Identification of Fluorescent Compounds with Non-Specific Binding Property via High Throughput Live Cell Microscopy

10.1371/journal.pone.0028802PLoS ONE71

Exon-Level Microarray Analyses Identify Alternative Splicing Programs in Breast Cancer

Protein isoforms produced by alternative splicing (AS) of many genes have been implicated in several aspects of cancer genesis and progression. These observations motivated a genome-wide assessment of AS in breast cancer. We accomplished this by measuring exon level expression in 31 breast cancer and nonmalignant immortalized cell lines representing luminal, basal, and claudin-low breast cancer subtypes using Affymetrix Human Junction Arrays. We analyzed these data using a computational pipeline specifically designed to detect AS with a low false-positive rate. This identified 181 splice events representing 156 genes as candidates for AS. Reverse transcription-PCR validation of a subset of predicted AS events confirmed 90%. Approximately half of the AS events were associated with basal, luminal, or claudin-low breast cancer subtypes. Exons involved in claudin-low subtype–specific AS were significantly associated with the presence of evolutionarily conserved binding motifs for the tissue-specific Fox2 splicing factor. Small interfering RNA knockdown of Fox2 confirmed the involvement of this splicing factor in subtype-specific AS. The subtype-specific AS detected in this study likely reflects the splicing pattern in the breast cancer progenitor cells in which the tumor arose and suggests the utility of assays for Fox-mediated AS in cancer subtype definition and early detection. These data also suggest the possibility of reducing the toxicity of protein-targeted breast cancer treatments by targeting protein isoforms that are not present in limiting normal tissues

A systems analysis of the chemosensitivity of breast cancer cells to the polyamine analogue PG-11047

<p>Abstract</p> <p>Background</p> <p>Polyamines regulate important cellular functions and polyamine dysregulation frequently occurs in cancer. The objective of this study was to use a systems approach to study the relative effects of PG-11047, a polyamine analogue, across breast cancer cells derived from different patients and to identify genetic markers associated with differential cytotoxicity.</p> <p>Methods</p> <p>A panel of 48 breast cell lines that mirror many transcriptional and genomic features present in primary human breast tumours were used to study the antiproliferative activity of PG-11047. Sensitive cell lines were further examined for cell cycle distribution and apoptotic response. Cell line responses, quantified by the GI₅₀(dose required for 50% relative growth inhibition) were correlated with the omic profiles of the cell lines to identify markers that predict response and cellular functions associated with drug sensitivity.</p> <p>Results</p> <p>The concentrations of PG-11047 needed to inhibit growth of members of the panel of breast cell lines varied over a wide range, with basal-like cell lines being inhibited at lower concentrations than the luminal cell lines. Sensitive cell lines showed a significant decrease in S phase fraction at doses that produced little apoptosis. Correlation of the GI₅₀values with the omic profiles of the cell lines identified genomic, transcriptional and proteomic variables associated with response.</p> <p>Conclusions</p> <p>A 13-gene transcriptional marker set was developed as a predictor of response to PG-11047 that warrants clinical evaluation. Analyses of the pathways, networks and genes associated with response to PG-11047 suggest that response may be influenced by interferon signalling and differential inhibition of aspects of motility and epithelial to mesenchymal transition.</p> <p>See the related commentary by Benes and Settleman: <url>http://www.biomedcentral.com/1741-7015/7/78</url></p

Declarative flow control for distributed instrumentation

We have developed a 'microscopy channel' to advertise a unique set of on-line scientific instruments and to let users join a particular session, perform an experiment, collaborate with other users, and collect data for further analysis. The channel is a collaborative problem solving environment (CPSE) that allows for both synchronous and asynchronous collaboration, as well as flow control for enhanced scalability. The flow control is a declarative feature that enhances software functionality at the experimental scale. Our testbed includes several unique electron and optical microscopes with applications ranging from material science to cell biology. We have built a system that leverages current commercial CORBA services, Web Servers, and flow control specifications to meet diverse requirements for microscopy and experimental protocols. In this context, we have defined and enhanced Instrument Services (IS), Exchange Services (ES), Computational Services (CS), and Declarative Services (DS) that sit on top of CORBA and its enabling services (naming, trading, security, and notification) IS provides a layer of abstraction for controlling any type of microscope. ES provides a common set of utilities for information management and transaction. CS provides the analytical capabilities needed for online microscopy. DS provides mechanisms for flow control for improving the dynamic behavior of the system

PHENOTYPIC CHARACTERIZATION OF BREAST INVASIVE CARCINOMA VIA TRANSFERABLE TISSUE MORPHOMETRIC PATTERNS LEARNED FROM GLIOBLASTOMA MULTIFORME.

Quantitative analysis of whole slide images (WSIs) in a large cohort may provide predictive models of clinical outcome. However, the performance of the existing techniques is hindered as a result of large technical variations (e.g., fixation, staining) and biological heterogeneities (e.g., cell type, cell state) that are always present in a large cohort. Although unsupervised feature learning provides a promising way in learning pertinent features without human intervention, its capability can be greatly limited due to the lack of well-curated examples. In this paper, we explored the transferability of knowledge acquired from a well-curated Glioblastoma Multiforme (GBM) dataset through its application to the representation and characterization of tissue histology from the Cancer Genome Atlas (TCGA) Breast Invasive Carcinoma (BRCA) cohort. Our experimental results reveals two major phenotypic subtypes with statistically significantly different survival curves. Further differential expression analysis of these two subtypes indicates enrichment of genes regulated by NF-kB in response to TNF and genes up-regulated in response to IFNG

PHENOTYPIC CHARACTERIZATION OF BREAST INVASIVE CARCINOMA VIA TRANSFERABLE TISSUE MORPHOMETRIC PATTERNS LEARNED FROM GLIOBLASTOMA MULTIFORME.

Quantitative analysis of whole slide images (WSIs) in a large cohort may provide predictive models of clinical outcome. However, the performance of the existing techniques is hindered as a result of large technical variations (e.g., fixation, staining) and biological heterogeneities (e.g., cell type, cell state) that are always present in a large cohort. Although unsupervised feature learning provides a promising way in learning pertinent features without human intervention, its capability can be greatly limited due to the lack of well-curated examples. In this paper, we explored the transferability of knowledge acquired from a well-curated Glioblastoma Multiforme (GBM) dataset through its application to the representation and characterization of tissue histology from the Cancer Genome Atlas (TCGA) Breast Invasive Carcinoma (BRCA) cohort. Our experimental results reveals two major phenotypic subtypes with statistically significantly different survival curves. Further differential expression analysis of these two subtypes indicates enrichment of genes regulated by NF-kB in response to TNF and genes up-regulated in response to IFNG

MORPHOMETRIC SUBTYPING FOR A PANEL OF BREAST CANCER CELL LINES

A panel of cell lines of diverse molecular background offers an improved model system for high-content screening, comparative analysis, and cell systems biology. A computational pipeline has been developed to collect images from cell-based assays, segment individual cells and colonies, represent segmented objects in a multidimensional space, and cluster them for identifying distinct subpopulations. While each segmentation strategy can vary for different imaging assays, representation and subpopulation analysis share a common thread. Application of this pipeline to a library of 41 breast cancer cell lines is demonstrated. These cell lines are grown in 2D and imaged through immunofluorescence microscopy. Subpopulations in this panel are identified and shown to correlate with previous subtyping literature that was derived from transcript data