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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

Project description: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. We studied 26 cases of NSCLC with both PET/CT and microarray data under IRB approval from Stanford University and the Veterans Administration Palo Alto Health Care System. The collection of tissue samples consisted of a distribution of poorly- to well-differentiated adenocarcinomas and squamous cell cancers. The surgeon had removed necrotic debris during excision and sampled cavitary lesions to include as much solid component as practical. Then, from the excised tumor, he cut a 3 to 5 mm thick slice along its longest axis, and froze it within 30 minutes of excision. We retrieved the frozen tissue and extracted the RNA that was then processed by the Stanford Functional Genomics Facility using Illumina Whole Genome Bead Chips (Human HT-12 v3.0)

2012-05-31 | E-GEOD-28827 | biostudies-arrayexpress

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Transcription profiling of human primary liver tumours vs. universal human reference reveals that dynamic imaging traits in non-invasive computed tomography (CT) systematically correlate with the global gene expression programs of primary human liver cancer

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Project description:Current prognostic gene expression profiles for breast cancer mainly reflect proliferation status and are most useful in ER-positive cancers. Triple-negative breast cancers (TNBCs) are clinically heterogeneous, and prognostic markers and biology-based therapies are needed to better treat this disease. We assembled Affymetrix gene expression data for 579 TNBCs and performed unsupervised analysis to define metagenes that distinguish molecular subsets within TNBC. We used n=394 cases for discovery and n=185 cases for validation. Sixteen metagenes emerged that identified basal-like, apocrine and claudin-low molecular subtypes, or reflected various non-neoplastic cell populations including immune cells, blood, adipocytes, stroma, angiogenesis, and inflammation within the cancer. The expressions of these metagenes were correlated with survival and multivariate analysis was performed including routine clinical and pathological variables. 73% of TNBCs displayed basal-like molecular subtype that correlated with high histological grade and younger age. Survival of basal-like TNBC was not different from non-basal-like TNBC. High expression of immune cell metagenes was associated with good and high expression of inflammation and angiogenesis-related metagenes were associated with poor prognosis. A ratio of high B-cell and low IL-8 metagenes identified 32% of TNBC with good prognosis (HR 0.37, 95% CI 0.22-0.61; P<0.001) and was the only significant predictor in multivariate analysis including routine clincopathological variables. We describe a ratio of high B-cell presence and low IL-8 activity as a powerful new prognostic marker for TNBC. Inhibition of the IL-8 pathway also represents an attractive novel therapeutic target for this disease.

2011-09-13 | GSE31519 | GEO

Decoding global gene expression programs in liver cancer by noninvasive imaging

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2007-04-07 | GSE7474 | GEO

89 NSCLC patients with gene expression profiles and matching CT imaging data available at TCIA.

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2014-06-19 | E-GEOD-58661 | biostudies-arrayexpress

Transcriptional response in the heart after low-dose gamma

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Homo sapiens

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

| PRJNA138787 | ENA

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