BACKGROUND: The tumor immune microenvironment can provide prognostic and therapeutic information. We aimed to develop noninvasive imaging biomarkers from computed tomography (CT) for comprehensive evaluation of immune context and investigate their associations with prognosis and immunotherapy response in gastric cancer (GC). METHODS: This study involved 2,600 patients with GC from 9 independent cohorts. We developed and validated 2 CT imaging biomarkers (lymphoid radiomics score [LRS] and myeloid radiomics score [MRS]) for evaluating the IHC-derived lymphoid and myeloid immune context respectively, and integrated them into a combined imaging biomarker [LRS/MRS: low(−) or high(+)] with 4 radiomics immune subtypes: 1 (−/−), 2 (+/−), 3 (−/+), and 4 (+/+). We further evaluated the imaging biomarkers’ predictive values on prognosis and immunotherapy response. RESULTS: The developed imaging biomarkers (LRS and MRS) had a high accuracy in predicting lymphoid (AUC range: 0.765–0.773) and myeloid (AUC range: 0.736–0.750) immune context. Further, similar to the IHC-derived immune context, 2 imaging biomarkers (HR range: 0.240–0.761 for LRS; 1.301–4.012 for MRS) and the combined biomarker were independent predictors for disease-free and overall survival in the training and all validation cohorts (all P < 0.05). Additionally, patients with high LRS or low MRS may benefit more from immunotherapy ( P < 0.001). Further, a highly heterogeneous outcome on objective response rate was observed in 4 imaging subtypes: 1 (−/−) with 27.3%, 2 (+/−) with 53.3%, 3 (−/+) with 10.2%, and 4 (+/+) with 30.0% ( P < 0.0001). CONCLUSION: The noninvasive imaging biomarkers could accurately evaluate the immune context and provide information regarding prognosis and immunotherapy for GC.

Background: Peritoneal recurrence (PR) is the predominant pattern of relapse after curative-intent surgery in gastric cancer (GC) and indicates a dismal prognosis. Accurate prediction of PR is crucial for patient management and treatment. We aimed to develop a noninvasive imaging biomarker from computed tomography (CT) for PR evaluation, and investigate its associations with prognosis and chemotherapy benefit. Methods: In this multicenter study including five independent cohorts of 2005 GC patients, we extracted 584 quantitative features from the intratumoral and peritumoral regions on contrast-enhanced CT images. The artificial intelligence algorithms were used to select significant PR-related features, and then integrated into a radiomic imaging signature. And improvements of diagnostic accuracy for PR by clinicians with the signature assistance were quantified. Using Shapley values, we determined the most relevant features and provided explanations to prediction. We further evaluated its predictive performance in prognosis and chemotherapy response. Results: The developed radiomics signature had a consistently high accuracy in predicting PR in the training cohort (area under the curve: 0.732), internal and SYSUCC validation cohorts (0.721 and 0.728). The radiomics signature was the most important feature in Shapley interpretation. The diagnostic accuracy of PR with the radiomics signature assistance was improved by 10.13% to 18.86% for clinicians (P<0.001). Furthermore, it was also applicable in the survival prediction. In multivariable analysis, the radiomics signature remained an independent predictor for PR and prognosis (P<0.001 for all). Importantly, patients with predicting high risk of PR from radiomics signature, could gain survival benefit from adjuvant chemotherapy. By contrast, chemotherapy had no impact on survival for patients with a predicted low risk of PR. Conclusion: The noninvasive and explainable model developed from preoperative CT images could accurately predict PR and chemotherapy benefit in patients with GC, which will allow the optimization of individual decision-making. Export

Background: Tertiary lymphoid structures (TLSs) are associated with favorable prognosis and enhanced response to anticancer therapy. A digital assessment of TLSs could provide an objective alternative that mitigates variability inherent in manual evaluation. This study aimed to develop and validate a digital gene panel based on biological prior knowledge for assessment of TLSs, and further investigate its associations with survival and multiple anticancer therapies. Materials and methods: The present study involved 1704 patients with gastric cancer from seven cancer centers. TLSs were identified morphologically through hematoxylin-and-eosin staining. The authors further developed a digital score based on targeted gene expression profiling to assess TLSs status, recorded as gene signature of tertiary lymphoid structures (gsTLS). For enhanced interpretability, we employed the SHapley Additive exPlanation (SHAP) analysis to elucidate its contribution to the prediction. The authors next evaluated the signature’s associations with prognosis, and investigated its predictive accuracy for multiple anticancer therapies, including adjuvant chemotherapy and immunotherapy. Results: The gsTLS panel with nine gene features achieved high accuracies in predicting TLSs status in the training, internal, and external validation cohorts (area under the curve, range: 0.729–0.791). In multivariable analysis, gsTLS remained an independent predictor of disease-free and overall survival (hazard ratio, range: 0.346–0.743, all P<0.05) after adjusting for other clinicopathological variables. SHAP analysis highlighted gsTLS as the strongest predictor of TLSs status compared with clinical features. Importantly, patients with high gsTLS (but not those with low gsTLS) exhibited substantial benefits from adjuvant chemotherapy (P<0.05). Furthermore, the authors found that the objective response rate to antiprogrammed cell death protein 1 (anti-PD-1) immunotherapy was significantly higher in the high-gsTLS group (40.7%) versus the low-gsTLS group (5.6%, P=0.036), and the diagnosis was independent from Epstein–Barr virus, tumor mutation burden, and programmed cell death-ligand 1 (PD-L1) expression. Conclusion: The gsTLS digital panel enables accurate assessment of TLSs status, and provides information regarding prognosis and responses to multiple therapies for gastric cancer.

Purpose:Tertiary lymphoid structures (TLS) are associated with improved survival and enhanced response to anticancer immunotherapy. This study aimed to develop a CT imaging–based approach for noninvasive assessment of TLSs and immunotherapy response.Experimental Design:This study involved 3,155 patients with gastric cancer. TLSs were classified into four stages (absence, Agg, FL-I, and FL-II) according to their maturation based on IHC staining. A CT imaging–based TLS scoring model (ctTLS) was developed to assess TLS status, subsequently classified into four classes (ctTLS-0/1/2/3). We next evaluated the model’s associations with prognosis and immunotherapy response. To enhance the model’s interpretability, we analyzed multiomics data and employed the Shapley value strategy.Results:The ctTLS model achieved high accuracies in predicting TLS status in the internal validation (AUC range, 0.727–0.809) and external validation (0.704–0.807) cohorts. In retrospective and prospective validation cohorts, ctTLSs exhibited significant associations with both disease-free and overall survival (HR range, 0.206–0.634; all P < 0.01). Shapley value analysis highlighted ctTLSs as the strongest predictor of TLS status. Upon analyzing multiomics data, we found that higher ctTLS levels positively correlated with tumor immune activation and apoptosis signaling, while displaying a negative correlation with tumor proliferation and metabolism signaling. Intriguingly, patients with high ctTLSs (but not low ctTLSs) exhibited substantial benefits from immunotherapy (P < 0.0001). The objective response rate of four ctTLS classes was 16.7% in ctTLS-0, 35.5% in ctTLS-1, 45.8% in ctTLS-2, and 53.8% in ctTLS-3.Conclusions:The ctTLS model could noninvasively assess TLS status, enabling improved prognosis evaluation and informed decisions about immunotherapy.

Supplementary Table 1. Clinicopathological characteristics of patients with gastric cancer in the retrospective, prospective, immunotherapy, and multi-omics cohorts. Supplementary Table 2. Clinical characteristics of patients according to the ctTLSs in the training cohort and internal validation cohort 1. Supplementary Table 3. Clinical characteristics of patients according to the ctTLSs in the internal validation cohort 2 and prospective validation cohort. Supplementary Table 4. Clinical characteristics of patients according to the ctTLSs in the external validation cohort 1 and external validation cohort 2. Supplementary Table 5. Clinical characteristics of patients according to the ctTLSs in the external validation cohort 3. Supplementary Table 6. Clinical characteristics of patients according to the TLSs in the training cohort and internal validation cohort 1. Supplementary Table 7. Clinical characteristics of patients according to the TLSs in the external validation cohort 1. Supplementary Table 8. Univariable association of the TLSs, clinicopathological characteristics with disease-free and overall survival. Supplementary Table 9. Multivariable association of the TLSs, clinicopathological characteristics with disease-free and overall survival. Supplementary Table 10. Multivariable Cox model with interaction testing across different clinicopathological characteristics with TLSs. Supplementary Table 11. Univariable association of the ctTLSs, clinicopathological characteristics with disease-free and overall survival in the training cohort, internal validation cohort 1 and internal validation cohort 2. Supplementary Table 12. Univariable association of the ctTLSs, clinicopathological characteristics with disease-free and overall survival in the external validation cohort 1, external validation cohort 2 and external validation cohort 3. Supplementary Table 13. Univariable association of the ctTLSs, clinicopathological characteristics with disease-free and overall survival in the prospective validation cohort. Supplementary Table 14. Multivariable cox regression analyses for disease-free survival and overall survival in patients with gastric cancer. Supplementary Table 15. Comparing the prediction accuracy of the integrated nomogram with ctTLSs and TNM stage in the training and validation cohorts. Supplementary Table 16. Univariable and multivariable cox regression analyses for disease-free survival and overall survival in patients with ctTLSs and TIL information. Supplementary Table 17. Clinical characteristics of patients according to the ctTLSs in the immunotherapy cohort 1 (SMU-5) and immunotherapy cohort 2 (SYSUCC-3). Supplementary Table 18. Clinical characteristics of patients according to the ctTLSs in the immunotherapy cohort 3 (PKUCH) and immunotherapy cohort 4 (GPHCM). Supplementary Table 19. Clinicopathological characteristics of patients with gastric cancer in immunotherapy cohorts. Supplementary Table 20. Univariable and multivariable logistic regression for ctTLSs and clinicopathological characteristics with responders (CR/PR ± SD) vs. non-responders (PD) in the immunotherapy cohort. Supplementary Table 21. Multinomial model across CR/PR/SD/PD for ctTLSs and clinicopathological characteristics in the immunotherapy cohort. Supplementary Table 22. Univariable association of the ctTLSs, clinicopathological characteristics with progression-free and overall survival in all immunotherapy cohort (SMU-5, SYSUCC-3, PKUCH, and GPHCM). Supplementary Table 23. Multivariable analyses for Progression -free survival and overall survival in the immunotherapy cohort. Supplementary Table 24. Clinical characteristics of patients according to the TLSs and ctTLSs in the multi-omics cohort. Supplementary Table 25. Antibody sources and staining conditions. Supplementary Table 26. Imaging Biomarker Standardization Initiative (IBSI) reporting structure of the study. Supplementary Table 27. Representativeness of Study Participants