A mouse model repository for cancer biomarker discovery

Early detection of cancer using biomarkers obtained from blood or other easily accessible tissues would have a significant impact on reducing cancer mortality. However, identifying new blood-based biomarkers has been hindered by the dynamic complexity of the human plasma proteome, confounded by genetic and environmental variability, and the scarcity of high quality controlled samples. In this report, we discuss a new paradigm for biomarker discovery through the use of mouse models. Inbred mouse models of cancer recapitulate many critical features of human cancer, while eliminating sources of environmental and genetic variability. The ability to collect samples from highly matched cases and controls under identical conditions further reduces variability which is critical for successful biomarker discovery. We describe the establishment of a repository containing tumor, plasma, urine, and other tissues from 10 different mouse models of human cancer, including two breast, two lung, two prostate, two gastrointestinal, one ovarian, and one skin tumor model. We present the overall design of this resource and its potential use by the research community for biomarker discovery.     

False discovery rates and related statistical concepts in mass spectrometry-based proteomics

Development of statistical methods for assessing the significance of peptide assignments to tandem mass spectra obtained using database searching remains an important problem. In the past several years, several different approaches have emerged, including the concept of expectation values, target-decoy strategy, and the probability mixture modeling approach of PeptideProphet. In this work, we provide a background on statistical significance analysis in the field of mass spectrometry-based proteomics, and present our perspective on the current and future developments in this area.     

Evaluation of fluorescence- and mass spectrometry-based CYP inhibition assays for use in drug discovery

The potential for metabolism-related drug-drug interactions by new chemical entities is assessed by monitoring the impact of these compounds on cytochrome P450 (CYP) activity using well-characterized CYP substrates. The conventional gold standard approach for in vitro evaluation of CYP inhibitory potential uses pooled human liver microsomes (HLM) in conjunction with prototypical drug substrates, often quantified by LC-MS/MS. However, fluorescent CYP inhibition assays, which use recombinantly expressed CYPs and fluorogenic probe substrates, have been employed in early drug discovery to provide low-cost, high-throughput assessment of new chemical entities. Despite its greatly enhanced throughput, this approach has been met with mixed success in predicting the data obtained with the conventional gold standard approach (HLM+LC-MS). The authors find that the predictivity of fluorogenic assays for the major CYP isoforms 3A4 and 2D6 may depend on the quality of the test compounds. Although the structurally more optimized marketed drugs yielded acceptable correlations between the fluorogenic and HLM+LC-MS/MS assays for CYPs 3A4, 2D6, and 2C9 (r2 = 0.5-0.7; p < 0.005), preoptimization, early discovery compounds yielded poorer correlations (r2 < or = 0.2) for 2 of these major isoforms, CYPs 3A4 and 2D6. Potential reasons for the observed differences are discussed.     

Secretome proteomics for discovery of cancer biomarkers

“Secretome” is referred to as the rich, complex set of molecules secreted from living cells. In a less strict definition frequently followed in “secretome” studies, the term also includes molecules shed from the surface of living cells. Proteins of secretome (will be referred to as secreted) play a key role in cell signaling, communication and migration. The need for developing more effective cancer biomarkers and therapeutic modalities has led to the study of cancer cell secretome as a means to identify and characterize diagnostic and prognostic markers and potential drug and therapeutic targets. Significant technological advances in the field of proteomics during the last two decades have greatly facilitated research towards this direction. Nevertheless, secretome analysis still faces some difficulties mainly related to sample collection and preparation. The goal of this article is to provide an overview of the main findings from the analysis of cancer cell secretome. Specifically, we focus on the presentation of main methodological approaches that have been developed for the study of secreted proteins and the results thereof from the analysis of secretome in different types of malignancies; special emphasis is given on correlation of findings with protein expression in body fluids.     

Coherent pipeline for biomarker discovery using mass spectrometry and bioinformatics

Background  

Robust biomarkers are needed to improve microbial identification and diagnostics. Proteomics methods based on mass spectrometry can be used for the discovery of novel biomarkers through their high sensitivity and specificity. However, there has been a lack of a coherent pipeline connecting biomarker discovery with established approaches for evaluation and validation. We propose such a pipeline that uses in silico methods for refined biomarker discovery and confirmation.     

Evaluating the potential of a novel oral lesion exudate collection method coupled with mass spectrometry-based proteomics for oral cancer biomarker discovery

Introduction  

Early diagnosis of Oral Squamous Cell Carcinoma (OSCC) increases the survival rate of oral cancer. For early diagnosis, molecular biomarkers contained in samples collected non-invasively and directly from at-risk oral premalignant lesions (OPMLs) would be ideal.     

Lectin chromatography/mass spectrometry discovery workflow identifies putative biomarkers of aggressive breast cancers

We used a lectin chromatography/MS-based approach to screen conditioned medium from a panel of luminal (less aggressive) and triple negative (more aggressive) breast cancer cell lines (n=5/subtype). The samples were fractionated using the lectins Aleuria aurantia (AAL) and Sambucus nigra agglutinin (SNA), which recognize fucose and sialic acid, respectively. The bound fractions were enzymatically N-deglycosylated and analyzed by LC-MS/MS. In total, we identified 533 glycoproteins, ～90% of which were components of the cell surface or extracellular matrix. We observed 1011 glycosites, 100 of which were solely detected in ≥3 triple negative lines. Statistical analyses suggested that a number of these glycosites were triple negative-specific and thus potential biomarkers for this tumor subtype. An analysis of RNaseq data revealed that approximately half of the mRNAs encoding the protein scaffolds that carried potential biomarker glycosites were up-regulated in triple negative vs luminal cell lines, and that a number of genes encoding fucosyl- or sialyltransferases were differentially expressed between the two subtypes, suggesting that alterations in glycosylation may also drive candidate identification. Notably, the glycoproteins from which these putative biomarker candidates were derived are involved in cancer-related processes. Thus, they may represent novel therapeutic targets for this aggressive tumor subtype.     

Integrated mass spectrometry-based analysis of plasma glycoproteins and their glycan modifications

We present a protocol for the identification of glycosylated proteins in plasma followed by elucidation of their individual glycan compositions. The study of glycoproteins by mass spectrometry is usually based on cleavage of glycans followed by separate analysis of glycans and deglycosylated proteins, which limits the ability to derive glycan compositions for individual glycoproteins. The methodology described here consists of 2D HPLC fractionation of intact proteins and liquid chromatography-multistage tandem mass spectrometry (LC-MS/MS(n)) analysis of digested protein fractions. Protein samples are separated by 1D anion-exchange chromatography (AEX) with an eight-step salt elution. Protein fractions from each of the eight AEX elution steps are transferred onto the 2D reversed-phase column to further separate proteins. A digital ion trap mass spectrometer with a wide mass range is then used for LC-MS/MS(n) analysis of intact glycopeptides from the 2D HPLC fractions. Both peptide and oligosaccharide compositions are revealed by analysis of the ion fragmentation patterns of glycopeptides with an intact glycopeptide analysis pipeline.     

Proteomics discovery of chemoresistant biomarkers for ovarian cancer therapy

Introduction: Chemoresistance is a major challenge to current ovarian cancer chemotherapy. It is important to identify biomarkers to distinguish chemosensitive and chemoresistant patients.

Areas covered: We review the medical literature, discuss MS-based technologies with respect to chemoresistant ovarian cancer and summarize the promising chemoresistant biomarkers identified. In addition, the challenges and future perspectives of biomarker discovery research are explored. With the employment of mass spectrometry-based (MS-based) proteomics, biomarker discovery of ovarian cancer has made great progress in the last decade. Many potential biomarkers were identified by MS-based proteomics technologies, some of which have been validated for further extensive studies in clinical settings.

Expert commentary: The discovery of chemoresistant biomarkers is a newly developing area and may provide a clue for predicting chemotherapeutic response and discover therapeutic targets for paving the way of personalized medicine. Multiple complementary MS-based proteomics approaches hold promise for finding novel therapeutic targets in ovarian cancer treatment.     

Low abundance protein enrichment for discovery of candidate plasma protein biomarkers for early detection of breast cancer

Molecular biomarkers of early stage breast cancer may improve the sensitivity and specificity of diagnosis. Plasma biomarkers have additional value in that they can be monitored with minimal invasiveness. Plasma biomarker discovery by genome-wide proteomic methods is impeded by the wide dynamic range of protein abundance and the heterogeneity of protein expression in healthy and disease populations which requires the analysis of a large number of samples. We addressed these issues through the development of a novel protocol that couples a combinatorial peptide ligand library protein enrichment strategy with isobaric label-based 2D LC-MS/MS for the identification of candidate biomarkers in high throughput. Plasma was collected from patients with stage I breast cancer or benign breast lesions. Low abundance proteins were enriched using a bead-based combinatorial library of hexapeptides. This resulted in the identification of 397 proteins, 22% of which are novel plasma proteins. Twenty-three differentially expressed plasma proteins were identified, demonstrating the effectiveness of the described protocol and defining a set of candidate biomarkers to be validated in independent samples. This work can be used as the basis for the design of properly powered investigations of plasma protein expression for biomarker discovery in larger cohorts of patients with complex disease.     

Mass spectrometry-based analysis of glycoproteins and its clinical applications in cancer biomarker discovery

Glycosylation is one of the most important posttranslational modifications of proteins and plays essential roles in various biological processes. Aberration in the glycan moieties of glycoproteins is associated with many diseases. It is especially critical to develop the rapid and sensitive methods for analysis of aberrant glycoproteins associated with diseases. Mass spectrometry (MS) has become a powerful tool for glycoprotein analysis. Especially, tandem mass spectrometry can provide highly informative fragments for structural identification of glycoproteins. This review provides an overview of the development of MS technologies and their applications in identification of abnormal glycoproteins and glycans in human serum to screen cancer biomarkers in recent years.     

Mass spectrometry-based study of the plasma proteome in a mouse intestinal tumor model

Early detection of cancer can greatly improve prognosis. Identification of proteins or peptides in the circulation, at different stages of cancer, would greatly enhance treatment decisions. Mass spectrometry (MS) is emerging as a powerful tool to identify proteins from complex mixtures such as plasma that may help identify novel sets of markers that may be associated with the presence of tumors. To examine this feature we have used a genetically modified mouse model, Apc(Min), which develops intestinal tumors with 100% penetrance. Utilizing liquid chromatography-tandem mass spectrometry (LC-MS/MS), we identified total plasma proteome (TPP) and plasma glycoproteome (PGP) profiles in tumor-bearing mice. Principal component analysis (PCA) and agglomerative hierarchial clustering analysis revealed that these protein profiles can be used to distinguish between tumor-bearing Apc(Min) and wild-type control mice. Leave-one-out cross-validation analysis established that global TPP and global PGP profiles can be used to correctly predict tumor-bearing animals in 17/19 (89%) and 19/19 (100%) of cases, respectively. Furthermore, leave-one-out cross-validation analysis confirmed that the significant differentially expressed proteins from both the TPP and the PGP were able to correctly predict tumor-bearing animals in 19/19 (100%) of cases. A subset of these proteins was independently validated by antibody microarrays using detection by two color rolling circle amplification (TC-RCA). Analysis of the significant differentially expressed proteins indicated that some might derive from the stroma or the host response. These studies suggest that mass spectrometry-based approaches to examine the plasma proteome may prove to be a valuable method for determining the presence of intestinal tumors.     

Proximal fluid proteomics for the discovery of digestive cancer biomarkers

Most digestive malignancies have asymptomatic course, often progressing to poor outcome stages. Surgical resection usually represents the only potentially curative option but a prior assumption of the malignant nature of the lesion is mandatory to avoid exposing patients to unnecessary risks. Unfortunately, currently available diagnostic tools lack accuracy in many cases, consequently more reliable markers are needed to improve detection of malignant lesions. In this challenging context, fluids surrounding digestive malignancies represent a valuable source for the search of new potential biomarkers and proteomic tools offer the opportunity to achieve this goal. The new field of proximal fluid proteomics is thus emerging in the arena of digestive cancer biomarker discovery.     

Protective immunosurveillance and therapeutic antitumor activity of gammadelta T cells demonstrated in a mouse model of prostate cancer

In contrast to Ag-specific alphabeta T cells, gammadelta T cells can kill malignantly transformed cells in a manner that does not require the recognition of tumor-specific Ags. Although such observations have contributed to the emerging view that gammadelta T cells provide protective innate immunosurveillance against certain malignancies, particularly those of epithelial origin, they also provide a rationale for developing novel clinical approaches to exploit the innate antitumor properties of gammadelta T cells for the treatment of cancer. Using TRAMP, a transgenic mouse model of prostate cancer, proof-of-concept studies were performed to first establish that gammadelta T cells can indeed provide protective immunosurveillance against spontaneously arising mouse prostate cancer. TRAMP mice, which predictably develop prostate adenocarcinoma, were backcrossed with gammadelta T cell-deficient mice (TCRdelta(-/-) mice) yielding TRAMP x TCRdelta(-/-) mice, a proportion of which developed more extensive disease compared with control TRAMP mice. By extension, these findings were then used as a rationale for developing an adoptive immunotherapy model for treating prostate cancer. Using TRAMP-C2 cells derived from TRAMP mice (C57BL/6 genetic background), disease was first established in otherwise healthy wild-type C57BL/6 mice. In models of localized and disseminated disease, tumor-bearing mice treated i.v. with supraphysiological numbers of syngeneic gammadelta T cells (C57BL/6-derived) developed measurably less disease compared with untreated mice. Disease-bearing mice treated i.v. with gammadelta T cells also displayed superior survival compared with untreated mice. These findings provide a biological rationale for clinical trials designed to adoptively transfer ex vivo expanded autologous gammadelta T cells for the treatment of prostate cancer.     

A liquid chromatography/mass spectrometry-based generic detection method for biochemical assay and hit discovery of histone methyltransferases

Epigenetic modifications of the genome, such as DNA methylation and posttranslational modifications of histone proteins, contribute to gene regulation. Growing evidence suggests that histone methyltransferases are associated with the development of various human diseases, including cancer, and are promising drug targets. High-quality generic assays will facilitate drug discovery efforts in this area. In this article, we present a liquid chromatography/mass spectrometry (LC/MS)-based S-adenosyl homocysteine (SAH) detection assay for histone methyltransferases (HMTs) and its applications in HMT drug discovery, including analyzing the activity of newly produced enzymes, developing and optimizing assays, performing focused compound library screens and orthogonal assays for hit confirmations, selectivity profiling against a panel of HMTs, and studying mode of action of select hits. This LC/MS-based generic assay has become a critical platform for our methyltransferase drug discovery efforts.     

Biomarker discovery using a comparative omics approach in a mouse model developing heterogeneous mammary cancer subtypes

Identification of biomarkers for early breast cancer detection in blood is a challenging task, since breast cancer is a heterogeneous disease with a wide range of tumor subtypes. This is envisioned to result in differences in serum protein levels. The p53(R270H/+) WAPCre mouse model is unique in that these mice spontaneously develop both ER- and ER+ tumors, in proportions comparable to humans. Therefore, these mice provide a well-suited model system to identify human relevant biomarkers for early breast cancer detection that are additionally specific for different tumor subtypes. Mammary gland tumors were obtained from p53(R270H/+) WAPCre mice and cellular origin, ER, and HER2 status were characterized. We compared gene expression profiles for tumors with different characteristics versus control tissue, and determined genes differentially expressed across tumor subtypes. By using literature data (Gene Ontology, UniProt, and Human Plasma Proteome), we further identified protein candidate biomarkers for blood-based detection of breast cancer. Functional overrepresentation analysis (using Gene Ontology, MSigDB, BioGPS, Cancer GeneSigDB, and proteomics literature data) showed enrichment for several processes relevant for human breast cancer. Finally, Human Protein Atlas data were used to obtain a prioritized list of 16 potential biomarkers that should facilitate further studies on blood-based breast cancer detection in humans.