Gaochao Dong

Background: Primary‐tumor major pathological response (MPR) and postoperative nodal status are both used to assess response after neoadjuvant therapy for esophageal squamous cell carcinoma (ESCC), but discordant response after neoadjuvant chemoimmunotherapy is poorly characterized. We evaluated a combined primary tumor‐lymph node (PT‐LN) framework for risk stratification. Methods: This retrospective single‐center cohort included patients treated with two cycles of paclitaxel, cisplatin, and camrelizumab followed by curative‐intent surgery between January 2019 and December 2024. Among 414 screened patients, 367 were included. MPR was defined as 10% or less residual viable tumor cells in the primary tumor. Patients were classified as PT‐MPR/ypN0, PT‐MPR/ypN+, PT‐non‐MPR/ypN0, or PT‐non‐MPR/ypN+. Disease‐free survival (DFS), overall survival (OS), first documented recurrence, and pathological model performance were evaluated. Results: Sixty‐nine patients (18.8%) had PT‐MPR/ypN+ disease. DFS and OS differed across groups (both overall log‐rank p < 0.001). Compared with PT‐MPR/ypN0, PT‐MPR/ypN+ was associated with worse DFS (baseline‐adjusted hazard ratio [HR], 5.85; 95% confidence interval [CI], 2.82–12.14) and OS (HR, 18.06; 95% CI, 5.53–59.06; both p < 0.001), with similar conclusions after additional pathological adjustment. Regional lymph node recurrence occurred in 44.9% of PT‐MPR/ypN+ patients versus 3.6%, 6.7%, and 16.7% in the other groups (global p < 0.001). The combined PT‐LN framework had 3‐year DFS and OS AUCs of 0.798 and 0.818, with modest gains over LN‐only classification. Conclusions: Residual nodal disease despite primary‐tumor MPR identifies an adverse phenotype with predominantly regional nodal recurrence. The combined framework is an interpretable stratification approach requiring external validation; surveillance and treatment implications remain hypothesis‐generating.

While effective against non-small cell lung cancer (NSCLC), PD-1 inhibitors can induce immune-related adverse events (irAEs), occurring in up to 15.2% of patients and potentially fatal. Currently, effective predictive biomarkers capable of simultaneously forecasting both irAEs and immune checkpoint inhibitor (ICI) responders remain elusive. This limitation hinders the safe clinical application of these agents. This study enrolled 333 advanced NSCLC patients treated with PD-1 inhibitor monotherapy or combination therapy. CT imaging features were extracted using radiomics and deep-learning approaches. Three unimodal and two multimodal models were constructed to predict irAEs (Grade ≥3) and ICI responders in parallel. The SHAP algorithm was used to identify clinical features contributing to the prediction of both irAEs and ICI responders. The CDML–DenseNet model, integrating clinical features with deep-learning–derived radiomics features (DenseNet), demonstrated superior performance in predicting irAEs (AUC = 0.85), outperforming single-modal radiomics models. For ICI responder prediction, the CDML–DenseNet model achieved an AUC of 0.866. The Prognostic Nutritional Index (PNI) was identified as a key feature in both irAEs and ICI responder prediction models. Patients who were non-responders to ICIs but experienced irAEs had significantly lower PNI (46.8 ± 8.779, P < 0.05) compared with ICI responders without irAEs. Our multimodal CDML–DenseNet model effectively predicts both irAEs and ICI responders in NSCLC patients receiving PD-1 inhibitors. This approach provides a novel framework for balancing immunotherapy efficacy and toxicity. Furthermore, the readily available and cost-effective PNI offers clinicians a practical tool to identify potential non-responders experiencing irAEs and to refine treatment decisions.

Supplementary Fig. 11. cEMSY expression level affects patient prognosis in multiple clinical cancer cohorts.

Supplementary Fig. 2. Characteristics of cEMSY circRNA and expression profiles in LUAD.

Supplementary Fig. 4. cEMSY knockout abrogates the ability of cellular stressors to induce ICD.

Supplementary Fig. 1. Filter ICD related circRNAs.

Supplementary Fig. 8. cEMSY activates antiviral immune signaling.

Supplementary Fig. 10. cEMSY modulates immune responses through facilitating the translocation of TDP-43 into mitochondria.

Supplementary Fig. 3. cEMSY induces ICD in vitro.