Building the world’s largest spatial multiomics dataset in oncology

DISCOVER MOSAIC

Large-scale spatial omics data is essential to fuel research in cancer

The convergence of large-scale spatial omics data and AI will power the next revolution of oncology research. There is an urgent need for the whole cancer community to have access to these data to reveal groups of patients with distinct tumour/immune biology.

The MOSAIC initiative will overcome this limitation by creating a large scale dataset of tumor spatial transcriptomes, allowing scientists to conduct research on data from cohorts larger than what is currently possible.

9
Cancer indications
6
Data modalities
100
Clinical variables per patient
2,055
Total patients included
1,833
Spatial omics data generated
1,449
Single-cell omics generated

Data modalities

MOSAIC deploys a deep multimodal approach, covering six data modalities to capture the complexity of disease biology at multiple scales.

Spatial transcriptomics
Spatial transcriptomics
Single-cell RNA-Seq
Single-cell RNA-Seq
RNA-Seq
Bulk RNA-Seq
Whole Exome Sequencing
Whole Exome Sequencing
Digitized H&E
Digitized H&E
Clinical data
Clinical data

Cancer therapy areas

MOSAIC focuses on a selected cohort that includes seven cancer indications to create the largest spatial and multiomic atlas to date.

NSCLC
Non-small cell lung cancer (NSCLC)
Ovarian Cancer
Ovarian cancer
Bladder Cancer
Bladder cancer
Mesothelioma
Mesothelioma
GBM
Glioblastoma (GBM)
Breast cancer
Breast cancer
DLBCL
Diffuse large B cell lymphoma (DLBCL)

Why spatial biology?

Spatial biology brings a new dimension to cancer research because it maps biological molecules to their location within a tissue, capturing the spatial context.

When combined with data from technologies like imaging and genomics, spatial omics contributes to a rich, multiscale understanding of disease mechanisms.

Learn More

How will MOSAIC help us fight cancer?

Through spatial omics MOSAIC will paint a picture of the dynamics between cells, highlighting key biological networks to enable precision medicine via the discovery of:

Disease Biology Illustration

New disease biology

  • Integrated, multimodal analysis of the tumor in its native architecture
  • A new understanding of the tumor microenvironment
Patient Subtypes

New patient subtypes

  • Identification of patients who are resistant or respond differently to certain drugs
  • A better understanding of how tumor-immune cell interactions differ in those patients
Biomarkers

New biomarkers

  • Discovery of biomarkers that characterize patient subtypes
  • Improved diagnostics, treatment decision and clinical trial design
Drug Targets

New drug targets

  • Discovery of patient-specific drug targets
  • Development of more effective combination and personalized therapies
Match Treatment

Matching the right patient to the right treatment

  • Improved connection between patient biology and target discovery
  • Prescription of personalized therapies to the right patients

Partners