Distinctive Responsiveness to Stromal Signaling Accompanies Histologic Grade Programming of Cancer Cells

Whether stromal components facilitate growth, invasion, and dissemination of cancer cells or suppress neoplastic lesions from further malignant progression is a continuing conundrum in tumor biology. Conceptualizing a dynamic picture of tumorigenesis is complicated by inter-individual heterogeneity. In the post genomic era, unraveling such complexity remains a challenge for the cancer biologist. Towards establishing a functional association between cellular crosstalk and differential cancer aggressiveness, we identified a signature of malignant breast epithelial response to stromal signaling. Proximity to fibroblasts resulted in gene transcript alterations of >2-fold for 107 probes, collectively designated as Fibroblast Triggered Gene Expression in Tumor (FTExT). The hazard ratio predicted by the FTExT classifier for distant relapse in patients with intermediate and high grade breast tumors was significant compared to routine clinical variables (dataset 1, n = 258, HR – 2.11, 95% CI 1.17–3.80, p-value 0.01; dataset 2, n = 171, HR - 3.07, 95% CI 1.21–7.83, p-value 0.01). Biofunctions represented by FTExT included inflammatory signaling, free radical scavenging, cell death, and cell proliferation. Unlike genes of the ‘proliferation cluster’, which are overexpressed in aggressive primary tumors, FTExT genes were uniquely repressed in such cases. As proof of concept for our correlative findings, which link stromal-epithelial crosstalk and tumor behavior, we show a distinctive differential in stromal impact on prognosis-defining functional endpoints of cell cycle progression, and resistance to therapy-induced growth arrest and apoptosis in low vs. high grade cancer cells. Our experimental data thus reveal aspects of ‘paracrine cooperativity’ that are exclusively contingent upon the histopathologically defined grade of interacting tumor epithelium, and demonstrate that epithelial responsiveness to the tumor microenvironment is a deterministic factor underlying clinical outcome. In this light, early attenuation of epithelial-stromal crosstalk could improve the management of cases prone to be clinically challenging

A gene signature of loss of oestrogen receptor (ER) function and oxidative stress links ER-positive breast tumours with an absent progesterone receptor and a poor prognosis

Prognostic gene signatures like the wound and hypoxia signature differ by assumptions of cellular growth. Although gene signatures show little overlap, they also track within the group of luminal breast tumours those with a high proliferation and poor prognosis. Oxidative stress is another assumption of cellular growth. It affects several pathological conditions through its influence on the regulation of protein kinases and signal transduction pathways. A comprehensive set of 62 core genes from cultured oestrogen- and oestrogen receptor-deprived epithelial breast cancer cells is responsive to three forms of oxidative stress. Evidence is presented that oxidative stress involves the development of an aggressive subset of primary oestrogen receptor-positive breast tumours

Transcription profiles of non-immortalized breast cancer cell lines

BACKGROUND: Searches for differentially expressed genes in tumours have made extensive use of array technology. Most samples have been obtained from tumour biopsies or from established tumour-derived cell lines. Here we compare cultures of non-immortalized breast cancer cells, normal non-immortalized breast cells and immortalized normal and breast cancer cells to identify which elements of a defined set of well-known cancer-related genes are differentially expressed. METHODS: Cultures of cells from pleural effusions or ascitic fluids from breast cancer patients (MSSMs) were used in addition to commercially-available normal breast epithelial cells (HMECs), established breast cancer cell lines (T-est) and established normal breast cells (N-est). The Atlas Human Cancer 1.2 cDNA expression array was employed. The data obtained were analysed using widely-available statistical and clustering software and further validated through real-time PCR. RESULTS: According to Significance Analysis of Microarray (SAM) and AtlasImage software, 48 genes differed at least 2-fold in adjusted intensities between HMECs and MSSMs (p < 0.01). Some of these genes have already been directly linked with breast cancer, metastasis and malignant progression, whilst others encode receptors linked to signal transduction pathways or are otherwise related to cell proliferation. Fifty genes showed at least a 2.5-fold difference between MSSMs and T-est cells according to AtlasImage, 2-fold according to SAM. Most of these classified as genes related to metabolism and cell communication. CONCLUSION: The expression profiles of 1176 genes were determined in finite life-span cultures of metastatic breast cancer cells and of normal breast cells. Significant differences were detected between the finite life-span breast cancer cell cultures and the established breast cancer cell lines. These data suggest caution in extrapolating information from established lines for application to clinical cancer research

UCP2 and PRMT1 are key prognostic markers for lung carcinoma patients

Cancer cells have developed unique strategies to meet their high energy demand. Therefore, they have established a setting of Ca2+-triggered high mitochondrial activity. But mitochondrial Ca2+ uptake has to be strictly controlled to avoid mitochondrial Ca2+ overload that would cause apoptotic cell death. Methylation by protein arginine methyl transferase 1 (PRMT1) desensitizes the mitochondrial Ca2+ uptake machinery and reduces mitochondrial Ca2+ accumulation in cancer cells. In case of PRMT1- driven methylation, proper mitochondrial Ca2+ uptake is reestablished by increased activity of uncoupling protein 2 (UCP2), pointing to an importance of these proteins for cancer cell survival and activity. Accordingly, in this study we investigated the impact of UCP2 and PRMT1 on the fate of human lung cancer cells (A549, Calu-3 and H1299) as well as on patients suffering from lung carcinoma. We show that combined overexpression of UCP2 and PRMT1 significantly enhances viability, proliferation as well as mitochondrial respiration. In line with these findings, the overall survival probability of lung carcinoma patients with high mRNA expression levels of UCP2 and PRMT1 is strongly reduced. Furthermore, analysis via The Cancer Genome Atlas (TCGA) reveals upregulation of both proteins, UCP2 and PRMT1, as common feature of various cancer types. These findings suggest that proper mitochondrial Ca2+ uptake is essential for devastating tumor growth, and highlight the importance of a tightly controlled mitochondrial Ca2+ uptake to ensure proper ATP biosynthesis while avoiding dangerous mitochondrial Ca2+ overload. By that, the study unveils proteins of the mitochondrial Ca2+ uptake as potential targets for cancer treatment. Copyright: Sokolowski et al

Single Cell Profiling of Circulating Tumor Cells: Transcriptional Heterogeneity and Diversity from Breast Cancer Cell Lines

BACKGROUND: To improve cancer therapy, it is critical to target metastasizing cells. Circulating tumor cells (CTCs) are rare cells found in the blood of patients with solid tumors and may play a key role in cancer dissemination. Uncovering CTC phenotypes offers a potential avenue to inform treatment. However, CTC transcriptional profiling is limited by leukocyte contamination; an approach to surmount this problem is single cell analysis. Here we demonstrate feasibility of performing high dimensional single CTC profiling, providing early insight into CTC heterogeneity and allowing comparisons to breast cancer cell lines widely used for drug discovery. METHODOLOGY/PRINCIPAL FINDINGS: We purified CTCs using the MagSweeper, an immunomagnetic enrichment device that isolates live tumor cells from unfractionated blood. CTCs that met stringent criteria for further analysis were obtained from 70% (14/20) of primary and 70% (21/30) of metastatic breast cancer patients; none were captured from patients with non-epithelial cancer (n = 20) or healthy subjects (n = 25). Microfluidic-based single cell transcriptional profiling of 87 cancer-associated and reference genes showed heterogeneity among individual CTCs, separating them into two major subgroups, based on 31 highly expressed genes. In contrast, single cells from seven breast cancer cell lines were tightly clustered together by sample ID and ER status. CTC profiles were distinct from those of cancer cell lines, questioning the suitability of such lines for drug discovery efforts for late stage cancer therapy. CONCLUSIONS/SIGNIFICANCE: For the first time, we directly measured high dimensional gene expression in individual CTCs without the common practice of pooling such cells. Elevated transcript levels of genes associated with metastasis NPTN, S100A4, S100A9, and with epithelial mesenchymal transition: VIM, TGFß1, ZEB2, FOXC1, CXCR4, were striking compared to cell lines. Our findings demonstrate that profiling CTCs on a cell-by-cell basis is possible and may facilitate the application of 'liquid biopsies' to better model drug discovery

Knowledge-based analysis of microarrays for the discovery of transcriptional regulation relationships

<p>Abstract</p> <p>Background</p> <p>The large amount of high-throughput genomic data has facilitated the discovery of the regulatory relationships between transcription factors and their target genes. While early methods for discovery of transcriptional regulation relationships from microarray data often focused on the high-throughput experimental data alone, more recent approaches have explored the integration of external knowledge bases of gene interactions.</p> <p>Results</p> <p>In this work, we develop an algorithm that provides improved performance in the prediction of transcriptional regulatory relationships by supplementing the analysis of microarray data with a new method of integrating information from an existing knowledge base. Using a well-known dataset of yeast microarrays and the Yeast Proteome Database, a comprehensive collection of known information of yeast genes, we show that knowledge-based predictions demonstrate better sensitivity and specificity in inferring new transcriptional interactions than predictions from microarray data alone. We also show that comprehensive, direct and high-quality knowledge bases provide better prediction performance. Comparison of our results with ChIP-chip data and growth fitness data suggests that our predicted genome-wide regulatory pairs in yeast are reasonable candidates for follow-up biological verification.</p> <p>Conclusion</p> <p>High quality, comprehensive, and direct knowledge bases, when combined with appropriate bioinformatic algorithms, can significantly improve the discovery of gene regulatory relationships from high throughput gene expression data.</p

MUC1-associated proliferation signature predicts outcomes in lung adenocarcinoma patients

Background: MUC1 protein is highly expressed in lung cancer. The cytoplasmic domain of MUC1 (MUC1-CD) induces tumorigenesis and resistance to DNA-damaging agents. We characterized MUC1-CD-induced transcriptional changes and examined their significance in lung cancer patients. Methods: Using DNA microarrays, we identified 254 genes that were differentially expressed in cell lines transformed by MUC1-CD compared to control cell lines. We then examined expression of these genes in 441 lung adenocarcinomas from a publicly available database. We employed statistical analyses independent of clinical outcomes, including hierarchical clustering, Student's t-tests and receiver operating characteristic (ROC) analysis, to select a seven-gene MUC1-associated proliferation signature (MAPS). We demonstrated the prognostic value of MAPS in this database using Kaplan-Meier survival analysis, log-rank tests and Cox models. The MAPS was further validated for prognostic significance in 84 lung adenocarcinoma patients from an independent database. Results: MAPS genes were found to be associated with proliferation and cell cycle regulation and included CCNB1, CDC2, CDC20, CDKN3, MAD2L1, PRC1 and RRM2. MAPS expressors (MAPS+) had inferior survival compared to non-expressors (MAPS-). In the initial data set, 5-year survival was 65% (MAPS-) vs. 45% (MAPS+, p < 0.0001). Similarly, in the validation data set, 5-year survival was 57% (MAPS-) vs. 28% (MAPS+, p = 0.005). Conclusions: The MAPS signature, comprised of MUC1-CD-dependent genes involved in the control of cell cycle and proliferation, is associated with poor outcomes in patients with adenocarcinoma of the lung. These data provide potential new prognostic biomarkers and treatment targets for lung adenocarcinoma