Cell graph neural networks enable the digital staging of tumor microenvironment and precise prediction of patient survival in gastric cancer

Gastric cancer is one of the deadliest cancers worldwide. Accurate prognosis is essential for effective clinical assessment and treatment. Spatial patterns in the tumor microenvironment (TME) are conceptually indicative of the staging and progression of gastric cancer patients. Using spatial patterns of the TME by integrating and transforming the multiplexed immunohistochemistry (mIHC) images as Cell-Graphs, we propose a novel graph neural network-based approach, termed Cell-Graph Signature or CG Signature , powered by artificial intelligence, for digital staging of TME and precise prediction of patient survival in gastric cancer. In this study, patient survival prediction is formulated as either a binary ( short-term and long-term ) or ternary ( short-term, medium-term , and long-term ) classification task. Extensive benchmarking experiments demonstrate that the CG Signature achieves outstanding model performance, with Area Under the Receiver-Operating Characteristic curve (AUROC) of 0.960±0.01, and 0.771±0.024 to 0.904±0.012 for the binary- and ternary-classification, respectively. Moreover, Kaplan-Meier survival analysis indicates that the ‘digital-grade’ cancer staging produced by CG Signature provides a remarkable capability in discriminating both binary and ternary classes with statistical significance ( p -value < 0.0001), significantly outperforming the AJCC 8th edition Tumor-Node-Metastasis staging system. Using Cell-Graphs extracted from mIHC images, CG Signature improves the assessment of the link between the TME spatial patterns and patient prognosis. Our study suggests the feasibility and benefits of such artificial intelligence-powered digital staging system in diagnostic pathology and precision oncology. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was supported by the Major Inter-Disciplinary Research (IDR) Grant awarded by Monash University ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study was approved by the Shanghai Ruijin Hospital under protocol 2021SQ015. All researchers were blinded to the patient private data during the experimental analysis. All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes The data used for the main analyses presented here is available for non-commercial use and can be accessible by request.