Computational pathology approach for assessment of prognosis and immunotherapy response in pan-gastrointestinal cancer.

BACKGROUND: Current cancer staging methods cannot accurately predict survival outcomes and therapeutic benefits in cancer patients. Digital pathomics, a rapidly evolving field, holds significant potential to revolutionize disease evaluation. METHODS: This international multicenter study included 2463 patients with pan-gastrointestinal cancer from twelve cohorts of seven cancer centers, of whom 1653 patients were diagnosed with gastric cancer (GC). We proposed a deep learning pathomics signature (DLPS) by integrating information on three scales from the whole slide images (WSIs) of H&E-stained tissue, including pathomic features related to the nucleus, microenvironment, and single-cell spatial distribution. Next, we assessed the predictive accuracy of the DLPS for prognosis, chemotherapy response, and immunotherapy response. RESULTS: Our findings revealed that the DLPS was significantly associated with overall survival in gastric cancer (GC) and pan-gastrointestinal cancers, exhibiting good accuracy with area under the curve values ranging from 0.723 to 0.840 across GC cohorts. Multivariable Cox regression analyses indicated that the DLPS is an independent prognostic factor. The nomogram that integrated the DLPS and TNM stage showed improved performance in predicting cancer prognosis compared to that with TNM stage alone (P < 0.05). Importantly, GC patients with low-DLPS (but not those with high-DLPS) exhibited substantial benefits from adjuvant chemotherapy (P < 0.05). Furthermore, the objective response rate to anti-PD-1 immunotherapy was significantly higher in the low-DLPS group (29.6%) compared to the high-DLPS group (8.3%, P < 0.05). CONCLUSIONS: The DLPS has the potential to enhance prognosis assessment and identify patients who are likely to benefit from adjuvant chemotherapy and immunotherapy in pan-gastrointestinal cancers, as well as in other solid tumors.