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Machine learning with autophagy-related proteins for discriminating renal cell carcinoma subtypes.
Scientific Reports ( IF 3.8 ) Pub Date : 2020-01-20 , DOI: 10.1038/s41598-020-57670-y Zhaoyue He 1, 2 , He Liu 1 , Holger Moch 3 , Hans-Uwe Simon 1, 4
Scientific Reports ( IF 3.8 ) Pub Date : 2020-01-20 , DOI: 10.1038/s41598-020-57670-y Zhaoyue He 1, 2 , He Liu 1 , Holger Moch 3 , Hans-Uwe Simon 1, 4
Affiliation
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Machine learning techniques have been previously applied for classification of tumors based largely on morphological features of tumor cells recognized in H&E images. Here, we tested the possibility of using numeric data acquired from software-based quantification of certain marker proteins, i.e. key autophagy proteins (ATGs), obtained from immunohistochemical (IHC) images of renal cell carcinomas (RCC). Using IHC staining and automated image quantification with a tissue microarray (TMA) of RCC, we found ATG1, ATG5 and microtubule-associated proteins 1A/1B light chain 3B (LC3B) were significantly reduced, suggesting a reduction in the basal level of autophagy with RCC. Notably, the levels of the ATG proteins expressed did not correspond to the mRNA levels expressed in these tissues. Applying a supervised machine learning algorithm, the K-Nearest Neighbor (KNN), to our quantified numeric data revealed that LC3B provided a strong measure for discriminating clear cell RCC (ccRCC). ATG5 and sequestosome-1 (SQSTM1/p62) could be used for classification of chromophobe RCC (crRCC). The quantitation of particular combinations of ATG1, ATG16L1, ATG5, LC3B and p62, all of which measure the basal level of autophagy, were able to discriminate among normal tissue, crRCC and ccRCC, suggesting that the basal level of autophagy would be a potentially useful parameter for RCC discrimination. In addition to our observation that the basal level of autophagy is reduced in RCC, our workflow from quantitative IHC analysis to machine learning could be considered as a potential complementary tool for the classification of RCC subtypes and also for other types of tumors for which precision medicine requires a characterization.
中文翻译：
使用自噬相关蛋白进行机器学习，以区分肾细胞癌亚型。
机器学习技术先前已主要基于在H＆E图像中识别的肿瘤细胞的形态特征而应用于肿瘤分类。在这里，我们测试了使用从某些标记蛋白（即关键自噬蛋白（ATG））的基于软件的量化中获取的数值数据的可能性，这些蛋白质是从肾细胞癌（RCC）的免疫组化（IHC）图像中获得的。使用IHC染色和RCC组织微阵列（TMA）进行自动图像定量，我们发现ATG1，ATG5和微管相关蛋白1A / 1B轻链3B（LC3B）显着减少，表明自噬的基础水平降低了RCC。值得注意的是，表达的ATG蛋白的水平与这些组织中表达的mRNA水平不对应。应用有监督的机器学习算法，K最近邻（KNN）对我们的量化数值数据显示，LC3B为区分透明单元RCC（ccRCC）提供了强有力的措施。ATG5和sequestosome-1（SQSTM1 / p62）可用于发色团RCC（crRCC）的分类。对ATG1，ATG16L1，ATG5，LC3B和p62的特定组合进行定量分析，所有这些都可以测量自噬的基础水平，能够区分正常组织，crRCC和ccRCC，这表明自噬的基础水平可能是有用的RCC判别参数。除了我们观察到RCC中自噬的基础水平降低外，
更新日期：2020-01-21
中文翻译：
使用自噬相关蛋白进行机器学习，以区分肾细胞癌亚型。
机器学习技术先前已主要基于在H＆E图像中识别的肿瘤细胞的形态特征而应用于肿瘤分类。在这里，我们测试了使用从某些标记蛋白（即关键自噬蛋白（ATG））的基于软件的量化中获取的数值数据的可能性，这些蛋白质是从肾细胞癌（RCC）的免疫组化（IHC）图像中获得的。使用IHC染色和RCC组织微阵列（TMA）进行自动图像定量，我们发现ATG1，ATG5和微管相关蛋白1A / 1B轻链3B（LC3B）显着减少，表明自噬的基础水平降低了RCC。值得注意的是，表达的ATG蛋白的水平与这些组织中表达的mRNA水平不对应。应用有监督的机器学习算法，K最近邻（KNN）对我们的量化数值数据显示，LC3B为区分透明单元RCC（ccRCC）提供了强有力的措施。ATG5和sequestosome-1（SQSTM1 / p62）可用于发色团RCC（crRCC）的分类。对ATG1，ATG16L1，ATG5，LC3B和p62的特定组合进行定量分析，所有这些都可以测量自噬的基础水平，能够区分正常组织，crRCC和ccRCC，这表明自噬的基础水平可能是有用的RCC判别参数。除了我们观察到RCC中自噬的基础水平降低外，
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