Добірка наукової літератури з теми "Rs of radiobiology"

Background: The outcome of radiation therapy, the duration and quality of life of cancer patients significantly depend on the radiosensitivity (RS) of a cancerous tumor, and the duration of the patient’s relapse-free period is largely determined by the degree of its radioresistance (RR). Today the results of molecular mechanism investigation of cancer radioresistance and the classification of cancer cells according to their radiophenotypes mostly contribute to improving prognosis methods of treatment outcomes and increasing effectiveness of radiation therapy. In this work, we developed a classification method of cancer cells according to their radiosensitivity using machine learning based on the data analysis of clonogenic cell survival under ionizing radiation. Material and methods: The method consists of clustering parameters of experimental dose-effect relationships, which were approximated using the equation of a linear-quadratic (LQ) model, which is used to evaluate RS of cancer cells in radiobiology. The training of the statistical model included published experimental dataset of 96 cancer cell lines, for which parameters a, b and their ratio a/b of the LQ model were determined. Classification of cancer cells according to their radiosensitivity was carried out based on principal component analysis (PCA) in the parameter space (a, a/b), k-means clustering and hierarchical clustering methods. Results: Application of the developed statistical model to a large dataset of cancer cells made it possible to reliably separate radiosensitive and radioresistant (RR) cells into two clusters according to the parameters a and a/b. Application of the model to cancer cells with acquired RR, in which RS was suppressed as a result of exposure to irradiation or hypoxia, allowed tracing the shift of parent cells’ parameters from the RS cluster to the RR cell cluster. To study the genetic mechanisms of radiosensitivity, we performed bioinformatic analysis of the mutation distribution in genes encoding proteins in the cellular signalling pathways of cancer cells, i.e. proliferation, apoptosis, repair of damaged DNA molecules and antioxidant defence cellular system. Conclusion: The developed statistical model of radiophenotypic classification of cancer cells based on their radiosensitivity can be used in the development of radiation therapy treatment plans taking into account radiosensitivity of patient’s tumour. The model may be also helpful in a joint analysis of the phenotypic and genotypic characteristics of cancer cells, aiming at the elucidation of the molecular and genetic mechanisms of radiosensitivity and development of biomarkers of radioresistance.

La radiothérapie est l'un des principaux traitements du cancer. Malgré son utilisation intensive en pratique clinique, son efficacité dépend de plusieurs facteurs. Plusieurs études ont montré que la réponse tumorale à la radiothérapie diffère d'un patient à l'autre. En effet, la réponse de la tumeur est influencée par plusieurs facteurs comme l'hypoxie et des multiples interactions entre le microenvironnement tumoral et les cellules saines. Cinq concepts biologiques majeurs appelés les « 5 Rs » qui résument ces interactions ont vu le jour. Ces concepts incluent la réoxygénation, la réparation cellulaire, la redistribution cellulaire dans le cycle, la radiosensibilité intrinsèque et la repopulation tumorale. La stratégie de traitement optimale doit tenir compte de ces « 5 Rs ». Dans cette étude, nous avons proposé dans un premier temps une approche de modélisation d'oxygénation qui peut être considérée comme un processus d'optimisation de traitement en absence de données concernant l'oxygène. Nous avons utilisé un modèle multi-échelle afin de prédire les effets de la radiothérapie sur la croissance tumorale en utilisant une base des images de tomographie par émission de positons (PET). Ensuite, nous avons inclus dans notre modèle les «5 Rs » de la radiothérapie, afin de prédire les effets des rayonnements sur la croissance tumorale. Enfin, nous avons présenté une étude sur l'effet de différents types de fractionnement sur la réponse tumorale à la radiothérapie
Radiotherapy is one of the principal cancer treatments. Despite its intensive use in clinical practice, itseffectiveness depends on several factors. Several studies showed that the tumor response to radiotherapy differ from one patient to another. The response of tumor is influenced by several factors like hypoxia and multiple interactions between the tumor microenvironment and healthy cells. Five major biologic concepts called “5 Rs” resume these interactions. These concepts include reoxygenation, DNA damage-repair, cell cycle redistribution, cellular radiosensitivity and cellular repopulation.The optimal treatment strategy must consider these “5 Rs". In this study, we proposed as a first an approach to oxygenation modeling that can be considered as an optimization process in the absence of data concerning oxygen. We used a multi-scale model to predict the effects of radiotherapy on tumor growth based on information extracted from positron-emission tomography (PET) images. Then, we included to our model the ‘’5 Rs’’ of radiotherapy, to predict the effects of radiation on tumor growth. Finally, we presented a study of the effect of different types of fractionations on tumor response to radiotherapy