Abstract 773: Analysis of oncogene affected networks in tumorigenesis of lung cancer

Abstract

Abstract Genetic abnormality is one of major causes of tumorigenesis by driving the progressive transformation of normal human cells into highly malignant derivatives. Tracing the downstream pathways through which and how the effects of the oncogenic-activating mutations are transmitted is an important and challenging problem in studying disease mechanisms, diagnoses and treatments. In this study, we designed a network-based computational method, called MAXMAP, to not only predict mutation downstream affected pathways, but also quantitatively measure the dynamics of the pathway genes. We first constructed a heterogeneous network by integrating protein-protein interaction networks and signaling and regulatory networks from available up-to-date curated databases, as well as RNASeq expression data. The RNAseq dataset of these mutated cell lines has 18,096 genes whereas the Protein Protein Interaction and Signalling, regulation dataset has 14,864 genes. We then used the maximal information flow method to globally optimize the effects from the mutations to target downstream genes. The method was applied to analyze the biological network dynamics that is caused by single (TP53), double (TP53+KRAS) and triple (TP53+KRAS+STK11) mutations of HBEC30KT, an immortalized lung cell line, and revealed the core dynamical subnetworks of these mutations. Only the triple mutant is known to have tumor-like properties and as shown in earlier studies these sequential mutation accumulations lead to tumorigenesis in mouse xenograft models. The experimental validation of a small subnetwork was performed using transient knock down of the pathway genes and expression was verified using qPCR. We showed the dynamical subnetworks through which a gene, SLC7A5 was affected by sequential single, double or triple mutations. We have experimentally validated the predicted small subnetwork for SLC7A5 which is an important transporter of neutral amino acids. It is associated with bladder and bile duct adeno carcinomas and overexpressed in lung cancers. We selected the subnetwork affected due to the single mutation (TP53) for validation, including THBS1, TNFRSF11B and MAPK1 and SLC7A5. Hence, using the MAXMAP method we have successfully identified a dynamical subnetwork affected by changes in the known network of genes. These results suggest that this computational method may allow us to predict drug targets or biomarkers or to study interesting alternative paths and predict the most likely path leading to certain phenotypic outcomes. Citation Format: Adwait Sathe, Yong Chen, Hyuntae Yoo, Michael Q. Zhang. Analysis of oncogene affected networks in tumorigenesis of lung cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 773.