Gargya Péter, Bálint Bálint László
Genomic Medicine and Bioinformatics Core Facility, Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen, Egyetem tér 1, 4032 Debrecen, Hungary.
Cancers (Basel). 2021 Aug 27;13(17):4348. doi: 10.3390/cancers13174348.
The tumor grade of endometrioid endometrial cancer is used as an independent marker of prognosis and a key component in clinical decision making. It is reported that between grades 1 and 3, however, the intermediate grade 2 carries limited information; thus, patients with grade 2 tumors are at risk of both under- and overtreatment. We used RNA-sequencing data from the TCGA project and machine learning to develop a model which can correctly classify grade 1 and grade 3 samples. We used the trained model on grade 2 patients to subdivide them into low-risk and high-risk groups. With iterative retraining, we selected the most relevant 12 transcripts to build a simplified model without losing accuracy. Both models had a high AUC of 0.93. In both cases, there was a significant difference in the relapse-free survivals of the newly identified grade 2 subgroups. Both models could identify grade 2 patients that have a higher risk of relapse. Our approach overcomes the subjective components of the histological evaluation. The developed method can be automated to perform a prescreening of the samples before a final decision is made by pathologists. Our translational approach based on machine learning methods could allow for better therapeutic planning for grade 2 endometrial cancer patients.
子宫内膜样子宫内膜癌的肿瘤分级用作独立的预后标志物及临床决策的关键组成部分。然而，据报道，在1级和3级之间，中间的2级所携带的信息有限；因此，2级肿瘤患者存在治疗不足和过度治疗的风险。我们利用来自癌症基因组图谱（TCGA）项目的RNA测序数据和机器学习来开发一个能够正确区分1级和3级样本的模型。我们将训练好的模型应用于2级患者，将他们分为低风险组和高风险组。通过迭代再训练，我们选择了最相关的12个转录本，构建了一个简化模型且不损失准确性。两个模型的曲线下面积（AUC）均高达0.93。在这两种情况下，新确定的2级亚组的无复发生存率均存在显著差异。两个模型都能够识别出复发风险较高的2级患者。我们的方法克服了组织学评估中的主观因素。所开发的方法可以自动化，以便在病理学家做出最终决定之前对样本进行预筛查。我们基于机器学习方法的转化方法能够为2级子宫内膜癌患者制定更好的治疗方案。
