Effects of p53 Mutation on Tumor Radiosensitivity Estimated by Predictive Models

p53 gene mutations are common in various cancers and may provide insights in predicting tumor radiosensitivity. This study aimed to assess the effect of p53 mutations on radiosensitivity using the intrinsic radiosensitivity index (RSI) across publicly available cancer cohorts. Gene expression data, mutation data, and clinical information were obtained from the Cancer Genome Atlas dataset. RSI, calculated from the expression of 10 specific genes, was used to evaluate radiosensitivity. Additional models were used to assess the tumor microenvironment status. p53 mutations were prevalent in several types of cancer. Notably, RSI models indicated reduced predicted radiosensitivity in patients with p53 mutations compared to those without mutations, only in head and neck squamous cell carcinoma (HNSC). In contrast, p53 mutations did not significantly decrease predicted radiosensitivity in other cancers. The association between p53 mutations and the predicted radioresistant phenotype disappeared when the cohort was controlled for p53 and p16 status in HNSC. Similarly, the estimated tumor microenvironment status was unaffected by p53 mutations. These findings suggest that predicted radiosensitivity is more strongly influenced by p16 status than by p53 mutations, indicating that p53 status alone may not be a reliable predictive marker for radiosensitivity in HNSC.

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Journal
Radiation Research
Published
2025-06-30
DOI
https://doi.org/10.1667/rade-24-00260.1
Citations
1
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
1.36
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article

Effects of p53 Mutation on Tumor Radiosensitivity Estimated by Predictive Models

Hitomi Nojima, Masahiko Miura, Atsushi Kaida
1 citations
Radiation Research
Radiomics and Machine Learning in Medical Imaging
1.36
article

Effects of p53 Mutation on Tumor Radiosensitivity Estimated by Predictive Models

Hitomi Nojima, Masahiko Miura, Atsushi Kaida
article en
1 citations

Abstract

p53 gene mutations are common in various cancers and may provide insights in predicting tumor radiosensitivity. This study aimed to assess the effect of p53 mutations on radiosensitivity using the intrinsic radiosensitivity index (RSI) across publicly available cancer cohorts. Gene expression data, mutation data, and clinical information were obtained from the Cancer Genome Atlas dataset. RSI, calculated from the expression of 10 specific genes, was used to evaluate radiosensitivity. Additional models were used to assess the tumor microenvironment status. p53 mutations were prevalent in several types of cancer. Notably, RSI models indicated reduced predicted radiosensitivity in patients with p53 mutations compared to those without mutations, only in head and neck squamous cell carcinoma (HNSC). In contrast, p53 mutations did not significantly decrease predicted radiosensitivity in other cancers. The association between p53 mutations and the predicted radioresistant phenotype disappeared when the cohort was controlled for p53 and p16 status in HNSC. Similarly, the estimated tumor microenvironment status was unaffected by p53 mutations. These findings suggest that predicted radiosensitivity is more strongly influenced by p16 status than by p53 mutations, indicating that p53 status alone may not be a reliable predictive marker for radiosensitivity in HNSC.

Radiation ResearchVol. 204(3)
Tokyo Medical and Dental University (JP), Institute of Science Tokyo (JP)
Good health and well-being
Openalex Percentile: Top 18%
Radiomics and Machine Learning in Medical Imaging
1.36
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Effects of p53 Mutation on Tumor Radiosensitivity Estimated by Predictive Models — Hitomi Nojima, Masahiko Miura, et al. · Radiation Research (2025) | TGRS Research Map | TGRS