Abstract 4939: Computational pathology approach for prognostic advancements and therapeutic benefits in gastrointestinal cancer: A multi-centric retrospective study

Abstract Background: The current cancer staging methods cannot accurately predict survival and therapeutic benefits in cancer patients. Digital pathomics is an emerging field with potential to revolutionize disease evaluation. The present study aims to develop and validate a deep learning pathomics signature from digital hematoxylin-eosin (H 0. 001), which was further validated in other gastrointestinal cancers (all p 0. 02). The DLPS remained an independent predictor of prognosis in multivariable analysis (all p 0. 001). Furthermore, a nomogram incorporating the DLPS and TNM stage shows significantly improved accuracy in predicting cancer prognosis compared to that with TNM stage alone (all p 0. 05). Shapley value analysis highlighted DLPS as the strongest predictor for prognosis. Importantly, GC patients with a low-DLPS (but not those with a high-DLPS) exhibited substantial benefits from adjuvant chemotherapy (all p 0. 05). Furthermore, we found the objective responses of anti-PD-1 immunotherapy is significantly higher in the low-DLPS group (29. 6%) than in the high-DLPS group (8. 3%, p 0. 05). Upon analyzing multi-omics data, we found that a higher DLPS was positively correlated with tumor promoting, chemotherapy resistance, immune tumor microenvironment and metabolic signaling. Conclusion: The DLPS enabled improved assessment on prognosis, and has the potential to identify patients who will benefit from adjuvant chemotherapy and immunotherapy, which can further be extended to many gastrointestinal cancers or other solid tumors. Citation Format: Taojun Zhang, Zepang Sun, Zhe Li, M. Usman Ahmad, Md Tauhidul Islam, Fan Yang, Zhenhui Li, Yuming Jiang. Computational pathology approach for prognostic advancements and therapeutic benefits in gastrointestinal cancer: A multi-centric retrospective study abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 4939.