IRS1 is highly expressed in localized breast tumors and regulates the sensitivity of breast cancer cells to chemotherapy, while IRS2 is highly expressed in invasive breast tumors.

Insulin receptor substrate (IRS) proteins have been shown to play an important role in breast cancer by differentially regulating cancer cell survival, proliferation, and motility. Furthermore, the IL-4-induced tyrosine phosphorylation of the transcription factor STAT6 was shown to protect breast cancer cells from apoptosis. Here, we analyzed human breast cancer tissues for the expression of IRS1, IRS2, STAT6, and tyrosine phosphorylated STAT6 (pSTAT6). We found that IRS1 and pSTAT6 were both highly expressed in ductal carcinoma in situ (DCIS). On the other hand, IRS2 expression was low in DCIS, but increased significantly in relation to tumor invasiveness. We utilized cell lines with disparate IRS1 expression, MDA-MB-231, MCF7, and MCF7 cells with depleted IRS1 due to shRNA lentiviral infection, to examine the role of IRS1 and IRS2 in the responsiveness of breast cancer cells to chemotherapy. We report that high IRS1 sensitized MCF7 cells to specific chemotherapeutic agents. These results suggest that high IRS1 with low IRS2 expression may predict the effectiveness of specific types of chemotherapy in breast cancer.

STAT3 is activated by JAK2 independent of key oncogenic driver mutations in non-small cell lung carcinoma.

Constitutive activation of STAT3 is a common feature in many solid tumors including non-small cell lung carcinoma (NSCLC). While activation of STAT3 is commonly achieved by somatic mutations to JAK2 in hematologic malignancies, similar mutations are not often found in solid tumors. Previous work has instead suggested that STAT3 activation in solid tumors is more commonly induced by hyperactive growth factor receptors or autocrine cytokine signaling. The interplay between STAT3 activation and other well-characterized oncogenic "driver" mutations in NSCLC has not been fully characterized, though constitutive STAT3 activation has been proposed to play an important role in resistance to various small-molecule therapies that target these oncogenes. In this study we demonstrate that STAT3 is constitutively activated in human NSCLC samples and in a variety of NSCLC lines independent of activating KRAS or tyrosine kinase mutations. We further show that genetic or pharmacologic inhibition of the gp130/JAK2 signaling pathway disrupts activation of STAT3. Interestingly, treatment of NSCLC cells with the JAK1/2 inhibitor ruxolitinib has no effect on cell proliferation and viability in two-dimensional culture, but inhibits growth in soft agar and xenograft assays. These data demonstrate that JAK2/STAT3 signaling operates independent of known driver mutations in NSCLC and plays critical roles in tumor cell behavior that may not be effectively inhibited by drugs that selectively target these driver mutations.

Hedgehog signaling pathway molecules and ALDH1A1 expression in early-stage non-small cell lung cancer.

INTRODUCTION: The Hedgehog Signaling Pathway (HHSP) has been implicated in the development of multiple cancers. HHSP activation may primarily be hedgehog ligand-dependent in non-small cell lung cancer (NSCLC); while a subset may be ligand-independent. In this study NSCLC primary tumors were used to identify correlations between multiple biomarkers thought to be involved in the HHSP and the clinical outcomes of patients with NSCLC. Identification of such correlations could be used to aid in NSCLC treatment and predicting patient prognosis. METHODS: A tissue microarray representing 248 clinically annotated stage I-II NSCLC cases was stained using immunohistochemistry (IHC) and scored for HHSP proteins namely, SHH, PTCH1, SMO, GLI1, and GLI2; as well as, ALDH1A1, a putative cancer stem cell marker. Data was analyzed for correlation between IHC staining, EGFR and KRAS mutations, and clinical characteristics including relapse-free survival (RFS) and overall survival (OS). RESULTS: In adenocarcinoma, there were significant correlations of IHC expression between SHH and downstream HHSP receptor SMO (p=0.017) and transcription factor GLI1 (p=0.001), while SMO correlated with GLI1 (p=0.007). In squamous cell carcinoma, SHH significantly correlated with GLI2 protein expression (p=0.003). After multiple testing correction, there was no significant correlation between any of the six markers and RFS or OS. CONCLUSIONS: Key downstream components of the HHSP show correlation with sonic hedgehog ligand (SHH) expression, suggesting that ligand-dependent signaling is more prevalent in primary NSCLC tumors. Surprisingly, in early-stage NSCLC, there were no significant correlations between HHSP proteins or ALDH1A1 and RFS or OS.

Pathway targets to explore in the treatment of small cell and large cell lung cancers.

INTRODUCTION: With low 5-year survival rates and little progress in therapy, improvements in therapeutic strategies for the treatment of small cell lung cancer (SCLC) and large cell lung cancer (LCC) are warranted. We hypothesized that different SCLC and LCC microarray data sets will share commonly perturbed pathways leading to the identification of new targets for therapeutic development. METHODS: Per gene mean expression fold change ratios were calculated from four publicly available microarray data sets consisting of a total of 113 profiled samples. This was followed by mapping of differentially expressed genes into functionally annotated biologic pathways using Ingenuity pathway analysis software. Common pathways containing genes whose expression levels differed between phenotypic classes defined by histology and gender were determined. RESULTS: Several significant common pathways overlapping these data sets were identified in silico. One of which, the Wnt/beta-catenin signaling pathway, is targeted by agents such as vorinostat and dasatinib, which are currently under exploration in SCLC trials. The remaining pathways are targeted by several drugs developed or under development in cancer therapeutics. CONCLUSION: This in silico pathway exploration in SCLC and LCC holds promise. Additional expression profiling of SCLC and LCC cases would likely add to the knowledge base. Further investigation into identified targets is warranted.

Community analysis of chronic wound bacteria using 16S rRNA gene-based pyrosequencing: impact of diabetes and antibiotics on chronic wound microbiota.

BACKGROUND: Bacterial colonization is hypothesized to play a pathogenic role in the non-healing state of chronic wounds. We characterized wound bacteria from a cohort of chronic wound patients using a 16S rRNA gene-based pyrosequencing approach and assessed the impact of diabetes and antibiotics on chronic wound microbiota. METHODOLOGY/PRINCIPAL FINDINGS: We prospectively enrolled 24 patients at a referral wound center in Baltimore, MD; sampled patients' wounds by curette; cultured samples under aerobic and anaerobic conditions; and pyrosequenced the 16S rRNA V3 hypervariable region. The 16S rRNA gene-based analyses revealed an average of 10 different bacterial families in wounds--approximately 4 times more than estimated by culture-based analyses. Fastidious anaerobic bacteria belonging to the Clostridiales family XI were among the most prevalent bacteria identified exclusively by 16S rRNA gene-based analyses. Community-scale analyses showed that wound microbiota from antibiotic treated patients were significantly different from untreated patients (p = 0.007) and were characterized by increased Pseudomonadaceae abundance. These analyses also revealed that antibiotic use was associated with decreased Streptococcaceae among diabetics and that Streptococcaceae was more abundant among diabetics as compared to non-diabetics. CONCLUSIONS/SIGNIFICANCE: The 16S rRNA gene-based analyses revealed complex bacterial communities including anaerobic bacteria that may play causative roles in the non-healing state of some chronic wounds. Our data suggest that antimicrobial therapy alters community structure--reducing some bacteria while selecting for others.

Pathway targets to explore in the treatment of non-small cell lung cancer.

INTRODUCTION: With a 5-year survival rate of only 16%, improvements in therapeutic strategies for the treatment of non-small cell lung cancer (NSCLC) are still warranted. Published prognostic gene expression classification signatures in NSCLC overlap poorly across studies. We hypothesized that different NSCLC microarray datasets will share common pathways leading to the identification of new targets for therapeutic development. METHODS: Per gene mean expression fold change ratios were calculated from 7 publicly-available microarray datasets consisting of a total of 725 profiled samples. This was followed by mapping of differentially expression genes into functionally annotated biologic pathways using Ingenuity pathway analysis software. Common pathways containing genes whose expression levels differed between phenotypic classes defined by NSCLC cases and/or histology, pathologic stage, and gender were determined. RESULTS: Several significant common pathways overlapping these datasets were identified in silico. One of which, Leukocyte Extravasation Signaling pathway, includes targeted agents such as imatinib, dasatinib, and temozolomide currently under exploration in NSCLC trials. The remaining pathways have targets with few drugs developed or under development in cancer therapeutics. Comparison of pathways derived from Eastern versus Western population datasets revealed several differing targets and potential target drugs. CONCLUSION: This in silico pathway exploration in NSCLC warrants further investigation and could open the door to potential new therapeutics that might improve NSCLC patient outcomes. This strategy can be applied to other cancer types.

Magellan: a web based system for the integrated analysis of heterogeneous biological data and annotations; application to DNA copy number and expression data in ovarian cancer.

Recent advances in high throughput biological methods allow researchers to generate enormous amounts of data from a single experiment. In order to extract meaningful conclusions from this tidal wave of data, it will be necessary to develop analytical methods of sufficient power and utility. It is particularly important that biologists themselves be able to perform many of these analyses, such that their background knowledge of the experimental system under study can be used to interpret results and direct further inquiries. We have developed a web-based system, Magellan, which allows the upload, storage, and analysis of multivariate data and textual or numerical annotations. Data and annotations are treated as abstract entities, to maximize the different types of information the system can store and analyze. Annotations can be used in analyses/visualizations, as a means of subsetting data to reduce dimensionality, or as a means of projecting variables from one data type or data set to another. Analytical methods are deployed within Magellan such that new functionalities can be added in a straightforward fashion. Using Magellan, we performed an integrated analysis of genome-wide comparative genomic hybridization (CGH), mRNA expression, and clinical data from ovarian tumors. Analyses included the use of permutation-based methods to identify genes whose mRNA expression levels correlated with patient survival, a nearest neighbor classifier to predict patient survival from CGH data, and curated annotations such as genomic position and derived annotations such as statistical computations to explore the quantitative relationship between CGH and mRNA expression data.

Genomic and transcriptional aberrations linked to breast cancer pathophysiologies.

This study explores the roles of genome copy number abnormalities (CNAs) in breast cancer pathophysiology by identifying associations between recurrent CNAs, gene expression, and clinical outcome in a set of aggressively treated early-stage breast tumors. It shows that the recurrent CNAs differ between tumor subtypes defined by expression pattern and that stratification of patients according to outcome can be improved by measuring both expression and copy number, especially high-level amplification. Sixty-six genes deregulated by the high-level amplifications are potential therapeutic targets. Nine of these (FGFR1, IKBKB, ERBB2, PROCC, ADAM9, FNTA, ACACA, PNMT, and NR1D1) are considered druggable. Low-level CNAs appear to contribute to cancer progression by altering RNA and cellular metabolism.

Evidence of unique genotypes of beak and feather disease virus in southern Africa.

Psittacine beak and feather disease (PBFD), caused by Beak and feather disease virus (BFDV), is the most significant infectious disease in psittacines. PBFD is thought to have originated in Australia but is now found worldwide; in Africa, it threatens the survival of the indigenous endangered Cape parrot and the vulnerable black-cheeked lovebird. We investigated the genetic diversity of putative BFDVs from southern Africa. Feathers and heparinized blood samples were collected from 27 birds representing 9 psittacine species, all showing clinical signs of PBFD. DNA extracted from these samples was used for PCR amplification of the putative BFDV coat protein (CP) gene. The nucleotide sequences of the CP genes of 19 unique BFDV isolates were determined and compared with the 24 previously described sequences of BFDV isolates from Australasia and America. Phylogenetic analysis revealed eight BFDV lineages, with the southern African isolates representing at least three distinctly unique genotypes; 10 complete genome sequences were determined, representing at least one of every distinct lineage. The nucleotide diversity of the southern African isolates was calculated to be 6.4% and is comparable to that found in Australia and New Zealand. BFDVs in southern Africa have, however, diverged substantially from viruses found in other parts of the world, as the average distance between the southern African isolates and BFDV isolates from Australia ranged from 8.3 to 10.8%. In addition to point mutations, recombination was found to contribute substantially to the level of genetic variation among BFDVs, with evidence of recombination in all but one of the genomes analyzed.