NanoPro 1000 analysis of Erk1/2, Mek1/2, and Akt isoform phosphorylation in clinical samples from 115 patients.

Abstract

e22227 Background: Proteomic analysis of cellular signaling networks has strongly gained in importance in the field of cancer research and treatment. For example, the inhibition of the EGFR pathway has been widely integrated into clinical practice for different tumor entities. Although, predictive genetic markers of response to targeted therapy are already in use, heterogeneity in responses has been clinically observed. In order to understand this heterogeneity on the proteome level, the activation status of cell signaling proteins has to be analyzed. Methods: Samples: Patient’s samples were collected according to Indivumed´s SOPs. Patient´s cohort consisted of breast cancer (n=26), non-small cell lung cancer (n=29), colorectal cancer (n= 30) and liver metastases of colorectal cancer (n= 30). Analysis of signaling pathways: Protein phosphorylations of signaling pathway molecules were analyzed using the NanoPro1000 technology (Protein Simple). For quantification of selected signaling proteins (total and phosphorylated Erk1/2, Mek1/2 and Akt), tissue slices were lysed and analyzed in replicates. Results: Statistical analysis of generated data revealed that isoform phosphorylation of Erk1/2, Mek1/2 and Akt significantly differed among individual patients, but not between tumor entities. This indicates that the in-depth analysis of isoform phosphorylation can reveal significant biological differences and might result in therapy relevant, predictive signatures independent of the mutation status. Hence, we established cut-off values for basal signaling molecule phosphorylation, to potentially enable the selection of patients, who will most likely benefit from targeted treatment. Based on the cut-off values we classified patients into statistical significant groups of high, moderate and low signaling molecule phosphorylation. Conclusions: The NanoPro 1000 technology can be used to robustly screen a large cohort of patients for signaling molecule phosphorylation. The analysis of patients on the proteome level may be helpful to identify predictive biomarker for the development of resistance to anti-EGF-receptor treatment and thus further improve personalized medicine.