ABSTRACT
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.
Competing Interest Statement
PKS is a co-founder and member of the BOD of Glencoe Software, a member of the BOD for Applied Biomath, and a member of the SAB for RareCyte, NanoString, and Montai Health; he holds equity in Glencoe, Applied Biomath, and RareCyte. PKS is a consultant for Merck and the Sorger lab has received research funding from Novartis and Merck in the past five years. YC is a consultant for RareCyte. DC, JC, EM, SR, and TG are employees of RareCyte. The DFCI receives funding for KLL research from the following entities: Amgen, Travera, and X4. DFCI and KLL have patents related to molecular diagnostics of cancer. SJR receives research support from Bristol-Myers-Squibb and KITE/Gilead. SJR is on the Scientific Advisory Board for Immunitas Therapeutics. The other authors declare no outside interests.
Footnotes
Human Tissue Atlas Center