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Background: Apatinib, a highly selective VEGFR2 inhibitor, significantly improved efficacy versus placebo as a third- and later-line treatment for advanced gastric cancer in phase 2 and 3 trials. This prospective, single-arm, multicenter phase IV AHEAD study was conducted to verify the safety and efficacy of apatinib in patients with advanced or metastatic gastric or gastroesophageal adenocarcinoma after at least two lines of systematic therapy in clinical practice settings. Methods: Patients with advanced gastric cancer who had previously failed at least two lines of chemotherapy received oral apatinib until disease progression, death or unacceptable toxicity. The primary endpoint was safety. The secondary endpoints included objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS) and overall survival (OS). Adverse events were summarized by the incidence rate. Median OS and PFS were estimated using the Kaplan–Meier method. ORR, DCR, OS at 3 and 6 months, and PFS at 3 and 6 months were calculated, and their 95% CIs were estimated according to the Clopper-Pearson method. Results: Between May 2015 and November 2019, a total of 2004 patients were enrolled, and 1999 patients who received at least one dose of apatinib were assessed for safety. In the safety population, 87.9% of patients experienced treatment-related adverse events (TRAEs), with the most common hypertension (45.2%), proteinuria (26.5%), and white blood cell count decreased (25.3%). Additionally, 51% of patients experienced grade ≥ 3 TRAEs. Fatal TRAEs occurred in 57 (2.9%) patients. No new safety concerns were reported. Among the 2004 patients included in the intention-to-treat population, the ORR was 4.4% (95% CI, 3.6–5.4%), and DCR was 35.8% (95% CI, 33.7–38.0%). The median PFS was 2.7 months (95% CI 2.2–2.8), and the median OS was 5.8 months (95% CI 5.4–6.1). Conclusions: The findings in the AHEAD study confirmed the acceptable and manageable safety profile and clinical benefit of apatinib in patients with advanced gastric cancer as a third- or later-line of treatment. Trial registration: This study was registered with ClinicalTrials.gov NCT02426034. Registration date was April 24, 2015. [ABSTRACT FROM AUTHOR]

Esophageal cancer (EC) is a malignant tumor with high mortality. We aimed to find the optimal examined lymph node (ELN) count threshold and develop a model to predict survival of patients after radical esophagectomy. Two cohorts were analyzed: the training cohort which included 734 EC patients from the Chinese registry and the external testing cohort which included 3208 EC patients from the Surveillance, Epidemiology, and End Results (SEER) registry. Cox proportional hazards regression analysis was used to determine the prognostic value of ELNs. The cut-off point of the ELNs count was determined using R-statistical software. The prediction model was developed using random survival forest (RSF) algorithm. Higher ELNs count was significantly associated with better survival in both cohorts (training cohort: HR = 0.98, CI = 0.97–0.99, P < 0.01; testing cohort: HR = 0.98, CI = 0.98–0.99, P < 0.01) and the cut-off point was 18 (training cohort: P < 0.01; testing cohort: P < 0.01). We developed the RSF model with high prediction accuracy (AUC: training cohort: 87.5; testing cohort: 79.3) and low Brier Score (training cohort: 0.122; testing cohort: 0.152). The ELNs count beyond 18 is associated with better overall survival. The RSF model has preferable clinical capability in terms of individual prognosis assessment in patients after radical esophagectomy. [ABSTRACT FROM AUTHOR]

Purpose: Immune checkpoint inhibitors (ICIs) have shown durable responses in various malignancies. However, the response to ICI therapy is unpredictable, and investigation of predictive biomarkers needs to be improved. Experimental design: In total, 120 patients receiving ICI therapy and 40 patients receiving non-ICI therapy were enrolled. Peripheral blood immune cell markers (PBIMs), as liquid biopsy biomarkers, were analyzed by flow cytometry before ICI therapy, and before the first evaluation. In the ICI cohort, patients were randomly divided into training (n = 91) and validation (n = 29) cohorts. Machine learning algorithms were applied to construct the prognostic and predictive immune-related models. Results: Using the training cohort, a peripheral blood immune cell-based signature (BICS) based on four hub PBIMs was developed. In both the training and the validation cohorts, and the whole cohort, the BICS achieved a high accuracy for predicting overall survival (OS) benefit. The high-BICS group had significantly shorter progression-free survival and OS than the low-BICS group. The BICS demonstrated the predictive ability of patients to achieve durable clinical outcomes. By integrating these PBIMs, we further constructed and validated the support vector machine-recursive and feature elimination classifier model, which robustly predicts patients who will achieve optimal clinical benefit. Conclusions: Dynamic PBIM-based monitoring as a noninvasive, cost-effective, highly specific and sensitive biomarker has broad potential for prognostic and predictive utility in patients receiving ICI therapy. [ABSTRACT FROM AUTHOR]