A Predictive Phosphorylation Signature of Lung Cancer

Background: Aberrant activation of signaling pathways drives many of the fundamental biological processes that accompany tumor initiation and progression. Inappropriate phosphorylation of intermediates in these signaling pathways are a frequently observed molecular lesion that accompanies the undesirable activation or repression of pro- and anti-oncogenic pathways. Therefore, methods which directly query signaling pathway activation via phosphorylation assays in individual cancer biopsies are expected to provide important insights into the molecular “logic” that distinguishes cancer and normal tissue on one hand, and enables personalized intervention strategies on the other. Results: We first document the largest available set of tyrosine phosphorylation sites that are, individually, differentially phosphorylated in lung cancer, thus providing an immediate set of drug targets. Next, we develop a novel computational methodology to identify pathways whose phosphorylation activity is strongly correlated with the lung cancer phenotype. Finally, we demonstrate the feasibility of classifying lung cancers based on multi-variate phosphorylation signatures. Conclusions: Highly predictive and biologically transparent phosphorylation signatures of lung cancer provide evidence for the existence of a robust set of phosphorylation mechanisms (captured by the signatures) present in the majority of lung cancers, and that reliably distinguish each lung cancer from normal. This approach should improve our understanding of cancer and help guide its treatment, since the phosphorylation signatures highlight proteins and pathways whose phosphorylation should be inhibited in order to prevent unregulated proliferation

Integration of protein phosphorylation, acetylation, and methylation data sets to outline lung cancer signaling networks

Integrated multiomics network analysis reveals signaling profiles in lung cancer.</jats:p

Integrative genomic and proteomic analyses identify targets for Lkb1 deficient metastatic lung tumors

SummaryIn mice, Lkb1 deletion and activation of KrasG12D results in lung tumors with a high penetrance of lymph node and distant metastases. We analyzed these primary and metastatic de novo lung cancers with integrated genomic and proteomic profiles and have identified gene and phosphoprotein signatures associated with Lkb1 loss and progression to invasive and metastatic lung tumors. These studies revealed that SRC is activated in Lkb1 deficient primary and metastatic lung tumors and that the combined inhibition of SRC, PI3K and MEK1/2 resulted in synergistic tumor regression. These studies demonstrate that integrated genomic and proteomic analyses can be used to identify signaling pathways that may be targeted for treatment

Clustergrammer Lung Cancer PTM Case Study Data

Lung cancer post-translational modification data