Table_12_From Mouse to Human: Cellular Morphometric Subtype Learned From Mouse Mammary Tumors Provides Prognostic Value in Human Breast Cancer.xlsx

Mouse models of cancer provide a powerful tool for investigating all aspects of cancer biology. In this study, we used our recently developed machine learning approach to identify the cellular morphometric biomarkers (CMB) from digital images of hematoxylin and eosin (H&E) micrographs of orthotopic Trp53-null mammary tumors (n = 154) and to discover the corresponding cellular morphometric subtypes (CMS). Of the two CMS identified, CMS-2 was significantly associated with shorter survival (p = 0.0084). We then evaluated the learned CMB and corresponding CMS model in MMTV-Erbb2 transgenic mouse mammary tumors (n = 53) in which CMS-2 was significantly correlated with the presence of metastasis (p = 0.004). We next evaluated the mouse CMB and CMS model on The Cancer Genome Atlas breast cancer (TCGA-BRCA) cohort (n = 1017). Kaplan–Meier analysis showed significantly shorter overall survival (OS) of CMS-2 patients compared to CMS-1 patients (p = 0.024) and added significant prognostic value in multi-variable analysis of clinical and molecular factors, namely, age, pathological stage, and PAM50 molecular subtype. Thus, application of CMS to digital images of routine workflow H&E preparations can provide unbiased biological stratification to inform patient care.

癌症小鼠模型是探究癌症生物学各维度的强有力工具。本研究借助课题组新近开发的机器学习方法，从154例原位Trp53缺失型乳腺肿瘤的苏木精-伊红（hematoxylin and eosin，H&E）显微数字图像中识别细胞形态计量生物标志物（cellular morphometric biomarkers，CMB），并挖掘得到对应的细胞形态计量亚型（cellular morphometric subtypes，CMS）。共鉴定出两类CMS，其中CMS-2与更短的生存期显著相关（p=0.0084）。随后，研究团队在53例MMTV-Erbb2转基因小鼠乳腺肿瘤中对习得的CMB及对应CMS模型开展验证，结果显示CMS-2与肿瘤转移的存在显著相关（p=0.004）。接下来，研究团队将该小鼠来源的CMB与CMS模型应用于癌症基因组图谱乳腺癌队列（The Cancer Genome Atlas breast cancer，TCGA-BRCA）的1017例样本。Kaplan-Meier分析显示，相较于CMS-1患者，CMS-2患者的总生存期（overall survival，OS）显著更短（p=0.024）；且在年龄、病理分期及PAM50分子亚型等临床与分子因素的多变量分析中，该模型具备显著的预后价值。综上，将CMS应用于常规流程制备的H&E染色数字图像，可实现无偏的生物学分层，为患者临床诊疗提供参考依据。