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Rapid identification of prognostic imaging biomarkers for non-small cell lung cancer by leveraging public gene expression microarray data

ABSTRACT: To rapidly identify new prognostic imaging biomarkers, we propose a bioinformatics approach that integrates gene expression and image data and leverages public gene expression data. We demonstrate our approach in non-small cell lung carcinoma patients for whom CT, PET/CT and gene expression data are available but without clinical follow-up. We extracted 180 image features and 56 high quality gene expression clusters, represented by metagenes. 115 image features were expressed in terms of metagenes, using sparse linear regression and cross-validation, with an accuracy of 65-86%. After mapping the signatures to a public gene expression dataset, 26 image features were significantly associated with recurrence-free survival and 22 with overall survival. A multivariate analysis identified multiple image features that were prognostic, independent of clinical covariates. Identifying prognostic imaging biomarkers by linking images and gene expression with outcomes in public gene expression datasets promises to accelerate the role of imaging in personalized medicine.

ORGANISM(S): Homo sapiens

PROVIDER: GSE28827 | GEO | 2012/06/01

SECONDARY ACCESSION(S): PRJNA138787

REPOSITORIES: GEO

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Rapid identification of prognostic imaging biomarkers for non-small cell lung cancer by leveraging public gene expression microarray data

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