Decrypting cancer's spatial code: from single cells to tissue niches

Abstract

Spatial transcriptomics (ST) has emerged as a powerful tool to map gene expression patterns to the local tissue structure in cancer, enabling unprecedented insights into cellular heterogeneity and tumour microenvironments. As the technology matures, developing new, spatially informed analytical frameworks will be essential to fully leverage its potential to elucidate the complex organisation and emerging properties of cancer tissues. Here, we highlight key challenges in cancer spatial transcriptomics, focusing on three emerging topics: (a) defining cell states, (b) delineating cellular niches and (c) integrating spatial data with other modalities that can pave the way towards clinical translation. We discuss multiple analytical approaches that are currently implemented or could be adapted in the future in order to tackle these challenges, including classical biostatistics methods as well as methods inherited from geospatial analytics or artificial intelligence. In the rapidly expanding landscape of ST, such methodologies lay the foundation for biological discoveries that conceptualise cancer as an evolving system of interconnected niches.

Keywords: AI; cancer; cell state; cellular niche; digital pathology; geospatial statistics; spatial transcriptomics.