A Survival Prediction Nomogram for Esophageal Squamous Cell Carcinoma Treated with Neoadjuvant Chemoradiotherapy Followed by Surgery

Prognostic significance of positive lymph node regression grade to neoadjuvant chemoradiation for esophageal squamous cell carcinoma

BACKGROUND AND PURPOSE

To assess the relationship between metastatic lymph node (LN) responder status and recurrence-free survival (RFS) in patients undergoing neoadjuvant chemoradiotherapy (NCRT).

MATERIALS AND METHODS

We retrospectively reviewed 304 patients with local advanced esophageal squamous cell carcinoma received NCRT followed by esophagectomy. For 112 patients with positive node, according to the proportion of residual viable tumor cells area within the whole tumor beds of all metastatic LNs, we classified LN-tumor regression grade (LN-TRG) into four categories: grade 1, 0%; 2, <10%; 3, 10%-50%; 4, >50%. Patients with grade 1-2 LN-TRG of were considered LN responders, and those with grades 3-4, as LN nonresponders. Univariate and multivariate analyses of RFS were estimated by a Cox regression model, Kaplan-Meier curve, and log-rank test.

RESULTS

The median follow-up time of a total of 112 patients was 29.6 months. Fifty-two (46.4%) patients have experienced recurrence. In Cox univariate analysis, differentiation, AJCC stage LN responder status, nerve invasion, and lymphovascular invasion significantly correlated with RFS. Multivariate analysis for RFS revealed that LN responder status and AJCC stage (p < 0.05) were independent prognostic factor. The 3-year RFS rates for patients with LN-TRG of 1-4 grades were 72.7%, 76.5%, 37.4%, and 28.5%, respectively, and the median RFS times were not reach, 43.56, 28.09, and 22.77, respectively.

CONCLUSIONS

LN responder status is an independent prognostic factor for RFS in esophageal cancer patients who received NCRT.

Development and Validation of a Recurrence-Free Survival Prediction Model for Locally Advanced Esophageal Squamous Cell Carcinoma with Neoadjuvant Chemoradiotherapy

BACKGROUND

A recurrence-free survival (RFS) prediction model was developed and validated for patients with locally advanced esophageal squamous cell carcinoma treated with neoadjuvant chemoradiotherapy (NCRT) in combination with surgery.

PATIENTS AND METHODS

We included 282 patients with esophageal squamous cell carcinoma who received neoadjuvant chemoradiotherapy (NCRT) combined with surgery, constructed three models incorporating pathological factors, investigated the discrimination and calibration of each model, and compared the clinical utility of each model using the net reclassification index (NRI) and the integrated discrimination index (IDI).

RESULTS

Multivariable analysis showed that pathologic complete response (pCR) and lymph node tumor regression grading (LN-TRG) (p < 0.05) were independent prognostic factors for RFS. LASSO regression screened six correlates of LN-TRG, vascular invasion, nerve invasion, degree of differentiation, platelet grade, and a total diameter of residual cancer in lymph nodes to build model three, which was consistent in terms of efficacy in the training set and validation set. Kaplan-Meier (K-M) curves showed that all three models were able to distinguish well between high- and low-risk groups (p < 0.01). The NRI and IDI showed that the clinical utility of model 2 was slightly better than that of model 1 (p > 0.05), and model 3 was significantly better than that of model 2 (p < 0.05).

CONCLUSIONS

Clinical prediction models incorporating LN-TRG factors have high predictive efficacy, can help identify patients at high risk of recurrence after neoadjuvant therapy, and can be used as a supplement to the  AJCC/TNM staging system while offering a scientific rationale for early postoperative intervention.

Nomogram based on pretreatment hepatic and renal function indicators for survival prediction of locally advanced esophageal squamous cell carcinoma with treatment of neoadjuvant chemoradiotherapy plus surgery

The parameters for survival prediction of esophageal squamous cell carcinoma (ESCC) patients treated with neoadjuvant chemoradiotherapy (NCRT) combined with surgery are unclear. Here, we aimed to construct a nomogram for survival prediction of ESCC patients treated with NCRT combined with surgery based on pretreatment serological hepatic and renal function tests. A total of 174 patients diagnosed as ESCC were enrolled as a training cohort from July 2007 to June 2019, and approximately 50% of the cases (n = 88) were randomly selected as an internal validation cohort. Univariate and multivariate Cox survival analyses were performed to identify independent prognostic factors to establish a nomogram. Predictive accuracy of the nomogram was evaluated by Harrell's concordance index (C-index) and calibration curve. ALT, ALP, TBA, TP, AST, TBIL and CREA were identified as independent prognostic factors and incorporated into the construction of the hepatic and renal function test nomogram (HRFTNomogram). The C-index of the HRFTNomogram for overall survival (OS) was 0.764 (95% CI 0.701-0.827) in the training cohort, which was higher than that of the TNM staging system (0.507 (95% CI 0.429-0.585), P < 0.001). The 5-year OS calibration curve of the training cohort demonstrated that the predictive accuracy of the HRFTNomogram was satisfactory. Moreover, patients in the high-risk group stratified by the HRFTNomogram had poorer 5-year OS than those in the low-risk group in the training cohort (27.4% vs. 80.3%, P < 0.001). Similar results were observed in the internal validation cohort. A novel HRFTNomogram might help predict the survival of locally advanced ESCC patients treated with NCRT followed by esophagectomy.

A prognostic nomogram that includes MPV in esophageal squamous cell carcinoma

BACKGROUND

Mean platelet volume (MPV), as a marker of platelet activity, has been shown to be an efficient prognostic biomarker in several types of cancer. Using MPV, this study aimed to create and validate a prognostic nomogram to the overall survival in esophageal squamous cell carcinoma (ESCC) patients.

METHODS

The nomogram was constructed and tested using data from a retrospective study of 1893 patients who were randomly assigned to the training and testing cohorts with a 7:3 randomization. In order to screen out the optimal predictors for overall survival (OS), we conducted the LASSO-cox regression, univariate, and multivariate cox regression analyses. Subsequently, the predictive accuracy of the nomogram was validated in both the training and the testing cohorts. Finally, decision curve analysis (DCA) was used to confirm clinical validity.

RESULTS

Age, MPV, nerve invasion, T stage, and N stage were found as independent prognostic variables for OS and were further developed into a nomogram. The nomogram's prediction accuracy for 1-, 3-, and 5-year OS was 0.736, 0.749, 0.774, and 0.724, 0.719, 0.704 in the training and testing cohorts, respectively. Furthermore, DCA results indicated that nomograms outperformed the AJCC 8th and conventional T, N staging systems in both the training and testing cohorts.

CONCLUSIONS

The nomogram, in conjunction with MPV and standard clinicopathological markers, could improve the accuracy of prediction of OS in ESCC patients.

A clinical prognostic model for patients with esophageal squamous cell carcinoma based on circulating tumor DNA mutation features

Background

Few predictive models have included circulating tumor DNA (ctDNA) indicators to predict prognosis of esophageal squamous cell carcinoma (ESCC) patients. Here, we aimed to explore whether ctDNA can be used as a predictive biomarker in nomogram models to predict the prognosis of patients with ESCC.

Methods

We included 57 patients who underwent surgery and completed a 5-year follow-up. With next-generation sequencing, a 61-gene panel was used to evaluate plasma cell-free DNA and white blood cell genomic DNA from patients with ESCC. We analyzed the relationship between the mutation features of ctDNA and the prognosis of patients with ESCC, identified candidate risk predictors by Cox analysis, and developed nomogram models to predict the 2- and 5-year disease-free survival (DFS) and overall survival (OS). The area under the curve of the receiver operating characteristic (ROC) curve, concordance index (C-index), calibration plot, and integrated discrimination improvement (IDI) were used to evaluate the performance of the nomogram model. The model was compared with the traditional tumor-nodes-metastasis (TNM) staging system.

Results

The ROC curve showed that the average mutant allele frequency (MAF) of ctDNA variants and the number of ctDNA variants were potential biomarkers for predicting the prognosis of patients with ESCC. The predictors included in the models were common candidate predictors of ESCC, such as lymph node stage, angiolymphatic invasion, drinking history, and ctDNA characteristics. The calibration curve demonstrated consistency between the observed and predicted results. Moreover, our nomogram models showed clear prognostic superiority over the traditional TNM staging system (based on C-index, 2-year DFS: 0.82 vs. 0.64; 5-year DFS: 0.78 vs. 0.65; 2-year OS: 0.80 vs. 0.66; 5-year OS: 0.77 vs. 0.66; based on IDI, 2-year DFS: 0.33, p <0.001; 5-year DFS: 0.18, p = 0.04; 2-year OS: 0.28, p <0.001; 5-year OS: 0.15, p = 0.04). The comprehensive scores of the nomogram models could be used to stratify patients with ESCC.

Conclusions

The novel nomogram incorporating ctDNA features may help predict the prognosis of patients with resectable ESCC. This model can potentially be used to guide the postoperative management of ESCC patients in the future, such as adjuvant therapy and follow-up.

Heparanase is a prognostic biomarker independent of tumor purity and hypoxia based on bioinformatics and immunohistochemistry analysis of esophageal squamous cell carcinoma

Background

Esophageal squamous cell carcinoma (ESCC) is a common malignant tumor of the digestive tract with a poor prognosis. The tumor microenvironment (TME) is mainly composed of tumor cells, stromal cells, and immune cells and plays an important role in ESCC development. There are substantial differences in tumor purity among different parts of ESCC tissues, consisting of distinct immune and stromal cells and variations in the status of hypoxia. Thus, prognostic models of ESCC based on bioinformatic analysis of tumor tissues are unreliable.

Method

Differentially expressed genes (DEGs) independent of tumor purity and hypoxia were screened by Spearman correlation analysis of public ESCC cohorts. Subsequently, the DEGs were subjected to Cox regression analysis. Then, we constructed a protein–protein interaction (PPI) network of the DEGs using Cytoscape. Intersection analysis of the univariate Cox and PPI results indicated that heparanase (HPSE), an endo-β-D-glucuronidase capable of cleaving heparan sulfate side chains, was a predictive factor. Gene set enrichment analysis (GSEA) was used to reveal the potential function of HPSE, and single-cell sequencing data were analyzed to evaluate the distribution of HPSE in immune cells. Furthermore, a human ESCC tissue microarray was used to validate the expression and prognostic value of HPSE.

Result

We found that HPSE was downregulated in ESCC tissues and was not correlated with tumor purity or hypoxia status. HPSE is involved in multiple biological processes. ESCC patients with low HPSE expression in cancerous tissues exhibited poor prognosis.

Conclusions

These results indicate that low HPSE expression in cancerous tissues correlates with poor prognosis in patients with ESCC. HPSE is a novel prognostic biomarker independent of tumor purity and hypoxia status in ESCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-022-02698-9.

Prognostic Nomogram for Predicting Long-Term Overall Survival of Esophageal Cancer Patients Receiving Neoadjuvant Chemoradiotherapy Plus Surgery: A Population-Based Study

Objective

Neoadjuvant chemoradiotherapy (nCRT) plays an important role in patients with locally advanced esophageal cancer (EC). We aim to determine the prognostic risk factors and establish a reliable nomogram to predict overall survival (OS) based on SEER population.

Methods

Patients with EC coded by 04–15 in the SEER database were included. The data were divided into training group and verification group (7:3). The Cox proportional-risk model was evaluated by using the working characteristic curve (receiver operating characteristic curve, ROC) and the area under the curve (AUC), and a nomogram was constructed. The calibration curve was used to measure the consistency between the predicted and the actual results. Decision curve analysis (DCA) was used to evaluate its clinical value. The best cut-off value of nomogram score in OS was determined by using X-tile software, and the patients were divided into low-risk, medium-risk, and high-risk groups.

Results

A total of 2,209 EC patients who underwent nCRT were included in further analysis, including 1,549 in the training cohort and 660 in the validation group. By Cox analysis, sex, marital status, T stage, N stage, M stage, and pathological grade were identified as risk factors. A nomogram survival prediction model was established to predict the 36-, 60-, and 84-month survival. The ROC curve and AUC showed that the model had good discrimination ability. The correction curve was in good agreement with the prediction results. DCA further proved the effective clinical value of the nomogram model. The results of X-tile analysis showed that the long-term prognosis of patients in the low-risk subgroup was better in the training cohort and the validation cohort (p &lt; 0.001).

Conclusion

This study established an easy-to-use nomogram risk prediction model consisting of independent prognostic factors in EC patients receiving nCRT, helping to stratify risk, identify high-risk patients, and provide personalized treatment options.