Transcriptome profiling of patient-derived tumor xenografts suggests novel extracellular matrix-related signatures for gastric cancer prognosis prediction.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

CRISPR-mediated MECOM depletion retards tumor growth by reducing cancer stem cell properties in lung squamous cell carcinoma.

Targeted therapy for lung squamous cell carcinoma (LUSC) remains a challenge due to the lack of robust targets. Here, we identified MECOM as a candidate of therapeutic target for LUSC by screening 38 genes that were commonly amplified in three pairs of primary tumors and patient-derived xenografts (PDXs) using a clustered regularly interspaced short palindromic repeats (CRISPR)-mediated approach. High MECOM expression levels were associated with poor prognosis. Forced expression of MECOM in LUSC cell lines promoted cancer stem cell (CSC) properties, and its knockout inhibited CSC phenotypes. Furthermore, systemic delivery of CRISPR-mediated MECOM depletion cassette using adenovirus with an adaptor, which is composed of a single-chain fragment variable (scFv) against epithelial cell adhesion molecules (EpCAM) fused to the ectodomain of coxsackievirus and adenovirus receptor, and a protector, which consists of the scFv connected to the hexon symmetry of the adenovirus, could specifically target subcutaneous and orthotopic LUSC and retard tumor growth. This study could provide a novel therapeutic strategy for LUSC with high efficacy and specificity.