Massive Multi-Omics Atlas MOSAIC Maps the Hidden Diversity Inside Tumors

Cancer has never been a single disease, and increasingly, oncologists are realizing that a single tumor is not a single entity either. Within one tumor mass, malignant cells can carry different genetic mutations, activate different signaling pathways, and recruit entirely different neighborhoods of immune and stromal cells. This phenomenon, known as intra-tumoral heterogeneity, is one of the central reasons why precision oncology so often falls short of its promise: a biopsy sampled from one region of a tumor may reveal a molecular profile that is dramatically different from another region just millimeters away, and a therapy designed against one dominant clone may leave resistant subpopulations untouched. A large international consortium now reports a systematic effort to confront this problem at unprecedented scale, publishing its design, early results, and first public dataset in the journal Genome Medicine.

The initiative, called MOSAIC (Multi-Omics Spatial Atlas in Cancer), is a multi-center clinical omics study that has profiled more than 2,700 cancer samples spanning multiple tumor types. The consortium brings together researchers from Owkin and clinical and academic partners including Gustave Roussy in France, Charité Universitätsmedizin Berlin and University Hospital Erlangen in Germany, Lausanne University Hospital in Switzerland, and the University of Pittsburgh in the United States. The study was funded by Owkin Inc. and conducted as a non-interventional, multicenter clinical protocol, registered as NCT06625203, with ethics approval at each participating site. What distinguishes MOSAIC from many previous atlas projects is not just its size but its deliberate integration of data modalities that are usually collected and analyzed in isolation.

Technically, each tumor sample in MOSAIC is characterized through several complementary lenses. Spatial transcriptomics captures gene expression across intact tissue sections, preserving the two-dimensional geography of the tumor and allowing researchers to measure which genes are active at which coordinates on a slide. Single-nuclei transcriptomics resolves expression at the level of individual cells, distinguishing malignant subclones from the diverse non-malignant cells of the tumor microenvironment. These are layered alongside digitized histology scans, bulk whole-transcriptome sequencing, and whole-exome sequencing, which together provide the genomic mutations, the aggregate expression landscape, and the classical pathological view of the tissue. Extensive curated clinical information, including treatment regimens and outcomes, is attached to each sample, so that molecular patterns can eventually be linked to how patients actually responded.

The rationale for this multi-modal design is that no single technology can capture the full complexity of a tumor. Histology reveals architecture but not molecular identity; bulk sequencing averages away the very heterogeneity researchers want to measure; single-cell methods lose spatial context; and spatial methods, until recently, lacked single-cell resolution. By generating all of these representations from the same samples under standardized protocols, MOSAIC aims to create a resource in which computational methods, including artificial intelligence, can learn to connect what a pathologist sees under the microscope with what the genome and transcriptome reveal, and ultimately to identify clinically relevant biomarkers and cancer subtypes that no single modality alone could expose.

Alongside the full study design, the consortium has released an initial public dataset called the MOSAIC Window, now available through the European Genome-phenome Archive under study ID EGAS50000000689. This first release covers 60 patients across five tumor types and includes quality-controlled data from spatial transcriptomics, single-nuclei RNA sequencing, bulk RNA sequencing, and whole-exome sequencing, together with curated clinical descriptors such as age, smoking status, disease stage, and survival. The consortium describes this release as a glimpse into the project’s potential, with further data releases planned as the study matures. The cohort design spans eleven major cancer types overall, including bladder, breast, colorectal, gastric, glioblastoma, head and neck squamous cell carcinoma, mesothelioma, non-small cell lung cancer, ovarian, and pancreatic cancers, sampled at baseline diagnosis, after neoadjuvant therapy, and at recurrence or progression.

The early analyses reported in the paper demonstrate the analytical power that comes from combining these modalities. Using the MOSAIC Window data, the researchers quantified both intra-patient and inter-patient heterogeneity, uncovering distinct malignant cell subsets within individual tumors. They then examined how these malignant subpopulations relate to their surroundings and found a correlation between the intrinsic oncogenic signaling activity of malignant cells and the colocalization of specific tumor microenvironment cell types. In other words, the signaling programs running inside cancer cells appear to be associated with which immune and stromal cells gather around them, a link that could help explain why some tumor regions are immunologically cold and others inflamed, and why immunotherapy can succeed in one part of a tumor while failing elsewhere.

To make these concepts concrete, the paper presents four diverse case studies, each illustrating a different way that integrated multi-omics data can decipher heterogeneity. In these examples, the team used computational tools such as differential expression analysis and gene set enrichment analysis to characterize malignant cell clusters identified from single-nuclei data, and quantified pathway activities with methods like PROGENy and GSVA to compare signaling states across subpopulations. Statistical measures, including variance analyses and Pearson correlations between spot-level cellular fractions, were used to test whether the associations between malignant signaling and microenvironment composition were robust across samples and indications. The case studies span different tumor types and different biological questions, collectively highlighting how the same standardized data framework can support a wide range of investigations.

The technical rigor behind the resource is considerable. Supplementary documentation accompanying the paper details quality metrics for every modality, from median gene counts per spatial spot and read-mapping percentages in transcriptomics to sequencing depth, contamination estimates, and GC content in whole-exome data. A detailed data management plan describes how samples are pseudonymized, how data flows across modalities are linked to a common study identifier, and how the resource adheres to FAIR principles, making the data findable, accessible, interoperable, and reusable. The clinical study protocol, covering objectives, endpoints, eligibility criteria, and the statistical analysis plan, is also published, giving the community full visibility into how the resource was constructed.

For the field of computational oncology, the significance of MOSAIC lies partly in addressing a persistent bottleneck: the scarcity of large, consistent, well-annotated datasets. Machine learning models for histology and multi-omics integration have advanced rapidly, but they are often trained on small or inconsistently processed cohorts, limiting their generalizability and clinical translation. A standardized atlas of thousands of tumors, each with matched spatial, single-cell, genomic, histological, and clinical data, provides exactly the kind of training substrate that multimodal artificial intelligence approaches require. The consortium explicitly states its aim to use artificial intelligence and other computational approaches to integrate all data modalities and extract clinically actionable insights, positioning MOSAIC as infrastructure for the next generation of biomarker discovery.

There are, of course, caveats worth noting. The study is funded entirely by a single company, Owkin, and several authors are current or former employees holding shares or stock options in the company, alongside a range of disclosed consulting relationships with pharmaceutical and diagnostics firms at the academic sites. The paper is published as a shared early version, citable with a permanent DOI and subject to further editorial updates. And while the MOSAIC Window release demonstrates feasibility and analytical promise, the clinical value of the resource will ultimately depend on whether the biomarkers and subtypes it reveals can be validated prospectively and shown to improve patient outcomes. Still, with more than 2,700 samples profiled, an open first dataset already in researchers’ hands, and a standardized framework linking molecular, spatial, and clinical dimensions of cancer, MOSAIC represents one of the most ambitious attempts yet to map the internal diversity of tumors at scale, and a foundation on which the precision oncology community can build for years to come.

Subject of Research: Multi-omics characterization of intra-tumoral heterogeneity using spatial and single-cell profiling in cancer

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

Article References: MOSAIC Consortium, Cornish, A. J., Bayard, Q., Karabajakian, A., Madissoon, E., Ferrarini, G., Youssef, A., Badoual, C., de Leval, L., Dressman, D., Durand, E. Y., Erber, R., Florian, S., Garberis, I., Haignere, C., Homicsko, K., Keilholz, U., Lee, A. V., Lehar, J., … Hoffmann, C. (2026). MOSAIC: Intra-tumoral heterogeneity characterization through large-scale spatial and cell-resolved multi-omics profiling. Genome Medicine. https://doi.org/10.1186/s13073-026-01791-y

Image Credits: AI Generated

DOI: 10.1186/s13073-026-01791-y

Keywords: MOSAIC, intra-tumoral heterogeneity, spatial transcriptomics, single-nuclei RNA-seq, multi-omics, tumor microenvironment, precision oncology, whole-exome sequencing, cancer biomarkers, artificial intelligence, Genome Medicine, open data