Kishan Amar U, McGreevy Kristen, Valle Luca, Steinberg Michael, Neilsen Beth, Casado Maria, Cao Minsong, Telesca Donatello, Weidhaas Joanne B
Department of Radiation Oncology, University of California Los Angeles, Los Angeles, California.
Department of Biostatistics, University of California Los Angeles, Los Angeles, California.
Clin Cancer Res. 2025 Jun 13;31(12):2530-2538. doi: 10.1158/1078-0432.CCR-24-3951.
Although radiotherapy (RT) is one of the primary treatment modalities used in the treatment of cancer, patients often experience toxicity during or after treatment. RT-induced genitourinary (GU) toxicity is a significant survivorship challenge for patients with prostate cancer, but identifying those at risk has been challenging. Herein, we attempt (i) to validate a previously identified biomarker of late RT-induced GU toxicity, PROSTOX, consisting primarily of miRNA-based germline biomarkers (mirSNPs), and (ii) investigate the possibility of temporally and genetically defining other forms of RT-associated GU toxicity.
We included 148 patients enrolled in Magnetic Resonance Imaging-Guided Stereotactic Body Radiotherapy for Prostate Cancer (MIRAGE; NCT04384770), a trial comparing MRI- versus CT-guided prostate stereotactic body RT. Linear regression was used to evaluate the association between PROSTOX score and late GU grade toxicity. Machine learning approaches were used to develop predictive models for acute toxicity and chronic GU toxicity, and the accuracy of all models was assessed using AUC metrics. A comparative Gene Ontology analysis was performed.
PROSTOX accurately predicts late GU toxicity, achieving an AUC of 0.76, and demonstrates strong correlation with GU toxicity grade (p-1.2E-9). mirSNP-based signatures can distinguish acute RT-associated GU toxicity and chronic RT-associated GU toxicity (AUCs of 0.770 and 0.763, respectively). Finally, Gene Ontology analysis identifies unique pathways involved in each form of GU toxicity: acute, chronic, and late.
These findings provide strong evidence for the continued application of mirSNPs to predict toxicity to RT and act as a path for the continued personalization of RT with improved patient outcomes.
尽管放射治疗（RT）是癌症治疗中使用的主要治疗方式之一，但患者在治疗期间或治疗后常出现毒性反应。RT引起的泌尿生殖系统（GU）毒性是前列腺癌患者生存的重大挑战，但识别有风险的患者一直具有挑战性。在此，我们试图（i）验证先前确定的晚期RT诱导的GU毒性生物标志物PROSTOX，其主要由基于miRNA的种系生物标志物（mirSNP）组成，以及（ii）研究在时间和基因上定义其他形式的RT相关GU毒性的可能性。
我们纳入了148名参加前列腺癌磁共振成像引导立体定向体部放射治疗（MIRAGE；NCT04384770）的患者，该试验比较了MRI引导与CT引导的前列腺立体定向体部RT。采用线性回归评估PROSTOX评分与晚期GU毒性分级之间的关联。使用机器学习方法开发急性毒性和慢性GU毒性的预测模型，并使用AUC指标评估所有模型的准确性。进行了比较基因本体分析。
PROSTOX准确预测晚期GU毒性，AUC为0.76，并与GU毒性分级显示出强相关性（p = 1.2E - 9）。基于mirSNP的特征可以区分急性RT相关GU毒性和慢性RT相关GU毒性（AUC分别为0.770和0.763）。最后，基因本体分析确定了每种形式的GU毒性（急性、慢性和晚期）所涉及的独特途径。
这些发现为继续应用mirSNP预测RT毒性提供了有力证据，并为通过改善患者预后实现RT的持续个性化提供了途径。
