Investigating lung cancer microenvironment from cell segmentation of pathological image and its application in prognostic stratification

Abstract

Lung cancer, particularly adenocarcinoma, ranks high in morbidity and mortality rates worldwide, with a relatively low five-year survival rate. To achieve precise prognostic assessment and clinical intervention for patients, thereby enhancing their survival prospects, there is an urgent need for more accurate stratification schemes. Currently, the TNM staging system is predominantly used in clinical practice for prognostic evaluation, but its accuracy is constrained by the reliance on physician experience. Although biomarker discovery based on molecular pathology offers a new perspective for prognostic assessment, its dependence on expensive gene panel testing limits its widespread clinical application. Pathological images contain abundant diagnostic information, providing a new avenue for prognostic evaluation. In this study, we employed advanced Hover-Net technology to accurately quantify the abundance of epithelial cells, lymphocytes, macrophages, and neutrophils from pathological images, and delved into the clinical and biological significance of these cellular abundances. Our research findings reveal that, in contrast to patients classified as N0 stage, those belonging to the N1 stage demonstrated a marked elevation in the infiltration of epithelial cells, lymphocytes, macrophages, and neutrophils. Notably, the infiltration patterns of lymphocytes and neutrophils exhibited an inverse relationship with the activation status of numerous pivotal gene pathways, including the HALLMARK_HEME_METABOLISM pathway. Furthermore, our analysis distinguished FABP7 as a prognostic biomarker, exhibiting pronounced differential expression between patients with high and low levels of neutrophil infiltration, indicate that cellular abundance analysis based on pathological images can provide a more accurate and cost-effective prognostic evaluation, offering new strategies for the clinical management of lung adenocarcinoma.

Keywords: Hover-net cell segmentation technology; Lung adenocarcinoma (LUAD); Multidimensional biological analysis; Prognostic biomarkers.