Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Division of Gastrointestinal Cancer Translational Research Laboratory, Peking University Cancer Hospital and Institute, Beijing, 100142, People's Republic of China.
Biological Sample Bank, Peking University Cancer Hospital and Institute, Beijing, 100142, People's Republic of China.
J Transl Med. 2023 Sep 19;21(1):638. doi: 10.1186/s12967-023-04473-0.
A major obstacle to the development of personalized therapies for gastric cancer (GC) is the prevalent heterogeneity at the intra-tumor, intra-patient, and inter-patient levels. Although the pathological stage and histological subtype diagnosis can approximately predict prognosis, GC heterogeneity is rarely considered. The extracellular matrix (ECM), a major component of the tumor microenvironment (TME), extensively interacts with tumor and immune cells, providing a possible proxy to investigate GC heterogeneity. However, ECM consists of numerous protein components, and there are no suitable models to screen ECM-related genes contributing to tumor growth and prognosis. We constructed patient-derived tumor xenograft (PDTX) models to obtain robust ECM-related transcriptomic signatures to improve GC prognosis prediction and therapy design.
One hundred twenty two primary GC tumor tissues were collected to construct PDTX models. The tumorigenesis rate and its relationship with GC prognosis were investigated. Transcriptome profiling was performed for PDTX-originating tumors, and least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied to extract prognostic ECM signatures and establish PDTX tumorigenicity-related gene (PTG) scores. The predictive ability of the PTG score was validated using two independent cohorts. Finally, we combined PTG score, age, and pathological stage information to establish a robust nomogram for GC prognosis prediction.
We found that PDTX tumorigenicity indicated a poor prognosis in patients with GC, even at the same pathological stage. Transcriptome profiling of PDTX-originating GC tissues and corresponding normal controls identified 383 differentially expressed genes, with enrichment of ECM-related genes. A robust prognosis prediction model using the PTG score showed robust performance in two validation cohorts. A high PTG score was associated with elevated M2 polarized macrophage and cancer-associated fibroblast infiltration. Finally, combining the PTG score with age and TNM stage resulted in a more effective prognostic model than age or TNM stage alone.
We found that ECM-related signatures may contribute to PDTX tumorigenesis and indicate a poor prognosis in GC. A feasible survival prediction model was built based on the PTG score, which was associated with immune cell infiltration. Together with patient ages and pathological TNM stages, PTG score could be a new approach for GC prognosis prediction.
胃癌（GC）个性化治疗发展的主要障碍是肿瘤内、患者内和患者间水平普遍存在异质性。虽然病理分期和组织学亚型诊断可以大致预测预后，但 GC 异质性很少被考虑。细胞外基质（ECM）是肿瘤微环境（TME）的主要组成部分，它与肿瘤和免疫细胞广泛相互作用，为研究 GC 异质性提供了一个可能的替代指标。然而，ECM 由许多蛋白质组成，目前还没有合适的模型来筛选与肿瘤生长和预后相关的 ECM 相关基因。我们构建了患者来源的肿瘤异种移植（PDTX）模型，以获得稳健的 ECM 相关转录组特征，从而改善 GC 预后预测和治疗设计。
收集了 122 例原发性 GC 肿瘤组织构建 PDTX 模型，研究了肿瘤发生的可能性及其与 GC 预后的关系。对 PDTX 起源肿瘤进行转录组谱分析，应用最小绝对收缩和选择算子（LASSO）Cox 回归分析提取预后 ECM 特征，并建立 PDTX 肿瘤发生相关基因（PTG）评分。利用两个独立的队列验证了 PTG 评分的预测能力。最后，我们将 PTG 评分、年龄和病理分期信息相结合，建立了一个稳健的 GC 预后预测列线图。
我们发现，即使在相同的病理分期，PDTX 肿瘤发生预示着 GC 患者预后不良。PDTX 起源的 GC 组织和相应的正常对照的转录组谱分析鉴定出 383 个差异表达基因，其中富含 ECM 相关基因。使用 PTG 评分建立的稳健预后预测模型在两个验证队列中表现出稳健的性能。高 PTG 评分与 M2 极化巨噬细胞和癌相关成纤维细胞浸润增加相关。最后，将 PTG 评分与年龄和 TNM 分期相结合，比单独使用年龄或 TNM 分期建立的预后模型更有效。
我们发现 ECM 相关特征可能有助于 PDTX 肿瘤发生，并预示 GC 预后不良。基于 PTG 评分构建了一种可行的生存预测模型，该模型与免疫细胞浸润有关。与患者年龄和病理 TNM 分期相结合，PTG 评分可能成为 GC 预后预测的新方法。
