Ouroboros: cross-linking protein expression perturbations and cancer histology imaging with generative-predictive modeling

TL;DR

This paper introduces a new framework called Ouroboros that connects protein expression data with cancer histology images to improve diagnosis and treatment insights.

Contribution

The novel generative-predictive framework Ouroboros enables bidirectional modeling between protein expression and histology images in glioblastoma.

Findings

Ouroboros significantly improves prediction of protein expression from H&E images compared to baseline methods.

The framework accurately generates virtual glioblastoma sample images from protein expression data.

The study reveals morphological patterns linked to protein expression changes in glioblastoma tissues.

Abstract

Imagine if we could simultaneously predict spatial protein expression in tissues from their routine Hematoxylin and Eosin (H&E) stained images, and create tissue images given protein expression profiles thus enabling virtual simulations of how protein expression alterations impact histology in complex diseases like cancer. Such an approach could lead to more informed diagnostic and therapeutic decisions for precision medicine at lower costs and shorter turnaround times, more detailed insights into underlying disease pathology as well as improvement in predictive and generative performance. In this study, we investigate the intricate correlation between protein expressions obtained from Hyperion mass cytometry and histopathological microstructures in conventional H&E stained glioblastoma (GBM) samples, unveiling morphological patterns and cellular-level spatial alterations associated with protein expression changes. To model these complex relationships, we propose a novel generative-predictive framework called Ouroboros for producing H&E images from protein expressions and simultaneously predicting protein expressions from H&E images. Our comprehensive sample-independent validation over 9920 tissue spots from 4 GBM samples encompassing visual image analysis, quantitative analysis, subspace alignment and perturbation experiments shows that the proposed generative-predictive approach offers significant improvements in predicting protein expression from images in comparison to baseline methods as well as accurate generation of virtual GBM sample images. This proof of concept study can contribute to advancing our understanding of histological responses to protein expression perturbations and lays the foundations for further developments in this area.

Implementation and associated data for the proposed approach are available at the URL: https://github.com/Srijay/Ouroboros.

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