Histopathological Images and Multi-Omics Integration Predict Molecular Characteristics and Survival in Lung Adenocarcinoma.
Histopathological Images and Multi-Omics Integration Predict Molecular Characteristics and Survival in Lung Adenocarcinoma.
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DOI：
10.3389/fcell.2021.720110
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发表时间：
2021
影响因子：
5.5
通讯作者：
Ma X
中科院分区：
文献类型：
--
作者：
Chen L;Zeng H;Xiang Y;Huang Y;Luo Y;Ma X
组织病理学图像和组学图谱在癌症患者的预后中发挥着重要作用。在这里，我们从组织病理学图像中提取定量特征来预测分子特征和预后，并将图像特征与突变、转录组学和蛋白质组学数据相结合，用于肺腺癌（LUAD）的预后预测。从癌症基因组图谱 (TCGA) 获得的患者分为训练集 (n = 235) 和测试集 (n = 235)。我们在训练集中开发了机器学习模型，并在测试集中估计了它们的预测性能。在测试集中，机器学习模型可以预测遗传畸变：ALK (AUC = 0.879)、BRAF (AUC = 0.847)、EGFR (AUC = 0.855)、ROS1 (AUC = 0.848) 和转录亚型：近端炎症 (AUC = 0.897)、近端增殖 (AUC = 0.861) 和终末期来自组织病理学图像的呼吸单位（AUC = 0.894）。此外，我们还获得了 316 名 LUAD 患者的组织微阵列，包括四个外部验证集。使用图像特征的预后模型可预测测试组和四个验证组中的总体生存率，5 年 AUC 为 0.717 至 0.825。按模型分层的高风险和低风险组在测试集（HR = 4.94，p < 0.0001）和三个验证集（HR = 1.64–2.20，p < 0.05）中显示出不同的生存率。图像特征和单一组学的结合在测试集中具有更大的预后能力，例如组织病理学+转录组学模型（5年AUC = 0.840；HR = 7.34，p < 0.0001）。最后，将图像特征与多组学相结合的模型取得了最佳性能（5年AUC = 0.908；HR = 19.98，p < 0.0001）。我们的结果表明，基于组织病理学图像特征的机器学习模型可以预测 LUAD 患者的遗传畸变、转录亚型和生存结果。组织病理学图像和多组学的整合可能为 LUAD 提供更好的生存预测。
Histopathological images and omics profiles play important roles in prognosis of cancer patients. Here, we extracted quantitative features from histopathological images to predict molecular characteristics and prognosis, and integrated image features with mutations, transcriptomics, and proteomics data for prognosis prediction in lung adenocarcinoma (LUAD). Patients obtained from The Cancer Genome Atlas (TCGA) were divided into training set (n = 235) and test set (n = 235). We developed machine learning models in training set and estimated their predictive performance in test set. In test set, the machine learning models could predict genetic aberrations: ALK (AUC = 0.879), BRAF (AUC = 0.847), EGFR (AUC = 0.855), ROS1 (AUC = 0.848), and transcriptional subtypes: proximal-inflammatory (AUC = 0.897), proximal-proliferative (AUC = 0.861), and terminal respiratory unit (AUC = 0.894) from histopathological images. Moreover, we obtained tissue microarrays from 316 LUAD patients, including four external validation sets. The prognostic model using image features was predictive of overall survival in test and four validation sets, with 5-year AUCs from 0.717 to 0.825. High-risk and low-risk groups stratified by the model showed different survival in test set (HR = 4.94, p < 0.0001) and three validation sets (HR = 1.64–2.20, p < 0.05). The combination of image features and single omics had greater prognostic power in test set, such as histopathology + transcriptomics model (5-year AUC = 0.840; HR = 7.34, p < 0.0001). Finally, the model integrating image features with multi-omics achieved the best performance (5-year AUC = 0.908; HR = 19.98, p < 0.0001). Our results indicated that the machine learning models based on histopathological image features could predict genetic aberrations, transcriptional subtypes, and survival outcomes of LUAD patients. The integration of histopathological images and multi-omics may provide better survival prediction for LUAD.
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影响因子：
5.7
作者：
Saito M;Shiraishi K;Kunitoh H;Takenoshita S;Yokota J;Kohno T
通讯作者：
Kohno T
影响因子：
9.8
作者：
McQuin C;Goodman A;Chernyshev V;Kamentsky L;Cimini BA;Karhohs KW;Doan M;Ding L;Rafelski SM;Thirstrup D;Wiegraebe W;Singh S;Becker T;Caicedo JC;Carpenter AE
通讯作者：
Carpenter AE
肿瘤 - 基质相互作用在肿瘤微环境中耐药性中的作用。
影响因子：
5.5
作者：
Ni Y;Zhou X;Yang J;Shi H;Li H;Zhao X;Ma X
通讯作者：
Ma X
影响因子：
5.4
作者：
Lee G;Veltri RW;Zhu G;Ali S;Epstein JI;Madabhushi A
通讯作者：
Madabhushi A
影响因子：
64.5
作者：
Gillette, Michael A.;Satpathy, Shankha;Carr, Steven A.
通讯作者：
Carr, Steven A.