Abstract
BackgroundRadiation therapy is among the most effective and commonly used therapeutic modalities of cancer treatments in current clinical practice. The fundamental paradigm that has guided radiotherapeutic regimens are ‘one-size-fits-all’, which are not in line with the dogma of precision medicine. While there were efforts to build radioresponse signatures using OMICS data, their ability to accurately predict in patients is still limited.MethodsWe proposed to integrate two large-scale radiogenomics datasets consisting of 511 with 23 tissues and 60 cancer cell lines with 9 tissues to build and validate radiation response biomarkers. We used intrinsic radiation sensitivity, i.e., surviving fraction of cells (SF2) as the radiation response indicator. Gene set enrichment analysis was used to examine the biological determinants driving SF2. Using SF2 as a continuous variable, we used five different approaches, univariate, rank gene ensemble, rank gene multivariate, mRMR and elasticNet to build genomic predictors of radiation response through a cross-validation framework.ResultsThrough the pathway analysis, we found 159 pathways to be statistically significant, out of which 54 and 105 were positively and negatively enriched with SF2. More importantly, we found cell cycle and repair pathways to be enriched with SF2, which are inline with the fundamental aspects of radiation biology. With regards to the radiation response gene signature, we found that all multivariate models outperformed the univariate model with a ranking based approach performing well compared to other models, indicating complex biological processes underpinning radiation response.ConclusionTo summarize, we found biological processes underpinning SF2 and systematically compared different machine learning approaches to develop and validate predictors of radiation response. With more patient data available in the future, the clinical value of these biomarkers can be assessed that would allow for personalization of radiotherapy.
Highlights
Radiation therapy is among the most effective and commonly used therapeutic modalities of cancer treatments in current clinical practice
For an false discovery approach (FDR) < 5%, we found a total of 159 molecular pathways that were enriched with surviving fraction at 2 Gy (SF2) (Fig. 2B)
In the last few years, radiation genomics has emerged as a new research field, which can help investigate changes in the transcriptome induced by radiotherapy as well as develop predictive biomarkers of radiation response
Summary
Radiation therapy is among the most effective and commonly used therapeutic modalities of cancer treatments in current clinical practice. To tailor radiation therapy to individual patients, it is important to build predictive assays that can potentially be used to design genomically-driven dosing, facilitating decision making between treatments such as neoadjuvant or adjuvant radiotherapy or chemoradiotherapy. This could augment the existing radiobiological therapeutic strategies to genomically-driven personalized radiation regimens. The application of transcriptomic fingerprinting to administer OMICS-driven therapies is making inroads to preclinical and clinical settings in precision radiation oncology [14]
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