William FitzHugh

Use of an Immunoaffinity-Mass Spectrometry-based Approach for the Quantification of Protein Biomarkers from Serum Samples of Lung Cancer Patients

It is a challenging task to verify and quantify potential biomarkers expressed at elevated levels in sera from cancer patients. An immunoaffinity-mass spectrometry-based approach has been developed using antibodies to enrich proteins of interest from sera followed by mass spectrometry-based quantification. Antibodies specific to the protein of interest were immobilized to hydrazide resin via the carbohydrate moiety on the Fc region of the antibody. Captured proteins were eluted, reduced, alkylated, and digested with trypsin. Peptides were analyzed by LC coupled with multiple reaction monitoring approach, and quantification was achieved by the addition of stable isotope-labeled (heavy) standard peptides. Using this methodology, we were able to achieve a linear response from 15 to 250 ng/ml for carcinoembryonic antigen (CEA), a known tumor biomarker. Moreover we observed elevated levels of CEA in sera samples from lung cancer patients that to our knowledge is the first time that circulating CEA has been detected by mass spectrometry-based analysis. This approach was further applied to potential protein biomarkers discovered from tumor cell lines and tumor tissues. A linear response was obtained from a multiplex spiking experiment in normal human sera for secretory leukocyte peptidase inhibitor (4–500 ng/ml), tissue factor pathway inhibitor (TFPI) (42–1000 ng/ml), tissue factor pathway inhibitor 2 (TFPI2) (2–250 ng/ml), and metalloproteinase inhibitor 1 (TIMP1) (430–1000 ng/ml). A replicate experiment for a single concentration value yielded a relative coefficient of variation better than 11% for TFPI, secretory leukocyte peptidase inhibitor, and TFPI2. The expression level of the proteins in lung cancer patient sera was assayed by an immunoaffinity-multiple reaction monitoring method, and the results were comparable with those obtained from ELISA. This immunoaffinity-mass spectrometry-based quantification approach thus provides a specific and accurate assay for verifying the expression of potential biomarkers in patient serum samples especially for those proteins for which the necessary reagents for ELISA development are unavailable.

Identification and Characterization of Angiogenesis Targets through Proteomic Profiling of Endothelial Cells in Human Cancer Tissues

Genomic and proteomic analysis of normal and cancer tissues has yielded abundant molecular information for potential biomarker and therapeutic targets. Considering potential advantages in accessibility to pharmacological intervention, identification of targets resident on the vascular endothelium within tumors is particularly attractive. By employing mass spectrometry (MS) as a tool to identify proteins that are over-expressed in tumor-associated endothelium relative to normal cells, we aimed to discover targets that could be utilized in tumor angiogenesis cancer therapy. We developed proteomic methods that allowed us to focus our studies on the discovery of cell surface/secreted proteins, as they represent key antibody therapeutic and biomarker opportunities. First, we isolated endothelial cells (ECs) from human normal and kidney cancer tissues by FACS using CD146 as a marker. Additionally, dispersed human colon and lung cancer tissues and their corresponding normal tissues were cultured ex-vivo and their endothelial content were preferentially expanded, isolated and passaged. Cell surface proteins were then preferentially captured, digested with trypsin and subjected to MS-based proteomic analysis. Peptides were first quantified, and then the sequences of differentially expressed peptides were resolved by MS analysis. A total of 127 unique non-overlapped (157 total) tumor endothelial cell over-expressed proteins identified from directly isolated kidney-associated ECs and those identified from ex-vivo cultured lung and colon tissues including known EC markers such as CD146, CD31, and VWF. The expression analyses of a panel of the identified targets were confirmed by immunohistochemistry (IHC) including CD146, B7H3, Thy-1 and ATP1B3. To determine if the proteins identified mediate any functional role, we performed siRNA studies which led to previously unidentified functional dependency for B7H3 and ATP1B3.

Immune modulator CD70 as a potential cisplatin resistance predictive marker in ovarian cancer

OBJECTIVE We have used mass-spectrometry (MS) based proteomics platform to identify cell surface proteins over-expressed on a cisplatin resistant derivative of an ovarian cancer cell line A2780. METHODS Membrane associated glycoproteins from A2780 and its cisplatin resistant derivative cell line, A2780cis, were processed for liquid chromatography (LC)-MS based analysis. The expression of proteins found at elevated levels in A2780cis cell line was confirmed using flow cytometry and Taqman analysis. The expression of these proteins was further evaluated in unrelated ovarian cancer cell lines using MS analysis and flow cytometry. Immunohistochemical (IHC) analysis was performed using ovarian tumor tissues to evaluate the protein density on the cell surface. Monoclonal antibodies were used in an alamar blue proliferation assay to examine the cytotoxic effects on cell proliferation in resistant cell lines. RESULTS Six proteins were identified by LC-MS as being over-expressed on cell surface of A2780cis cell line. Mass spectrometry and flow cytometry confirmed the over-expression of CD49f, CD70 and Her-2/neu in other cisplatin resistant ovarian cancer cell lines. Immunohistochemical analysis revealed that only CD70 was expressed at moderate levels in ovarian tumors. When cisplatin resistant ovarian cell lines A2780cis and SKOV-3 were treated with antibody against CD70, there was a significant decrease in cell proliferation. CONCLUSION Using a MS based proteomics approach we have shown that expression of CD70 is associated with cisplatin resistance in ovarian cancer cell lines. Follow-up examination of these tumor cell line findings in clinical tumor specimens with available pathology staging and cisplatin treatment history is warranted.

Rna Interference Characterization of Proteins Discovered by Proteomic Analysis of Pancreatic Cancer Reveals Function in Cell Growth and Survival

Objectives There is a clear need for better therapeutics and diagnostics for pancreatic cancer. We aimed to discover plasma membrane-associated proteins overexpressed in pancreatic cancer using quantitative proteomics and apply RNA interference (RNAi) to uncover proteins associated with cancer cell survival. Methods Cell surface glycoproteins from 5 pancreatic cancer cell lines were isolated, and differential analyses were performed using mass spectrometry and the “normoid” cell line Hs766T as the comparator. For validation, immunohistochemistry was performed on tissues from 10 independent patients and 2 normal donors. Correlation of protein and mRNA expression level was determined, and functional activity characterized using RNAi. Results Integrin &bgr;6, CD46, tissue factor, and a novel protein, chromosome 14 open reading frame 1, were identified as overexpressed on pancreatic cancer cell lines. Immunohistochemistry demonstrated the 4 targets were overexpressed in 20% to 70% of primary pancreatic tumor specimens. Small interfering RNA knockdown resulted in a reduction of cellular proliferation by inhibiting DNA synthesis, blocking S-phase progression or induction of apoptosis. Conclusions By combining a mass spectrometry identification platform and an RNAi validation platform, we have identified a panel of cell surface glycoproteins that not only are overexpressed, but also play a functional role in pancreatic tumor cell survival.

Reference map for liquid chromatography–mass spectrometry-based quantitative proteomics

The accurate mass and time (AMT) tag strategy has been recognized as a powerful tool for high-throughput analysis in liquid chromatography-mass spectrometry (LC-MS)-based proteomics. Due to the complexity of the human proteome, this strategy requires highly accurate mass measurements for confident identifications. We have developed a method of building a reference map that allows relaxed criteria for mass errors yet delivers high confidence for peptide identifications. The samples used for generating the peptide database were produced by collecting cysteine-containing peptides from T47D cells and then fractionating the peptides using strong cationic exchange chromatography (SCX). LC-tandem mass spectrometry (MS/MS) data from the SCX fractions were combined to create a comprehensive reference map. After the reference map was built, it was possible to skip the SCX step in further proteomic analyses. We found that the reference-driven identification increases the overall throughput and proteomic coverage by identifying peptides with low intensity or complex interference. The use of the reference map also facilitates the quantitation process by allowing extraction of peptide intensities of interest and incorporating models of theoretical isotope distribution.

Predicting continuous epitopes in proteins

The ability to predict antigenic sites on proteins is crucial for the production of synthetic peptide vaccines and synthetic peptide probes of antibody structure. Large number of amino acid propensity scales based on various properties of the antigenic sites like hydrophilicity, flexibility/mobility, turns and bends have been proposed and tested previously. However these methods are not very accurate in predicting epitopes and non-epitope regions. We propose algorithms that combine 14 best performing individual propensity scales and give better prediction accuracy as compared to individual scales.