Dataset Information

A predictive phosphorylation signature of lung cancer.

ABSTRACT:

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.

SUBMITTER: Wu CJ

PROVIDER: S-EPMC2777383 | biostudies-literature | 2009 Nov

REPOSITORIES: biostudies-literature

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Publications

A predictive phosphorylation signature of lung cancer.

Wu Chang-Jiun CJ Cai Tianxi T Rikova Klarisa K Merberg David D Kasif Simon S Steffen Martin M

PloS one 20091125 11

<h4>Background</h4>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 ...[more]

PMID: 19946374

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Project description:PURPOSE:Response to adjuvant chemotherapy after tumor resection varies widely among patients with non-small cell lung cancer (NSCLC); therefore, it is of clinical importance to prospectively predict who will benefit from adjuvant chemotherapy before starting the treatment. The goal of this study is to validate a 12-gene adjuvant chemotherapy predictive signature developed from a previous study using a clinical-grade assay. EXPERIMENTAL DESIGN:We developed a clinical-grade assay for formalin-fixed, paraffin-embedded (FFPE) samples using the NanoString nCounter platform to measure the mRNA expression of the previously published 12-gene set. The predictive performance was validated in a cohort of 207 patients with early-stage resected NSCLC with matched propensity score of adjuvant chemotherapy. RESULTS:The effects of adjuvant chemotherapy were significantly different in patients from the predicted adjuvant chemotherapy benefit group and those in the predicted adjuvant chemotherapy nonbenefit group (P = 0.0056 for interaction between predicted risk group and adjuvant chemotherapy). Specifically, in the predicted adjuvant chemotherapy benefit group, the patients receiving adjuvant chemotherapy had significant recurrence-free survival (RFS) benefit (HR = 0.34; P = 0.016; adjuvant chemotherapy vs. nonadjuvant chemotherapy), while in the predicted adjuvant chemotherapy nonbenefit group, the patients receiving adjuvant chemotherapy actually had worse RFS (HR = 1.86; P = 0.14; adjuvant chemotherapy vs. nonadjuvant chemotherapy) than those who did not receive adjuvant chemotherapy. CONCLUSIONS:This study validated that the 12-gene signature and the FFPE-based clinical assay predict that patients whose resected lung adenocarcinomas exhibit an adjuvant chemotherapy benefit gene expression pattern and who then receive adjuvant chemotherapy have significant survival advantage compared with patients whose tumors exhibit the benefit pattern but do not receive adjuvant chemotherapy.

Project description:OBJECTIVE:Smoking is a prominent risk factor for lung cancer. However, it is not an established prognostic factor for lung cancer in clinics. To date, no gene test is available for diagnostic screening of lung cancer risk or prognostication of clinical outcome in smokers. This study sought to identify a smoking associated gene signature in order to provide a more precise diagnosis and prognosis of lung cancer in smokers. METHODS AND MATERIALS:An implication network based methodology was used to identify biomarkers by modeling crosstalk with major lung cancer signaling pathways. Specifically, the methodology contains the following steps: (1) identifying genes significantly associated with lung cancer survival; (2) selecting candidate genes which are differentially expressed in smokers versus non-smokers from the survival genes identified in Step 1; (3) from these candidate genes, constructing gene coexpression networks based on prediction logic for the smoker group and the non-smoker group, respectively; (4) identifying smoking-mediated differential components, i.e., the unique gene coexpression patterns specific to each group; and (5) from the differential components, identifying genes directly co-expressed with major lung cancer signaling hallmarks. RESULTS:A smoking-associated 6-gene signature was identified for prognosis of lung cancer from a training cohort (n=256). The 6-gene signature could separate lung cancer patients into two risk groups with distinct post-operative survival (log-rank P<0.04, Kaplan-Meier analyses) in three independent cohorts (n=427). The expression-defined prognostic prediction is strongly related to smoking association and smoking cessation (P<0.02; Pearson's Chi-squared tests). The 6-gene signature is an accurate prognostic factor (hazard ratio=1.89, 95% CI: [1.04, 3.43]) compared to common clinical covariates in multivariate Cox analysis. The 6-gene signature also provides an accurate diagnosis of lung cancer with an overall accuracy of 73% in a cohort of smokers (n=164). The coexpression patterns derived from the implication networks were validated with interactions reported in the literature retrieved with STRING8, Ingenuity Pathway Analysis, and Pathway Studio. CONCLUSIONS:The pathway-based approach identified a smoking-associated 6-gene signature that predicts lung cancer risk and survival. This gene signature has potential clinical implications in the diagnosis and prognosis of lung cancer in smokers.

Dataset Information

A predictive phosphorylation signature of lung cancer.

Background

Results

Conclusions

Publications

A predictive phosphorylation signature of lung cancer.

Similar Datasets

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