In this study, we report a plasma proteomic analysis of a mouse MCF7 xenograft, using a novel platform named M-LAC (multilectin affinity chromatography), in an attempt to identify putative serum biomarkers of tumor presence and response to therapy. The use of the M-LAC platform enabled us to focus on secreted proteins as well as remove interference from serum albumin and other nonglycosylated proteins. The study focused on the MCF7 human xenograft tumor model which enabled us to distinguish tumor proteins (human peptide sequences) from host-derived murine proteins, potentially discriminating tumor- versus supporting tissue-derived markers. A large set of murine proteins was identified in this study, including several signaling molecules such as EGFR, interleukin-6 receptor, protein-kinase C, and phosphatidylinositol kinase which changed in plasma levels relative to tumor-free animals. We also detected in the samples with maximal tumor growth a number of human tumor-derived proteins linked to cell signaling, immune response, and transcriptional regulation. This is the first report where tumor-derived peptides could be detected in the serum of a xenograft model. We conclude that the M-LAC approach may be used to detect plasma proteins of potential biological significance in tumor-bearing animals and warrants further study in terms of increasing the sensitivity of the method for the characterization of low level tumor markers and to explore the applicability of these markers for human studies.

Liquid Chromatography Mass Spectrometry (LC-MS) based proteomics is an important tool in detecting changes in peptide/protein abundances in samples potentially leading to the discovery of disease biomarker candidates. We present CLUE-TIPS (Clustering Using Euclidean distance in Tanimoto Inter-Point Space), an approach that compares complex proteomic samples for similarity/dissimilarity analysis. In CLUE-TIPS, an intersample distance feature map is generated from filtered, aligned and binarized raw LC-MS data by applying the Tanimoto distance metric to obtain normalized similarity scores between all sample pairs for each m/z value. We developed clustering and visualization methods for the intersample distance map to analyze various samples for differences at the sample level as well as the individual m/z level. An approach to query for specific m/z values that are associated with similarity/dissimilarity patterns in a set of samples was also briefly described. CLUE-TIPS can also be used as a tool in assessing the quality of LC-MS runs. The presented approach does not rely on tandem mass-spectrometry (MS/MS), isotopic labels or gels and also does not rely on feature extraction methods. CLUE-TIPS suite was applied to LC-MS data obtained from plasma samples collected at various time points and treatment conditions from immunosuppressed mice implanted with MCF-7 human breast cancer cells. The generated raw LC-MS data was used for pattern analysis and similarity/dissimilarity detection. CLUE-TIPS successfully detected the differences/similarities in samples at various time points taken during the progression of tumor, and also recognized differences/similarities in samples representing various treatment conditions.

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