Dataset Information

Tumor-Associated Neutrophils Can Predict Lymph Node Metastasis in Early Gastric Cancer.

ABSTRACT: The consensus of endoscopic therapy for early gastric cancer (EGC) mainly depends on its clinicopathological features. However, the roles of tumor-associated neutrophils (TANs) in EGC remain uncertain. Here, we explored its predictive role for lymph node metastasis (LNM) in EGC. Three hundred twenty-two patients who underwent radical gastrectomy for EGC were enrolled. Preoperative peripheral blood was used to analyze the neutrophil-to-lymphocyte ratio (NLR), and the different status of TANs was determined by hematoxylin-and-eosin staining (H&E) and immunohistochemistry (IHC). TANs, rather than NLR, were positively associated with tumor size, Lauren classification, lymphovascular invasion (LVI), and LNM. Univariate analysis revealed that TANs were associated with LNM as well as tumor size, depth of invasion, Lauren classification, histological classification, LVI, and perineural invasion. In addition to histological classification and LVI, TANs were found to be an independent risk factor for LNM in EGC (P = 0.013). Stratification analysis by depth of invasion showed LVI in SM1 tumor, and both LVI and TANs (P = 0.042) in SM2 tumor were independent risk factors for LNM. In conclusion, TANs in EGC can predict LNM, and TANs may help to estimate LNM precisely in addition to the current criteria.

SUBMITTER: Wang Y

PROVIDER: S-EPMC7537418 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Tumor-Associated Neutrophils Can Predict Lymph Node Metastasis in Early Gastric Cancer.

Wang Yaohui Y Zhai Jing J Zhang Tiancheng T Han Shutang S Zhang Yifen Y Yao Xuequan X Shen Lizong L

Frontiers in oncology 20200921

The consensus of endoscopic therapy for early gastric cancer (EGC) mainly depends on its clinicopathological features. However, the roles of tumor-associated neutrophils (TANs) in EGC remain uncertain. Here, we explored its predictive role for lymph node metastasis (LNM) in EGC. Three hundred twenty-two patients who underwent radical gastrectomy for EGC were enrolled. Preoperative peripheral blood was used to analyze the neutrophil-to-lymphocyte ratio (NLR), and the different status of TANs was ...[more]

PMID: 33072602

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Project description:BackgroundLymph node metastasis (LNM) is an important factor for both treatment and prognosis of early gastric cancer (EGC). Current methods are insufficient to evaluate LNM in EGC due to suboptimal accuracy. Herein, we aim to identify methylation signatures for LNM of EGC, facilitate precision diagnosis, and guide treatment modalities.MethodsFor marker discovery, genome-wide methylation sequencing was performed in a cohort (marker discovery) using 47 fresh frozen (FF) tissue samples. The identified signatures were subsequently characterized for model development using formalin-fixed paraffin-embedded (FFPE) samples by qPCR assay in a second cohort (model development cohort, n = 302, training set: n = 151, test set: n = 151). The performance of the established model was further validated using FFPE samples in a third cohorts (validation cohort, n = 130) and compared with image-based diagnostics, conventional clinicopathology-based model (conventional model), and current standard workups.ResultsFifty LNM-specific methylation signatures were identified de novo and technically validated. A derived 3-marker methylation model for LNM diagnosis was established that achieved an AUC of 0.87 and 0.88, corresponding to the specificity of 80.9% and 85.7%, sensitivity of 80.6% and 78.1%, and accuracy of 80.8% and 83.8% in the test set of model development cohort and validation cohort, respectively. Notably, this methylation model outperformed computed tomography (CT)-based imaging with a superior AUC (0.88 vs. 0.57, p < 0.0001) and individual clinicopathological features in the validation cohort. The model integrated with clinicopathological features demonstrated further enhanced AUCs of 0.89 in the same cohort. The 3-marker methylation model and integrated model reduced 39.4% and 41.5% overtreatment as compared to standard workups, respectively.ConclusionsA novel 3-marker methylation model was established and validated that shows diagnostic potential to identify LNM in EGC patients and thus reduce unnecessary gastrectomy in EGC.

Project description:Importance:Lymph node status is the primary determinant in treatment decision making in early gastric cancer (EGC). Current evaluation methods are not adequate for estimating lymph node metastasis (LNM) in EGC. Objective:To develop and validate a prediction model based on a fully quantitative collagen signature in the tumor microenvironment to estimate the individual risk of LNM in EGC. Design, Setting, and Participants:This retrospective study was conducted from August 1, 2016, to May 10, 2018, at 2 medical centers in China (Nanfang Hospital and Fujian Provincial Hospital). Participants included a primary cohort (n = 232) of consecutive patients with histologically confirmed gastric cancer who underwent radical gastrectomy and received a T1 gastric cancer diagnosis from January 1, 2008, to December 31, 2012. Patients with neoadjuvant radiotherapy, chemotherapy, or chemoradiotherapy were excluded. An additional consecutive cohort (n = 143) who received the same diagnosis from January 1, 2011, to December 31, 2013, was enrolled to provide validation. Baseline clinicopathologic data of each patient were collected. Collagen features were extracted in specimens using multiphoton imaging, and the collagen signature was constructed. An LNM prediction model based on the collagen signature was developed and was internally and externally validated. Main Outcomes and Measures:The area under the receiver operating characteristic curve (AUROC) of the prediction model and decision curve were analyzed for estimating LNM. Results:In total, 375 patients were included. The primary cohort comprised 232 consecutive patients, in whom the LNM rate was 16.4% (n = 38; 25 men [65.8%] with a mean [SD] age of 57.82 [10.17] years). The validation cohort consisted of 143 consecutive patients, in whom the LNM rate was 20.9% (n = 30; 20 men [66.7%] with a mean [SD] age of 54.10 [13.19] years). The collagen signature was statistically significantly associated with LNM (odds ratio, 5.470; 95% CI, 3.315-9.026; P < .001). Multivariate analysis revealed that the depth of tumor invasion, tumor differentiation, and the collagen signature were independent predictors of LNM. These 3 predictors were incorporated into the new prediction model, and a nomogram was established. The model showed good discrimination in the primary cohort (AUROC, 0.955; 95% CI, 0.919-0.991) and validation cohort (AUROC, 0.938; 95% CI, 0.897-0.981). An optimal cutoff value was selected in the primary cohort, which had a sensitivity of 86.8%, a specificity of 93.3%, an accuracy of 92.2%, a positive predictive value of 71.7%, and a negative predictive value of 97.3%. The validation cohort had a sensitivity of 90.0%, a specificity of 90.3%, an accuracy of 90.2%, a positive predictive value of 71.1%, and a negative predictive value of 97.1%. Among the 375 patients, a sensitivity of 87.3%, a specificity of 92.1%, an accuracy of 91.2%, a positive predictive value of 72.1%, and a negative predictive value of 96.9% were found. Conclusions and Relevance:This study's findings suggest that the collagen signature in the tumor microenvironment is an independent indicator of LNM in EGC, and the prediction model based on this collagen signature may be useful in treatment decision making for patients with EGC.

Project description:BackgroundLymph node metastasis (LNM) is a critical factor in determining the prognosis of gastric cancer (GC), but its underlying mechanism remains unclear. The tumor mutational burden (TMB) has recently been recognized as a biomarker for predicting prognosis and response to immune checkpoint inhibitors, while mucin 16, cell surface associated (MUC16) is frequently mutated in GC. This study explored whether MUC16 mutation status is associated with TMB, LNM, and prognosis in patients with GC.MethodsSomatic mutation data were downloaded from three GC cohorts. TMB values were calculated and associations between the TMB and clinical characteristics were analyzed. The mutational landscapes of these three GC cohorts were individually explored and visualized using waterfall diagrams. Univariate logistic regression and Kaplan-Meier survival analysis were performed to screen for mutated genes associated with LNM and overall survival (OS). Associations between MUC16 mutations and TMB, microsatellite instability (MSI), LNM, and tumor microenvironment signatures were explored.ResultsTMB was associated with LNM and OS in patients with GC. Analyzing the three GC cohorts (The Cancer Genome Atlas-Stomach Adenocarcinoma, International Cancer Genome Consortium [ICGC]-China, and ICGC-Japan) revealed that MUC16 was one of the most frequently mutated genes in patients with GC. MUC16 mutations were associated with better prognosis, including lower LNM rates and improved OS rates. In addition, MUC16 mutation status was associated with TMB and MSI statuses. Fifteen upregulated and 222 downregulated genes were identified in patients with MUC16 mutations, compared to in those in patients with wild-type MUC16. An altered tumor microenvironment signature was also identified in GC samples with MUC16 mutations; it was characterized by significantly decreased infiltration regarding stromal cells, CD4+ T cells, and macrophages.ConclusionMUC16 mutation status was associated with TMB, microsatellite status, LNM, and survival in patients with GC. These findings may provide new insights into the mechanism of LNM and could act as a signpost for prognostic predictions and immunotherapy guidance for patients with GC.

Dataset Information

Tumor-Associated Neutrophils Can Predict Lymph Node Metastasis in Early Gastric Cancer.

Publications

Tumor-Associated Neutrophils Can Predict Lymph Node Metastasis in Early Gastric Cancer.

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