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Santiago Carmona is a computational biologist working at the interface of data science, genomics and cancer immunology. He obtained his degree and PhD in biotechnology and bioinformatics in Buenos Aires (Argentina). As part of his thesis, he pioneered the application of high-density peptide microarrays and machine learning to characterize pathogen-specific antibody repertoires. Supported by a Fulbright fellowship, part of his research was conducted at La Jolla Institute (USA). In 2015, he moved to Switzerland for a postdoc at the Department of Oncology of the AV¶ÌÊÓƵ of Lausanne. In 2019, as an SNF Ambizione fellow, he established the Cancer Systems Immunology laboratory at the Ludwig Institute for Cancer Research, AV¶ÌÊÓƵ of Lausanne. In 2021, he was nominated SIB Swiss Institute of Bioinformatics group leader. His research group has developed many highly-used, open-source computational methods for single-cell omics data analysis and made significant contributions in immunology and cancer research. He is actively involved in training and mentoring students and postdocs in bioinformatics. Santiago Carmona joins UNIGE in March 2025 as associate professor.

Research and/or clinical aims:

We combine data science methods development with high-throughput single-cell and spatial omics technologies to identify general principles of the immune system regulation during cancer progression and response to therapy.  

Advancing single-cell omics data science Recent high-throughput molecular profiling technologies generate large amounts of complex, high-dimensional data that present new challenges for processing, analysis, and interpretation. Our lab develops computational and statistical methods to translate such complex data into biological understanding that advances basic biology and clinical applications.

Dissecting the tumor microenvironment (TME) variations. The TME components and their complex interaction network largely determine disease progression and response to therapies. We use high-resolution spatial and single-cell technologies to study intratumoral heterogeneity as well as patient-to-patient variation. This approach has the potential to reveal the immune regulation mechanisms underpinning cancer progression and therapy resistance, leading to novel therapeutic targets.

Predictive models for immunotherapies We are interested in understanding how adaptive immune receptors and immune cell states in tperipheral blood or other tissues are altered by disease, and leveraging this knowledge to identify disease drivers and to develop machine learning models that can predict disease state and response to therapy.

Core expertise:

Analysis and computational modeling of single-cell and spatial omics data in cancer and immunology; analysis of cell states; TCR and BCR repertoires; development of new computational methods 

Main publications:

Laura Yerly, Massimo Andreatta, Jeremy Di Domizio, Michel Gilliet, Santiago J. Carmona*, François Kuonen*. Wounding drives infiltrative progression in human basal cell carcinoma (*co-last corresponding authors)
BioRxiv (2025) –

Massimo Andreatta, Leonard Herault, Paul Gueguen, David Gfeller, Ariel J Berenstein, Santiago J Carmona*.  Semi-supervised integration of single-cell transcriptomics data

Nature Communications (2024) –

Massimo Andreatta, Zachary Sherman, Ariel Tjitropranoto, Michael C. Kelly, Thomas Ciucci*,  Santiago J. Carmona*. A single-cell reference map delineates CD4+ T cell subtype-specific adaptation during acute and chronic viral infections.  eLife (2022) –

Andreatta M, Berenstein, A & Carmona, SJ*. scGate: marker-based purification of cell types from heterogeneous single-cell RNA-seq datasets.  Bioinformatics (2022) –https://doi.org/10.1093/bioinformatics/btac141

M Andreatta, JC Osorio, S Muller, R Cubas, G Coukos, SJ Carmona*. Interpretation of T cell states from single-cell transcriptomics data using reference atlases.
Nature Communications (2021)  – https://doi.org/10.1038/s41467-021-23324-4