Integrative Radiogenomic Profiling of Squamous Cell Lung Cancer
Radiation Oncology, Harvard Radiation Oncology Program. Cancer Research
(Impact Factor: 9.33).
08/2013; 73(20). DOI: 10.1158/0008-5472.CAN-13-1616
Radiation therapy is one of the mainstays of anti-cancer 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 by comparison to 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 radiation therapy, 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 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 (ssGSEA) 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, 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.
Available from: Tyler Lincoln Fowler
- "Recently, Abazeed and colleagues (Abazeed et al., 2013) reported on the development of a high-throughput platform for radiogenomic profiling in vitro. The group optimized clonogenic assay in a 384-well plate format. "
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ABSTRACT: This paper describes the development and characterization of a fully automated in vitro cell irradiator using an electronic brachytherapy source to perform radiation sensitivity bioassays. This novel irradiator allows complex variable dose and dose rate schemes to be delivered to multiple wells of 96-well culture plates used in standard biological assays. The Xoft Axxent® eBx™ was chosen as the x-ray source due to its ability to vary tube current up to 300 µA for a 50 kVp spectrum using clinical surface applicators. Translation of the multiwell plate across the fixed radiation field is achieved using a precision motor driven computer controlled positioning system. A series of measurements was performed to characterize dosimetric performance of the system. Measurements have shown that the radiation output measured with an end window ionization chamber is stable between operating currents of 50-300 µA. In addition, radiochromic film was used to characterize the field flatness and symmetry. The average field flatness in the in-plane and cross-plane direction was 2.9 ±1.0% and 4.0 ±1.7%, respectively. The average symmetry in the in-plane and cross-plane direction was 1.8 ±0.9% and 1.6 ±0.5%, respectively. The optimal focal spot resolution at the cellular plane was determined by measuring sequential irradiations on radiochromic film for three different well spacing schemes. It was determined that the current system can irradiate every other well with negligible impact on the radiation field characteristics. Finally, a performance comparison between this system and a common cabinet irradiator is presented.
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ABSTRACT: NRF2 is a transcription factor that mediates stress responses. Oncogenic mutations in NRF2 localize to one of its two binding interfaces with KEAP1, an E3 ubiquitin ligase that promotes proteasome-dependent degradation of NRF2. Somatic mutations in KEAP1 occur commonly in human cancer, where KEAP1 may function as a tumor suppressor. These mutations distribute throughout the KEAP1 protein but little is known about their functional impact. In this study, we characterized 18 KEAP1 mutations defined in a lung squamous cell carcinoma tumor set. Four mutations behaved as wild-type KEAP1, thus are likely passenger events. R554Q, W544C, N469fs, P318fs, and G333C mutations attenuated binding and suppression of NRF2 activity. The remaining mutations exhibited hypomorphic suppression of NRF2, binding both NRF2 and CUL3. Proteomic analysis revealed that the R320Q, R470C, G423V, D422N, G186R, S243C, and V155F mutations augmented the binding of KEAP1 and NRF2. Intriguingly, these 'super-binder' mutants exhibited reduced degradation of NRF2. Cell-based and in vitro biochemical analyses demonstrated that despite its inability to suppress NRF2 activity, the R320Q 'superbinder' mutant maintained the ability to ubiquitinate NRF2. These data strengthen the genetic interactions between KEAP1 and NRF2 in cancer and provide new insight into KEAP1 mechanics.
Available from: Amit Das
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ABSTRACT: We proposed a nonlinear model to perform a novel quantitative radiation sensitivity prediction. We used the NCI-60 panel, which consists of nine different cancer types, as the platform to train our model. Important radiation therapy (RT) related genes were selected by significance analysis of microarrays (SAM). Orthogonal latent variables (LVs) were then extracted by the partial least squares (PLS) method as the new compressive input variables. Finally, support vector machine (SVM) regression model was trained with these LVs to predict the SF2 (the surviving fraction of cells after a radiation dose of 2 Gy
-ray) values of the cell lines. Comparison with the published results showed significant improvement of the new method in various ways: (a) reducing the root mean square error (RMSE) of the radiation sensitivity prediction model from 0.20 to 0.011; and (b) improving prediction accuracy from 62% to 91%. To test the predictive performance of the gene signature, three different types of cancer patient datasets were used. Survival analysis across these different types of cancer patients strongly confirmed the clinical potential utility of the signature genes as a general prognosis platform. The gene regulatory network analysis identified six hub genes that are involved in canonical cancer pathways.
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