Research and Development, Advanced Core Technology Japan Unit 2, Evident Corp. Hachioji, 192-0033, Tokyo, Japan.
Translational Research Center, Fukushima Medical University, 960-1295, Fukushima, Japan.
Sci Rep. 2024 Oct 1;14(1):22769. doi: 10.1038/s41598-024-73725-w.
Genotypic and phenotypic diversity, which generates heterogeneity during disease evolution, is common in cancer. The identification of features specific to each patient and tumor is central to the development of precision medicine and preclinical studies for cancer treatment. However, the complexity of the disease due to inter- and intratumor heterogeneity increases the difficulty of effective analysis. Here, we introduce a sequential deep learning model, preprocessing to organize the complexity due to heterogeneity, which contrasts with general approaches that apply a single model directly. We characterized morphological heterogeneity using microscopy images of patient-derived organoids (PDOs) and identified gene subsets relevant to distinguishing differences among original tumors. PDOs, which reflect the features of their origins, can be reproduced in large quantities and varieties, contributing to increasing the variation by enhancing their common characteristics, in contrast to those from different origins. This resulted in increased efficiency in the extraction of organoid morphological features sharing the same origin. Linking these tumor-specific morphological features to PDO gene expression data enables the extraction of genes strongly correlated with intertumor differences. The relevance of the selected genes was assessed, and the results suggest potential applications in preclinical studies and personalized clinical care.
肿瘤的遗传和表型多样性在疾病演变过程中产生异质性，这在癌症中很常见。鉴定每个患者和肿瘤特有的特征是精准医学和癌症治疗临床前研究的核心。然而，由于肿瘤内和肿瘤间的异质性增加了疾病的复杂性，这增加了有效分析的难度。在这里，我们介绍了一种顺序深度学习模型，预处理以组织由于异质性产生的复杂性，这与直接应用单一模型的一般方法形成对比。我们使用患者来源的类器官（PDO）的显微镜图像来描述形态异质性，并确定与区分原始肿瘤之间差异相关的基因子集。PDO 反映了它们起源的特征，可以大量和多样化地复制，通过增强它们的共同特征来增加变异，与来自不同起源的 PDO 相反。这提高了提取具有相同起源的类器官形态特征的效率。将这些肿瘤特异性形态特征与 PDO 基因表达数据联系起来，可以提取与肿瘤间差异强烈相关的基因。评估了所选基因的相关性，结果表明其在临床前研究和个性化临床护理中有潜在的应用。
