Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States.
Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States.
Radiother Oncol. 2023 Aug;185:109723. doi: 10.1016/j.radonc.2023.109723. Epub 2023 May 25.
Late radiation-induced hematuria can develop in prostate cancer patients undergoing radiotherapy and can negatively impact the quality-of-life of survivors. If a genetic component of risk could be modeled, this could potentially be the basis for modifying treatment for high-risk patients. We therefore investigated whether a previously developed machine learning-based modeling method using genome-wide common single nucleotide polymorphisms (SNPs) can stratify patients in terms of the risk of radiation-induced hematuria.
We applied a two-step machine learning algorithm that we previously developed for genome-wide association studies called pre-conditioned random forest regression (PRFR). PRFR includes a pre-conditioning step, producing adjusted outcomes, followed by random forest regression modeling. Data was from germline genome-wide SNPs for 668 prostate cancer patients treated with radiotherapy. The cohort was stratified only once, at the outset of the modeling process, into two groups: a training set (2/3 of samples) for modeling and a validation set (1/3 of samples). Post-modeling bioinformatics analysis was conducted to identify biological correlates plausibly associated with the risk of hematuria.
The PRFR method achieved significantly better predictive performance compared to other alternative methods (all p < 0.05). The odds ratio between the high and low risk groups, each of which consisted of 1/3 of samples in the validation set, was 2.87 (p = 0.029), implying a clinically useful level of discrimination. Bioinformatics analysis identified six key proteins encoded by CTNND2, GSK3B, KCNQ2, NEDD4L, PRKAA1, and TXNL1 genes as well as four statistically significant biological process networks previously shown to be associated with the bladder and urinary tract.
The risk of hematuria is significantly dependent on common genetic variants. The PRFR algorithm resulted in a stratification of prostate cancer patients at differential risk levels of post-radiotherapy hematuria. Bioinformatics analysis identified important biological processes involved in radiation-induced hematuria.
接受放疗的前列腺癌患者可能会出现迟发性放射性血尿，这会对生存者的生活质量产生负面影响。如果能够对风险的遗传因素进行建模，那么这可能为修改高危患者的治疗提供依据。因此，我们研究了先前使用全基因组常见单核苷酸多态性（SNP）开发的基于机器学习的建模方法是否可以根据放射性血尿的风险对患者进行分层。
我们应用了一种两步机器学习算法，即我们之前开发的用于全基因组关联研究的预条件随机森林回归（PRFR）。PRFR 包括一个预条件步骤，生成调整后的结果，然后进行随机森林回归建模。数据来自接受放疗的 668 例前列腺癌患者的种系全基因组 SNPs。该队列仅在建模过程开始时进行了一次分层，分为两组：建模用的训练集（样本的 2/3）和验证集（样本的 1/3）。建模后进行了生物信息学分析，以确定与血尿风险相关的生物学相关性。
与其他替代方法相比，PRFR 方法的预测性能显著提高（均 p<0.05）。验证集中每组包含 1/3 的样本，高风险组和低风险组之间的优势比为 2.87（p=0.029），表明具有临床有用的区分能力。生物信息学分析确定了 CTNND2、GSK3B、KCNQ2、NEDD4L、PRKAA1 和 TXNL1 基因编码的六个关键蛋白，以及四个先前显示与膀胱和泌尿道相关的统计学显著的生物学过程网络。
血尿风险与常见遗传变异显著相关。PRFR 算法导致前列腺癌患者在接受放疗后血尿的风险分层中存在差异。生物信息学分析确定了与放射性血尿相关的重要生物学过程。
