Personalization Of Radiotherapy Research Articles

Leveraging Blood-Based Diagnostics to Predict Tumor Biology and Extend the Application and Personalization of Radiotherapy in Liver Cancers.

While the incidence of primary liver cancers has been increasing worldwide over the last few decades, the mortality has remained consistently high. Most patients present with underlying liver disease and have limited treatment options. In recent years, radiotherapy has emerged as a promising approach for some patients; however, the risk of radiation induced liver disease (RILD) remains a limiting factor for some patients. Thus, the discovery and validation of biomarkers to measure treatment response and toxicity is critical to make progress in personalizing radiotherapy for liver cancers. While tissue biomarkers are optimal, hepatocellular carcinoma (HCC) is typically diagnosed radiographically, making tumor tissue not readily available. Alternatively, blood-based diagnostics may be a more practical option as blood draws are minimally invasive, widely availability and may be performed serially during treatment. Possible blood-based diagnostics include indocyanine green test, plasma or serum levels of HGF or cytokines, circulating blood cells and genomic biomarkers. The albumin–bilirubin (ALBI) score incorporates albumin and bilirubin to subdivide patients with well-compensated underlying liver dysfunction (Child–Pugh score A) into two distinct groups. This review provides an overview of the current knowledge on circulating biomarkers and blood-based scores in patients with malignant liver disease undergoing radiotherapy and outlines potential future directions.

Development and validation of genomic predictors of radiation sensitivity using preclinical data

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.

Adaptive Magnetic Resonance-Guided Stereotactic Body Radiotherapy: The Next Step in the Treatment of Renal Cell Carcinoma.

Adaptive MR-guided radiotherapy (MRgRT) is a new treatment paradigm and its role as a non-invasive treatment option for renal cell carcinoma is evolving. The early clinical experience to date shows that real-time plan adaptation based on the daily MRI anatomy can lead to improved target coverage and normal tissue sparing. Continued technological innovations will further mitigate the challenges of organ motion and enable more advanced treatment adaptation, and potentially lead to enhanced oncologic outcomes and preservation of renal function. Future applications look promising to make a positive clinical impact and further the personalization of radiotherapy in the management of renal cell carcinoma.

OC-0488: Prognostic biomarkers and targets for personalization of radiotherapy of HNSCC: CD44v6

OC-0488: Prognostic biomarkers and targets for personalization of radiotherapy of HNSCC: CD44v6