Chromosome-breakage genomic instability and chromothripsis in breast cancer

Background

Chromosomal breakage followed by faulty DNA repair leads to gene amplifications and deletions in cancers. However, the mere assessment of the extent of genomic changes, amplifications and deletions may reduce the complexity of genomic data observed by array comparative genomic hybridization (array CGH). We present here a novel approach to array CGH data analysis, which focuses on putative breakpoints responsible for rearrangements within the genome.

Results

We performed array comparative genomic hybridization in 29 primary tumors from high risk patients with breast cancer. The specimens were flow sorted according to ploidy to increase tumor cell purity prior to array CGH. We describe the number of chromosomal breaks as well as the patterns of breaks on individual chromosomes in each tumor. There were differences in chromosomal breakage patterns between the 3 clinical subtypes of breast cancers, although the highest density of breaks occurred at chromosome 17 in all subtypes, suggesting a particular proclivity of this chromosome for breaks. We also observed chromothripsis affecting various chromosomes in 41% of high risk breast cancers.

Conclusions

Our results provide a new insight into the genomic complexity of breast cancer. Genomic instability dependent on chromosomal breakage events is not stochastic, targeting some chromosomes clearly more than others. We report a much higher percentage of chromothripsis than described previously in other cancers and this suggests that massive genomic rearrangements occurring in a single catastrophic event may shape many breast cancer genomes.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-579) contains supplementary material, which is available to authorized users.     

Development and characterization of highly polymorphic long TC repeat microsatellite markers for genetic analysis of peanut

Background

The immune system has paradoxical roles during cancer development and the prognostic significance of immune modulating factors is controversial. The aim of this study was to determine the expression of cyclooxygenase 2 (COX-2), transforming growth factor-beta (TGF- beta), interleukin-10 (IL-10) and their prognostic significance in breast cancers. Ki67 was included as a measure of growth fraction of tumor cells.

Methods

On immunohistochemical stained slides from 38 breast cancer patients, we performed digital video analysis of tumor cell areas and adjacent tumor stromal areas from the primary tumors and their corresponding lymph node metastases. COX-2 was recorded as graded staining intensity.

Results

The expression of TGF-beta, IL-10 and Ki67 were recorded in tumor cell areas and adjacent tumor stromal areas. In both primary tumors and metastases, the expression of COX-2 was higher in the tumor stromal areas than in the tumor cell areas (both P < 0.001). High stromal staining intensity in the primary tumors was associated with a 3.9 (95% CI 1.1-14.2) times higher risk of death compared to the low staining group (P = 0.036). The expression of TGF-beta was highest in the tumor cell areas of both primary tumors and metastases (both P < 0.001). High stromal expression of TGF-beta was associated with increased mortality. For IL-10, the stromal expression was highest in the primary tumors (P < 0.001), whereas in the metastases the expression was highest in tumor cell areas (P < 0.001). High IL-10 expression in tumor- and stromal cell areas of primary tumors predicted mortality. Ki67 was higher expressed in tumor stromal areas of the metastases, and in tumor cell areas of the primary tumors (P < 0.001). Ki67 expression in tumor cell areas and stromal areas of the metastases was independently associated with breast cancer mortality.

Conclusions

Stromal expression of COX-2, TGF-beta and Ki67 may facilitate tumor progression in breast cancer.     

Ribosomal RNA secondary structure: compensatory mutations and implications for phylogenetic analysis

Using sequence data from the 28S ribosomal RNA (rRNA) genes of selected vertebrates, we investigated the effects that constraints imposed by secondary structure have on the phylogenetic analysis of rRNA sequence data. Our analysis indicates that characters from both base-pairing regions (stems) and non-base-pairing regions (loops) contain phylogenetic information, as judged by the level of support of the phylogenetic results compared with a well-established tree based on both morphological and molecular data. The best results (the greatest level of support of well-accepted nodes) were obtained when the complete data set was used. However, some previously supported nodes were resolved using either the stem or loop bases alone. Stem bases sustain a greater number of compensatory mutations than would be expected at random, but the number is < 40% of that expected under a hypothesis of perfect compensation to maintain secondary structure. Therefore, we suggest that in phylogenetic analyses, the weighting of stem characters be reduced by no more than 20%, relative to that of loop characters. In contrast to previous suggestions, we do not recommend weighting of stem positions by one-half, compared with that of loop positions, because this overcompensates for the constraints that selection imposes on the secondary structure of rRNA.      

Integrative analysis of multiple gene expression profiles with quality-adjusted effect size models

Background  

With the explosion of microarray studies, an enormous amount of data is being produced. Systematic integration of gene expression data from different sources increases statistical power of detecting differentially expressed genes and allows assessment of heterogeneity. The challenge, however, is in designing and implementing efficient analytic methodologies for combination of data generated by different research groups.     

Cytoskeletal rearrangements in synovial fibroblasts as a novel pathophysiological determinant of modeled rheumatoid arthritis

Rheumatoid arthritis is a chronic inflammatory disease with a high prevalence and substantial socioeconomic burden. Despite intense research efforts, its aetiology and pathogenesis remain poorly understood. To identify novel genes and/or cellular pathways involved in the pathogenesis of the disease, we utilized a well-recognized tumour necrosis factor-driven animal model of this disease and performed high-throughput expression profiling with subtractive cDNA libraries and oligonucleotide microarray hybridizations, coupled with independent statistical analysis. This twin approach was validated by a number of different methods in other animal models of arthritis as well as in human patient samples, thus creating a unique list of disease modifiers of potential therapeutic value. Importantly, and through the integration of genetic linkage analysis and Gene Ontology–assisted functional discovery, we identified the gelsolin-driven synovial fibroblast cytoskeletal rearrangements as a novel pathophysiological determinant of the disease.     

Exenatide promotes cognitive enhancement and positive brain metabolic changes in PS1-KI mice but has no effects in 3xTg-AD animals

Recent studies have shown that type 2 diabetes mellitus (T2DM) is a risk factor for cognitive dysfunction or dementia. Insulin resistance is often associated with T2DM and can induce defective insulin signaling in the central nervous system as well as increase the risk of cognitive impairment in the elderly. Glucagone like peptide-1 (GLP-1) is an incretin hormone and, like GLP-1 analogs, stimulates insulin secretion and has been employed in the treatment of T2DM. GLP-1 and GLP-1 analogs also enhance synaptic plasticity and counteract cognitive deficits in mouse models of neuronal dysfunction and/or degeneration. In this study, we investigated the potential neuroprotective effects of long-term treatment with exenatide, a GLP-1 analog, in two animal models of neuronal dysfunction: the PS1-KI and 3xTg-AD mice. We found that exenatide promoted beneficial effects on short- and long-term memory performances in PS1-KI but not in 3xTg-AD animals. In PS1-KI mice, the drug increased brain lactate dehydrogenase activity leading to a net increase in lactate levels, while no effects were observed on mitochondrial respiration. On the contrary, exenatide had no effects on brain metabolism of 3xTg-AD mice. In summary, our data indicate that exenatide improves cognition in PS1-KI mice, an effect likely driven by increasing the brain anaerobic glycolysis rate.