Translating Gene Signatures Into a Pathologic Feature: Tumor Necrosis Predicts Disease Relapse in Operable and Stage I Lung Adenocarcinoma.

PURPOSE: The high 5-year disease relapse rate in patients with stage I lung adenocarcinoma indicates the need for additional risk stratification parameters. This study assessed whether gene signatures translate into a pathologic feature for better disease stratification. MATERIALS AND METHODS: The mutual interdependence and risk stratification power of three gene signatures, cell cycle, hypoxia, and mammalian target of rapamycin (mTOR), were investigated in nine cohorts of patients with lung adenocarcinoma and five cohorts of patients with lung squamous cell carcinoma. The translation from gene signatures to a pathologic feature, tumor necrosis, was validated in The Cancer Genome Atlas lung adenocarcinoma cohort. The translation of signature score to pathway activity was further investigated by integrative analyses using The Cancer Genome Atlas and The Cancer Protein Atlas lung adenocarcinoma data sets. RESULTS: The results showed that the three gene signatures were mutually interdependent in lung adenocarcinoma but not in lung squamous cell carcinoma. The signature activities were higher in necrosis-positive tumors than in necrosis-negative tumors. The signature score correlated with the expression level of the representative protein that implicated the activity of each pathway. These signatures stratified patients with operable and stage I lung adenocarcinomas into different risk groups independent of age and stage. Furthermore, the signatures translated to a pathologic feature, tumor necrosis, which predicted shorter overall and relapse-free survival in patients with operable and stage I lung adenocarcinomas. CONCLUSION: This study showed that gene signatures could translate into a pathologic feature, tumor necrosis, with risk stratification ability in patients with operable and stage I lung adenocarcinomas.

Liver Gene Expression Profiles Correlate with Virus Infection and Response to Interferon Therapy in Chronic Hepatitis B Patients.

The natural course of chronic hepatitis B (CHB) infection and treatment response are determined mainly by the genomic characteristics of the individual. We investigated liver gene expression profiles to reveal the molecular basis associated with chronic hepatitis B and IFN-alpha (IFNα) treatment response in CHB patients. Expression profiles were compared between seven paired liver biopsy samples taken before and 6 months after successful IFNα treatment or between pretreatment biopsy samples of 11 IFNα responders and 11 non-responders. A total of 132 differentially up-regulated and 39 down-regulated genes were identified in the pretreated livers of CHB patients. The up-regulated genes were mainly related to cell proliferation and immune response, with IFNγ and B cell signatures significantly enriched. Lower intrahepatic HBV pregenomic RNA levels and 25 predictive genes were identified in IFNα responders. The predictive gene set in responders significantly overlapped with the up-regulated genes associated with the pretreated livers of CHB patients. The mechanisms responsible for IFNα treatment responses are different between HBV and HCV patients. HBV infection evokes significant immune responses even in chronic infection. The up-regulated genes are predictive of responsiveness to IFNα therapy, as are lower intrahepatic levels of HBV pregenomic RNA and pre-activated host immune responses.

Utilizing signature-score to identify oncogenic pathways of cholangiocarcinoma.

Extracting maximal information from gene signature sets (GSSs) via microarray-based transcriptional profiling involves assigning function to up and down regulated genes. Here we present a novel sample scoring method called Signature-score (S-score) which can be used to quantify the expression pattern of tumor samples from previously identified gene signature sets. A simulation result demonstrated an improved accuracy and robustness by S-score method comparing with other scoring methods. By applying the S-score method to cholangiocarcinoma (CAC), an aggressive hepatic cancer that arises from bile ducts cells, we identified enriched oncogenic pathways in two large CAC data sets. Thirteen pathways were enriched in CAC compared with normal liver and bile duct. Moreover, using S-score, we were able to dissect correlations between CAC-associated oncogenic pathways and Gene Ontology function. Two major oncogenic clusters and associated functions were identified. Cluster 1, which included beta-catenin and Ras, showed a positive correlation with the cell cycle, while cluster 2, which included TGF-beta, cytokeratin 19 and EpCAM was inversely correlated with immune function. We also used S-score to identify pathways that are differentially expressed in CAC and hepatocellular carcinoma (HCC), the more common subtype of liver cancer. Our results demonstrate the utility and effectiveness of S-score in assigning functional roles to tumor-associated gene signature sets and in identifying potential therapeutic targets for specific liver cancer subtypes.