Identification of circadian-related gene expression profiles in entrained breast cancer cell lines

<p>Cancer cells have broken circadian clocks when compared to their normal tissue counterparts. Moreover, it has been shown in breast cancer that disruption of common circadian oscillations is associated with a more negative prognosis. Numerous studies, focused on canonical circadian genes in breast cancer cell lines, have suggested that there are no mRNA circadian-like oscillations. Nevertheless, cancer cell lines have not been extensively characterized and it is unknown to what extent the circadian oscillations are disrupted. We have chosen representative non-cancerous and cancerous breast cell lines (MCF-10A, MCF-7, ZR-75-30, MDA-MB-231 and HCC-1954) in order to determine the degree to which the circadian clock is damaged. We used serum shock to synchronize the circadian clocks in culture. Our aim was to initially observe the time course of gene expression using cDNA microarrays in the non-cancerous MCF-10A and the cancerous MCF-7 cells for screening and then to characterize specific genes in other cell lines. We used a cosine function to select highly correlated profiles. Some of the identified genes were validated by quantitative polymerase chain reaction (qPCR) and further evaluated in the other breast cancer cell lines. Interestingly, we observed that breast cancer and non-cancerous cultured cells are able to generate specific circadian expression profiles in response to the serum shock. The rhythmic genes, suggested via microarray and measured in each particular subtype, suggest that each breast cancer cell type responds differently to the circadian synchronization. Future results could identify circadian-like genes that are altered in breast cancer and non-cancerous cells, which can be used to propose novel treatments. Breast cell lines are potential models for <i>in vitro</i> studies of circadian clocks and clock-controlled pathways.</p

Performance and representation the two NSCLC biomarkers.

<p>Kaplan-Meier curves as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0074250#pone-0074250-g003" target="_blank">Figure 3</a>. Heat map shows the expression of each gene (rows) along samples (columns) in risk groups. Low expression is represented in green grades and high expression in red grades. Corresponding beta coefficients from the Cox fitting is shown. Two stars (**) marks genes whose fitting p-value <0.05, one star (*) for marginal significant genes having p-value <0.10, and no stars for genes whose p-value is >0.1. Box plots compare the difference of gene expression between risk groups using a t-test.</p

Datasets and clinical for the OncotypeDX example.

<p>ER and LN stand for Estrogen Receptor and Lymph Node respectively.</p

Kaplan-Meier curves and performance of the OncoTypeDX biomarker in the breast cancer Ivshina dataset across three tumor grades.

<p>Legends as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0074250#pone-0074250-g003" target="_blank">Figure 3</a>.</p

Current content of the SurvExpress database per cancer type.

<p>Current content of the SurvExpress database per cancer type.</p

Kaplan-Meier curves and performance of the OncoTypeDX biomarker in four datasets.

<p>Censoring samples are shown as “+” marks. Horizontal axis represents time to event. Dataset, outcome event, time scale, concordance index (CI), and p-value of the log-rank test are shown. Red and Green curves denote High- and Low-risk groups respectively. The red and green numbers below horizontal axis represent the number of individuals not presenting the event of the corresponding risk group along time. The number of individuals, the number of censored, and the CI of each risk group are shown in the top-right insets.</p

Datasets and results of the Boutros and Chen biomarkers for the lung cancer example.

<p>p-Risk Groups column show the p-value of the equality between survival curves among risk groups.</p

Overview of the SurvExpress web tool.

<p>Panel A shows a schematic diagram of the SurvExpress workflow while Panel B shows snapshots of the interfaces tagging the required input fields. In the first <i>Input</i> web page, the user can paste the list of genes (tagged with the number 1, which can be symbols, entrez gene identifier and others identifiers) and choose the dataset from around 140 available datasets (tagged with 2 and 3). SurvExpress validates and searches the genes and dataset to show the <i>Analysis</i> web page where the user selects the censored outcome (tag 4) and visualizes the results (right-bottom expanded in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0074250#pone-0074250-g002" target="_blank">Figure 2</a>). The whole process can be achieved in less than one minute for a sensible number of genes.</p

Results of the Oncotype DX in four breast cancer datasets.

<p>CI stands for Concordance Index. DEG means differential expressed genes.</p>*<p>Response time of the results page.</p>**<p>SurvivalROC was estimated around time = 6 years, curves took one order of magnitude more than the response time shown.</p

Common outputs of the SurvExpress Results page.

<p>This figure shows the results from a breast cancer meta-base included in SurvExpress. Panel A shows the Kaplan-Meier curve for risk groups, concordance index, and p-value of the log-rank testing equality of survival curves. Panel B shows clinical information available related to risk group, prognostic index, and outcome data. Panel C shows a heat map representation of the gene expression values. Panel D shows a box plot across risk groups, including the p-value testing for difference using t-test (or f-test for more than two groups). Panel E shows the relation between risk groups and prognostic index. Panel F shows fragments of tables with the summary of the Cox fitting and the prognostic indexes. Details are provided in SurvExpress Tutorial.</p