Detection of Phenotypic Alterations Using High-Content Analysis of Whole-Slide Images

Tumors exhibit spatial heterogeneity, as manifested in immunohistochemistry (IHC) staining patterns. Current IHC quantification methods lose information by reducing this heterogeneity in each whole-slide image (WSI) or in selective fields of view to a single staining index. The aim of this study was to investigate the sensitivity of an IHC quantification method that uses this heterogeneity to reliably compare IHC staining patterns. We virtually partitioned WSIs by a grid of square tiles, and computed the staining index distributions to quantify heterogeneities. We used samples from these distributions as inputs to non-parametric statistical comparisons. We applied our grid method to fixed tumor samples from 26 tumors obtained from a double-blind preclinical study of a patient-derived orthotopic xenograft model of pediatric neuroblastoma in CD1 nude mice. We compared the results of our grid method to the results based on whole-slide indices, the current practice. We show that our grid method reliably detects phenotypic alterations that other tests based on whole-slide indices fail to detect. Based on robustness and increased sensitivity of statistical inference, we conclude that our method of whole-slide grid quantification is superior to existing whole-slide quantification techniques.