The interactions between tumor cells and the tumor microenvironment (TME) dictate therapeutic efficacy of radiation and many systemic therapies in breast cancer. However, to date, there is not a widely available method to reproducibly measure tumor and immune phenotypes for each patient's tumor. Given this unmet clinical need, we applied multiple instance learning (MIL) algorithms to assess activity of ten biologically relevant pathways from the hematoxylin and eosin (H&E) slide of primary breast tumors. We employed different feature extraction approaches and state-of-the-art model architectures. Using binary classification, our models attained area under the receiver operating characteristic (AUROC) scores above 0.70 for nearly all gene expression pathways and on some cases, exceeded 0.80. Attention maps suggest that our trained models recognize biologically relevant spatial patterns of cell sub-populations from H&E. These efforts represent a first step towards developing computational H&E biomarkers that reflect facets of the TME and hold promise for augmenting precision oncology.

翻译：肿瘤细胞与肿瘤微环境（TME）之间的相互作用决定了乳腺癌放疗及多种系统治疗的效果。然而，迄今为止，尚缺乏一种可广泛用于可重复测量每位患者肿瘤表型与免疫表型的方法。针对这一尚未满足的临床需求，我们应用多实例学习（MIL）算法，从原发性乳腺肿瘤的苏木精-伊红（H&E）切片中评估了十条生物学相关通路的活性。我们采用了不同的特征提取方法与最先进的模型架构。通过二分类任务，我们的模型在几乎所有基因表达通路上均取得了高于0.70的受试者工作特征曲线下面积（AUROC）分数，部分案例甚至超过0.80。注意力热图显示，训练后的模型能够从H&E染色中识别出具有生物学意义的细胞亚群空间模式。这些工作标志着开发能够反映肿瘤微环境特征的H&E计算生物标志物迈出了第一步，有望推动精准肿瘤学的发展。