现代先进的放射治疗技术提高了放射治疗实施的精确度和准确性，最终的计划是根据个体解剖结构高度个性化的。对个体肿瘤生物学的适应仍然难以捉摸。对具有内在放射敏感性的生物标志物的需求尚未得到满足，这些生物标志物可以预测肿瘤对放射的反应，以促进个体化决策、剂量和治疗计划。在过去的几十年里，高通量分子生物学技术的使用导致了新发现的癌症生物标志物的爆炸式增长。基因表达特征现在在临床中常规使用，以帮助做出有关辅助全身治疗的决策。它们作为放射治疗生物标志物具有巨大的潜力。 2015 年发表的一项系统综述仅报告了五项特征研究，评估了特征在临床队列中预测放疗益处的能力。这项更新的系统综述涵盖了过去十年报告的更多研究。另外还确定了 27 项研究。总共识别了 22 个不同的签名（2015 年之前 5 个，2015 年之后 17 个）。十七个特征是“放射敏感性”特征，五个是乳腺癌预后特征，旨在识别局部复发风险增加的患者，因此更有可能从辅助放射中受益。大多数签名 (15/22) 尚未超越开发的发现阶段，没有合适的经过验证的临床级检测可供应用。很少有特征（4/17“放射敏感性”特征）经过任何基于实验室的生物学验证其预测肿瘤放射敏感性的能力。迄今为止，尚未在 III 期生物标志物主导的试验中对特征进行前瞻性评估，也没有推荐在临床指南中常规使用。对两种乳腺癌预后特征的 III 期前瞻性评估正在进行中。最有希望的放射敏感性特征仍然是放射敏感性指数（RSI），它用于计算基因组调整辐射剂量（GARD）。目前正在进行一项针对三阴性乳腺癌的 RSI/GARD 的 II 期前瞻性生物标志物主导研究。未来几年人们热切期待这些试验的结果。该领域未来的工作应重点关注（1）稳健的生物学验证； (2) 与具有剂量方差的大型放射治疗随机对照试验一起建立生物库（以证明放射敏感性特征与剂量之间的相互作用）； (3) 对可在当前医疗保健基础设施内提供的临床级成本效益测定进行验证； (4) 与辐射反应其他决定因素的生物标志物整合。

Modern advanced radiotherapy techniques have improved the precision and accuracy of radiotherapy delivery, with resulting plans being highly personalised based on individual anatomy. Adaptation for individual tumour biology remains elusive. There is an unmet need for biomarkers of intrinsic radiosensitivity that can predict tumour response to radiation to facilitate individualised decision-making, dosing and treatment planning. Over the last few decades, the use of high throughput molecular biology technologies has led to an explosion of newly discovered cancer biomarkers. Gene expression signatures are now used routinely in clinic to aid decision-making regarding adjuvant systemic therapy. They have great potential as radiotherapy biomarkers. A previous systematic review published in 2015 reported only five studies of signatures evaluated for their ability to predict radiotherapy benefits in clinical cohorts. This updated systematic review encompasses the expanded number of studies reported in the last decade. An additional 27 studies were identified. In total, 22 distinct signatures were recognised (5 pre-2015, 17 post-2015). Seventeen signatures were 'radiosensitivity' signatures and five were breast cancer prognostic signatures aiming to identify patients at an increased risk of local recurrence and therefore were more likely to benefit from adjuvant radiation. Most signatures (15/22) had not progressed beyond the discovery phase of development, with no suitable validated clinical-grade assay for application. Very few signatures (4/17 'radiosensitivity' signatures) had undergone any laboratory-based biological validation of their ability to predict tumour radiosensitivity. No signatures have been assessed prospectively in a phase III biomarker-led trial to date and none are recommended for routine use in clinical guidelines. A phase III prospective evaluation is ongoing for two breast cancer prognostic signatures. The most promising radiosensitivity signature remains the radiosensitivity index (RSI), which is used to calculate a genomic adjusted radiation dose (GARD). There is an ongoing phase II prospective biomarker-led study of RSI/GARD in triple negative breast cancer. The results of these trials are eagerly anticipated over the coming years. Future work in this area should focus on (1) robust biological validation; (2) building biobanks alongside large radiotherapy randomised controlled trials with dose variance (to demonstrate an interaction between radiosensitivity signature and dose); (3) a validation of clinical-grade cost-effective assays that are deliverable within current healthcare infrastructure; and (4) an integration with biomarkers of other determinants of radiation response.