Single-cell RNA transcriptomic analyses of tumor microenvironment of ovarian metastasis in gastric cancer.

PURPOSE: Ovarian metastasis of gastric cancer (GC), commonly referred to as Krukenberg tumors, leads to a poor prognosis. However, the cause of metastasis remains unknown. Here, we present an integrated single-cell RNA sequencing (scRNA-Seq) analysis of the immunological microenvironment of two paired clinical specimens with ovarian metastasis of GC. METHODS: scRNA-Seq was performed to determine the immunological microenvironment in ovarian metastasis of gastric cancer. CellChat was employed to analyze cell-cell communications across different cell types. Functional enrichment analysis was done by enrichKEGG in clusterProfiler. GEPIA2 was used to assess the influence of certain genes and gene signatures on prognosis. RESULTS: The ovarian metastasis tissues exhibit a heterogenous immunological microenvironment compared to the primary tumors. Exhaustion of T and B cells is observed in the ovarian metastasis tissues. Compared to the paired adjacent non-tumoral and primary tumors, the ratio of endothelial cells and fibroblasts is high in the ovarian metastasis tissues. Compared to primary ovarian cancers, we identify a specific group of tumor-associated fibroblasts with MFAP4 and CAPNS1 expression in the ovarian metastatic tissues of GC. We further define metastasis-related-endothelial and metastasis-related-fibroblast signatures and indicate that patients with these high signature scores have a poor prognosis. In addition, the ovarian metastasis tissue has a lower level of intercellular communications compared to the primary tumor. CONCLUSION: Our findings reveal the immunological microenvironment of ovarian metastasis of gastric cancer and will promote the discovery of new therapeutic strategies for ovarian metastasis in gastric cancer.

Building consensus on the application of organoid-based drug sensitivity testing in cancer precision medicine and drug development.

Patient-derived organoids (PDOs) have emerged as a promising platform for clinical and translational studies. A strong correlation exists between clinical outcomes and the use of PDOs to predict the efficacy of chemotherapy and/or radiotherapy. To standardize interpretation and enhance scientific communication in the field of cancer precision medicine, we revisit the concept of PDO-based drug sensitivity testing (DST). We present an expert consensus-driven approach for medication selection aimed at predicting patient responses. To further standardize PDO-based DST, we propose guidelines for clarification and characterization. Additionally, we identify several major challenges in clinical prediction when utilizing PDOs.

Predictive value of positive lymph node ratio in patients with locally advanced gastric remnant cancer.

BACKGROUND: Traditional lymph node stage (N stage) has limitations in advanced gastric remnant cancer (GRC) patients; therefore, establishing a new predictive stage is necessary. AIM: To explore the predictive value of positive lymph node ratio (LNR) according to clinicopathological characteristics and prognosis of locally advanced GRC. METHODS: Seventy-four patients who underwent radical gastrectomy and lymphadenectomy for locally advanced GRC were retrospectively reviewed. The relationship between LNR and clinicopathological characteristics was analyzed. The survival analysis was performed using Kaplan-Meier survival curves and Cox regression model. RESULTS: Number of metastatic LNs, tumor diameter, depth of tumor invasion, Borrmann type, serum tumor biomarkers, and tumor-node-metastasis (TNM) stage were correlated with LNR stage and N stage. Univariate analysis revealed that the factors affecting survival included tumor diameter, anemia, serum tumor biomarkers, vascular or neural invasion, combined resection, LNR stage, N stage, and TNM stage (all P < 0.05). The median survival time for those with LNR0, LNR1, LNR2 and LNR3 stage were 61, 31, 23 and 17 mo, respectively, and the differences were significant (P = 0.000). Anemia, tumor biomarkers and LNR stage were independent prognostic factors for survival in multivariable analysis (all P < 0.05). CONCLUSION: The new LNR stage is uniquely based on number of metastatic LNs, with significant prognostic value for locally advanced GRC, and could better differentiate overall survival, compared with N stage.

Gut microbiome for predicting immune checkpoint blockade-associated adverse events.

BACKGROUND: The impact of the gut microbiome on the initiation and intensity of immune-related adverse events (irAEs) prompted by immune checkpoint inhibitors (ICIs) is widely acknowledged. Nevertheless, there is inconsistency in the gut microbial associations with irAEs reported across various studies. METHODS: We performed a comprehensive analysis leveraging a dataset that included published microbiome data (n = 317) and in-house generated data from 16S rRNA and shotgun metagenome samples of irAEs (n = 115). We utilized a machine learning-based approach, specifically the Random Forest (RF) algorithm, to construct a microbiome-based classifier capable of distinguishing between non-irAEs and irAEs. Additionally, we conducted a comprehensive analysis, integrating transcriptome and metagenome profiling, to explore potential underlying mechanisms. RESULTS: We identified specific microbial species capable of distinguishing between patients experiencing irAEs and non-irAEs. The RF classifier, developed using 14 microbial features, demonstrated robust discriminatory power between non-irAEs and irAEs (AUC = 0.88). Moreover, the predictive score from our classifier exhibited significant discriminative capability for identifying non-irAEs in two independent cohorts. Our functional analysis revealed that the altered microbiome in non-irAEs was characterized by an increased menaquinone biosynthesis, accompanied by elevated expression of rate-limiting enzymes menH and menC. Targeted metabolomics analysis further highlighted a notably higher abundance of menaquinone in the serum of patients who did not develop irAEs compared to the irAEs group. CONCLUSIONS: Our study underscores the potential of microbial biomarkers for predicting the onset of irAEs and highlights menaquinone, a metabolite derived from the microbiome community, as a possible selective therapeutic agent for modulating the occurrence of irAEs.