A proteomic analysis of the plasma glycoproteins of a MCF-7 mouse xenograft: a model system for the detection of tumor markers.

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.

Identification of genotype-correlated sensitivity to selective kinase inhibitors by using high-throughput tumor cell line profiling.

Kinase inhibitors constitute an important new class of cancer drugs, whose selective efficacy is largely determined by underlying tumor cell genetics. We established a high-throughput platform to profile 500 cell lines derived from diverse epithelial cancers for sensitivity to 14 kinase inhibitors. Most inhibitors were ineffective against unselected cell lines but exhibited dramatic cell killing of small nonoverlapping subsets. Cells with exquisite sensitivity to EGFR, HER2, MET, or BRAF kinase inhibitors were marked by activating mutations or amplification of the drug target. Although most cell lines recapitulated known tumor-associated genotypes, the screen revealed low-frequency drug-sensitizing genotypes in tumor types not previously associated with drug susceptibility. Furthermore, comparing drugs thought to target the same kinase revealed striking differences, predictive of clinical efficacy. Genetically defined cancer subsets, irrespective of tissue type, predict response to kinase inhibitors, and provide an important preclinical model to guide early clinical applications of novel targeted inhibitors.