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.
@article{arxiv.2502.00979,
title = {MorphoITH: A Framework for Deconvolving Intra-Tumor Heterogeneity Using Tissue Morphology},
author = {Aleksandra Weronika Nielsen and Hafez Eslami Manoochehri and Hua Zhong and Vandana Panwar and Vipul Jarmale and Jay Jasti and Mehrdad Nourani and Dinesh Rakheja and James Brugarolas and Payal Kapur and Satwik Rajaram},
journal= {arXiv preprint arXiv:2502.00979},
year = {2025}
}