Prediction of clinicopathological features, multi-omics events and prognosis based on digital pathology and deep learning in HR(+)/HER2(-) breast cancer.
Prediction of clinicopathological features, multi-omics events and prognosis based on digital pathology and deep learning in HR(+)/HER2(-) breast cancer.
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DOI：
10.21037/jtd-23-445
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发表时间：
2023-05-30
影响因子：
2.5
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中科院分区：
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乳腺癌在全世界妇女中的发病率和死亡率最高。激素受体（HR）+/人表皮生长因子受体2（HER 2）-乳腺癌是最常见的分子亚型，占乳腺癌的50-79%。深度学习已被广泛应用于癌症图像分析，特别是用于预测与精确治疗和患者预后相关的目标。然而，缺乏针对HR+/HER 2 −乳腺癌治疗靶点和预后预测的研究。本研究回顾性收集了2013年1月至2014年12月期间复旦大学上海肿瘤防治中心（FUSCC）HR+/HER 2 −乳腺癌患者的苏木精和伊红（H&E）染色切片，并扫描生成全切片图像（WSIs）。然后，我们建立了一个基于深度学习的工作流程来训练和验证模型，以预测临床病理特征、多组学分子特征和预后;使用受试者工作特征（ROC）的曲线下面积（AUC）和测试集的一致性指数（C-index）来评估模型的有效性。我们的研究共纳入了421例HR+/HER 2 −乳腺癌患者。关于临床病理学特征，III级可以用AUC 0. 90 [95%置信区间（CI）：0. 84 - 0. 97]预测。关于体细胞突变，TP 53和GATA 3突变可分别预测AUC为0. 68（95% CI：0. 56 - 0. 81）和0. 68（95% CI：0. 47 - 0. 89）。关于基因集富集分析（GSEA）途径，预测G2-M检查点途径的AUC为0.79（95% CI：0.69-0.90）。关于免疫治疗应答的标志物，预测肿瘤内肿瘤浸润淋巴细胞（iTIL）、间质肿瘤浸润淋巴细胞（sTIL）、CD 8A和PDCD 1的AUC为0.78（95% CI：0.55-1.00）、0.76（95% CI：0.65-0.87）、0.71（95% CI：0.60-0.82）和0.74（95% CI：0.63-0.85）。此外，我们发现，临床预后变量和图像的深层特征的整合可以改善患者预后的分层。使用基于深度学习的工作流程，我们开发了模型来预测HR+/HER 2 −乳腺癌患者的临床病理特征，多组学特征和预后。这项工作可能有助于有效的患者分层，以促进HR+/HER 2 −乳腺癌的个性化管理。
Breast cancer has the highest incidence and mortality rates among women worldwide. Hormone receptor (HR)+/human epidermal growth factor receptor 2 (HER2)− breast cancer is the most common molecular subtype, accounting for 50–79% of breast cancers. Deep learning has been widely used in cancer image analysis, especially for predicting targets related to precise treatment and patient prognosis. However, studies focusing on therapeutic target and prognosis predicting in HR+/HER2− breast cancer are lacking. This study retrospectively collected hematoxylin and eosin (H&E)-stained slides of HR+/HER2− breast cancer patients between January 2013 and December 2014 at Fudan University Shanghai Cancer Center (FUSCC) and scanned to generate whole-slide images (WSIs). Then, we built a deep-learning-based workflow to train and validate model to predict clinicopathological features, multi-omics molecular features and prognosis; the area under the curve (AUC) of the receiver operating characteristic (ROC) and the concordance index (C-index) of the test set were used to assess model effectiveness. A total of 421 HR+/HER2− breast cancer patients were included in our study. Regarding clinicopathological features, grade III could be predicted with an AUC of 0.90 [95% confidence interval (CI): 0.84–0.97]. Regarding somatic mutations, TP53 and GATA3 mutation could be predicted with AUCs of 0.68 (95% CI: 0.56–0.81) and 0.68 (95% CI: 0.47–0.89), respectively. Regarding gene set enrichment analysis (GSEA) pathways, the G2-M checkpoint pathway was predicted with an AUC of 0.79 (95% CI: 0.69–0.90). Regarding markers of immunotherapy response, intratumoral tumor-infiltrating lymphocytes (iTILs), stromal tumor-infiltrating lymphocytes (sTILs), CD8A, and PDCD1 were predicted with AUCs of 0.78 (95% CI: 0.55–1.00), 0.76 (95% CI: 0.65–0.87), 0.71 (95% CI: 0.60–0.82), and 0.74 (95% CI: 0.63–0.85), respectively. In addition, we found that the integration of clinical prognostic variables and deep features of images can improve the stratification of patient prognosis. Using a deep-learning-based workflow, we developed models to predict the clinicopathological features, multi-omics features and prognosis of patients with HR+/HER2− breast cancer using pathological WSIs. This work may contribute to efficient patient stratification to promote the personalized management of HR+/HER2− breast cancer.