Division of Pharmacy, University of Manchester, Manchester, UK.
Clin Oncol (R Coll Radiol). 2023 Sep;35(9):565-570. doi: 10.1016/j.clon.2023.02.018. Epub 2023 Mar 8.
To 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.
The 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.
Preclinically, 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).
These results suggest that RSI does not explain a sufficient amount of the outcome variance to be used within dose-adjustment algorithms.
探索被称为“辐射敏感性指数”（RSI）的放射敏感性特征的临床前和最新临床证据，评估其作为剂量调整算法输入的适用性。
从发表 RSI 的原始临床前测试集数据中收集并重新分析，通过比较 2 Gy（SF2）的观察与预测生存率来进行比较。此外，还将 RSI 的预测能力与随机猜测进行比较。临床数据来自最近发表的数据集，其中包括 RSI 值、总生存结果、放疗剂量和六种癌症（脑胶质瘤、三阴性乳腺癌、子宫内膜癌、黑色素瘤、胰腺癌和肺癌）的肿瘤部位。使用 Cox 比例风险模型评估：（i）是否调整 RSI 可以阐明剂量反应，（ii）RSI 和剂量之间是否存在交互作用，且具有良好的精度。
临床前，RSI 与观察到的 SF2 之间呈负相关（Spearman 的 rho = -0.61），在去除白血病细胞系后仍然为负相关。此外，随机猜测与 SF2 的相关性比 RSI 更好，在整个数据集上有 98%的时间，在去除白血病细胞系后有 80%的时间。临床前数据表明，RSI 不能比随机猜测更好地解释 SF2 的方差。在调整 RSI 后，并未观察到剂量反应（风险比= 1.00，95%置信区间 0.97-1.04；P = 0.876），也未发现 RSI 和剂量之间存在交互作用（P = 0.844）。
这些结果表明，RSI 不能解释足够数量的结果方差，因此不能在剂量调整算法中使用。
