Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, Kentucky.
Markey Cancer Center, University of Kentucky, Lexington, Kentucky; Department of Pathology and Laboratory Medicine, University of Kentucky, Lexington, Kentucky.
Lab Invest. 2023 Aug;103(8):100176. doi: 10.1016/j.labinv.2023.100176. Epub 2023 May 12.
Lung cancer heterogeneity is a major barrier to effective treatments and encompasses not only the malignant epithelial cell phenotypes and genetics but also the diverse tumor-associated cell types. Current techniques used to investigate the tumor microenvironment can be time-consuming, expensive, complicated to interpret, and often involves destruction of the sample. Here we use standard hematoxylin and eosin-stained tumor sections and the HALO AI nuclear phenotyping software to characterize 6 distinct cell types (epithelial, mesenchymal, macrophage, neutrophil, lymphocyte, and plasma cells) in both murine lung cancer models and human lung cancer samples. CD3 immunohistochemistry and lymph node sections were used to validate lymphocyte calls, while F4/80 immunohistochemistry was used for macrophage validation. Consistent with numerous prior studies, we demonstrated that macrophages predominate the adenocarcinomas, whereas neutrophils predominate the squamous cell carcinomas in murine samples. In human samples, we showed a strong negative correlation between neutrophils and lymphocytes as well as between mesenchymal cells and lymphocytes and that higher percentages of mesenchymal cells correlate with poor prognosis. Taken together, we demonstrate the utility of this AI software to identify, quantify, and compare distributions of cell types on standard hematoxylin and eosin-stained slides. Given the simplicity and cost-effectiveness of this technique, it may be widely beneficial for researchers designing new therapies and clinicians working to select favorable treatments for their patients.
肺癌异质性是有效治疗的主要障碍，不仅包括恶性上皮细胞表型和遗传学，还包括多种与肿瘤相关的细胞类型。目前用于研究肿瘤微环境的技术可能既耗时、昂贵、解释复杂，又常常涉及样本的破坏。在这里，我们使用标准的苏木精和伊红染色肿瘤切片和 HALO AI 核表型分析软件，对两种小鼠肺癌模型和人类肺癌样本中的 6 种不同的细胞类型（上皮细胞、间充质细胞、巨噬细胞、中性粒细胞、淋巴细胞和浆细胞）进行特征分析。我们使用 CD3 免疫组织化学和淋巴结切片来验证淋巴细胞的分类，而使用 F4/80 免疫组织化学来验证巨噬细胞。与许多先前的研究一致，我们发现在小鼠样本中，腺癌以巨噬细胞为主，而鳞状细胞癌则以中性粒细胞为主。在人类样本中，我们显示出中性粒细胞和淋巴细胞之间以及间充质细胞和淋巴细胞之间呈强烈负相关，而且间充质细胞的比例越高，与预后不良相关。综上所述，我们证明了这种人工智能软件在识别、定量和比较标准苏木精和伊红染色载玻片上的细胞类型分布方面的实用性。鉴于这种技术的简单性和成本效益，它可能对设计新疗法的研究人员和为患者选择有利治疗方案的临床医生都有广泛的益处。
