Integrated pipeline for mass spectrometry-based discovery and confirmation of biomarkers demonstrated in a mouse model of breast cancer

Despite their potential to impact diagnosis and treatment of cancer, few protein biomarkers are in clinical use. Biomarker discovery is plagued with difficulties ranging from technological (inability to globally interrogate proteomes) to biological (genetic and environmental differences among patients and their tumors). We urgently need paradigms for biomarker discovery. To minimize biological variation and facilitate testing of proteomic approaches, we employed a mouse model of breast cancer. Specifically, we performed LC-MS/MS of tumor and normal mammary tissue from a conditional HER2/Neu-driven mouse model of breast cancer, identifying 6758 peptides representing >700 proteins. We developed a novel statistical approach (SASPECT) for prioritizing proteins differentially represented in LC-MS/MS datasets and identified proteins over- or under-represented in tumors. Using a combination of antibody-based approaches and multiple reaction monitoring-mass spectrometry (MRM-MS), we confirmed the overproduction of multiple proteins at the tissue level, identified fibulin-2 as a plasma biomarker, and extensively characterized osteopontin as a plasma biomarker capable of early disease detection in the mouse. Our results show that a staged pipeline employing shotgun-based comparative proteomics for biomarker discovery and multiple reaction monitoring for confirmation of biomarker candidates is capable of finding novel tissue and plasma biomarkers in a mouse model of breast cancer. Furthermore, the approach can be extended to find biomarkers relevant to human disease.     

Discovery of novel human breast cancer microRNAs from deep sequencing data by analysis of pri-microRNA secondary structures

MicroRNAs (miRNAs) are key regulators of gene expression and contribute to a variety of biological processes. Abnormal miRNA expression has been reported in various diseases including pathophysiology of breast cancer, where they regulate protumorigenic processes including vascular invasiveness, estrogen receptor status, chemotherapy resistance, invasion and metastasis. The miRBase sequence database, a public repository for newly discovered miRNAs, has grown rapidly with approximately >10,000 entries to date. Despite this rapid growth, many miRNAs have not yet been validated, and several others are yet to be identified. A lack of a full complement of miRNAs has imposed limitations on recognizing their important roles in cancer, including breast cancer. Using deep sequencing technology, we have identified 189 candidate novel microRNAs in human breast cancer cell lines with diverse tumorigenic potential. We further show that analysis of 500-nucleotide pri-microRNA secondary structure constitutes a reliable method to predict bona fide miRNAs as judged by experimental validation. Candidate novel breast cancer miRNAs with stem lengths of greater than 30 bp resulted in the generation of precursor and mature sequences in vivo. On the other hand, candidates with stem length less than 30 bp were less efficient in producing mature miRNA. This approach may be used to predict which candidate novel miRNA would qualify as bona fide miRNAs from deep sequencing data with approximately 90% accuracy.     

Identification of a potential tumor differentiation factor receptor candidate in prostate cancer cells

Tumor differentiation factor (TDF) is a pituitary protein that is secreted into the bloodstream and has an endocrine function. TDF and TDF-P1, a 20-residue peptide selected from the ORF of TDF, induce differentiation in human breast and prostate cancer cells, but not in other cells. TDF has no known mechanism of action. In our recent study, we identified heat shock 70 kDa proteins (HSP70s) as TDF receptors (TDF-Rs) in breast cancer cells. Therefore, we sought to investigate whether TDF-R candidates from prostate cancer cells are the same as those identified in breast cancer cells. Here, we used TDF-P1 to purify the potential TDF-R candidates by affinity purification chromatography from DU145 and PC3 steroid-resistant prostate cancer cells, LNCaP steroid-responsive prostate cancer cells, and nonprostate NG108 neuroblastoma and BLK CL.4 fibroblast-like cells. We identified the purified proteins by MS, and validated them by western blotting, immunofluorescence microscopy, immunoaffinity purification chromatography, and structural biology. We identified seven candidate proteins, of which three were from the HSP70 family. These three proteins were validated as potential TDF-R candidates in LNCaP steroid-responsive and in DU145 and PC3 steroid-resistant prostate cancer cells, but not in NG108 neuroblastoma and BLK CL.4 fibroblast-like cells. Our previous study and the current study suggest that GRP78, and perhaps HSP70s, are strong TDF-R candidates, and further suggest that TDF interacts with its receptors exclusively in breast and prostate cells, inducing cell differentiation through a novel, steroid-independent pathway.     

Exploring metabolic pathway disruption in the subchronic phencyclidine model of schizophrenia with the Generalized Singular Value Decomposition

Background

One of the major goals in gene and protein expression profiling of cancer is to identify biomarkers and build classification models for prediction of disease prognosis or treatment response. Many traditional statistical methods, based on microarray gene expression data alone and individual genes' discriminatory power, often fail to identify biologically meaningful biomarkers thus resulting in poor prediction performance across data sets. Nonetheless, the variables in multivariable classifiers should synergistically interact to produce more effective classifiers than individual biomarkers.

Results

We developed an integrated approach, namely network-constrained support vector machine (netSVM), for cancer biomarker identification with an improved prediction performance. The netSVM approach is specifically designed for network biomarker identification by integrating gene expression data and protein-protein interaction data. We first evaluated the effectiveness of netSVM using simulation studies, demonstrating its improved performance over state-of-the-art network-based methods and gene-based methods for network biomarker identification. We then applied the netSVM approach to two breast cancer data sets to identify prognostic signatures for prediction of breast cancer metastasis. The experimental results show that: (1) network biomarkers identified by netSVM are highly enriched in biological pathways associated with cancer progression; (2) prediction performance is much improved when tested across different data sets. Specifically, many genes related to apoptosis, cell cycle, and cell proliferation, which are hallmark signatures of breast cancer metastasis, were identified by the netSVM approach. More importantly, several novel hub genes, biologically important with many interactions in PPI network but often showing little change in expression as compared with their downstream genes, were also identified as network biomarkers; the genes were enriched in signaling pathways such as TGF-beta signaling pathway, MAPK signaling pathway, and JAK-STAT signaling pathway. These signaling pathways may provide new insight to the underlying mechanism of breast cancer metastasis.

Conclusions

We have developed a network-based approach for cancer biomarker identification, netSVM, resulting in an improved prediction performance with network biomarkers. We have applied the netSVM approach to breast cancer gene expression data to predict metastasis in patients. Network biomarkers identified by netSVM reveal potential signaling pathways associated with breast cancer metastasis, and help improve the prediction performance across independent data sets.     

Pathway Correlation Profile of Gene-Gene Co-Expression for Identifying Pathway Perturbation

Identifying perturbed or dysregulated pathways is critical to understanding the biological processes that change within an experiment. Previous methods identified important pathways that are significantly enriched among differentially expressed genes; however, these methods cannot account for small, coordinated changes in gene expression that amass across a whole pathway. In order to overcome this limitation, we use microarray gene expression data to identify pathway perturbation based on pathway correlation profiles. By identifying the distribution of gene-gene pair correlations within a pathway, we can rank the pathways based on the level of perturbation and dysregulation. We have shown this successfully for differences between two experimental conditions in Escherichia coli and changes within time series data in Saccharomyces cerevisiae, as well as two estrogen receptor response classes of breast cancer. Overall, our method made significant predictions as to the pathway perturbations that are involved in the experimental conditions.     

Use of Genome-Wide Association Studies for Cancer Research and Drug Repositioning

Although genome-wide association studies have identified many risk loci associated with colorectal cancer, the molecular basis of these associations are still unclear. We aimed to infer biological insights and highlight candidate genes of interest within GWAS risk loci. We used an in silico pipeline based on functional annotation, quantitative trait loci mapping of cis-acting gene, PubMed text-mining, protein-protein interaction studies, genetic overlaps with cancer somatic mutations and knockout mouse phenotypes, and functional enrichment analysis to prioritize the candidate genes at the colorectal cancer risk loci. Based on these analyses, we observed that these genes were the targets of approved therapies for colorectal cancer, and suggested that drugs approved for other indications may be repurposed for the treatment of colorectal cancer. This study highlights the use of publicly available data as a cost effective solution to derive biological insights, and provides an empirical evidence that the molecular basis of colorectal cancer can provide important leads for the discovery of new drugs.     

Breast Tumors with Elevated Expression of 1q Candidate Genes Confer Poor Clinical Outcome and Sensitivity to Ras/PI3K Inhibition

Genomic aberrations are common in cancers and the long arm of chromosome 1 is known for its frequent amplifications in breast cancer. However, the key candidate genes of 1q, and their contribution in breast cancer pathogenesis remain unexplored. We have analyzed the gene expression profiles of 1635 breast tumor samples using meta-analysis based approach and identified clinically significant candidates from chromosome 1q. Seven candidate genes including exonuclease 1 (EXO1) are consistently over expressed in breast tumors, specifically in high grade and aggressive breast tumors with poor clinical outcome. We derived a EXO1 co-expression module from the mRNA profiles of breast tumors which comprises 1q candidate genes and their co-expressed genes. By integrative functional genomics investigation, we identified the involvement of EGFR, RAS, PI3K / AKT, MYC, E2F signaling in the regulation of these selected 1q genes in breast tumors and breast cancer cell lines. Expression of EXO1 module was found as indicative of elevated cell proliferation, genomic instability, activated RAS/AKT/MYC/E2F1 signaling pathways and loss of p53 activity in breast tumors. mRNA–drug connectivity analysis indicates inhibition of RAS/PI3K as a possible targeted therapeutic approach for the patients with activated EXO1 module in breast tumors. Thus, we identified seven 1q candidate genes strongly associated with the poor survival of breast cancer patients and identified the possibility of targeting them with EGFR/RAS/PI3K inhibitors.     

A regulatory network comprising let-7 miRNA and SMUG1 is associated with good prognosis in ER+ breast tumours

Single-strand selective uracil–DNA glycosylase 1 (SMUG1) initiates base excision repair (BER) of uracil and oxidized pyrimidines. SMUG1 status has been associated with cancer risk and therapeutic response in breast carcinomas and other cancer types. However, SMUG1 is a multifunctional protein involved, not only, in BER but also in RNA quality control, and its function in cancer cells is unclear. Here we identify several novel SMUG1 interaction partners that functions in many biological processes relevant for cancer development and treatment response. Based on this, we hypothesized that the dominating function of SMUG1 in cancer might be ascribed to functions other than BER. We define a bad prognosis signature for SMUG1 by mapping out the SMUG1 interaction network and found that high expression of genes in the bad prognosis network correlated with lower survival probability in ER⁺ breast cancer. Interestingly, we identified hsa-let-7b-5p microRNA as an upstream regulator of the SMUG1 interactome. Expression of SMUG1 and hsa-let-7b-5p were negatively correlated in breast cancer and we found an inhibitory auto-regulatory loop between SMUG1 and hsa-let-7b-5p in the MCF7 breast cancer cells. We conclude that SMUG1 functions in a gene regulatory network that influence the survival and treatment response in several cancers.     

Identification of potential binding sites for the FHA domain of human Chk2 by in vitro binding studies

Human Chk2 is a newly identified tumor suppressor protein involved in signaling pathways in response to DNA damage. The protein consists of a forkhead-associated (FHA) domain and a kinase domain. Identification of binding partners of the Chk2FHA domain is important in understanding the roles of Chk2 in signaling. We report development of an approach involving the use of combinatorial libraries, pull-down assays, surface plasmon resonance (SPR), and nuclear magnetic resonance (NMR) methods to identify possible candidates for the binding sites of Chk2FHA. The approach has been used to identify Thr329 of p53 and Thr1852 of breast cancer type 1 susceptibility protein (BRCA1) as very likely biological binding sites of Chk2FHA. The results provide useful leads for further biological analyses of cell signaling involving the FHA domain of Chk2 protein.     

Identification of Relevant Conformational Epitopes on the HER2 Oncoprotein by Using Large Fragment Phage Display (LFPD)

We developed a new phage-display based approach, the Large Fragment Phage Display (LFPD), that can be used for mapping conformational epitopes on target molecules of immunological interest. LFPD uses a simplified and more effective phage-display approach in which only a limited set of larger fragments (about 100 aa in length) are expressed on the phage surface. Using the human HER2 oncoprotein as a target, we identified novel B-cell conformational epitopes. The same homologous epitopes were also detected in rat HER2 and all corresponded to the epitopes predicted by computational analysis (PEPITO software), showing that LFPD gives reproducible and accurate results. Interestingly, these newly identified HER2 epitopes seem to be crucial for an effective immune response against HER2-overexpressing breast cancers and might help discriminating between metastatic breast cancer and early breast cancer patients. Overall, the results obtained in this study demonstrated the utility of LFPD and its potential application to the detection of conformational epitopes on many other molecules of interest, as well as, the development of new and potentially more effective B-cell conformational epitopes based vaccines.     

Downregulation of long noncoding RNA DGCR5 contributes to the proliferation,migration, and invasion of cervical cancer by activating Wnt signaling pathway

Accumulating research works have reported that long noncoding RNAs (lncRNAs) are involved in various cancers, including cervical cancer. LncRNA DGCR5 has been identified in many cancers. However, the biological role of DGCR5 in cervical cancer remains barely known. We aimed to investigate the biological function of DGCR5 in cervical cancer progression. Here, in our current study, we observed that DGCR5 was downregulated in human cervical cancer cell lines (MS751, SiHa, HeLa, and HT-3) compared with the primary normal cervical squamous cells (NCSC1 and NCSC2). Then, DGCR5 was restrained by transfection with lenti-virus-short hairpin RNA (LV-shRNA) while induced by LV-DGCR5 in HeLa and C33A cells. Silence of DGCR5 obviously induced cervical cancer cell viability and cell proliferation. Reversely, upregulation of DGCR5 inhibited HeLa and C33A cell survival and proliferation. Furthermore, silencing of DGCR5 increased cervical cancer cell colony formation ability and decreased cell apoptosis, whereas its overexpression exhibited an opposite process. Moreover, DGCR5 suppressed migration and invasion capacity of cervical cancer cells. The Wnt signaling is integral in numerous biological processes. Here, we found that Wnt signaling was strongly activated in cervical cancer cells. Downregulation of DGCR5 contributed to cervical cancer progression by activating Wnt signaling. Subsequently, in vivo animal models were used to confirm that DGCR5 suppressed cervical cancer via targeting Wnt signaling. In conclusion, we reported that DGCR5 was involved in cervical cancer progression via modulating the Wnt pathway.     

Identification of potential tumor differentiation factor (TDF) receptor from steroid-responsive and steroid-resistant breast cancer cells

Tumor differentiation factor (TDF) is a recently discovered protein, produced by the pituitary gland and secreted into the bloodstream. TDF and TDF-P1, a 20-amino acid peptide selected from the open reading frame of TDF, induce differentiation in human breast and prostate cancer cells but not in other cells. TDF protein has no identified site of action or receptor, and its mechanism of action is unknown. Here, we used TDF-P1 to purify and identify potential TDF receptor (TDF-R) candidates from MCF7 steroid-responsive breast cancer cells and non-breast HeLa cancerous cells using affinity purification chromatography (AP), and mass spectrometry (MS). We identified four candidate proteins from the 70-kDa heat shock protein (HSP70) family in MCF7 cells. Experiments in non-breast HeLa cancerous cells did not identify any TDF-R candidates. AP and MS experiments were validated by AP and Western blotting (WB). We additionally looked for TDF-R in steroid-resistant BT-549 cells and human dermal fibroblasts (HDF-a) using AP and WB. TDF-P1 interacts with potential TDF-R candidates from MCF7 and BT-549 breast cells but not from HeLa or HDF-a cells. Immunofluorescence (IF) experiments identified GRP78, a TDF-R candidate, at the cell surface of MCF7, BT-549 breast cells, and HeLa cells but not HDF-a cells. IF of other HSP70 proteins demonstrated labeling on all four cell types. These results point toward GRP78 and HSP70 proteins as strong TDF-R candidates and suggest that TDF interacts with its receptor, exclusively on breast cells, through a steroid-independent pathway.     

Quantitative proteomics combined with BAC TransgeneOmics reveals in vivo protein interactions

Protein interactions are involved in all cellular processes. Their efficient and reliable characterization is therefore essential for understanding biological mechanisms. In this study, we show that combining bacterial artificial chromosome (BAC) TransgeneOmics with quantitative interaction proteomics, which we call quantitative BAC–green fluorescent protein interactomics (QUBIC), allows specific and highly sensitive detection of interactions using rapid, generic, and quantitative procedures with minimal material. We applied this approach to identify known and novel components of well-studied complexes such as the anaphase-promoting complex. Furthermore, we demonstrate second generation interaction proteomics by incorporating directed mutational transgene modification and drug perturbation into QUBIC. These methods identified domain/isoform-specific interactors of pericentrin- and phosphorylation-specific interactors of TACC3, which are necessary for its recruitment to mitotic spindles. The scalability, simplicity, cost effectiveness, and sensitivity of this method provide a basis for its general use in small-scale experiments and in mapping the human protein interactome.