MOSAIC: Intra-tumoral heterogeneity characterization through large-scale spatial and cell-resolved multi-omics profiling.

Precision oncology remains challenging due to gaps in understanding tumor biology and immunology, as well as the scarcity of consistent data across large cohorts. Here, we introduce MOSAIC (Multi-Omics Spatial Atlas in Cancer), a multi-center clinical Omics study designed to systematically profile thousands of cancer samples, with a focus on spatial and single-cell data across multiple tumor types. MOSAIC exploits recent technological advances to integrate spatial and single-cell data with complementary data modalities, including hematoxylin-eosin histology scans, bulk RNA and exome sequences, to generate comprehensive representations of cancer histology, genomics, and transcriptomics. MOSAIC also collects extensive and curated clinical information to ensure that patients meet the precise inclusion criteria for each cohort. The consortium aims to integrate all data modalities using artificial intelligence and other computational approaches in order to identify clinically-relevant biomarkers and cancer subtypes. This paper outlines the core objectives of MOSAIC, its potential impact, early proofs-of-concept, design, and experimental considerations. Additionally, we introduce the MOSAIC Window initiative, featuring the first released dataset from 60 patients, offering a glimpse into the projects groundbreaking potential. Using four selected patients, we demonstrate the power of multi-omic approaches to analyze and interpret intra-tumoral variability within and across patients, providing insights on specific drug sensitivity of cancer subpopulations and revealing potential impacts on therapeutic recommendations.