mRNA profiling reveals determinants of trastuzumab efficiency in HER2-positive breast cancer.

Intrinsic and acquired resistance to the monoclonal antibody drug trastuzumab is a major problem in the treatment of HER2-positive breast cancer. A deeper understanding of the underlying mechanisms could help to develop new agents. Our intention was to detect genes and single nucleotide polymorphisms (SNPs) affecting trastuzumab efficiency in cell culture.

Circulating microRNAs in plasma as early detection markers for breast cancer.

In recent years, circulating miRNAs have attracted a great deal of attention as promising novel markers for various diseases. Here, we investigated their potential to serve as minimally invasive, early detection markers for breast cancer in blood plasma. We profiled miRNAs extracted from the plasma of early stage breast cancer patients (taken at the time-point of diagnosis) and healthy control individuals using TaqMan low-density arrays (TLDA).

Deterministic Effects Propagation Networks for reconstructing protein signaling networks from multiple interventions.

Background: Modern gene perturbation techniques, like RNA interference (RNAi), enable us to study effects of targeted interventions in cells efficiently. In combination with mRNA or protein expression data this allows to gain insights into the behavior of complex biological systems.

Results: In this paper, we propose Deterministic Effects Propagation Networks (DEPNs) as a special Bayesian Network approach to reverse engineer signaling networks from a combination of protein expression and perturbation data.

Reverse-phase protein arrays for application-orientated cancer research.

A detailed and quantitative analysis of disease-relevant signaling will greatly contribute to our understanding of tumorigenesis and cancer progression, and thus open new strategies for drug discovery. However, throughput and sensitivity of currently established methods available for proteome profiling do not comply with the needs of clinical research such as high sample capacity and low sample consumption. Protein microarrays emerged as a promising alternative to analyze the abundance of proteins and their phosphorylation status on a high-throughput level.

Modeling ERBB receptor-regulated G1/S transition to find novel targets for de novo trastuzumab resistance.

Background: In breast cancer, overexpression of the transmembrane tyrosine kinase ERBB2 is an adverse prognostic marker, and occurs in almost 30% of the patients. For therapeutic intervention, ERBB2 is targeted by monoclonal antibody trastuzumab in adjuvant settings; however, de novo resistance to this antibody is still a serious issue, requiring the identification of additional targets to overcome resistance. In this study, we have combined computational simulations, experimental testing of simulation results, and finally reverse engineering of a protein interaction network to define potential therapeutic strategies for de novo trastuzumab resistant breast cancer.

Contact spotting of protein microarrays coupled with spike-in of normalizer protein permits time-resolved analysis of ERBB receptor signaling.

Protein microarrays allow highly accurate comparison and quantification of numerous biological samples in parallel while requiring only little material. This qualifies protein arrays for systems biology and clinical research where only limited sample material is available, but a precise readout is required. With the introduction of signal normalization steps to monitor the drop size of manually contact-spotted RP protein arrays, the usefulness of normalizer proteins to ensure a high-throughput but inexpensive protein analysis was demonstrated.

Extending pathways based on gene lists using InterPro domain signatures.

Background: High-throughput technologies like functional screens and gene expression analysis produce extended lists of candidate genes. Gene-Set Enrichment Analysis is a commonly used and well established technique to test for the statistically significant over-representation of particular pathways. A shortcoming of this method is however, that most genes that are investigated in the experiments have very sparse functional or pathway annotation and therefore cannot be the target of such an analysis.

High-throughput flow cytometry-based assay to identify apoptosis-inducing proteins.

After sequencing the human genome, the challenge ahead is to systematically analyze the functions and disease relation of the proteins encoded. Here the authors describe the application of a flow cytometry-based high-throughput assay to screen for apoptosis-activating proteins in transiently transfected cells. The assay is based on the detection of activated caspase-3 with a specific antibody, in cells overexpressing proteins tagged C- or N-terminally with yellow fluorescent protein.

Combinatorial RNAi for quantitative protein network analysis.

The elucidation of cross-talk events between intersecting signaling pathways is one main challenge in biological research. The complexity of protein networks, composed of different pathways, requires novel strategies and techniques to reveal relevant interrelations. Here, we established a combinatorial RNAi strategy for systematic single, double, and triple knockdown, and we measured the residual mRNAs and proteins quantitatively by quantitative real-time PCR and reverse-phase protein arrays, respectively, as a prerequisite for data analysis.

Statistical methods and software for the analysis of highthroughput reverse genetic assays using flow cytometry readouts.

Highthroughput cell-based assays with flow cytometric readout provide a powerful technique for identifying components of biologic pathways and their interactors. Interpretation of these large datasets requires effective computational methods. We present a new approach that includes data pre-processing, visualization, quality assessment, and statistical inference.

The LIFEdb database in 2006.

LIFEdb (http://www.LIFEdb.de) integrates data from large-scale functional genomics assays and manual cDNA annotation with bioinformatics gene expression and protein analysis.

Systematic comparison of surface coatings for protein microarrays.

To process large numbers of samples in parallel is one potential of protein microarrays for research and diagnostics. However, the application of protein arrays is currently hampered by the lack of comprehensive technological knowledge about the suitability of 2-D and 3-D slide surface coatings. We have performed a systematic study to analyze how both surface types perform in combination with different fluorescent dyes to generate significant and reproducible data.

Functional profiling: from microarrays via cell-based assays to novel tumor relevant modulators of the cell cycle.

Cancer transcription microarray studies commonly deliver long lists of "candidate" genes that are putatively associated with the respective disease. For many of these genes, no functional information, even less their relevance in pathologic conditions, is established as they were identified in large-scale genomics approaches. Strategies and tools are thus needed to distinguish genes and proteins with mere tumor association from those causally related to cancer.

From ORFeome to biology: a functional genomics pipeline.

As several model genomes have been sequenced, the elucidation of protein function is the next challenge toward the understanding of biological processes in health and disease. We have generated a human ORFeome resource and established a functional genomics and proteomics analysis pipeline to address the major topics in the post-genome-sequencing era: the identification of human genes and splice forms, and the determination of protein localization, activity, and interaction. Combined with the understanding of when and where gene products are expressed in normal and diseased conditions, we create information that is essential for understanding the interplay of genes and proteins in the complex biological network.

Expression of the cholecystokinin 2-receptor in normal human thyroid gland and medullary thyroid carcinoma.

Objective: The cholecystokinin(2)-receptor (CCK(2)R) promotes secretion and cell growth induced by its ligands cholecystokinin (CCK) and gastrin. The receptor has recently been shown to be expressed in human medullary thyroid carcinomas (MTCs). The objective of this study was to analyze CCK(2)R expression in MTC samples of different tumor stages as well as in non-malignant thyroid tissues.