Targeted drugs are less toxic than traditional chemotherapeutic therapies; however, the proportion of patients that benefit from these drugs is often smaller. A marker that confidently predicts patient response to a specific therapy would allow an individual therapy selection most likely to benefit the patient. Here, we used quantitative mass spectrometry to globally profile the basal phosphoproteome of a panel of non-small cell lung cancer cell lines. The effect of the kinase inhibitor dasatinib on cellular growth was tested against the same panel. From the phosphoproteome profiles, we identified 58 phosphorylation sites, which consistently differ between sensitive and resistant cell lines. Many of the corresponding proteins are involved in cell adhesion and cytoskeleton organization. We showed that a signature of only 12 phosphorylation sites is sufficient to accurately predict dasatinib sensitivity. The introduction of targeted drugs for treating cancer is a major biomedical achievement of the past decade (1, 2). Because these drugs selectively block molecular pathways that are typically overactivated in tumor cells, they are more precise and less toxic than traditional chemotherapeutics. However, although many cancer patients benefit from a specific targeted therapy, many others do not. Therefore, predictive molecular markers are needed to confidently predict patient response to a specific therapy. Such markers would facilitate therapy personalization, where the selected therapy is based on the molecular profile of the patient.Predictive tests currently used in the clinic are frequently based on one particular marker that is often linked to the drug target. A well known example for a predictive test is assessing HER2/neu overexpression using immunohistochemistry or fluorescent in situ hybridization to predict the response to therapy with trastuzumab (Herceptinா; Roche) (3, 4). However, in some cases the expression or mutational status of the target or other singleton markers might not be sufficient to predict a therapeutic response. Recently, several studies tried to identify molecular signatures comprising multiple markers for response predictions, usually based on gene expression profiling (5, 6). To our knowledge, no study successfully identified a signature from global phosphoproteomic profiles so far.Recent advances in mass spectrometry, methods for enriching phosphorylated proteins or peptides, and computer algorithms for analyzing proteomics data have enabled the application of mass spectrometry-based proteomics to monitor phosphorylation events in a global and unbiased manner. These methods have become sufficiently sensitive and robust to localize and quantify the phosphorylation sites within a peptide sequence (7-9). Phosphorylation events are important in signal transduction, where signals caused by external stimuli are transmitted from the cell membrane to the nucleus. Aberrations in these signal transduction pathways are particularly important for understanding the mechanisms of certain diseases,...

Since targeted drugs selectively block molecular pathways that are typically over-activated in tumour cells, they are more precise and show fewer adverse effects than traditional chemotherapeutic agents. At the same time the proportion of patients that benefit from targeted drugs is smaller. Therefore, predictive molecular markers are needed to confidently predict the patient's response to a specific therapy. Such markers would facilitate therapy personalization, where the selected therapy is based on the molecular profile of the patient. We sought to identify a signature of protein phosphorylations that predicts the response to dasatinb in non-small cell lung cancer cell lines. A panel of cell lines were profiled in a global, unbiased, phosphoproteomics study yielding quantitative information for roughly 25,000 phosphorylation sites. The identical cell lines were tested for their response to dasatinib. From the phosphoproteome profiles, we identified a signature of twelve phosphorylation sites that can accurately predict dasatinib sensitivity. Four of the phosphorylation sites belong to integrin α4, a protein that mediates cell-matrix or cell-cell adhesion. We evaluated the performance of this signature in a cross-validation set-up and investigated the robustness of the selected predictive features. Finally, we confirmed the predictive power of the signature in an independent set of breast cancer cell lines. We showed that the phosphorylations of integrin β4 as well as eight further proteins are candidate biomarkers for predicting response to dasatinib in solid tumours. Furthermore, we demonstrated that identifying predictive phosphorylation signatures from global, quantitative phosphoproteomic data is possible, and opens a new path to discovering molecular markers for response prediction. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4808. doi:1538-7445.AM2012-4808