Abstract
Breast cancer is a highly heterogeneous disease with diverse outcomes, and intra-tumoral heterogeneity plays a significant role in both diagnosis and treatment. Despite its importance, the spatial distribution of intra-tumoral heterogeneity is not fully elucidated. Spatial transcriptomics has emerged as a promising tool to study the molecular mechanisms behind many diseases. It offers accurate measurements of RNA abundance, providing powerful tools to correlate the morphologies of cellular neighborhoods with localized gene expression patterns. However, the spot-based spatial transcriptomic tools, including the most widely used platform, Visium, do not achieve single-cell resolution readouts, which hinders data interpretability. In this study, we present a computational pathology image analysis pipeline (i.e., computational tissue annotation, CTA) that utilizes machine learning algorithms to accurately map tumor, stroma, and immune compartments within Visium-assayed tumor sections. Using a cohort of 23 breast tumor sections from four patients, we demonstrate that CTA can provide high-resolution annotations on the hematoxylin-and-eosin-stained images alongside the paired sequencing data, support the evaluation of deconvolution methods, deepen insights into intra-tumoral heterogeneity by increasing data analysis resolution, assist with spatially resolved intrinsic subtyping, and enhance the visualization of lymphocyte clones at single-cell resolution. The proposed pipeline provides valuable insights into the complex spatial architecture of breast cancer, contributing to more personalized diagnostics and treatment strategies.