Cross-species comparisons of cancer signaling

Animal-SNPAtlas: a comprehensive SNP database for multiple animals

Single-nucleotide polymorphisms (SNPs) as the most important type of genetic variation are widely used in describing population characteristics and play vital roles in animal genetics and breeding. Large amounts of population genetic variation resources and tools have been developed in human, which provided solid support for human genetic studies. However, compared with human, the development of animal genetic variation databases was relatively slow, which limits the genetic researches in these animals. To fill this gap, we systematically identified ∼ 499 million high-quality SNPs from 4784 samples of 20 types of animals. On that basis, we annotated the functions of SNPs, constructed high-density reference panels and calculated genome-wide linkage disequilibrium (LD) matrixes. We further developed Animal-SNPAtlas, a user-friendly database (http://gong_lab.hzau.edu.cn/Animal_SNPAtlas/) which includes high-quality SNP datasets and several support tools for multiple animals. In Animal-SNPAtlas, users can search the functional annotation of SNPs, perform online genotype imputation, explore and visualize LD information, browse variant information using the genome browser and download SNP datasets for each species. With the massive SNP datasets and useful tools, Animal-SNPAtlas will be an important fundamental resource for the animal genomics, genetics and breeding community.

Fatty Acid Binding Protein 4 (FABP4) Overexpression in Intratumoral Hepatic Stellate Cells within Hepatocellular Carcinoma with Metabolic Risk Factors

Metabolic syndrome is a newly identified risk factor for hepatocellular carcinoma (HCC); however, tumor-specific biomarkers still remain unclear. We performed cross-species analysis to compare gene signatures of HCC from human patients and melanocortin 4 receptor-knockout mice, which develop HCC with obesity, insulin resistance, and dyslipidemia. Unsupervised hierarchical clustering and principle component analysis of 746 differentially expressed orthologous genes classified HCC of 152 human patients and melanocortin 4 receptor-knockout mice into two distinct subgroups, one of which included mouse HCC and was causatively associated with metabolic risk factors. Nine genes commonly overexpressed in human and mouse metabolic disease-associated HCC were identified; fatty acid binding protein 4 (FABP4) was remarkably enriched in intratumoral activated hepatic stellate cells (HSCs). Subclones constitutively expressing FABP4 were established from a human HSC cell line in which expression levels of inflammatory chemokines, including IL-1A and IL-6, were up-regulated through NF-κB nuclear translocation, resulting in recruitment of macrophages. An immunohistochemical validation study of 106 additional human HCC samples indicated that FABP4-positive HSCs were distributed in tumors of 38 cases, and the FABP4-high group consisted of patients with nonviral and nonalcoholic HCC (P = 0.027) and with multiple metabolic risk factors (P < 0.001) compared with the FABP4-low group. Thus, FABP4 overexpression in HSCs may contribute to hepatocarcinogenesis in patients with metabolic risk factors by modulation of inflammatory pathways.

The CCL2 chemokine is a negative regulator of autophagy and necrosis in luminal B breast cancer cells

Luminal A and B breast cancers are the most prevalent forms of breast cancer diagnosed in women. Compared to luminal A breast cancer patients, patients with luminal B breast cancers experience increased disease recurrence and lower overall survival. The mechanisms that regulate the luminal B subtype remain poorly understood. The chemokine CCL2 is overexpressed in breast cancer, correlating with poor patient prognosis. The purpose of this study was to determine the role of CCL2 expression in luminal B breast cancer cells. Breast tissues, MMTV-PyVmT and MMTV-Neu transgenic mammary tumors forming luminal B-like lesions, were immunostained for CCL2 expression. To determine the role of CCL2 in breast cancer cells, CCL2 gene expression was silenced in mammary tumor tissues and cells using TAT cell-penetrating peptides non-covalently cross linked to siRNAs (Ca-TAT/siRNA). CCL2 expression was examined by ELISA and flow cytometry. Cell growth and survival were analyzed by flow cytometry, immunocytochemistry, and fluorescence microscopy. CCL2 expression was significantly increased in luminal B breast tumors, MMTV- PyVmT and MMTV-Neu mammary tumors, compared or normal breast tissue or luminal A breast tumors. Ca-TAT delivery of CCL2 siRNAs significantly reduced CCL2 expression in PyVmT mammary tumors, and decreased cell proliferation and survival. CCL2 gene silencing in PyVmT carcinoma cells or BT474 luminal B breast cancer cells decreased cell growth and viability associated with increased necrosis and autophagy. CCL2 expression is overexpressed in luminal B breast cancer cells and is important for regulating cell growth and survival by inhibiting necrosis and autophagy.

Age-specific gene expression signatures for breast tumors and cross-species conserved potential cancer progression markers in young women

Breast cancer in young women is more aggressive with a poorer prognosis and overall survival compared to older women diagnosed with the disease. Despite recent research, the underlying biology and molecular alterations that drive the aggressive nature of breast tumors associated with breast cancer in young women have yet to be elucidated. In this study, we performed transcriptomic profile and network analyses of breast tumors arising in Middle Eastern women to identify age-specific gene signatures. Moreover, we studied molecular alterations associated with cancer progression in young women using cross-species comparative genomics approach coupled with copy number alterations (CNA) associated with breast cancers from independent studies. We identified 63 genes specific to tumors in young women that showed alterations distinct from two age cohorts of older women. The network analyses revealed potential critical regulatory roles for Myc, PI3K/Akt, NF-κB, and IL-1 in disease characteristics of breast tumors arising in young women. Cross-species comparative genomics analysis of progression from pre-invasive ductal carcinoma in situ (DCIS) to invasive ductal carcinoma (IDC) revealed 16 genes with concomitant genomic alterations, CCNB2, UBE2C, TOP2A, CEP55, TPX2, BIRC5, KIAA0101, SHCBP1, UBE2T, PTTG1, NUSAP1, DEPDC1, HELLS, CCNB1, KIF4A, and RRM2, that may be involved in tumorigenesis and in the processes of invasion and progression of disease. Array findings were validated using qRT-PCR, immunohistochemistry, and extensive in silico analyses of independently performed microarray datasets. To our knowledge, this study provides the first comprehensive genomic analysis of breast cancer in Middle Eastern women in age-specific cohorts and potential markers for cancer progression in young women. Our data demonstrate that cancer appearing in young women contain distinct biological characteristics and deregulated signaling pathways. Moreover, our integrative genomic and cross-species analysis may provide robust biomarkers for the detection of disease progression in young women, and lead to more effective treatment strategies.

Unravelling the complexity of metastasis - molecular understanding and targeted therapies

Despite recognizing the devastating consequences of metastasis, we are not yet able to effectively treat cancer that has spread to vital organs. The inherent complexity of genomic alterations in late-stage cancers, coupled with numerous heterotypic interactions that occur between tumour and stromal cells, represent fundamental challenges in our quest to understand and control metastatic disease. The incorporation of genomic and other systems level approaches, as well as technological breakthroughs in imaging and animal modelling, have galvanized the effort to overcome gaps in our understanding of metastasis. Future research carries with it the potential to translate the wealth of new knowledge and conceptual advances into effective targeted therapies.

Genetically engineered mouse models of diffuse gliomas

Over the last decade, genetically engineered mouse models have been extensively used to dissect the genetic requirements for neoplastic initiation and progression of diffuse gliomas. While these models faithfully recapitulate the histopathological features of human gliomas, comparative genomic analyses are increasingly being utilized to comprehensively assess their fidelity to recently identified molecular subtypes of these tumors. Future progress with these models will rely on incorporating insights not only from oncogenomics studies of cancer, but also from the developmental neuroscience and stem cell biology fields to design accurate and experimentally tractable models for use in translational cancer research, particularly for experimental therapeutics studies of molecularly defined subtypes of gliomas.

Rank-rank hypergeometric overlap: identification of statistically significant overlap between gene-expression signatures

Comparing independent high-throughput gene-expression experiments can generate hypotheses about which gene-expression programs are shared between particular biological processes. Current techniques to compare expression profiles typically involve choosing a fixed differential expression threshold to summarize results, potentially reducing sensitivity to small but concordant changes. We present a threshold-free algorithm called Rank–rank Hypergeometric Overlap (RRHO). This algorithm steps through two gene lists ranked by the degree of differential expression observed in two profiling experiments, successively measuring the statistical significance of the number of overlapping genes. The output is a graphical map that shows the strength, pattern and bounds of correlation between two expression profiles. To demonstrate RRHO sensitivity and dynamic range, we identified shared expression networks in cancer microarray profiles driving tumor progression, stem cell properties and response to targeted kinase inhibition. We demonstrate how RRHO can be used to determine which model system or drug treatment best reflects a particular biological or disease response. The threshold-free and graphical aspects of RRHO complement other rank-based approaches such as Gene Set Enrichment Analysis (GSEA), for which RRHO is a 2D analog. Rank–rank overlap analysis is a sensitive, robust and web-accessible method for detecting and visualizing overlap trends between two complete, continuous gene-expression profiles. A web-based implementation of RRHO can be accessed at http://systems.crump.ucla.edu/rankrank/.

An Industry Perspective on the Utility of Short-Term Carcinogenicity Testing in Transgenic Mice in Pharmaceutical Development

International guidelines allow for use of a short-term cancer bioassay (twenty-six weeks) in transgenic mice as a substitute for one of the two required long-term rodent bioassays in the preclinical safety evaluation of pharmaceuticals. The two models that have gained the widest acceptance by sponsors and regulatory authorities are the CB6F1-RasH2 mouse hemizygous for a human H-ras transgene and the B6.129N5-Trp53 mouse heterozygous for a p53 null allele. The p53+/- model is of particular value for compounds with residual concern that genotoxic activity may contribute to tumorigenesis. The rasH2 model is an appropriate alternative without regard to evidence of genotoxic potential. Since results from a short-term bioassay can be obtained relatively early in drug development, there is the potential for more timely assessment of cancer risk for individuals in long-term clinical trials. Use of these models in preclinical safety evaluation also significantly reduces animal use, time, and manpower. Preliminary findings indicate that prediction of two-year rat bioassay outcomes based on data from chronic rat toxicity studies, together with early assessment of carcinogenic potential in short-term transgenic models, may have the potential to increase the timeliness and efficiency of strategies for the identification of human carcinogenic hazards.

[Biomarkers: "Found in translation"]

Despite continued increase in global Pharma R & D expenditure, the number of innovative drugs obtaining market approval has declined since 1994. The pharmaceutical industry is now entering a crucial juncture where increasing rates of attrition in clinical drug development as well as increasing development timelines are impacted by external factors such as intense regulatory pricing and safety pressures, increasing sales erosion due to generics, as well as exponential increases in the costs of bringing a drug to market. Despite these difficulties, numerous opportunities exist such as multiple unmet medical needs, the increasing incidence of certain diseases such as Alzheimer's disease, cancer, diabetes and obesity due to demographic changes, as well as the emergence of evolving markets such as China, India, and Eastern Europe. Consequently, Pharma is now responding to this challenge by improving both the productivity and the innovation in its drug discovery and development pipelines. In this regard, the advent of new technologies and expertise such as genomics, proteomics, structural biology, and molecular informatics in an integrated systems biology approach also provides a powerful opportunity for Pharma to address some of these difficulties. The key features behind this new strategy imply a discovery process based on an improved understanding of the molecular mechanism of diseases and drugs, translational research that places the patient at the center of the research process, and the application of biomarkers throughout the discovery and development phases. Moreover, new paradigms are required to improve target validation and develop more predictive cellular and animal models of human pathologies, a greater capacity in informatics-based analysis, and, consequently, a greater access to the vast sources of accumulating biological data and its integrated analysis. In the present review, we will address some of these issues and in particular emphasize how the application of biomarkers could potentially lead to improved productivity, quality, and innovation in drug discovery and ultimately better and safer medicines with improved therapeutic efficacy in specific pathologies for targeted patients.

Cross-species comparison of human and mouse intestinal polyps reveals conserved mechanisms in adenomatous polyposis coli (APC)-driven tumorigenesis

Expression profiling is a well established tool for the genome-wide analysis of human cancers. However, the high sensitivity of this approach combined with the well known cellular and molecular heterogeneity of cancer often result in extremely complex expression signatures that are difficult to interpret functionally. The majority of sporadic colorectal cancers are triggered by mutations in the adenomatous polyposis coli (APC) tumor suppressor gene, leading to the constitutive activation of the Wnt/beta-catenin signaling pathway and formation of adenomas. Despite this common genetic basis, colorectal cancers are very heterogeneous in their degree of differentiation, growth rate, and malignancy potential. Here, we applied a cross-species comparison of expression profiles of intestinal polyps derived from hereditary colorectal cancer patients carrying APC germline mutations and from mice carrying a targeted inactivating mutation in the mouse homologue Apc. This comparative approach resulted in the establishment of a conserved signature of 166 genes that were differentially expressed between adenomas and normal intestinal mucosa in both species. Functional analyses of the conserved genes revealed a general increase in cell proliferation and the activation of the Wnt/beta-catenin signaling pathway. Moreover, the conserved signature was able to resolve expression profiles from hereditary polyposis patients carrying APC germline mutations from those with bi-allelic inactivation of the MYH gene, supporting the usefulness of such comparisons to discriminate among patients with distinct genetic defects.

Novel insights into the relationships between dendritic cell subsets in human and mouse revealed by genome-wide expression profiling

Genome-wide expression profiling of mouse and human leukocytes reveal conserved transcriptional programs of plasmacytoid or conventional dendritic cell subsets.

Background

Dendritic cells (DCs) are a complex group of cells that play a critical role in vertebrate immunity. Lymph-node resident DCs (LN-DCs) are subdivided into conventional DC (cDC) subsets (CD11b and CD8α in mouse; BDCA1 and BDCA3 in human) and plasmacytoid DCs (pDCs). It is currently unclear if these various DC populations belong to a unique hematopoietic lineage and if the subsets identified in the mouse and human systems are evolutionary homologs. To gain novel insights into these questions, we sought conserved genetic signatures for LN-DCs and in vitro derived granulocyte-macrophage colony stimulating factor (GM-CSF) DCs through the analysis of a compendium of genome-wide expression profiles of mouse or human leukocytes.

Results

We show through clustering analysis that all LN-DC subsets form a distinct branch within the leukocyte family tree, and reveal a transcriptomal signature evolutionarily conserved in all LN-DC subsets. Moreover, we identify a large gene expression program shared between mouse and human pDCs, and smaller conserved profiles shared between mouse and human LN-cDC subsets. Importantly, most of these genes have not been previously associated with DC function and many have unknown functions. Finally, we use compendium analysis to re-evaluate the classification of interferon-producing killer DCs, lin^-CD16⁺HLA-DR⁺ cells and in vitro derived GM-CSF DCs, and show that these cells are more closely linked to natural killer and myeloid cells, respectively.

Conclusion

Our study provides a unique database resource for future investigation of the evolutionarily conserved molecular pathways governing the ontogeny and functions of leukocyte subsets, especially DCs.

Comparison of gene expression data from human and mouse breast cancers: identification of a conserved breast tumor gene set

The aim of our work was to establish a database for breast cancer gene expression data in order to compare human and mouse breast cancer. We identified human and mouse homologues genes and compared the expression profile of 24 human breast tumors with 6 WAP-SVT/t breast tumors (WAP-SVT/t animals, line 8). Our studies confirmed the heterogeneity in gene expression of human as well as mouse breast cancer cells. However, 63 genes were found to be differentially expressed (upregulated: 40; downregulated: 23 genes) in at least 75% of the breast tumors of both species. To differentiate between early and late events in tumor formation, we compared the 63 differentially expressed genes with a mouse data set obtained from hyperplastic mammary glands. This revealed that the majority of the early deregulated genes are cell proliferation specific. These early changes seem to be necessary although not sufficient for breast cancer formation. Late alterations concern mainly genes belonging to the category of cell communication and metabolism. Interestingly, most of the 63 conserved genes are commonly associated with tumorigenesis.

Mouse models for human head and neck squamous cell carcinomas

Mouse models of human cancer play an important role in understanding the mechanisms of carcinogenesis and have accelerated the search for finding new molecular targets for cancer therapy. However, genetically engineered mouse models for head and neck squamous cell carcinoma (HNSCC) have only recently overcome major technical obstacles and begun to be explored. Here we review the current progress in the development of mouse models for human HNSCC, with emphasis on conditional transgenic and knockout mouse models. These new models faithfully recapitulate human HNSCC at both the pathologic and molecular levels. These animal models will not only be useful to define the roles of specific genes in HNSCC development and progression but will also provide a unique tool for developing and testing new therapeutic approaches.

Precancer in mice: animal models used to understand, prevent, and treat human precancers

We present a status report from the NCI Mouse Models of Human Cancers Consortium (MMHCC) Precancers Workshop held November 8 and 9, 2004. An expert panel, the Mouse Models Group (MMG) evaluated the status of mouse models of precancer emphasizing genetically engineered mouse models, especially of lining epithelium and their utilitarian value to human carcinogenesis. An outline of the background for the panel's considerations is provided with examples of past and current precancerous lesions in mice. The experimental use of oncogenic viruses and chemical carcinogens in mice led to operational definitions of initiation, promotion, and preneoplasia Preneoplastic and precancerous lesions are found in these models. In this precancer concept, most preneoplastic lesions are considered as potentially precancerous or at least an earlier stage in cancer development than typical pre-invasive epithelial lesions, which are often seen in these mouse models. Genetically engineered mice, used to test the oncogenicity of individual genes, develop precancers that are initiated by defined molecular and histopathologic changes. The mouse can be used to isolate and study precancers in detail, thereby providing a level of biological understanding not readily available in clinical disease. These studies suggest that genetically engineered mice are very useful preclinical models for chemoprevention and therapy.

Expression and genomic profiling of colorectal cancer

Colorectal cancer still represents a paradigm for the elucidation of the cellular, genetic and molecular mechanisms that underly solid tumor initiation, progression to malignancy, and metastasis to distal organ sites. The relative ease with which pathological specimens can be obtained by either surgery or endoscopy from different stages of tumor progression has facilitated the application of omics technologies to allow the genome-wide analysis both at the RNA (gene expression) and DNA (aneuploidy) levels. Here, we have reviewed the multiplicity of studies appeared to date in the scientific literature on the expression and genomic analysis of colorectal cancer, and attempted an integration of the profiling data generated and made available in the public domain. This approach is likely to pinpoint specific chromosomal loci and the corresponding genes which (i) play rate-limiting roles in colorectal cancer, (ii) represent putative diagnostic and prognostic markers for the accurate prediction of clinical outcome and response to treatment, and (iii) encompass potential therapeutic targets. Moreover, cross-species data mining and integration of the human colorectal cancer profiles with those obtained from mouse models of intestinal tumorigenesis will even more contribute to the elucidation of highly conserved pathways and cellular functions underlying malignancy in the GI tract. Notwithstanding the above promises, tumor heterogeneity, limited cohort sizes, and methodological differences among experimental and bioinformatic approaches still poses main obstacles towards the optimal utilization and integration of omics profiles.