Multi-modal digital pathology for colorectal cancer diagnosis by high-plex immunofluorescence imaging and traditional histology of the same tissue section

Precision medicine is critically dependent on better methods for diagnosing and staging disease and predicting drug response. Histopathology using Hematoxylin and Eosin (H&E) stained tissue - not genomics – remains the primary diagnostic modality in cancer. Moreover, recently developed, highly multiplexed tissue imaging represents a means of enhancing histology workflows with single cell mechanisms. Here we describe an approach for collecting and analyzing H&E and high-plex immunofluorescence (IF) images from the same cells in a whole-slide format suitable for translational and clinical research and eventual deployment in diagnosis. Using data from 40 human colorectal cancer resections (60 million cells) we show that IF and H&E images provide human experts and machine learning algorithms with complementary information. We demonstrate the automated generation and ranking of computational models, based either on immune infiltration or tumor-intrinsic features, that are highly predictive of progression-free survival. When these models are combined, a hazard ratio of ∼0.045 is achieved, demonstrating the ability of multi-modal digital pathology to generate high-performance and interpretable biomarkers.