Evaluating and comparing immunostaining and computational methods for spatial profiling of drug response in patient-derived explants

Patient-derived explants (PDEs) represent the direct culture of fragments of freshly-resected tumour tissue under conditions that retain the original architecture of the tumour. PDEs have advantages over other preclinical cancer models as platforms for predicting patient-relevant drug responses in that they preserve the tumour microenvironment and tumour heterogeneity. At endpoint, PDEs may either be processed for generation of histological sections or homogenised and processed for ʻomic’ evaluation of biomarker expression. A significant advantage of spatial profiling is the ability to co-register drug responses with tumour pathology, tumour heterogeneity and changes in the tumour microenvironment. Spatial profiling of PDEs relies on the utilisation of robust immunostaining approaches for validated biomarkers and incorporation of appropriate image analysis methods to quantitatively and qualitatively monitor changes in biomarker expression in response to anti-cancer drugs. Automation of immunostaining and image analysis would provide a significant advantage for the drug discovery pipeline and therefore, here, we have sought to optimise digital pathology approaches. We compare three image analysis software platforms (QuPath, ImmunoRatio and VisioPharm) for evaluating Ki67 as a marker for proliferation, cleaved PARP (cPARP) as a marker for apoptosis and pan-cytokeratin (CK) as a marker for tumour areas and find that all three generate comparable data to the views of a histomorphometrist. We also show that Virtual Double Staining of sequential sections by immunohistochemistry results in imperfect section alignment such that CK-stained tumour areas are over-estimated. Finally, we demonstrate that multi-immunofluorescence combined with digital image analysis is a superior method for monitoring multiple biomarkers simultaneously in tumour and stromal areas in PDEs