Association Of The Collagen Alteration In The Tumor Microenvironment With Lymph Node Metastasis In Early Gastric Cancer And Peritoneal Metastasis In T4 Gastric Cancer

For early gastric cancer(EGC),accuracy assessment the lymph node status is integral to the determinant of endoscopic or surgical dissection.Meanwhile,in gastric cancer with serosal invasion(T4),peritoneal metastasis is the most common recurrence type after radical surgery.Once happen,the prognosis is definitely poor.Currently,radiological examination and scoring system based on clinicopathological characteristics are not adequate to predict lymph node and peritoneal metastasis accurately.In this study,we focused on lymph node metastasis(LNM)of early gastric cancer and peritoneal metastasis of stage T4 gastric cancer.As the main component of tumor microenvironment,collagen alteration directs the biological behaviors of tumor cells,and regulates the mechanisms of invasion and metastasis.Multiphoton imaging,which based on the physical origin of nonlinear optics,is emerged as a powerful modality for collagen imaging.Moreover,the collagen could be quantified via various features by multiphoton imaging.Therefore,we hypothesized that the collagen alteration in the tumor microenvironment was associated with lymph node metastasis in early gastric cancer and peritoneal metastasis in T4 gastric cancer.Based on this hypothesis,we investigated the association between the collagen alteration in tumor microenvironment with lymph node metastasis in early gastric cancer and peritoneal metastasis in T4 gastric cancer.In our study,we extracted the morphological and textural features of collagen from the tumor microenvironment in early gastric cancer and T4 gastric cancer using multiphoton imaging,respectively.Then,the least-absolute shrinkage and selection operator(LASSO)was applied to select the most predictive features,and Collagen signature was constructed,respectively.Two nomograms were built based on Collagen signature and potential risk clinicopathologic characteristics.Section1.Association of the collagen signature in the tumor microenvironment with lymph node metastasis in early gastric cancer[Background and objective]:Lymph node status is the primary determinant in treatment decision making in EGC.Current evaluation methods are not adequate for predicting LNM in EGC.The objective of this study was 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.[Methods]:A total of 375 eligible patients with EGC were divided into a training cohort(n=232)and a validation cohort(n=143).Patients with histologically confirmed gastric cancer who underwent radical gastrectomy and were diagnosed as T1 gastric cancer without neoadjuvant therapy after radical surgery were included.The training cohort of this study was retrospectively collected in our medical database from January 2008 to December 2012.A validation cohort for external validation between January 2011 and December 2013 at another medical center and meeting the same criteria was enrolled.Collagen features were extracted in each specimen using multiphoton imaging,and the collagen signature was then constructed.Binary logistic regression analysis was applied to identify the independent predictors of LNM,and a prediction model was finally developed.The performance of the prediction model was internally and externally validated.[Results]:A 6 feature-based collagen signature was constructed using LASSO binary regression.The collagen signature was significantly associated with LNM(OR,5.470;95%CI,3.315-9.026;P<0.001).Multivariate analysis revealed that the depth of tumor invasion,tumor differentiation and the collagen signature were independent predictors of LNM.These three predictors were incorporated into the prediction model,and a nomogram was established.The AUROC for predicting LNM in the primary and validation cohorts was 0.955(95%CI,0.919-0.991)and 0.938(95%CI,0.897-0.981),respectively.An optimal cutoff value was selected in the primary cohort,which had a sensitivity of 86.8%(95%CI,73.6-97.4%)and a specificity of 93.3%(95%CI,89.796.4%).Similarly,the sensitivity was 90.0%(95%CI,76.7-96.7%)and the specificity was 90.3%(95%CI,84.1-94.7%)in the validation cohort.Moreover,the overall diagnostic accuracy was 91.2%(95%CI,89.3-94.7%)in all 375 patients.[Conclusion]:The collagen signature in the tumor microenvironment is an independent predictor of LNM in EGC.The prediction model is useful for decision making to facilitate tailored surgery in patients with EGC.Section 2.Association of the collagen signature in the serosa tumor microenvironment with peritoneal metastasis in T4 gastric cancer[Background and objective]:Accurate assessment the risk of peritoneal metastasis is important for T4 gastric cancer after radical surgery.Current evaluation methods are not adequate for predicting peritoneal metastasis in T4 gastric cancer.The objective of this study was to develop and validate a prediction model based on a fully quantitative collagen signature in the tumor microenvironment of serosa to estimate the individual risk of peritoneal metastasis in T4 gastric cancer.[Methods]:A total of 313 eligible patients with T4 gastric cancer were divided into a training cohort(n=198)and a validation cohort(n=115).Patients with histologically confirmed gastric cancer who underwent radical gastrectomy and were diagnosed as T4 gastric cancer without neoadjuvant therapy were included.The training cohort of this study was retrospectively collected in our medical database from July 2011 to July 2014.A validation cohort for external validation between July 2008 and September 2010 at another medical center and meeting the same criteria was enrolled.Collagen features were extracted in each specimen using multiphoton imaging,and the collagen signature was then constructed.Competing risk regression analysis was applied to identify the predictors of peritoneal metastasis,and a prediction model was finally developed.The performance of the prediction model was internally and externally validated.[Results]:A 4 feature-based collagen signature was constructed using LASSO regression analysis.The high collagen signature was significantly associated with higher risk of peritoneal metastasis and poorer prognosis in both training and validation cohort.Multivariate compering risk regression analysis revealed that the tumor size,tumor differentiation,lymph node metastasis stage and the collagen signature were predictors of peritoneal metastasis.These four predictors were incorporated into the prediction model,and a nomogram was established.The C-index for predicting peritoneal metastasis in the training and validation cohorts was 0.794(95%CI:0.790797)and 0.716(95%CI:0.707-0.726),respectively,which could predict peritoneal metastasis well.The nomogram obtained from this model could individually predict the peritoneal metastasis probability of T4 gastric cancer within 1 year,2 years and 3 years after radical operation,which was helpful to improve the prognosis of patients with T4 gastric cancer.[Conclusion]:The collagen signature in the tumor microenvironment serosa is associated with peritoneal metastasis in T4 gastric cancer.The prediction model is useful for decision making to facilitate precision medicine in patients with T4 gastric cancer.