Systems biology in personalized medicine in oncology: A case study in non-small cell lung cancer.

Abstract

e21096 Background: Systems biology together with translational oncology is a new approach to discover sensitive pathways in specific cancers. In this study, we propose a computational modeling technique to investigate the possible effects various alterations, such as protein overexpression, gene amplification or mutations, may have on signaling, through of the EGFR and IGF1R pathways, in non-small cell lung cancer (NSCLC). Methods: EGFR and IGF1R pathways and the downstream MAPK and PIK3 networks have been reproduced through a mathematical model. One hundred-twenty five tumors from surgical NSCLC patients were evaluated for EGFR and IGF1R protein expression, by immunohistochemistry (IHC) and gene amplification, by fluorescence in situ hybridization (FISH). KRAS mutations (exons 2 and 3) were evaluated by direct sequencing Results: To correlate EGFR and IGF1R expression levels, and KRAS mutations to tumor cell proliferation, we focused on the ERK signaling pathway, which plays a central role in several steps of cancer development including proliferation and cancer cell migration. The mathematical model predicts a relationship between a simultaneous high expression level of both receptors and a modification on ERK time behavior, implying a stronger attitude for cell proliferation. Furthermore KRAS activating mutations predict high level of active ERK and high probability to have cell proliferation. Cell growth can be closely related to disease progression and act, in survival analysis, as DFS estimator. Patients with concomitant IGF1R/EGFR FISH/IHC positivity had a worse DFS ( p=0.005). KRAS mutations have a statistically significant shorter DFS (p<0.001) as well Conclusions: We propose a Systems Biology approach, combined with Translational Oncology methodologies, to understand the interaction between EGFR, IGF1R and KRAS pathways in NSCLC. Computational model predictions confirm clinical evidences of survival analysis. Future work will validate our model with experiments on various NSCLC cell cultures and further investigate the response to drug administration. We thank AIRC and Fondazione Cassa di Risparmio for supporting the study.