Development of an Ex Vivo Functional Assay for Prediction of Irradiation Related Toxicity in Healthy Oral Mucosa Tissue.

Radiotherapy in the head-and-neck area is one of the main curative treatment options. However, this comes at the cost of varying levels of normal tissue toxicity, affecting up to 80% of patients. Mucositis can cause pain, weight loss and treatment delays, leading to worse outcomes and a decreased quality of life. Therefore, there is an urgent need for an approach to predicting normal mucosal responses in patients prior to treatment. We here describe an assay to detect irradiation responses in healthy oral mucosa tissue. Mucosa specimens from the oral cavity were obtained after surgical resection, cut into thin slices, irradiated and cultured for three days. Seven samples were irradiated with X-ray, and three additional samples were irradiated with both X-ray and protons. Healthy oral mucosa tissue slices maintained normal morphology and viability for three days. We measured a dose-dependent response to X-ray irradiation and compared X-ray and proton irradiation in the same mucosa sample using standardized automated image analysis. Furthermore, increased levels of inflammation-inducing factors-major drivers of mucositis development-could be detected after irradiation. This model can be utilized for investigating mechanistic aspects of mucositis development and can be developed into an assay to predict radiation-induced toxicity in normal mucosa.

A Radiosensitivity Prediction Model Developed Based on Weighted Correlation Network Analysis of Hypoxia Genes for Lower-Grade Glioma.

Background and Purpose: Hypoxia is one of the basic characteristics of the physical microenvironment of solid tumors. The relationship between radiotherapy and hypoxia is complex. However, there is no radiosensitivity prediction model based on hypoxia genes. We attempted to construct a radiosensitivity prediction model developed based on hypoxia genes for lower-grade glioma (LGG) by using weighted correlation network analysis (WGCNA) and least absolute shrinkage and selection operator (Lasso). Methods: In this research, radiotherapy-related module genes were selected after WGCNA. Then, Lasso was performed to select genes in patients who received radiotherapy. Finally, 12 genes (AGK, ETV4, PARD6A, PTP4A2, RIOK3, SIGMAR1, SLC34A2, SMURF1, STK33, TCEAL1, TFPI, and UROS) were included in the model. A radiosensitivity-related risk score model was established based on the overall rate of The Cancer Genome Atlas (TCGA) dataset in patients who received radiotherapy. The model was validated in TCGA dataset and two Chinese Glioma Genome Atlas (CGGA) datasets. A novel nomogram was developed to predict the overall survival of LGG patients. Results: We developed and verified a radiosensitivity-related risk score model based on hypoxia genes. The radiosensitivity-related risk score served as an independent prognostic indicator. This radiosensitivity-related risk score model has prognostic prediction ability. Moreover, a nomogram integrating risk score with age and tumor grade was established to perform better for predicting 1-, 3-, and 5-year survival rates. Conclusions: We developed and validated a radiosensitivity prediction model that can be used by clinicians and researchers to predict patient survival rates and achieve personalized treatment of LGG.

Development and Validation of a Radiosensitivity Prediction Model for Lower Grade Glioma Based on Spike-and-Slab Lasso.

BACKGROUND AND PURPOSE: Lower grade glioma (LGG) is one of the leading causes of death world worldwide. We attempted to develop and validate a radiosensitivity model for predicting the survival of lower grade glioma by using spike-and-slab lasso Cox model. METHODS: In this research, differentially expressed genes based on tumor microenvironment was obtained to further analysis. Log-rank test was used to identify genes in patients who received radiotherapy and patients who did not receive radiotherapy, respectively. Then, spike-and-slab lasso was performed to select genes in patients who received radiotherapy. Finally, three genes (INA, LEPREL1 and PTCRA) were included in the model. A radiosensitivity-related risk score model was established based on overall rate of TCGA dataset in patients who received radiotherapy. The model was validated in TCGA dataset that PFS as endpoint and two CGGA datasets that OS as endpoint. A novel nomogram integrated risk score with age and tumor grade was developed to predict the OS of LGG patients. RESULTS: We developed and verified a radiosensitivity-related risk score model. The radiosensitivity-related risk score is served as an independent prognostic indicator. This radiosensitivity-related risk score model has prognostic prediction ability. Moreover, the nomogram integrated risk score with age and tumor grade was established to perform better for predicting 1, 3, 5-year survival rate. CONCLUSIONS: This model can be used by clinicians and researchers to predict patient's survival rates and achieve personalized treatment of LGG.

Relationship between thyroid tumor radiosensitivity and nuclear localization of DNA-dependent protein kinase catalytic subunit.

Thyroid tumors are the most common types of endocrine malignancies and are commonly treated with radioactive iodine (RAI) to destroy remaining cancer cells following surgical intervention. We previously reported that the expression levels of double-stranded DNA-dependent protein kinase catalytic subunit (DNA-PKcs), which plays a key role in non-homologous end joining, are correlated with the radiosensitivity of cancer cells. Specifically, cells expressing high levels of DNA-PKcs exhibited radiation resistance, whereas cells expressing low levels were sensitive to radiation treatment. In this study, we observed full-length native DNA-PKcs (460 kDa) in radiation-resistant FRO and KTC-2 cells through western blot analysis using an antibody against the C-terminus of DNA-PKcs. In contrast, cleaved DNA-PKcs (175 kDa) were observed in radiation-sensitive TPC-1 and KTC-1 cells. Almost equal amounts of DNA-PKcs were observed in moderately radiation-sensitive WRO cells. We also describe a simple method for the prediction of radiation therapy efficacy in individual cases of thyroid cancers based on staining for DNA-PKcs in human cancer cell lines. Immunofluorescent staining showed that native DNA-PKcs was localized largely in the cytoplasm and only rarely localized in the nuclei of radiation-resistant thyroid cancer cells, whereas in radiation-sensitive cancer cells a 175-kDa cleaved C-terminal fragment of DNA-PKcs was localized mainly inside the nuclei. Therefore, DNA-PKcs moved to the nucleus after γ-ray irradiation. Our results suggest a new method for classifying human thyroid tumors based on their cellular distribution patterns of DNA-PKcs in combination with their radiosensitivity.

Identification of a radiation sensitivity gene expression profile in primary fibroblasts derived from patients who developed radiotherapy-induced fibrosis.

BACKGROUND AND PURPOSE: During radiotherapy, normal tissue is unavoidably exposed to radiation which results in severe normal tissue reactions in a small fraction of patients. Because those who are sensitive cannot be determined prior to radiotherapy, the doses are limited to all patients to avoid an unacceptable number of severe adverse normal tissue responses. This limitation restricts the optimal treatment for individuals who are more tolerant to radiation. Genetic variation is a likely source for the normal tissue radiosensitivity variation observed between individuals. Therefore, understanding the radiation response at the genomic level may provide knowledge to develop individualized treatment and improve radiotherapy outcomes. MATERIAL AND METHODS: Exon arrays were utilized to compare the basal expression profile between cell lines derived from six cancer patients with and without severe fibrosis. These data were supported by qRT-PCR and RNA-Seq techniques. RESULTS: A set of genes (FBN2, FST, GPRC5B, NOTCH3, PLCB1, DPT, DDIT4L and SGCG) were identified as potential predictors for radiation-induced fibrosis. Many of these genes are associated with TGFß or retinoic acid both having known links to fibrosis. CONCLUSION: A combinatorial gene expression approach provides a promising strategy to predict fibrosis in cancer patients prior to radiotherapy.