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

Understanding the tumor microenvironment has the potential to significantly advance precision oncology, but this effort has been hampered by the scarcity of consistent large data sets. Here, we introduce MOSAIC (Multi-Omics Spatial Atlas in Cancer), a multi-center clinical omics study which profiled over 2,700 cancer samples across multiple tumor types. MOSAIC exploits recent technological advances to integrate spatial and single-nuclei transcriptomics with complementary data modalities, including histology scans, bulk whole transcriptome and whole exome sequencing, to generate comprehensive representations of cancer histology, genomics, and transcriptomics, to unravel insights that no single modality alone can explain. MOSAIC also collects extensive and curated clinical information. The MOSAIC consortium aims to integrate all data modalities using artificial intelligence and other computational approaches to identify clinically relevant biomarkers and cancer subtypes. This paper outlines the core objectives of MOSAIC, proofs-of-concept, design, and experimental considerations. Additionally, we introduce the MOSAIC Window initiative, featuring the first released dataset from 60 patients and 5 tumor types, offering a glimpse into the project’s potential, and available on the European Genome-phenome Archive (EGA; Study ID: EGAS50000000689). We demonstrate the power of the multi-omics approach to quantify intra- and inter-patient heterogeneity, uncovering distinct tumor subsets and revealing a correlation between malignant cell intrinsic oncogenic signaling and TME cell colocalization. These findings, exemplified by four diverse case studies, highlight the power of integrated multi-omics data to decipher cancer heterogeneity, and inform treatment strategies. MOSAIC establishes a large, standardized multi-omics resource for studying intra-tumoral heterogeneity, with an initial public release (MOSAIC Window) already available to the research community and further releases planned.