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. Three HER2-positive breast cancer cell lines with different resistance phenotypes were analyzed. We chose BT474 as model of trastuzumab sensitivity, HCC1954 as model of intrinsic resistance, and BTR50, derived from BT474, as model of acquired resistance. Based on RNA-Seq data, we performed differential expression analyses on these cell lines with and without trastuzumab treatment. Differentially expressed genes between the resistant cell lines and BT474 are expected to contribute to resistance. Differentially expressed genes between untreated and trastuzumab treated BT474 are expected to contribute to drug efficacy. To exclude false positives from the candidate gene set, we removed genes that were also differentially expressed between untreated and trastuzumab treated BTR50. We further searched for SNPs in the untreated cell lines which could contribute to trastuzumab resistance. The analysis resulted in 54 differentially expressed candidate genes that might be connected to trastuzumab efficiency. 90% of 40 selected candidates were validated by RT-qPCR. ALPP, CALCOCO1, CAV1, CYP1A2 and IGFBP3 were significantly higher expressed in the trastuzumab treated than in the untreated BT474 cell line. GDF15, IL8, LCN2, PTGS2 and 20 other genes were significantly higher expressed in HCC1954 than in BT474, while NCAM2, COLEC12, AFF3, TFF3, NRCAM, GREB1 and TFF1 were significantly lower expressed. Additionally, we inferred SNPs in HCC1954 for CAV1, PTGS2, IL8 and IGFBP3. The latter also had a variation in BTR50. 20% of the validated subset have already been mentioned in literature. For half of them we called and analyzed SNPs. These results contribute to a better understanding of trastuzumab action and resistance mechanisms.

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). Selected candidates identified in the initial screen were further validated in an extended study cohort of 207 individuals including 127 sporadic breast cancer cases and 80 healthy controls via RT-qPCR. Four miRNAs (miR-148b, miR-376c, miR-409-3p and miR-801) were shown to be significantly upregulated in the plasma of breast cancer patients. ROC curve analysis showed that the combination of only three miRNAs (miR-148b, miR-409-3p and miR-801) had an equal discriminatory power between breast cancer cases and healthy controls as all four miRNAs together (AUC = 0.69). In conclusion, the identified miRNAs might be of potential use in the development of a multimarker blood-based test to complement and improve early detection of breast cancer. Such a multimarker blood test might for instance provide a prescreening tool, especially for younger women, to facilitate decisions about which individuals to recommend for further diagnostic tests.

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. DEPNs allow to reconstruct protein networks based on combinatorial intervention effects, which are monitored via changes of the protein expression or activation over one or a few time points. Our implementation of DEPNs allows for latent network nodes (i.e. proteins without measurements) and has a built in mechanism to impute missing data. The robustness of our approach was tested on simulated data. We applied DEPNs to reconstruct the ERBB signaling network in de novo trastuzumab resistant human breast cancer cells, where protein expression was monitored on Reverse Phase Protein Arrays (RPPAs) after knockdown of network proteins using RNAi. CONCLUSION: DEPNs offer a robust, efficient and simple approach to infer protein signaling networks from multiple interventions. The method as well as the data have been made part of the latest version of the R package "nem" available as a supplement to this paper and via the Bioconductor repository.

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. RESULTS: First, we employed Boolean logic to model regulatory interactions and simulated single and multiple protein loss-of-functions. Then, our simulation results were tested experimentally by producing single and double knockdowns of the network components and measuring their effects on G1/S transition during cell cycle progression. Combinatorial targeting of ERBB2 and EGFR did not affect the response to trastuzumab in de novo resistant cells, which might be due to decoupling of receptor activation and cell cycle progression. Furthermore, examination of c-MYC in resistant as well as in sensitive cell lines, using a specific chemical inhibitor of c-MYC (alone or in combination with trastuzumab), demonstrated that both trastuzumab sensitive and resistant cells responded to c-MYC perturbation. CONCLUSION: In this study, we connected ERBB signaling with G1/S transition of the cell cycle via two major cell signaling pathways and two key transcription factors, to model an interaction network that allows for the identification of novel targets in the treatment of trastuzumab resistant breast cancer. Applying this new strategy, we found that, in contrast to trastuzumab sensitive breast cancer cells, combinatorial targeting of ERBB receptors or of key signaling intermediates does not have potential for treatment of de novo trastuzumab resistant cells. Instead, c-MYC was identified as a novel potential target protein in breast cancer cells.

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. Here we summarize recent methodological advancements in the field of reverse-phase protein arrays and demonstrate their potential for clinical research as well as for in vitro applications.

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. This approach was applied for the analysis of signaling through ERBB receptor activated kinases in the breast cancer cell line MCF-7. Activation of ERK1/2 and AKT by ERBB1 (EGFR), ERRB2 (HER2/neu), and ERBB3-4 was monitored in a time-resolved manner. Analysis of pathway activation by stimulation with epidermal growth factor and heregulin, or inhibition by blocking with gefitinib or herceptin allowed a characterization of the distinct signaling properties of the different ERBB receptor subtypes.

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. The approach presented here aims to assign lists of genes with limited annotation to previously described functional gene collections or pathways. This works by comparing InterPro domain signatures of the candidate gene lists with domain signatures of gene sets derived from known classifications, e.g. KEGG pathways. RESULTS: In order to validate our approach, we designed a simulation study. Based on all pathways available in the KEGG database, we create test gene lists by randomly selecting pathway genes, removing these genes from the known pathways and adding variable amounts of noise in the form of genes not annotated to the pathway. We show that we can recover pathway memberships based on the simulated gene lists with high accuracy. We further demonstrate the applicability of our approach on a biological example. CONCLUSION: Results based on simulation and data analysis show that domain based pathway enrichment analysis is a very sensitive method to test for enrichment of pathways in sparsely annotated lists of genes. An R based software package domainsignatures, to routinely perform this analysis on the results of high-throughput screening, is available via Bioconductor.

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. Fluorescence intensities are measured using a flow cytometer integrated with a high-throughput autosampler. The applicability of this screen has been tested in a pilot screen with 200 proteins. The candidate proteins were all verified in an independent microscopy-based nuclear fragmentation assay, finally resulting in the identification of 6 apoptosis inducers.

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. Our results show that the parallel knockdown of at least three different genes is feasible while keeping both untargeted silencing and cytotoxicity low. The technique was validated by investigating the interplay of tyrosine kinase receptor ErbB2 and its downstream targets Akt-1 and MEK1 in cell invasion. This experimental approach combines multiple gene knockdown with a subsequent quantitative validation of reduced protein expression and is a major advancement toward the analysis of signaling pathways in systems biology.

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 software is freely available in the Bioconductor package prada. The method permits analysis of large screens to detect the effects of molecular interventions in cellular systems.

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. New features of LIFEdb include (i) an updated user interface with enhanced query capabilities, (ii) a configurable output table and the option to download search results in XML, (iii) the integration of data from cell-based screening assays addressing the influence of protein-overexpression on cell proliferation and (iv) the display of the relative expression ('Electronic Northern') of the genes under investigation using curated gene expression ontology information. LIFEdb enables researchers to systematically select and characterize genes and proteins of interest, and presents data and information via its user-friendly web-based interface.

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. In total, we analyzed more than 100 slides containing 1152 spots each. Slides were probed against different monoclonal antibodies (mAbs) and recombinant fusion proteins. We found two surface coatings to be most suitable for protein and antibody (Ab) immobilization. These were further subjected to quantitative analyses by evaluating intraslide and slide-to-slide reproducibilities, and the linear range of target detection. In summary, we demonstrate that only suitable combinations of surface and fluorescent dyes allow the generation of highly 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. Here, we describe a functional profiling approach, where we analyzed 103 previously uncharacterized genes in cancer relevant assays that probed their effects on DNA replication (cell proliferation). The genes had previously been identified as differentially expressed in genome-wide microarray studies of tumors. Using an automated high-throughput assay with single-cell resolution, we discovered seven activators and nine repressors of DNA replication. These were further characterized for effects on extracellular signal-regulated kinase 1/2 (ERK1/2) signaling (G1-S transition) and anchorage-independent growth (tumorigenicity). One activator and one inhibitor protein of ERK1/2 activation and three repressors of anchorage-independent growth were identified. Data from tumor and functional profiling make these proteins novel prime candidates for further in-depth study of their roles in cancer development and progression. We have established a novel functional profiling strategy that links genomics to cell biology and showed its potential for discerning cancer relevant modulators of the cell cycle in the candidate lists from microarray studies.

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. We have implemented bioinformatics tools and databases that are suitable to store, analyze, and integrate the different types of data from high-throughput experiments and to include further annotation that is based on external information. All information is presented in a Web database (http://www.dkfz.de/LIFEdb). It is exploited for the identification of disease-relevant genes and proteins for diagnosis and therapy.

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. DESIGN AND METHODS: Using RT-PCR we investigated 19 MTC samples and TT-cells (a human MTC cell line), as well as samples of normal thyroid. In addition, we performed immunohistochemistry using calcitonin- and CCK(2)R-specific antibodies on MTCs and samples of C-cell hyperplasia. RESULTS: We demonstrate for the first time that CCK(2)R is expressed not only in MTCs but in all samples of normal thyroid tissue. Using immunohistochemistry the receptor could be localized on calcitonin-secreting C-cells. The highest incidence of CCK(2)R expression in MTCs was observed in early-tumor stages, whereas CCK(2)R could not be detected in advanced or metastasized tumors. CONCLUSIONS: The expression of CCK(2)R in C-cells suggests a physiological function for gastrin and/or CCK in the regulation of calcitonin release, presumably related to bone and calcium metabolism. Moreover, these ligands might act as growth factors in MTCs. Efforts in the development of CCK(2)R scintigraphy for the detection of MTC lesions might have to consider a lower incidence of the receptor in advanced tumor stages.