Dataset Information

Integrative radiogenomic profiling of squamous cell lung cancer.

ABSTRACT: Radiotherapy is one of the mainstays of anticancer treatment, but the relationship between the radiosensitivity of cancer cells and their genomic characteristics is still not well defined. Here, we report the development of a high-throughput platform for measuring radiation survival in vitro and its validation in comparison with conventional clonogenic radiation survival analysis. We combined results from this high-throughput assay with genomic parameters in cell lines from squamous cell lung carcinoma, which is standardly treated by radiotherapy, to identify parameters that predict radiation sensitivity. We showed that activation of NFE2L2, a frequent event in lung squamous cancers, confers radiation resistance. An expression-based, in silico screen nominated inhibitors of phosphoinositide 3-kinase (PI3K) as NFE2L2 antagonists. We showed that the selective PI3K inhibitor, NVP-BKM120, both decreased NRF2 protein levels and sensitized NFE2L2 or KEAP1-mutant cells to radiation. We then combined results from this high-throughput assay with single-sample gene set enrichment analysis of gene expression data. The resulting analysis identified pathways implicated in cell survival, genotoxic stress, detoxification, and innate and adaptive immunity as key correlates of radiation sensitivity. The integrative and high-throughput methods shown here for large-scale profiling of radiation survival and genomic features of solid-tumor-derived cell lines should facilitate tumor radiogenomics and the discovery of genotype-selective radiation sensitizers and protective agents.

SUBMITTER: Abazeed ME

PROVIDER: S-EPMC3856255 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature

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Integrative radiogenomic profiling of squamous cell lung cancer.

Abazeed Mohamed E ME Adams Drew J DJ Hurov Kristen E KE Tamayo Pablo P Creighton Chad J CJ Sonkin Dmitriy D Giacomelli Andrew O AO Du Charles C Fries Daniel F DF Wong Kwok-Kin KK Mesirov Jill P JP Loeffler Jay S JS Schreiber Stuart L SL Hammerman Peter S PS Meyerson Matthew M

Cancer research 20130826 20

Radiotherapy is one of the mainstays of anticancer treatment, but the relationship between the radiosensitivity of cancer cells and their genomic characteristics is still not well defined. Here, we report the development of a high-throughput platform for measuring radiation survival in vitro and its validation in comparison with conventional clonogenic radiation survival analysis. We combined results from this high-throughput assay with genomic parameters in cell lines from squamous cell lung ca ...[more]

PMID: 23980093

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Project description:Graphical abstract Highlights • Bayesian tensor factorization is used to integrate multiomics data for radiogenomics analysis.• A regression framework is proposed to handle the unmatched data issue in radiogenomics analysis.• Deep learning is used to identify prognostic meaningful radiogenomic biomarkers for cancer. In precise medicine, it is with great value to develop computational frameworks for identifying prognostic biomarkers which can capture both multi-genomic and phenotypic heterogeneity of breast cancer (BC). Radiogenomics is a field where medical images and genomic measurements are integrated and mined to solve challenging clinical problems. Previous radiogenomic studies suffered from data incompleteness, feature subjectivity and low interpretability. For example, the majority of the radiogenomic studies miss one or two of medical imaging data, genomic data, and clinical outcome data, which results in the data incomplete issue. Feature subjectivity issue comes from the extraction of imaging features with significant human involvement. Thus, there is an urgent need to address above-mentioned limitations so that fully automatic and transparent radiogenomic prognostic biomarkers could be identified for BC. We proposed a novel framework for BC prognostic radiogenomic biomarker identification. This framework involves an explainable DL model for image feature extraction, a Bayesian tensor factorization (BTF) processing for multi-genomic feature extraction, a leverage strategy to utilize unpaired imaging, genomic, and survival outcome data, and a mediation analysis to provide further interpretation for identified biomarkers. This work provided a new perspective for conducting a comprehensive radiogenomic study when only limited resources are given. Compared with baseline traditional radiogenomic biomarkers, the 23 biomarkers identified by the proposed framework performed better in indicating patients’ survival outcome. And their interpretability is guaranteed by different levels of build-in and follow-up analyses.

Dataset Information

Integrative radiogenomic profiling of squamous cell lung cancer.

Publications

Integrative radiogenomic profiling of squamous cell lung cancer.

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