MorphoITH: A Framework for Deconvolving Intra-Tumor Heterogeneity Using Tissue Morphology

TL;DR

MorphoITH is a scalable framework that uses tissue morphology from histopathology slides to deconvolve intra-tumor heterogeneity, capturing phenotypic variation and linking it to genetic subclonal changes in cancer.

Contribution

It introduces MorphoITH, a novel deep learning-based method that integrates tissue morphology analysis with molecular heterogeneity deconvolution in tumors.

Findings

Captures clinically-significant features like vascular architecture and nuclear grades.

Recognizes biological states associated with subclonal genetic changes.

Recapitulates genetic evolution trajectories from phenotypic data.

Abstract

The ability of tumors to evolve and adapt by developing subclones in different genetic and epigenetic states is a major challenge in oncology. Traditional tools like multi-regional sequencing used to study tumor evolution and the resultant intra-tumor heterogeneity (ITH) are often impractical because of their resource-intensiveness and limited scalability. Here, we present MorphoITH, a novel framework that leverages histopathology slides to deconvolve molecular ITH through tissue morphology. MorphoITH integrates a self-supervised deep learning similarity measure to capture phenotypic variation across multiple dimensions (cytology, architecture, and microenvironment) with rigorous methods to eliminate spurious sources of variation. Using a prototype of ITH, clear cell renal cell carcinoma (ccRCC), we show that MorphoITH captures clinically-significant biological features, such as vascular architecture and nuclear grades. Furthermore, we find that MorphoITH recognizes differential biological states corresponding to subclonal changes in key driver genes (BAP1/PBRM1/SETD2). Finally, by applying MorphoITH to a multi-regional sequencing experiment, we postulate evolutionary trajectories that largely recapitulate genetic evolution. In summary, MorphoITH provides a scalable phenotypic lens that bridges the gap between histopathology and genomics, advancing precision oncology.