Identification of a stable molecular signature in mammary tumor endothelial cells that persists in vitro

Long-term, in vitro propagation of tumor-specific endothelial cells (TEC) allows for functional studies and genome-wide expression profiling of clonally-derived, well-characterized subpopulations. Using a genetically engineered mouse model (GEMM) of mammary adenocarcinoma, we have optimized an isolation procedure and defined growth conditions for long-term propagation of mammary TEC. The isolated TEC maintain their endothelial specification and phenotype in culture. Furthermore, gene expression profiling of multiple TEC subpopulations revealed striking, persistent overexpression of several candidate genes including Irx2 and Zfp503 (transcription factors), Alcam and Cd133 (cell surface markers), Ccl4 and neurotensin (Nts) (angiocrine factors), and Gpr182 and Cnr2 (G protein-coupled receptors, GPCRs). Taken together, we have developed an effective method for isolating and culture-expanding mammary TEC, and uncovered several new TEC-selective genes whose overexpression persists even after long-term in vitro culture. These results suggest that the tumor microenvironment may induce changes in vascular endothelium in vivo that are stably transmittable in vitro

Joint and individual analysis of breast cancer histologic images and genomic covariates

A key challenge in modern data analysis is understanding connections between complex and differing modalities of data. For example, two of the main approaches to the study of breast cancer are histopathology (analyzing visual characteristics of tumors) and genetics. While histopathology is the gold standard for diagnostics and there have been many recent breakthroughs in genetics, there is little overlap between these two fields. We aim to bridge this gap by developing methods based on Angle-based Joint and Individual Variation Explained (AJIVE) to directly explore similarities and differences between these two modalities. Our approach exploits Convolutional Neural Networks (CNNs) as a powerful, automatic method for image feature extraction to address some of the challenges presented by statistical analysis of histopathology image data. CNNs raise issues of interpretability that we address by developing novel methods to explore visual modes of variation captured by statistical algorithms (e.g. PCA or AJIVE) applied to CNN features. Our results provide many interpretable connections and contrasts between histopathology and genetics

Radiation-Induced Gene Signature Predicts Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients

The identification of biomarkers predictive of neoadjuvant chemotherapy response in breast cancer patients would be an important advancement in personalized cancer therapy. In this study, we hypothesized that due to similarities between radiation- and chemotherapy-induced cellular response mechanisms, radiation-responsive genes may be useful in predicting response to neoadjuvant chemotherapy. Murine p53 null breast cancer cell lines representative of the luminal, basal-like and claudin-low human breast cancer subtypes were irradiated to identify radiation-responsive genes across subtypes. These murine tumor radiation-induced genes were then converted to their human orthologs, and subsequently tested as a predictor of pathologic complete response (pCR), which was validated on two independent published neoadjuvant chemotherapy datasets of genomic data with chemotherapy response. A radiation-induced gene signature consisting of 30 genes was identified on a training set of 337 human primary breast cancer tumor samples that was prognostic for survival. Mean expression of this signature was calculated for individual samples on two independent published datasets and was found to be significantly predictive of pCR. Multivariate logistic regression analysis in both independent datasets showed that this 30 gene signature added significant predictive information independent of that provided by standard clinical predictors and other gene expression-based predictors of pCR. This study provides new information for radiation-induced biology, as well as information regarding response to neoadjuvant chemotherapy and a possible means of improving the prediction of pCR

Docetaxel-Loaded PLGA Nanoparticles Improve Efficacy in Taxane-Resistant Triple-Negative Breast Cancer

Novel treatment strategies, including nanomedicine, are needed for improving management of triple-negative breast cancer. Patients with triple-negative breast cancer, when considered as a group, have a worse outcome after chemotherapy than patients with breast cancers of other subtypes, a finding that reflects the intrinsically adverse prognosis associated with the disease. The aim of this study was to improve the efficacy of docetaxel by incorporation into a novel nanoparticle platform for the treatment of taxane-resistant triple-negative breast cancer. Rod-shaped nanoparticles encapsulating docetaxel were fabricated using an imprint lithography based technique referred to as Particle Replication in Nonwetting Templates (PRINT). These rod-shaped PLGA-docetaxel nanoparticles were tested in the C3(1)-T-antigen (C3Tag) genetically engineered mouse model (GEMM) of breast cancer that represents the basal-like subtype of triple-negative breast cancer and is resistant to therapeutics from the taxane family. Thi..

Oncogenic PI3K Mutations Lead to NF- B-Dependent Cytokine Expression following Growth Factor Deprivation

The PI3K pathway is one of the most commonly misregulated signaling pathways in human cancers, but its impact on the tumor microenvironment has not been considered as deeply as its autonomous impact on tumor cells. In this study we demonstrate that NF-κB is activated by the two most common PI3K mutations, PIK3CA E545K and H1047R. We found that markers of NF-κB are most strongly upregulated under conditions of growth factor deprivation. Gene expression analysis performed on cells deprived of growth factors identified the repertoire of genes altered by oncogenic PI3K mutations following growth factor deprivation. This gene set most closely correlated with gene signatures from claudin-low and basal-like breast tumors, subtypes frequently exhibiting constitutive PI3K/Akt activity. An NF-κB-dependent subset of genes driven by oncogenic PI3K mutations was also identified that encoded primarily secreted proteins, suggesting a paracrine role for this gene set. Interestingly, while NF-κB activated by oncogenes such as Ras and EGFR leads to cell-autonomous effects, abrogating NF-κB in PI3K-transformed cells did not decrease proliferation or induce apoptosis. However, conditioned media from PI3K mutant-expressing cells led to increased STAT3 activation in recipient THP-1 monocytes or normal epithelial cells in a NF-κB and IL-6-dependent manner. Together, our findings describe a PI3K-driven, NF-κB-dependent transcriptional profile which may play a critical role in promoting a microenvironment amenable to tumor progression. These data also indicate that NF-κB plays diverse roles downstream from different oncogenic signaling pathways

SWISS MADE: Standardized WithIn Class Sum of Squares to Evaluate Methodologies and Dataset Elements

Contemporary high dimensional biological assays, such as mRNA expression microarrays, regularly involve multiple data processing steps, such as experimental processing, computational processing, sample selection, or feature selection (i.e. gene selection), prior to deriving any biological conclusions. These steps can dramatically change the interpretation of an experiment. Evaluation of processing steps has received limited attention in the literature. It is not straightforward to evaluate different processing methods and investigators are often unsure of the best method. We present a simple statistical tool, Standardized WithIn class Sum of Squares (SWISS), that allows investigators to compare alternate data processing methods, such as different experimental methods, normalizations, or technologies, on a dataset in terms of how well they cluster a priori biological classes. SWISS uses Euclidean distance to determine which method does a better job of clustering the data elements based on a priori classifications. We apply SWISS to three different gene expression applications. The first application uses four different datasets to compare different experimental methods, normalizations, and gene sets. The second application, using data from the MicroArray Quality Control (MAQC) project, compares different microarray platforms. The third application compares different technologies: a single Agilent two-color microarray versus one lane of RNA-Seq. These applications give an indication of the variety of problems that SWISS can be helpful in solving. The SWISS analysis of one-color versus two-color microarrays provides investigators who use two-color arrays the opportunity to review their results in light of a single-channel analysis, with all of the associated benefits offered by this design. Analysis of the MACQ data shows differential intersite reproducibility by array platform. SWISS also shows that one lane of RNA-Seq clusters data by biological phenotypes as well as a single Agilent two-color microarray

Intrinsic Breast Tumor Subtypes, Race, and Long-Term Survival in the Carolina Breast Cancer Study

Previous research identified differences in breast cancer-specific mortality across four "intrinsic" tumor subtypes: luminal A, luminal B, basal-like, and human epidermal growth factor receptor 2 positive/estrogen receptor negative (HER2+/ER−)

Genome-wide profiles of CtBP link metabolism with genome stability and epithelial reprogramming in breast cancer

epithelial reprogramming in breast cance

ReQON: a Bioconductor package for recalibrating quality scores from next-generation sequencing data

Background Next-generation sequencing technologies have become important tools for genome-wide studies. However, the quality scores that are assigned to each base have been shown to be inaccurate. If the quality scores are used in downstream analyses, these inaccuracies can have a significant impact on the results. Results Here we present ReQON, a tool that recalibrates the base quality scores from an input BAM file of aligned sequencing data using logistic regression. ReQON also generates diagnostic plots showing the effectiveness of the recalibration. We show that ReQON produces quality scores that are both more accurate, in the sense that they more closely correspond to the probability of a sequencing error, and do a better job of discriminating between sequencing errors and non-errors than the original quality scores. We also compare ReQON to other available recalibration tools and show that ReQON is less biased and performs favorably in terms of quality score accuracy. Conclusion ReQON is an open source software package, written in R and available through Bioconductor, for recalibrating base quality scores for next-generation sequencing data. ReQON produces a new BAM file with more accurate quality scores, which can improve the results of downstream analysis, and produces several diagnostic plots showing the effectiveness of the recalibration