Histopathology using Hematoxylin and Eosin (H&E) stained tissue sections plays a central role in the diagnosis and staging of diseases. The transition to digital H&E pathology affords an opportunity for integration with recently developed, highly multiplexed tissue imaging methods. Here we describe an approach (and instrument) for collecting and analyzing H&E and high-plex immunofluorescence (IF) images from the same cells at subcellular-resolution in a whole-slide format suitable for translational and clinical research and eventual deployment in a diagnostic setting. IF and H&E images provide highly complementary information for analysis by human experts and machine learning algorithms. Using images of 40 human colorectal cancer resections, 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 can be achieved, suggesting the potential of integrated H&E and high-plex imaging in the generation of high performance prognostic biomarkers.

Citation Format: Jia-Ren Lin, Yu-An Chen, Daniel Campton, Jeremy Cooper, Shannon Coy, Clarence Yapp, Erin McCarty, Keith L. Ligon, Steven Reese, Tad George, Sandro Santagata, Peter Sorger. Multi-modal digital pathology by sequential acquisition and joint analysis of highly multiplexed immunofluorescence and hematoxylin and eosin images [abstract]. In: Proceedings of the AACR Special Conference on Colorectal Cancer; 2022 Oct 1-4; Portland, OR. Philadelphia (PA): AACR; Cancer Res 2022;82(23 Suppl_1):Abstract nr A028.