PARP1 modulates METTL3 promoter chromatin accessibility and associated LPAR5 RNA m6A methylation to control cancer cell radiosensitivity

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Targeting phosphomevalonate kinase enhances radiosensitivity via ubiquitination of the replication protein A1 in lung cancer cells

Radiotherapy (RT) plays an important role in localized lung cancer treatments. Although RT locally targets and controls malignant lesions, RT resistance prevents RT from being an effective treatment for lung cancer. In this study, we identified phosphomevalonate kinase (PMVK) as a novel radiosensitizing target and explored its underlying mechanism. We found that cell viability and survival fraction after RT were significantly decreased by PMVK knockdown in lung cancer cell lines. RT increased apoptosis, DNA damage, and G2/M phase arrest after PMVK knockdown. Also, after PMVK knockdown, radiosensitivity was increased by inhibiting the DNA repair pathway, homologous recombination, via downregulation of replication protein A1 (RPA1). RPA1 downregulation was induced through the ubiquitin–proteasome system. Moreover, a stable shRNA PMVK mouse xenograft model verified the radiosensitizing effects of PMVK in vivo. Furthermore, PMVK expression was increased in lung cancer tissues and significantly correlated with patient survival and recurrence. Our results demonstrate that PMVK knockdown enhances radiosensitivity through an impaired HR repair pathway by RPA1 ubiquitination in lung cancer, suggesting that PMVK knockdown may offer an effective therapeutic strategy to improve the therapeutic efficacy of RT.     

Radiosensitivity Index is Not Fit to be Used for Dose Adjustments: A Pan-Cancer Analysis

AimsTo explore the preclinical and latest clinical evidence of the radiation sensitivity signature termed ‘radiosensitivity index’ (RSI), to assess its suitability as an input into dose-adjustment algorithms.Materials and methodsThe original preclinical test-set data from the publication where RSI was derived were collected and reanalysed by comparing the observed versus predicted survival fraction at 2 Gy (SF2). In addition, the predictive capability of RSI was also compared to random guessing. Clinical data were collected from a recently published dataset that included RSI values, overall survival outcomes, radiotherapy dose and tumour site for six cancers (glioma, triple-negative breast, endometrial, melanoma, pancreatic and lung cancer). Cox proportional hazards models were used to assess: (i) does adjusting for RSI elucidate a dose response and (ii) does an interaction between RSI and dose exist with good precision.ResultsPreclinically, RSI showed a negative correlation (Spearman's rho = –0.61) between observed and predicted SF2, which remained negative after removing leukaemia cell lines. Furthermore, random guesses showed better correlation to SF2 than RSI, 98% of the time on the full dataset and 80% after removing leukaemia cell lines. The preclinical data show that RSI does not explain the variance in SF2 better than random guessing. Clinically, a dose response was not seen after adjusting for RSI (hazard ratio = 1.00, 95% confidence interval 0.97–1.04; P = 0.876) and no evidence of an interaction between RSI and dose was found (P = 0.844).ConclusionsThese results suggest that RSI does not explain a sufficient amount of the outcome variance to be used within dose-adjustment algorithms.