Association of circulating tumor cell-white blood cell clusters with survival outcomes in patients with colorectal cancer after curative intent surgery.

BACKGROUND: The analysis of circulating tumor cell-associated white blood cell (CTC-WBC) clusters represented the progress in the liquid biopsy of malignant tumors, however, related research in patients with colorectal cancer is still absent. METHODS: To explore associations between CTC-WBC clusters and the prognosis of these patients, we conducted an independent cohort of 329 colorectal cancer patients after curative intent surgery and pre-operative CTC detection in Nanfang Hospital, Southern Medical University, Guangzhou, China between January 1, 2017, and September 31, 2019. The primary cohort referred to patients with CTC-WBC clusters positive. The control cohort was defined as those with exclusively CTCs positive. CTCs were enriched and distinguished by The CanPatrol™ system (SurExam, China). The Kaplan-Meier curve was used to compare the progressive-free survival (PFS) and overall survival (OS) between two groups. The COX regression model was used to assess the predictive value of CTC-WBC clusters. RESULTS: Sixty three patients presented CTC-WBC clusters positive (CTC-WBC group) and 266 patients showed solely CTCs (CTC group). The number of CTCs was significantly different between two groups (P < 0.001) and the rest of clinical characteristics were not markedly associated with the presence of CTC-WBC clusters. Kaplan-Meier curves of PFS and OS exhibited that the CTC-WBC group had significantly shorter PFS (P = 0.011), while not for OS. The multivariate model further suggested that the CTC-WBC clusters (Hazard Ratio = 1.89, 95% Confidence Interval 1.02-3.51, P = 0.042) was an independent predictor for the PFS of in post-operation CRC patients. CONCLUSION: The CTC-WBC cluster is significantly associated with recurrence after operation in CRC patients. This finding facilitates the evaluation of this indicator in tumor progression.

Dysbiosis of Gastric Mucosal Fungal Microbiota in the Gastric Cancer Microenvironment.

Background: Microbes have been shown to contribute to gastric cancer (GC), gastric bacteria and viruses are associated with gastric carcinogenesis. However, the relationship between gastric fungi and GC is still unclear. Our aim was to evaluate the gastric fungal microbiota in the GC microenvironment. Methods: Gastric fungal microbiome profiling was performed with internal transcribed spacer (ITS) rDNA sequencing in primary tumor and corresponding paired normal mucosal tissues from 61 GC patients. Differences in microbial composition, taxa diversity, and predicted function were further analyzed. Results: Dysbiosis of gastric mucosal fungal microbiome was observed between the tumor and normal groups in GC. The tumor group had a higher abundance of certain taxa than the normal group. In the taxa classification, the abundances of Pezizomycetes, Sordariales, Chaetomiaceae, and Rozellomycota were lower in the tumor group than in the normal group. At the genus level, Solicoccozyma (P = 0.033) was found in higher abundance and was differentially enriched in the tumor group with Lefse analysis. Additionally, Solicoccozyma accounted for 0.3% of gastric fungi in the GC microenvironment. Twenty-seven of the 61 GC patients showed positive Solicoccozyma expression in tumors. Solicoccozyma-positive expression in tumors was associated with the Bormann classification and nerve invasion. Solicoccozyma was considered a gastric fungal marker to classify stage I and stage II-IV GC patients with an area under the receiver-operating curve (AUC) of 0.7061, as well as to classify the nerve invasive and nonnerve invasive tumors from GC patients with an AUC of 0.6978. Functional prediction indicated that the positive expression of Solicoccozyma in tumors was associated with the amino acid- and carbohydrate-related metabolic pathways in GC. Conclusions: This study revealed a novel perspective on the role of Solicoccozyma in tumors and a theoretical basis for therapeutic targets against GC.

Identification of Malassezia globosa as a Gastric Fungus Associated with PD-L1 Expression and Overall Survival of Patients with Gastric Cancer.

Background: Microbiotas affected the prognosis of cancer patients by regulating programmed death ligand-1 (PD-L1) expression. However, the relationship between gastric fungi and PD-L1 expression is still unclear in gastric cancer (GC). We aimed at exploring the association of gastric fungi with PD-L1 expression and overall survival in GC. Methods: A total of 61 GC patients were divided into the two groups based on the PD-L1 combined positive scores (CPS). Fungal profiling was performed by internal transcribed spacer rDNA sequencing, and the survival analyses were performed by Kaplan-Meier curves. Results: We observed a taxonomic difference of fungi between the PD-L1-High (CPS ≥ 10) and PD-L1-Low group (CPS < 10) by principal coordinates analysis (PCoA) (P = 0.014 for Bray-Curtis and P = 0.042 for Jaccard). Malassezia had a higher abundance in the PD-L1-High group compared to the PD-L1-Low group (P = 0.045). Malassezia globosa elevated significantly in the PD-L1-High group. GC patients with PD-L1 low expression and low abundance of Malassezia globosa had a longer overall survival (OS) than others (P = 0.047). Malassezia globosa was associated with PD-L1 expression (Odds Ratio = 3.509, 95% Confidence Interval: 1.056-11.656, P = 0.040). Malassezia globosa was associated with the tumor size (P = 0.031) and PD-L1 status (P = 0.024). GC patients with a high abundance of Malassezia globosa had shorter OS than others (P = 0.028). Malassezia globosa was an independent factor (Hazard Ratio = 3.080, 95% Confidence Interval: 1.140-8.323, P = 0.027) for OS after adjusting for tumor stage. Malassezia globosa was figured out to be associated with- fatty acid and lipid biosynthesis and degradation via LIPASYN pathway. Conclusions. Malassezia globosa was identified as a PD-L1 expression-associated gastric fungus and associated with OS of GC patients, which calls for more studies to further explore its potential in PD-L1/PD-1 targeted immunotherapy.

An artificial intelligence model to predict survival and chemotherapy benefits for gastric cancer patients after gastrectomy development and validation in international multicenter cohorts.

BACKGROUND: Gastric cancer (GC) is a major health problem worldwide, with high prevalence and mortality. The present GC staging system provides inadequate prognostic information and does not reflect the chemotherapy benefit of GC. METHODS: Two hundred fifty-five patients who underwent surgical resection were enrolled in our study (training cohort = 212, internal validation cohort = 43). Nine clinicopathologic features were obtained to construct an support vector machine (SVM) model. The cohorts from 4 domestic centres and The Cancer Genome Atlas (TCGA) were used for external validation. RESULTS: In the training cohort, the AUCs were 0.773 (95% CI 0.708-0.838) for 5-year overall survival (OS) and 0.751 (95% CI 0.683-0.820) for 5-year disease-free survival (DFS); in the domestic validation cohort, the AUCs were 0.852 (95% CI 0.810-0.894) and 0.837 (95% CI 0.792-0.882), respectively. The model performed better than the TNM staging system according to the receiver operator characteristic(ROC) curve. GC patients were significantly divided into low, moderate and high risk based on the SVM. High-risk TNM stage Ⅱ and Ⅲ patients were more likely to benefit from adjuvant chemotherapy than low-risk patients. CONCLUSIONS: The SVM-based model may be used to predict OS and DFS in GC patients and the benefit of adjuvant chemotherapy in TNM stage Ⅱ and Ⅲ GC patients.