in nine independent HGSOC datasets. We first identified 488 significantly deregulated genes in HGSOC patients, of which 232 were found to be significantly associated with their OS. These genes were significantly enriched for cell cycle division, epithelial cell differentiation, p53 signaling pathway, vasculature development, and other processes. A novel 11-gene prognostic signature was identified and a prognostic scoring system was developed, which robustly predicted OS in HGSOC patients in 100 sampling test sets. The scoring system was further validated successfully in nine additional HGSOC public datasets. In conclusion, our integrative bioinformatics study combining transcriptomic and clinical data established an 11-gene prognostic signature for robust and reproducible prediction of OS in HGSOC patients. This signature could be of clinical value for guiding therapeutic selection and individualized treatment

Overcoming the challenges of cancer drug resistance through bacterial-mediated therapy.

Despite tremendous efforts to fight cancer, it remains a major public health problem and a leading cause of death worldwide. With increased knowledge of cancer pathways and improved technological platforms, precision therapeutics that specifically target aberrant cancer pathways have improved patient outcomes. Nevertheless, a primary cause of unsuccessful cancer therapy remains cancer drug resistance. In this review, we summarize the broad classes of resistance to cancer therapy, particularly pharmacokinetics, the tumor microenvironment, and drug resistance mechanisms. Furthermore, we describe how bacterial-mediated cancer therapy, a bygone mode of treatment, has been revitalized by synthetic biology and is uniquely suited to address the primary resistance mechanisms that confound traditional therapies. Through genetic engineering, we discuss how bacteria can be potent anticancer agents given their tumor targeting potential, anti-tumor activity, safety, and coordinated delivery of anti-cancer drugs