To address tumor diversity and their varied responses to chemotherapy, we require the ability to custom design treatment for each tumor. The goal of pharmacogenomics is to generate gene expression signatures for tissue using microarrays that are then correlated with prognosis or response to therapeutics. However, much of the complexity and diversity of response may be due to proteomic differences. Thus far, probing for proteomic diversity and linking this to therapeutic efficacy has been difficult. Our overall goals are to develop surface proteome signatures (SPS) and perform functional proteomic target validation analysis directly on primary tumor tissue. There is a great need to assess the efficacy of different chemotherapy combinations directly on patient tissue samples. A major difficulty is this respect is our inability to assess the small amounts of primary tumor tissue available from patient-derived samples. As part of our previous IMAT-funded research, we developed a library of single chain (scFv) phage display antibodies that recognize approximately 500 components of the surface proteome. In this work we also developed high-throughput methods for immunocytochemistry (ICC) using scFvs and apoptosis assays that use small number of cells (< 500) per assay. Together, these developments allow us to generate SPS and measure apoptosis after chemotherapy treatment using small amounts of primary tumor tissue. Cells will be tested with a battery of drugs alone and in combination and analyzed for apoptosis. Thus, a differential response to therapeutics will be correlated with a SPS. We will develop and test these assays in the R21 phase using thymic lymphoma mouse cell lines derived from three mouse models: transgenic MyrAkt and two genetic deletions, PTEN-/+ or p53-/-. This is an ideal model system, as the primary tumors are genetically defined by single oncogenic mutations. We will establish SPS for cell lines derived from thymic lymphoma lines from these mice. We will test for the efficacy of different drug regimens to obtain the optimum combination for inducing apoptosis of the cells. We will then test these drug combinations in primary tumor tissue from MyrAkt mice. We will also address the causal link between SPS and drug response and will test the functional role of scFvs that are biomarker candidates. In the R33 phase we will test the predicted optimal drug regimen on thymic lymphomas in the three mouse models, examining tumor load and survival. We will also characterize ten of the scFvs as potential biomarkers or targets for drug discovery. Our studies complement pharmacogenomics and provide a novel route to pharmacoproteomics.
| Cain, Jason W; Hauptschein, Robert S; Stewart, Jean K et al. (2011) Identification of CD44 as a surface biomarker for drug resistance by surface proteome signature technology. Mol Cancer Res 9:637-47 |
