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Abstract：
Triple-negative breast cancer (TNBC) is the most challenging breast cancer subtype. Molecular stratification and target therapy bring clinical benefit for TNBC patients, but it is difficult to implement comprehensive molecular testing in clinical practice. Here, using our multi-omics TNBC cohort (N = 425), a deep learning-based framework was devised and validated for comprehensive predictions of molecular features, subtypes and prognosis from pathological whole slide images. The framework first incorporated a neural network to decompose the tissue on WSIs, followed by a second one which was trained based on certain tissue types for predicting different targets. Multi-omics molecular features were analyzed including somatic mutations, copy number alterations, germline mutations, biological pathway activities, metabolomics features and immunotherapy biomarkers. It was shown that the molecular features with therapeutic implications can be predicted including the somatic PIK3CA mutation, germline BRCA2 mutation and PD-L1 protein expression (area under the curve [AUC]: 0.78, 0.79 and 0.74 respectively). The molecular subtypes of TNBC can be identified (AUC: 0.84, 0.85, 0.93 and 0.73 for the basal-like immune-suppressed, immunomodulatory, luminal androgen receptor, and mesenchymal-like subtypes respectively) and their distinctive morphological patterns were revealed, which provided novel insights into the heterogeneity of TNBC. A neural network integrating image features and clinical covariates stratified patients into groups with different survival outcomes (log-rank P < 0.001). Our prediction framework and neural network models were externally validated on the TNBC cases from TCGA (N = 143) and appeared robust to the changes in patient population. For potential clinical translation, we built a novel online platform, where we modularized and deployed our framework along with the validated models. It can realize real-time one-stop prediction for new cases. In summary, using only pathological WSIs, our proposed framework can enable comprehensive stratifications of TNBC patients and provide valuable information for therapeutic decision-making. It had the potential to be clinically implemented and promote the personalized management of TNBC.
摘要：
三阴性乳腺癌(TNBC)是最具挑战性的乳腺癌亚型。分子分层和靶向治疗为TNBC患者带来临床益处，但是在临床实践中很难实施全面的分子检测。这里,使用我们的多组学TNBC队列(N=425)，设计并验证了基于深度学习的框架，以全面预测分子特征，来自病理全幻灯片图像的亚型和预后。该框架首先结合了神经网络来分解WSI上的组织，然后是第二个，根据某些组织类型进行训练，以预测不同的目标。分析了多组学分子特征，包括体细胞突变，拷贝数更改，种系突变,生物途径活性，代谢组学特征和免疫治疗生物标志物。研究表明，可以预测具有治疗意义的分子特征，包括体细胞PIK3CA突变，种系BRCA2突变和PD-L1蛋白表达(曲线下面积[AUC]:分别为0.78、0.79和0.74)。可以鉴定TNBC的分子亚型（对于基底样免疫抑制的AUC：0.84、0.85、0.93和0.73，免疫调节,腔雄激素受体，和间充质样亚型）及其独特的形态模式被揭示，这为TNBC的异质性提供了新的见解。整合图像特征和临床协变量的神经网络将患者分成不同生存结果的组(log-rankP<0.001)。我们的预测框架和神经网络模型在TCGA（N=143）的TNBC病例上进行了外部验证，并且对患者人群的变化表现出稳健。对于潜在的临床翻译，我们建立了一个小说在线平台，在这里，我们模块化并部署了我们的框架以及经过验证的模型。它可以实现对新病例的实时一站式预测。总之,仅使用病理性WSI，我们提出的框架可以对TNBC患者进行全面分层,并为治疗决策提供有价值的信息.它有可能在临床上实施并促进TNBC的个性化管理。
