Integrative genome-wide expression profiling identifies three distinct molecular subgroups of renal cell carcinoma with different patient outcome

ABSTRACT: BACKGROUND: Renal cell carcinoma (RCC) is characterized by a number of diverse molecular aberrations that differ among individuals. Recent approaches to molecularly classify RCC were based on clinical, pathological as well as on single molecular parameters. As a consequence, gene expression patterns reflecting the sum of genetic aberrations in individual tumors may not have been recognized. In an attempt to uncover such molecular features in RCC, we used a novel, unbiased and integrative approach. METHODS: We integrated gene expression data from 97 primary RCCs of different pathologic parameters, 15 RCC metastases as well as 34 cancer cell lines for two-way nonsupervised hierarchical clustering using gene groups suggested by the PANTHER Classification System. We depicted the genomic landscape of the resulted tumor groups by means of Single Nuclear Polymorphism (SNP) technology. Finally, the achieved results were immunohistochemically analyzed using a tissue microarray (TMA) composed of 254 RCC. Results: We found robust, genome wide expression signatures, which split RCC into three distinct molecular subgroups. These groups remained stable even if randomly selected gene sets were clustered. Notably, the pattern obtained from RCC cell lines was clearly distinguishable from that of primary tumors. SNP array analysis demonstrated differing frequencies of chromosomal copy number alterations among RCC subgroups. TMA analysis with group-specific markers showed a prognostic significance of the different groups. Conclusion: We propose the existence of characteristic and histologically independent genome-wide expression outputs in RCC with potential biological and clinical relevance

Microarrays and Renal Cell Cancer Biomarkers

The incidence of renal cell carcinoma (RCC) has steadily increased during the last decades. Although technological advances for early recognition of RCC exist, many tumors are accidentally detected, and clinical decision-making is still mainly based on morphological evaluation. Being a relatively chemotherapeutic-resistant and very heterogenic disease, the biological behavior of this tumor type is difficult to predict. Histologic subtyping, tumor staging, and grading are still the pathologic parameters with most valid prognostic and diagnostic significance. Novel high-throughput methodologies have been developed to depict the molecular constitution of individual tumors at the DNA, RNA, and protein levels in order to find relevant biomarkers for optimizing cancer patient care. In this chapter we recapitulate previous published efforts with different microarray platforms which were used to identify biomarkers in RCC. As a result, a large number of such markers, including pathways and gene signatures, have been described as promising biomarkers with significant prognostic and predictive value. However, at present and in contrast to other tumor types such as breast cancer, lung cancer, or melanoma, there is no RCC biomarker that can unrestrictedly be recommended for the use in routine diagnostics. In light of the increasing demand of targeted cancer therapies, vigorous biomedical studies will be needed to translate the molecular findings into clinical applications

Lactose-over-Glucose Preference in Bifidobacterium longum NCC2705: glcP, Encoding a Glucose Transporter, Is Subject to Lactose Repression

Analysis of culture supernatants obtained from Bifidobacterium longum NCC2705 grown on glucose and lactose revealed that glucose utilization is impaired until depletion of lactose. Thus, unlike many other bacteria, B. longum preferentially uses lactose rather than glucose as the primary carbon source. Glucose uptake experiments with B. longum cells showed that glucose transport was repressed in the presence of lactose. A comparative analysis of global gene expression profiling using DNA arrays led to the identification of only one gene repressed by lactose, the putative glucose transporter gene glcP. The functionality of GlcP as glucose transporter was demonstrated by heterologous complementation of a glucose transport-deficient Escherichia coli strain. Additionally, GlcP exhibited the highest substrate specificity for glucose. Primer extension and real-time PCR analyses confirmed that expression of glcP was mediated by lactose. Hence, our data demonstrate that the presence of lactose in culture medium leads to the repression of glucose transport and transcriptional down-regulation of the glucose transporter gene glcP. This may reflect the highly adapted life-style of B. longum in the gastrointestinal tract of mammals

Extended focus Fourier domain optical coherence microscopy assists developmental biology

We present a novel detection scheme for Fourier domain optical coherence microscopy (FDOCM). A Bessel-like interference pattern with a strong central lobe was created with an axicon lens. This pattern was then imaged by a telescopic system into the sample space to obtain a laterally highly confined illumination needle, extending over a long axial range. For increased efficiency, the detection occurs decoupled from the illumination, avoiding a double pass through the axicon. Nearly constant transverse resolution of ~1.5µm along a focal range of 200µm with a maximum sensitivity of 105dB was obtained. A broad bandwidth Ti:Sapphire laser allowed for an axial resolution of 3µm in air, providing the nearly isotropic resolution necessary to access the microstructure of biological tissues. Together with the speed- and sensitivity-advantage of FDOCT, this system can perform in vivo measurements in a minimally invasive way. Tomograms of the mouse mammary gland and the mouse follicle, recorded in vitro, revealed biologically relevant structural details. Images acquired with classical microscopy techniques, involving stained and fluorescent samples, validate these structures and emphasize the high contrast of the tomograms. It is comparable to the contrast achieved with classical techniques, but employing neither staining, labeling nor slicing of the samples, stressing the high potential of FDOCM for minimally invasive in vivo small animal imaging

Discretization of Gene Expression Data Unmasks Molecular Subgroups Recurring in Different Human Cancer Types.

Despite the individually different molecular alterations in tumors, the malignancy associated biological traits are strikingly similar. Results of a previous study using renal cell carcinoma (RCC) as a model pointed towards cancer-related features, which could be visualized as three groups by microarray based gene expression analysis. In this study, we used a mathematic model to verify the presence of these groups in RCC as well as in other cancer types. We developed an algorithm for gene-expression deviation profiling for analyzing gene expression data of a total of 8397 patients with 13 different cancer types and normal tissues. We revealed three common Cancer Transcriptomic Profiles (CTPs) which recurred in all investigated tumors. Additionally, CTPs remained robust regardless of the functions or numbers of genes analyzed. CTPs may represent common genetic fingerprints, which potentially reflect the closely related biological traits of human cancers

Integrative genome-wide expression profiling identifies three distinct molecular subgroups of renal cell carcinoma with different patient outcome

Abstract Background Renal cell carcinoma (RCC) is characterized by a number of diverse molecular aberrations that differ among individuals. Recent approaches to molecularly classify RCC were based on clinical, pathological as well as on single molecular parameters. As a consequence, gene expression patterns reflecting the sum of genetic aberrations in individual tumors may not have been recognized. In an attempt to uncover such molecular features in RCC, we used a novel, unbiased and integrative approach. Methods We integrated gene expression data from 97 primary RCC of different pathologic parameters, 15 RCC metastases as well as 34 cancer cell lines for two-way nonsupervised hierarchical clustering using gene groups suggested by the PANTHER Classification System. We depicted the genomic landscape of the resulted tumor groups by means of Single Nuclear Polymorphism (SNP) technology. Finally, the achieved results were immunohistochemically analyzed using a tissue microarray (TMA) composed of 254 RCC. Results We found robust, genome wide expression signatures, which split RCC into three distinct molecular subgroups. These groups remained stable even if randomly selected gene sets were clustered. Notably, the pattern obtained from RCC cell lines was clearly distinguishable from that of primary tumors. SNP array analysis demonstrated differing frequencies of chromosomal copy number alterations among RCC subgroups. TMA analysis with group-specific markers showed a prognostic significance of the different groups. Conclusion We propose the existence of characteristic and histologically independent genome-wide expression outputs in RCC with potential biological and clinical relevance.</p

Graphical illustration of CTPs and patient classification.

<p>Shown is a graphical overview on the nature of generated RCC-CTPs as well as their comparison with the CTP of one patient with a different cancer type. The deviation from the mean expression for all probe sets and their relative correlation to each other (continuous curves) define the RCC-CTP target profiles. For CTP assignment, the CTP profile of an individual patient with another cancer type (dashed curve) is compared to the target RCC-CTPs.</p

The secreted protease Adamts18 links hormone action to activation of the mammary stem cell niche

Estrogens and progesterone control breast development and carcinogenesis via their cognate receptors expressed in a subset of luminal cells in the mammary epithelium. How they control the extracellular matrix, important to breast physiology and tumorigenesis, remains unclear. Here we report that both hormones induce the secreted protease Adamts18 in myoepithelial cells by controlling Wnt4 expression with consequent paracrine canonical Wnt signaling activation. Adamts18 is required for stem cell activation, has multiple binding partners in the basement membrane and interacts genetically with the basal membrane-specific proteoglycan, Col18a1, pointing to the basement membrane as part of the stem cell niche. In vitro, ADAMTS18 cleaves fibronectin; in vivo, Adamts18 deletion causes increased collagen deposition during puberty, which results in impaired Hippo signaling and reduced Fgfr2 expression both of which control stem cell function. Thus, Adamts18 links luminal hormone receptor signaling to basement membrane remodeling and stem cell activation. How hormonal signaling in the mammary epithelium controls the surrounding extracellular matrix is unclear. Here, the authors show that a secreted protease, Adamts18, induced by upstream estrogen-progesterone activated Wnt4 in myoepithelial cells, remodels the basement membrane and contributes to mammary epithelial stemness