## 4 The Importance of Biomarkers in Monitoring RT Toxicity
### Cell-Free DNA-Based Biomarkers
On the other hand, the low patient numbers in the proof-of-concept studies are still a critical issue for the validity of these biomarkers, since there is still an urgent need for randomized control trials with large comparable cohorts. Another important disadvantage in such investigations is the absence of patient groups having undergone RT under the variation in other factors affecting potentially the same investigated biomarker. Last but not least, most studies addressing radiosensitivity biomarkers do not discern those cases where cancer patients have undergone only RT (notcombined with chemotherapy), so as to observe promptly the response of the cells/tissues to irradiation and conclude the pure connection of biomarkers to IR. When RT takes place following chemotherapy, the latter therapy affects the whole biochemistry of the patient and complicates the study of possible biomarkers related to radiosensitivity. It is true that in most oncological cases, nowadays, a combined therapeutical strategy is applied, comprising both RT and chemotherapy. Accordingly, a priority in the search for radiosensitivity biomarkers would be to conduct large studies in the cases of cancer patients who undergo only RT. Another future trend in this field should be the use of highly reproducible assays capable of providing rapidly reliable results. Moreover, the use of easy-to-collect tissues (e.g., blood or other readily accessible biological fluids) and economically affordable assays and equipment limits the clinical translation of radiosensitivity biomarkers. Computational approaches and Artificial Intelligence (AI) have revolutionized biomarker identification, although such approaches in the field of radiotherapy and responses are still evolving. Transcriptomics, genomics, proteomics, metabolomics, and glycomics are some of the \"-omics\" data that are converging to identify critical bioindicators and biomarkers that can be used to train models. These can be further combined with radiomics to identify populations that could be radiosensitive [186; 187; 188]. For example, [PERSON] et al. combined gene expression, DNA methylation, and clinical data to identify eight radiosensitivity-related genes (AR, WBP1, AKR1E2, FANCG, NR2C2 AP, CXCR4, SYNE4, and WFDC2) [189], while [PERSON] et al. identified 12 genes (BEST2, TMPRSS15, FGF19, ALP1, KCNB2, CLDN6, IL17 REL, RORB, DDX25, TDRD9, CELF3, and FABP7) that can aid in identifying population responses in head and neck cancer patients [190]. Big data from clinical patient cohorts are becoming publicly available. At the same time, machine learning algorithms are employed for radiosensitivity predictions [191; 192], while artificial neural networks are increasingly being utilized to uncover new patterns  
Figure 1: Biomarker overview for assessing radiosensitivity and radiotoxicity. On the bottom right-hand corner, the nucleus is depicted in apoptosis (the nuclear envelope is shown disassembled and the chromatin broken up into fragments) (the figure was created using Servier Medical Art, available at ”[[http://smart.servier.com/](http://smart.servier.com/)]([http://smart.servier.com/](http://smart.servier.com/)) (accessed on 10 September 2023)”. and increase radiosensitivity predictions [193]. Starting in 2020, the Department of Energy (DOE) Office of Science directed National Laboratories in the United States to explore the potential of machine learning and AI capabilities based on models generated through a DOE--National Cancer Institute (NCI) collaboration, termed CANDLE (CANcer Distributed Learning Environment) that focused on precision medicine [194,195]. This particular field is gaining considerable momentum and may provide significant guidance in the field of radiotherapy for effective personalized therapeutic regimens that are dynamic in nature based on daily responses to the treatments. Research on genome and proteome deregulation in relation to radiosensitivity vs toxicity is the key to the future development of'smart' and adaptive oncologic therapies.