Computationally-Guided Development of a Stromal Inflammation Histologic Biomarker in Lung Squamous Cell Carcinoma

Abstract

The goal of this study is to use computational pathology to help guide the development of human-based prognostic H&E biomarker(s) suitable for research and potential clinical use in lung squamous cell carcinoma (SCC). We started with high-throughput computational image analysis with tissue microarrays (TMAs) to screen for histologic features associated with patient overall survival, and found that features related to stromal inflammation were the most strongly prognostic. Based on this, we developed an H&E stromal inflammation (SI) score. The prognostic value of the SI score was validated by two blinded human observers on two large cohorts from a single institution. The SI score was found to be reproducible on TMAs (Spearman rho = 0.88 between the two observers), and highly prognostic (e.g. hazard ratio = 0.32; 95% confidence interval: 0.19–0.54; p-value = 2.5 × 10−5 in multivariate analyses), particularly in comparison to established histologic biomarkers. Guided by downstream molecular/biomarker correlation studies starting with TCGA cases, we investigated the hypothesis that epithelial PD-L1 expression modified the prognostic value of SI. Our research demonstrates that computational pathology can be an efficient hypothesis generator for human pathology research, and support the histologic evaluation of SI as a prognostic biomarker in lung SCCs.

IASLC Multidisciplinary Recommendations for Pathologic Assessment of Lung Cancer Resection Specimens After Neoadjuvant Therapy

“…There is no established grading system for the extent of inflammation in the neoadjuvant setting on the basis of light microscopic review of hematoxylin and eosin (H&E) stained slides, although several approaches have been proposed in studies without neoadjuvant therapy. [75][76][77] Junker et al 30 described marked swelling of tumor cells after neoadjuvant therapy more often in adenocarcinomas than in squamous cell carcinomas. In rare cases, it may be difficult to differentiate single tumor cells or small clusters of tumor cells after neoadjuvant therapy from cells of a histiocytic reaction on the basis of H&E alone.…”

The state of the art for artificial intelligence in lung digital pathology

“…Wu et al [108] developed a U‐Net‐based end‐to‐end DL model to automatically detect tumors, segment cells, and calculate the TPS of PD‐L1 by highlighting PD‐L1‐positive tumor cells. In a study with 437 NSCLC patients, Xia et al [109] demonstrated the utility of computational pathology approaches in the discovery of newer biomarkers. Using tissue microarrays, they discovered a new histologic feature, the stroma inflammation score, which was highly correlated with patient overall survival in NSCLC.…”

The Role of Pathology-Based Methods in Qualitative and Quantitative Approaches to Cancer Immunotherapy

“…The potential benefits are more repeatable cell counting and measurement, and saving pathologists time in their everyday work routine [ 27 ]. Additionally, by measuring many cell and inter-cell parameters, AI can potentially help find new HE phenomena that can later be used [ 140 , 141 , 142 ]. Some commercially available software is made for breast cancers and scoring ER, PGR, Ki67, and HER-2.…”