Verily AI, Tel Aviv, Israel.
PLoS Comput Biol. 2024 Sep 30;20(9):e1012501. doi: 10.1371/journal.pcbi.1012501. eCollection 2024 Sep.
Solid tumors are characterized by complex interactions between the tumor, the immune system and the microenvironment. These interactions and intra-tumor variations have both diagnostic and prognostic significance and implications. However, quantifying the underlying processes in patient samples requires expensive and complicated molecular experiments. In contrast, H&E staining is typically performed as part of the routine standard process, and is very cheap. Here we present HIPI (H&E Image Interpretation and Protein Expression Inference) for predicting cell marker expression from tumor H&E images. We process paired H&E and CyCIF images taken from serial sections of colorectal cancers to train our model. We show that our model accurately predicts the spatial distribution of several important cell markers, on both held-out tumor regions as well as new tumor samples taken from different patients. Moreover, using only the tissue image morphology, HIPI is able to colocalize the interactions between different cell types, further demonstrating its potential clinical significance.
实体瘤的特征是肿瘤、免疫系统和微环境之间的复杂相互作用。这些相互作用和肿瘤内的变异既有诊断意义,也有预后意义和影响。然而,要在患者样本中量化潜在的过程,需要昂贵而复杂的分子实验。相比之下,H&E 染色通常作为常规标准程序的一部分进行,而且非常便宜。在这里,我们提出了 HIPI(H&E 图像解释和蛋白质表达推断),用于从肿瘤 H&E 图像预测细胞标志物的表达。我们处理从结直肠癌的连续切片中获取的配对的 H&E 和 CyCIF 图像来训练我们的模型。我们表明,我们的模型可以准确地预测几个重要细胞标志物的空间分布,无论是在保留的肿瘤区域还是从不同患者获取的新的肿瘤样本上。此外,仅使用组织图像形态学,HIPI 就能够对不同细胞类型之间的相互作用进行共定位,进一步证明了其潜在的临床意义。