Histopathological characteristics and artificial intelligence for predicting tumor mutational burden-high colorectal cancer
Histopathological characteristics and artificial intelligence for predicting tumor mutational burden-high colorectal cancer
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DOI：
10.1007/s00535-021-01789-w
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发表时间：
2021-04-28
影响因子：
6.3
通讯作者：
Wakai, Toshifumi
中科院分区：
文献类型：
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作者：
Shimada, Yoshifumi;Okuda, Shujiro;Wakai, Toshifumi
肿瘤突变负荷高（Tumor mutational burden-high, TMB-H）是免疫检查点抑制剂（ici）在结直肠癌（CRC）中很有前景的生物标志物，可通过基因面板检测到。然而，在临床实践中，并不是每个患者都使用基因面板检测TMB-H。我们的目的是确定TMB-H结直肠癌的组织病理学特征，以便有效地选择应该进行基因面板检测的患者。此外，我们试图开发一种基于卷积神经网络（CNN）的算法，直接从苏木精和伊红（H&E）载片预测TMB-H CRC。方法我们使用了两个CRC队列进行TMB-H检测，并从队列中获得全玻片H&E数字图像。对日本结直肠癌（JP-CRC）队列（N = 201）进行评估，利用H&E玻片检测TMB-H的组织病理学特征。采用j.p -CRC队列和The Cancer Genome Atlas (TCGA) CRC队列（N = 77），从H&E数字图像中建立基于cnn的TMB-H预测模型。结果肿瘤浸润淋巴细胞（til）与TMB-H型CRC有显著相关性（P < 0.001）。曲线下面积（AUC）预测TMB-H CRC为0.910。我们开发了一个基于cnn的TMB-H预测模型。随机选择载玻片进行10次验证试验，预测TMB-H载玻片的平均AUC为0.934。结论TILs是H&E玻片检测到的一种组织病理学特征，与TMB-H型CRC有关。我们基于cnn的模型有可能直接从H&E玻片预测TMB-H CRC，从而减轻病理学家的负担。这些方法将为临床医生提供有关低成本ici应用的重要信息。
Background Tumor mutational burden-high (TMB-H), which is detected with gene panel testing, is a promising biomarker for immune checkpoint inhibitors (ICIs) in colorectal cancer (CRC). However, in clinical practice, not every patient is tested for TMB-H using gene panel testing. We aimed to identify the histopathological characteristics of TMB-H CRC for efficient selection of patients who should undergo gene panel testing. Moreover, we attempted to develop a convolutional neural network (CNN)-based algorithm to predict TMB-H CRC directly from hematoxylin and eosin (H&E) slides. Methods We used two CRC cohorts tested for TMB-H, and whole-slide H&E digital images were obtained from the cohorts. The Japanese CRC (JP-CRC) cohort (N = 201) was evaluated to detect the histopathological characteristics of TMB-H using H&E slides. The JP-CRC cohort and The Cancer Genome Atlas (TCGA) CRC cohort (N = 77) were used to develop a CNN-based TMB-H prediction model from the H&E digital images. Results Tumor-infiltrating lymphocytes (TILs) were significantly associated with TMB-H CRC (P < 0.001). The area under the curve (AUC) for predicting TMB-H CRC was 0.910. We developed a CNN-based TMB-H prediction model. Validation tests were conducted 10 times using randomly selected slides, and the average AUC for predicting TMB-H slides was 0.934. Conclusions TILs, a histopathological characteristic detected with H&E slides, are associated with TMB-H CRC. Our CNN-based model has the potential to predict TMB-H CRC directly from H&E slides, thereby reducing the burden on pathologists. These approaches will provide clinicians with important information about the applications of ICIs at low cost.