Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio; Department of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio; Case Western Reserve University School of Medicine, Cleveland, Ohio.
Case Western Reserve University School of Medicine, Cleveland, Ohio.
J Thorac Oncol. 2021 Mar;16(3):428-438. doi: 10.1016/j.jtho.2020.11.008. Epub 2020 Dec 8.
Cancer sequencing efforts have revealed that cancer is the most complex and heterogeneous disease that affects humans. However, radiation therapy (RT), one of the most common cancer treatments, is prescribed on the basis of an empirical one-size-fits-all approach. We propose that the field of radiation oncology is operating under an outdated null hypothesis: that all patients are biologically similar and should uniformly respond to the same dose of radiation.
We have previously developed the genomic-adjusted radiation dose, a method that accounts for biological heterogeneity and can be used to predict optimal RT dose for an individual patient. In this article, we use genomic-adjusted radiation dose to characterize the biological imprecision of one-size-fits-all RT dosing schemes that result in both over- and under-dosing for most patients treated with RT. To elucidate this inefficiency, and therefore the opportunity for improvement using a personalized dosing scheme, we develop a patient-specific competing hazards style mathematical model combining the canonical equations for tumor control probability and normal tissue complication probability. This model simultaneously optimizes tumor control and toxicity by personalizing RT dose using patient-specific genomics.
Using data from two prospectively collected cohorts of patients with NSCLC, we validate the competing hazards model by revealing that it predicts the results of RTOG 0617. We report how the failure of RTOG 0617 can be explained by the biological imprecision of empirical uniform dose escalation which results in 80% of patients being overexposed to normal tissue toxicity without potential tumor control benefit.
Our data reveal a tapestry of radiosensitivity heterogeneity, provide a biological framework that explains the failure of empirical RT dose escalation, and quantify the opportunity to improve clinical outcomes in lung cancer by incorporating genomics into RT.
癌症测序工作表明，癌症是影响人类的最复杂和异质的疾病。然而，放射治疗（RT）是最常见的癌症治疗方法之一，它是基于经验主义的一刀切的方法来开处方的。我们认为放射肿瘤学领域正在使用过时的无效假设：所有患者在生物学上都是相似的，应该均匀地对相同剂量的辐射做出反应。
我们之前开发了基因组调整后的辐射剂量，这是一种考虑生物学异质性的方法，可用于预测个体患者的最佳 RT 剂量。在本文中，我们使用基因组调整后的辐射剂量来描述导致大多数接受 RT 治疗的患者过度和剂量不足的一刀切 RT 剂量方案的生物学不准确性。为了阐明这种低效性，从而为使用个性化剂量方案提供改进的机会，我们开发了一种患者特异性的竞争风险式数学模型，该模型结合了肿瘤控制概率和正常组织并发症概率的标准方程。该模型通过使用患者特异性基因组学个性化 RT 剂量来同时优化肿瘤控制和毒性。
使用来自两个前瞻性收集的 NSCLC 患者队列的数据，我们通过揭示它可以预测 RTOG 0617 的结果来验证竞争风险模型。我们报告了 RTOG 0617 的失败如何可以通过经验性统一剂量递增的生物学不准确性来解释，这导致 80%的患者暴露于正常组织毒性而没有潜在的肿瘤控制益处。
我们的数据揭示了放射敏感性异质性的织锦，提供了一个可以解释经验性 RT 剂量递增失败的生物学框架，并量化了通过将基因组学纳入 RT 来改善肺癌临床结果的机会。
