Development and interpretation of a pathomics-based model for the prediction of microsatellite instability in Colorectal Cancer.
Development and interpretation of a pathomics-based model for the prediction of microsatellite instability in Colorectal Cancer.
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用于预测结直肠癌微卫星不稳定性的病理组学模型的开发和解释
DOI：
10.7150/thno.49864
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发表时间：
2020
期刊：
影响因子：
12.4
通讯作者：
Dong ZY
中科院分区：
文献类型：
--
作者：
Cao R;Yang F;Ma SC;Liu L;Zhao Y;Li Y;Wu DH;Wang T;Lu WJ;Cai WJ;Zhu HB;Guo XJ;Lu YW;Kuang JJ;Huan WJ;Tang WM;Huang K;Huang J;Yao J;Dong ZY
微卫星不稳定性（MSI）已被批准为免疫检查点阻断（ICB）治疗的泛癌症生物标志物。然而，目前的MSI鉴定方法并不适用于所有患者。我们提出了一种基于组织病理学图像的集成多实例深度学习模型来预测微卫星状态，并利用多组学相关性解释了基于病理学的模型。研究方法：收集了两个队列的患者，包括来自癌症基因组图谱（TCGA-COAD）的429例和来自亚洲结直肠癌（CRC）队列（Asian-CRC）的785例。我们建立了基于两个连续阶段：斑块水平预测和WSI水平预测的病理组学模型，命名为集成斑块似然聚集（EPLA）。初始模型在TCGA-COAD中开发和验证，然后通过迁移学习在Asian-CRC中推广。从模型中提取的病理特征用基因组和转录组谱进行分析，用于模型解释。结果如下：迁移学习后，EPLA模型在TCGA-COAD测试集中实现了0.8848（95%CI：0.8185-0.9512）的曲线下面积（AUC），在外部验证集Asian-CRC中实现了0.8504（95%CI：0.7591-0.9323）的AUC。EPLA能反映低分化病理表型与MSI的关系（P < 0.001）。此外，从EPLA模型中鉴定的5个病理成像特征与基因组谱中的突变负荷和DNA损伤修复相关基因型以及转录组谱中的抗肿瘤免疫激活途径相关。结论：我们基于病理学的深度学习模型可以有效地从组织病理学图像预测MSI，并可转移到新的患者队列。我们的模型与病理学、基因组学和转录组学表型相关的可解释性为该人工智能（AI）平台在ICB治疗中的应用的前瞻性临床试验奠定了基础。
Microsatellite instability (MSI) has been approved as a pan-cancer biomarker for immune checkpoint blockade (ICB) therapy. However, current MSI identification methods are not available for all patients. We proposed an ensemble multiple instance deep learning model to predict microsatellite status based on histopathology images, and interpreted the pathomics-based model with multi-omics correlation. Methods: Two cohorts of patients were collected, including 429 from The Cancer Genome Atlas (TCGA-COAD) and 785 from an Asian colorectal cancer (CRC) cohort (Asian-CRC). We established the pathomics model, named Ensembled Patch Likelihood Aggregation (EPLA), based on two consecutive stages: patch-level prediction and WSI-level prediction. The initial model was developed and validated in TCGA-COAD, and then generalized in Asian-CRC through transfer learning. The pathological signatures extracted from the model were analyzed with genomic and transcriptomic profiles for model interpretation. Results: The EPLA model achieved an area-under-the-curve (AUC) of 0.8848 (95% CI: 0.8185-0.9512) in the TCGA-COAD test set and an AUC of 0.8504 (95% CI: 0.7591-0.9323) in the external validation set Asian-CRC after transfer learning. Notably, EPLA captured the relationship between pathological phenotype of poor differentiation and MSI (P < 0.001). Furthermore, the five pathological imaging signatures identified from the EPLA model were associated with mutation burden and DNA damage repair related genotype in the genomic profiles, and antitumor immunity activated pathway in the transcriptomic profiles. Conclusions: Our pathomics-based deep learning model can effectively predict MSI from histopathology images and is transferable to a new patient cohort. The interpretability of our model by association with pathological, genomic and transcriptomic phenotypes lays the foundation for prospective clinical trials of the application of this artificial intelligence (AI) platform in ICB therapy.
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影响因子：
7.3
作者：
Gao J;Aksoy BA;Dogrusoz U;Dresdner G;Gross B;Sumer SO;Sun Y;Jacobsen A;Sinha R;Larsson E;Cerami E;Sander C;Schultz N
通讯作者：
Schultz N
影响因子：
29.4
作者：
Boland CR;Goel A
通讯作者：
Goel A
WGCNA：用于加权相关网络分析的 R 包。
影响因子：
3
作者：
Langfelder P;Horvath S
通讯作者：
Horvath S
影响因子：
16.6
作者：
Cortes-Ciriano I;Lee S;Park WY;Kim TM;Park PJ
通讯作者：
Park PJ
影响因子：
29.4
作者：
Jenkins, Mark A.;Hayashi, Shinichi;Jass, Jeremy R.
通讯作者：
Jass, Jeremy R.