Recent advances in cancer research and diagnostics largely rely on new developments in microscopic or molecular profiling techniques, offering high levels of detail with respect to either spatial or molecular features, but usually not both. Here, we present an explainable machine-learning approach for the integrated profiling of morphological, molecular and clinical features from breast cancer histology. First, our approach allows for the robust detection of cancer cells and tumour-infiltrating lymphocytes in histological images, providing precise heatmap visualizations explaining the classifier decisions. Second, molecular features, including DNA methylation, gene expression, copy number variations, somatic mutations and proteins are predicted from histology. Molecular predictions reach balanced accuracies up to 78%, whereas accuracies of over 95% can be achieved for subgroups of patients. Finally, our explainable AI approach allows assessment of the link between morphological and molecular cancer properties. The resulting computational multiplex-histology analysis can help promote basic cancer research and precision medicine through an integrated diagnostic scoring of histological, clinical and molecular features.

癌症研究和诊断的最新进展很大程度上依赖于微观或分子分析技术的新发展，提供了关于空间或分子特征的高水平细节，但通常不是两者兼而有之。在这里，我们提出了一种可解释的机器学习方法，用于综合分析乳腺癌组织学的形态、分子和临床特征。首先，我们的方法允许在组织学图像中稳健地检测癌细胞和肿瘤浸润淋巴细胞，提供精确的热图可视化来解释分类器的决策。其次，从组织学中预测分子特征，包括 DNA 甲基化、基因表达、拷贝数变异、体细胞突变和蛋白质。分子预测达到平衡精度高达 78%，而对于亚组患者，准确率可以达到 95% 以上。最后，我们可解释的 AI 方法允许评估形态学和分子癌症特性之间的联系。由此产生的计算多重组织学分析可以通过组织学、临床和分子特征的综合诊断评分来帮助促进基础癌症研究和精准医学。