Arrayed adenoviral expression libraries for functional screening

With the publication of the sequence of the human genome, we are challenged to identify the functions of an estimated 70,000 human genes and the much larger number of proteins encoded by these genes. Of particular interest is the identification of gene products that play a role in human disease pathways, as these proteins include potential new targets that may lead to improved therapeutic strategies. This requires the direct measurement of gene function on a genomic scale in cell-based, functional assays. We have constructed and validated an individually arrayed, replication-defective adenoviral library harboring human cDNAs, termed PhenoSelect library. The adenoviral vector guarantees efficient transduction of diverse cell types, including primary cells. The arrayed format allows screening of this library in a variety of cellular assays in search for gene(s) that, by overexpression, induce a particular disease-related phenotype. The great majority of phenotypic assays, including morphological assays, can be screened with arrayed libraries. In contrast, pooled-library approaches often rely on phenotype-based isolation or selection of single cells by employing a flow cytometer or screening for cell survival. An arrayed placental PhenoSelect library was screened in cellular assays aimed at identifying regulators of osteogenesis, metastasis, and angiogenesis. This resulted in the identification of known regulators, as well as novel sequences that encode proteins hitherto not known to play a role in these pathways. These results establish the value of the PhenoSelect platform, in combination with cellular screens, for gene function discovery.     

Plasma proteome profiling of a mouse model of breast cancer identifies a set of up-regulated proteins in common with human breast cancer cells

We have applied an in-depth quantitative proteomic approach, combining isotopic labeling extensive intact protein separation and mass spectrometry, for high confidence identification of protein changes in plasmas from a mouse model of breast cancer. We hypothesized that a wide spectrum of proteins may be up-regulated in plasma with tumor development and that comparisons with proteins expressed in human breast cancer cell lines may identify a subset of up-regulated proteins in common with proteins expressed in breast cancer cell lines that may represent candidate biomarkers for breast cancer. Plasma from PyMT transgenic tumor-bearing mice and matched controls were obtained at two time points during tumor growth. A total of 133 proteins were found to be increased by 1.5-fold or greater at one or both time points. A comparison of this set of proteins with published findings from proteomic analysis of human breast cancer cell lines yielded 49 proteins with increased levels in mouse plasma that were identified in breast cancer cell lines. Pathway analysis comparing the subset of up-regulated proteins known to be expressed in breast cancer cell lines with other up-regulated proteins indicated a cancer related function for the former and a host-response function for the latter. We conclude that integration of proteomic findings from mouse models of breast cancer and from human breast cancer cell lines may help identify a subset of proteins released by breast cancer cells into the circulation and that occur at increased levels in breast cancer.     

Network-Based Integration of Disparate Omic Data To Identify "Silent Players" in Cancer

Development of high-throughput monitoring technologies enables interrogation of cancer samples at various levels of cellular activity. Capitalizing on these developments, various public efforts such as The Cancer Genome Atlas (TCGA) generate disparate omic data for large patient cohorts. As demonstrated by recent studies, these heterogeneous data sources provide the opportunity to gain insights into the molecular changes that drive cancer pathogenesis and progression. However, these insights are limited by the vast search space and as a result low statistical power to make new discoveries. In this paper, we propose methods for integrating disparate omic data using molecular interaction networks, with a view to gaining mechanistic insights into the relationship between molecular changes at different levels of cellular activity. Namely, we hypothesize that genes that play a role in cancer development and progression may be implicated by neither frequent mutation nor differential expression, and that network-based integration of mutation and differential expression data can reveal these “silent players”. For this purpose, we utilize network-propagation algorithms to simulate the information flow in the cell at a sample-specific resolution. We then use the propagated mutation and expression signals to identify genes that are not necessarily mutated or differentially expressed genes, but have an essential role in tumor development and patient outcome. We test the proposed method on breast cancer and glioblastoma multiforme data obtained from TCGA. Our results show that the proposed method can identify important proteins that are not readily revealed by molecular data, providing insights beyond what can be gleaned by analyzing different types of molecular data in isolation.     

RNA recognition by a human antibody against brain cytoplasmic 200 RNA

Diverse functional RNAs participate in a wide range of cellular processes. The RNA structure is critical for function, either on its own or as a complex form with proteins and other ligands. Therefore, analysis of the RNA conformation in cells is essential for understanding their functional mechanisms. However, no appropriate methods have been established as yet. Here, we developed an efficient strategy for panning and affinity maturation of anti-RNA human monoclonal antibodies from a naïve antigen binding fragment (Fab) combinatorial phage library. Brain cytoplasmic 200 (BC200) RNA, which is also highly expressed in some tumors, was used as an RNA antigen. We identified MabBC200-A3 as the optimal binding antibody. Mutagenesis and SELEX experiments showed that the antibody recognized a domain of BC200 in a structure- and sequence-dependent manner. Various breast cancer cell lines were further examined for BC200 RNA expression using conventional hybridization and immunoanalysis with MabBC200-A3 to see whether the antibody specifically recognizes BC200 RNA among the total purified RNAs. The amounts of antibody-recognizable BC200 RNA were consistent with hybridization signals among the cell lines. Furthermore, the antibody was able to discriminate BC200 RNA from other RNAs, supporting the utility of this antibody as a specific RNA structure-recognizing probe. Intriguingly, however, when permeabilized cells were subjected to immunoanalysis instead of purified total RNA, the amount of antibody-recognizable RNA was not correlated with the cellular level of BC200 RNA, indicating that BC200 RNA exists as two distinct forms (antibody-recognizable and nonrecognizable) in breast cancer cells and that their distribution depends on the cell type. Our results clearly demonstrate that anti-RNA antibodies provide an effective novel tool for detecting and analyzing RNA conformation.     

Design and implementation of cell-based assays to model human disease.

Cell-based assays, if appropriately designed, can be used to rapidly identify molecular mechanisms of human disease and develop novel therapeutics. In the last 20 years, many genes that cause or contribute to diverse disorders, including cancer and neurodegenerative disease, have been identified. With such genes in hand, scientists have created numerous model systems to dissect the molecular mechanisms of basic cellular and developmental biology. Meanwhile, techniques for high-throughput screening that use large chemical libraries have been developed, as have cDNA and RNA interference libraries that cover the entire human genome. By combining cell-based assays with chemical and genetic screens, we now have vastly improved our ability to dissect molecular mechanisms of disease and to identify therapeutic targets and therapeutic lead compounds. However, cell-based screening systems have yet to yield many fundamental insights into disease pathogenesis, and the development of therapeutic leads is frustratingly slow. This may be due to a failure of such assays to accurately reflect key aspects of pathogenesis. This Review attempts to guide the design of productive cellular models of human disease that may be used in high-throughput chemical and genetic screens. We emphasize two points: (i) model systems should use quantifiable molecular indicators of a pathogenic process, and (ii) small chemical libraries that include molecules with known biological activity and/or acceptable safety profiles are very useful.     

Proteomic screen with the proto-oncogene beta-catenin identifies interaction with Golgi coatomer complex I

Beta-catenin is well-known as a key effector of Wnt signalling and aberrant expression is associated with several human cancers. Stabilisation of and atypical subcellular localisation of beta-catenin, regulated in part through specific protein-protein interactions has been linked to cancer development, however the mechanisms behind these pathologies is yet to be fully elucidated. Affinity purification and mass spectrometry were used to identify potential β-catenin interacting proteins in SW480 colon cancer cells. Recombinant β-catenin constructs were used to co-isolate interacting proteins from stable isotope labelled cells followed by detection using mass spectrometry. Several known and new putative interactors were observed. In particular, we identified interaction with a set of coatomer complex I subunits implicated in retrograde transport at the Golgi, and confirmed endogenous interaction of β-catenin with coatomer subunit COPB using immunoprecipitation assays and immunofluorescence microscopy. These observations suggest a hitherto unrecognised role for β-catenin in the secretory pathway and warrant further functional studies to unravel its activity at this cellular location.     

Plasma Membrane Proteomics of Human Breast Cancer Cell Lines Identifies Potential Targets for Breast Cancer Diagnosis and Treatment

The use of broad spectrum chemotherapeutic agents to treat breast cancer results in substantial and debilitating side effects, necessitating the development of targeted therapies to limit tumor proliferation and prevent metastasis. In recent years, the list of approved targeted therapies has expanded, and it includes both monoclonal antibodies and small molecule inhibitors that interfere with key proteins involved in the uncontrolled growth and migration of cancer cells. The targeting of plasma membrane proteins has been most successful to date, and this is reflected in the large representation of these proteins as targets of newer therapies. In view of these facts, experiments were designed to investigate the plasma membrane proteome of a variety of human breast cancer cell lines representing hormone-responsive, ErbB2 over-expressing and triple negative cell types, as well as a benign control. Plasma membranes were isolated by using an aqueous two-phase system, and the resulting proteins were subjected to mass spectrometry analysis. Overall, each of the cell lines expressed some unique proteins, and a number of proteins were expressed in multiple cell lines, but in patterns that did not always follow traditional clinical definitions of breast cancer type. From our data, it can be deduced that most cancer cells possess multiple strategies to promote uncontrolled growth, reflected in aberrant expression of tyrosine kinases, cellular adhesion molecules, and structural proteins. Our data set provides a very rich and complex picture of plasma membrane proteins present on breast cancer cells, and the sorting and categorizing of this data provides interesting insights into the biology, classification, and potential treatment of this prevalent and debilitating disease.     

Discovery of pathway biomarkers from coupled proteomics and systems biology methods

Background

Breast cancer is worldwide the second most common type of cancer after lung cancer. Plasma proteome profiling may have a higher chance to identify protein changes between plasma samples such as normal and breast cancer tissues. Breast cancer cell lines have long been used by researches as model system for identifying protein biomarkers. A comparison of the set of proteins which change in plasma with previously published findings from proteomic analysis of human breast cancer cell lines may identify with a higher confidence a subset of candidate protein biomarker.

Results

In this study, we analyzed a liquid chromatography (LC) coupled tandem mass spectrometry (MS/MS) proteomics dataset from plasma samples of 40 healthy women and 40 women diagnosed with breast cancer. Using a two-sample t-statistics and permutation procedure, we identified 254 statistically significant, differentially expressed proteins, among which 208 are over-expressed and 46 are under-expressed in breast cancer plasma. We validated this result against previously published proteomic results of human breast cancer cell lines and signaling pathways to derive 25 candidate protein biomarkers in a panel. Using the pathway analysis, we observed that the 25 “activated” plasma proteins were present in several cancer pathways, including ‘Complement and coagulation cascades’, ‘Regulation of actin cytoskeleton’, and ‘Focal adhesion’, and match well with previously reported studies. Additional gene ontology analysis of the 25 proteins also showed that cellular metabolic process and response to external stimulus (especially proteolysis and acute inflammatory response) were enriched functional annotations of the proteins identified in the breast cancer plasma samples. By cross-validation using two additional proteomics studies, we obtained 86% and 83% similarities in pathway-protein matrix between the first study and the two testing studies, which is much better than the similarity we measured with proteins.

Conclusions

We presented a ‘systems biology’ method to identify, characterize, analyze and validate panel biomarkers in breast cancer proteomics data, which includes 1) t statistics and permutation process, 2) network, pathway and function annotation analysis, and 3) cross-validation of multiple studies. Our results showed that the systems biology approach is essential to the understanding molecular mechanisms of panel protein biomarkers.

     

Differential expression of novel tyrosine kinase substrates during breast cancer development

To identify novel tyrosine kinase substrates that have never been implicated in cancer, we studied the phosphoproteomic changes in the MCF10AT model of breast cancer progression using a combination of phosphotyrosyl affinity enrichment, iTRAQ technology, and LC-MS/MS. Using complementary MALDI- and ESI-based mass spectrometry, 57 unique proteins comprising tyrosine kinases, phosphatases, and other signaling proteins were detected to undergo differential phosphorylation during disease progression. Seven of these proteins (SPAG9, Toll-interacting protein (TOLLIP), WBP2, NSFL1C, SLC4A7, CYFIP1, and RPS2) were validated to be novel tyrosine kinase substrates. SPAG9, TOLLIP, WBP2, and NSFL1C were further proven to be authentic targets of epidermal growth factor signaling and Iressa (gefitinib). A closer examination revealed that the expression of SLC4A7, a bicarbonate transporter, was down-regulated in 64% of the 25 matched normal and tumor clinical samples. The expression of TOLLIP in clinical breast cancers was heterogeneous with 25% showing higher expression in tumor compared with normal tissues and 35% showing the reverse trend. Preliminary studies on SPAG9, on the other hand, did not show differential expression between normal and diseased states. This is the first time SLC4A7 and TOLLIP have been discovered as novel tyrosine kinase substrates that are also associated with human cancer development. Future molecular and functional studies will provide novel insights into the roles of TOLLIP and SLC4A7 in the molecular etiology of breast cancer.     

Computational exploration of the activated pathways associated with DNA damage response in breast cancer

Molecular signaling events regulate cellular activity. Cancer stimulating signals trigger cellular responses that evade the regulatory control of cell development. To understand the mechanism of signaling regulation in cancer, it is necessary to identify the activated pathways in cancer. We have developed RepairPATH, a computational algorithm that explores the activated signaling pathways in cancer. The RepairPATH integrates RepairNET, an assembled protein interaction network associated with DNA damage response, with the gene expression profiles derived from the microarray data. Based on the observation that cofunctional proteins often exhibit correlated gene expression profiles, it identifies the activated signaling pathways in cancer by systematically searching the RepairNET for proteins with significantly correlated gene expression profiles. Analyzing the gene expression profiles of breast cancer, we found distinct similarities and differences in the activated signaling pathways between the samples from the patients who developed metastases and the samples from the patients who were disease free within 5 years. The cellular pathways associated with the various DNA repair mechanisms and the cell-cycle checkpoint controls are found to be activated in both sample groups. One of the most intriguing findings is that the pathways associated with different cellular processes are functionally coordinated through BRCA1 in the disease-free sample group, whereas such functional coordination is absent in the samples from patients who developed metastases. Our analysis revealed the potential cellular pathways that regulate the signaling events in breast cancer.     

Autocrine and paracrine growth regulation of human breast cancer

Previous work from our laboratory has demonstrated that human breast cancer (BC) cells in culture can be stimulated by physiologic concentrations of estrogen. In an effort to further understand this process, we have examined the biochemical and biological properties of proteins secreted by human BC cells in vitro. We have developed a defined medium system which simultaneously allows the collection of factors secreted by the BC cells, facilitates their purification and allows for an unequivocal assay of their effect on other BC cells. By both biochemical and radioimmunoassay procedures, MCF-7 cells secrete large quantities of IGF-I-like activity. The cells contain receptors for IGF-I and are stimulated by physiologic concentrations of IGF-I. Multiple additional peaks of growth stimulatory activity can be obtained by partial purification of conditioned media from human BC cells by sequential dialysis, acid extraction and Biogel P60 chromatography. These peaks are induced up to 200-fold by physiologic concentrations of estrogen. Several of these peaks cross-react in a radioreceptor assay with EGF and are thus candidates for transforming growth factors. Monoclonal antibodies (MCA) have been prepared which react with secreted proteins from the MCF-7 cells. One of these MCAs binds to material from MCF-7 and ZR-75-1 hormone-dependent BC cells only when these two lines are treated with estrogen but reacts with conditioned medium from several other hormone-independent cell lines in the absence of estrogen stimulation. This MCA is currently undergoing further characterization and evaluation of its biological potency. We conclude that with estrogen stimulation, hormone-dependent human BC cells secrete peptides which when partially purified can replace estrogen as a mitogen. Their role as autocrine or paracrine growth factors and their effects on surrounding nonneoplastic stroma may suggest a means of interfering with tumor proliferation.     

TIP,a T-cell factor identified using high-throughput screening increases survival in a graft-versus-host disease model

A coordinated effort combining bioinformatic tools with high-throughput cell-based screening assays was implemented to identify novel factors involved in T-cell biology. We generated a unique library of cDNAs encoding predicted secreted and transmembrane domain-containing proteins generated by analyzing the Human Genome Sciences cDNA database with a combination of two algorithms that predict signal peptides. Supernatants from mammalian cells transiently transfected with this library were incubated with primary T cells and T-cell lines in several high-throughput assays. Here we describe the discovery of a T cell factor, TIP (T cell immunomodulatory protein), which does not show any homology to proteins with known function. Treatment of primary human and murine T cells with TIP in vitro resulted in the secretion of IFN-gamma, TNF-alpha, and IL-10, whereas in vivo TIP had a protective effect in a mouse acute graft-versus-host disease (GVHD) model. Therefore, combining functional genomics with high-throughput cell-based screening is a valuable and efficient approach to identifying immunomodulatory activities for novel proteins.     

Functional profiling of nucleotide Excision repair in breast cancer

Homologous recombination deficiency conferred by alterations in BRCA1 or BRCA2 are common in breast tumors and can drive sensitivity to platinum chemotherapy and PARP inhibitors. Alterations in nucleotide excision repair (NER) activity can also impact sensitivity to DNA damaging agents, but NER activity in breast cancer has been poorly characterized. Here, we apply a novel immunofluorescence-based cellular NER assay to screen a large panel of breast epithelial and cancer cell lines. Although the majority of breast cancer models are NER proficient, we identify an example of a breast cancer cell line with profound NER deficiency. We show that NER deficiency in this model is driven by epigenetic silencing of the ERCC4 gene, leading to lack of expression of the NER nuclease XPF, and that ERCC4 methylation is also strongly correlated with ERCC4 mRNA and XPF protein expression in primary breast tumors. Re-expression of XPF in the ERCC4-deficient breast cancer rescues NER deficiency and cisplatin sensitivity, but does not impact PARP inhibitor sensitivity. These findings demonstrate the potential to use functional assays to identify novel mechanisms of DNA repair deficiency and nominate NER deficiency as a platinum sensitivity biomarker in breast cancer.     

Pinning down cell signaling, cancer and Alzheimer's disease

Protein phosphorylation on certain serine or threonine residues preceding proline (Ser/Thr-Pro) is a pivitol signaling mechanism in diverse cellular processes and its deregulation can lead to human disease. However, little is known about how these phosphorylation events actually control cell signaling. Pin1 is a highly conserved enzyme that isomerizes only the phosphorylated Ser/Thr-Pro bonds in certain proteins, thereby inducing conformational changes. Recent results indicate that such conformational changes following phosphorylation are a novel signaling mechanism pivotal in regulating many cellular functions. This mechanism also offers new insights into the pathogenesis and treatment of human disease, most notably cancer and Alzheimer's disease. Thus, Pin1 plays a key role in linking signal transduction to the pathogenesis of cancer and Alzheimer's disease - two major age-related diseases.     

Plasma Proteomic Profiling in Hereditary Breast Cancer Reveals a BRCA1-Specific Signature: Diagnostic and Functional Implications

Background

Breast cancer (BC) is a leading cause of death among women. Among the major risk factors, an important role is played by familial history of BC. Germ-line mutations in BRCA1/2 genes account for most of the hereditary breast and/or ovarian cancers. Gene expression profiling studies have disclosed specific molecular signatures for BRCA1/2-related breast tumors as compared to sporadic cases, which might help diagnosis and clinical follow-up. Even though, a clear hallmark of BRCA1/2-positive BC is still lacking. Many diseases are correlated with quantitative changes of proteins in body fluids. Plasma potentially carries important information whose knowledge could help to improve early disease detection, prognosis, and response to therapeutic treatments. The aim of this study was to develop a comprehensive approach finalized to improve the recovery of specific biomarkers from plasma samples of subjects affected by hereditary BC.

Methods

To perform this analysis, we used samples from patients belonging to highly homogeneous population previously reported. Depletion of high abundant plasma proteins, 2D gel analysis, liquid chromatography-tandem mass spectrometry (LC-MS/MS) and bioinformatics analysis were used into an integrated approach to investigate tumor-specific changes in the plasma proteome of BC patients and healthy family members sharing the same BRCA1 gene founder mutation (5083del19), previously reported by our group, with the aim to identify specific signatures.

Results

The comparative analysis of the experimental results led to the identification of gelsolin as the most promising biomarker.

Conclusions

Further analyses, performed using a panel of breast cancer cell lines, allowed us to further elucidate the signaling network that might modulate the expression of gelsolin in breast cancer.     

Circulating proteins reveal prior use of menopausal hormonal therapy and increased risk of breast cancer

Accessible risk predictors are crucial for improving the early detection and prognosis of breast cancer. Blood samples are widely available and contain proteins that provide important information about human health and disease, however, little is still known about the contribution of circulating proteins to breast cancer risk prediction. We profiled EDTA plasma samples collected before diagnosis from the Swedish KARMA breast cancer cohort to evaluate circulating proteins as molecular predictors. A data-driven analysis strategy was applied to the molecular phenotypes built on 700 circulating proteins to identify and annotate clusters of women. The unsupervised analysis of 183 future breast cancer cases and 366 age-matched controls revealed five stable clusters with distinct proteomic plasma profiles. Among these women, those in the most stable cluster (N = 19; mean Jaccard index: 0.70 ± 0.29) were significantly more likely to have used menopausal hormonal therapy (MHT), get a breast cancer diagnosis, and were older compared to the remaining clusters. The circulating proteins associated with this cluster (FDR < 0.001) represented physiological processes related to cell junctions (F11R, CLDN15, ITGAL), DNA repair (RBBP8), cell replication (TJP3), and included proteins found in female reproductive tissue (PTCH1, ZP4). Using a data-driven approach on plasma proteomics data revealed the potential long-lasting molecular effects of menopausal hormonal therapy (MHT) on the circulating proteome, even after women had ended their treatment. This provides valuable insights concerning proteomics efforts to identify molecular markers for breast cancer risk prediction.