Carmona lab

Working at the crossroads of single-cell omics, data science, and cancer immunology, we develop and apply computational and statistical methods to translate high-dimensional data into biomedical insights. Our research is focused on characterizing the immune response to cancer and developing predictive models of disease progression and response to treatment. Our lab is affiliated to the Department of Oncology at the University of Lausanne (UNIL), the Ludwig Institute for Cancer Research (LICR) and the Swiss Institute of Bioinformatics (SIB). ...

Research projects

Advancing computational methods for single-cell omics

Recent advances in single-cell and spatial omics technologies have opened a unique opportunity to explore biological systems at an unprecedented resolution and scale. Our lab develops novel computational and statistical methods to translate the complex data generated by these technologies into biological understanding. We have created several widely-used computational methods tackling multiple challenges in single-cell data science including integration, celltype classification, reference atlas mapping, gene signature scoring, and T cell clonal analyses.

Dissecting the tumor microenvironment and its variation across human cancers

The tumor microenvironment (TME) is the complex niche surrounding tumors, composed of various cell types and molecules that influence tumor growth and treatment responses. Beyond cancer cells, it contains a striking diversity of immune cells, fibroblasts, endothelial cells, and other tissue-specific cells, all of which evolve with cancer progression. The components of the TME and their complex interactions determine to a large extent disease progression and response to therapies. Deep characterization of the TME holds the potential to reveal new therapeutic targets and mechanisms of therapy resistance. Single-cell and spatial transcriptomics technologies provide the most powerful tools to resolve cell heterogeneity in complex tissues. Thanks to our cutting-edge computational methods, we can analyze single-cell transcriptomics data at scale, across thousands of patients and millions of cells, to dissect the TME composition at high resolution and characterize patient-to-patient variation within and across cancer types. We aim to identify recurrent TME patterns across patients that are associated with clinical outcome to infer resistance mechanisms and predict new therapeutic targets.

Predictive models for immuno-oncology

Therapies that modulate the immune system, such as immune checkpoint blockade, are revolutionizing the landscape of cancer treatment. Yet, only a fraction of patients successfully respond to these therapies across cancer types. Profiling the immune landscape presents a great potential for the discovery of biomarkers of therapy response. In particular, blood samples emerge as accessible, minimally invasive, and inexpensive windows on the immune status of an individual that could be routinely measured in the clinic. We are developing statistical and machine learning models to predict disease state and response to therapy in cancer patients based on single-cell transcriptomics data from tumor biopsies and blood samples. 

Deciphering the landscape of T cell differentiation states

T cells are crucial players in the adaptive immune response with the capacity to recognize and eliminate infected and malignant cells and to mediate autoimmune disease. Depending on the immunological context, T cells acquire distinct differentiation states with different functions – e.g. Th1 vs Tfh CD4+ T cell states. A T cell state that might be beneficial in one immunological context (e.g. viral infection) might be less beneficial or detrimental in another one (e.g. autoimmune disease). Thus, it is critical to understand what is the full spectrum of T cell differentiation states, their functions and role in disease outcome. Moreover, in vitro T cell manipulation – e.g. by metabolic reprogramming or genetic engineering – can expand the landscape of T cell states and functionalities observed in vivo. These are key for developing successful T cell-based therapies, such as CARs, TCR-engineered T cells and TIL therapies. In collaboration with our experimental partners, we are conducting meta-analyses of single-cell omics data across individuals, tissues, diseasesand in vitro conditions in mouse and human to characterize the landscape of T cell states, their potential functions, their couplings with T-cell receptor sequence and antigen specificity, and their associations with disease outcome.


Santiago Carmona

PhD SNF Ambizione Fellow, Group Leader, Cancer systems immunology, SIB, Department of Oncology UNIL & CHUV CarmonaLab

Other members

Massimo Andreatta

Postdoctoral Fellow, UNIL & CHUV, SIB

Josep Garnica

Postdoctoral Fellow, UNIL & CHUV, SIB

Kieran McGourty

Visiting Scientist, UNIL & CHUV, SIB

Selected Publications

Activation of the transcription factor NFAT5 in the tumor microenvironment enforces CD8(+) T cell exhaustion.

Tillé L, Cropp D, Charmoy M, (...), Carmona SJ, Held W, Verdeil G

Nature immunology – 2023 Sep 14

Orthogonal cytokine engineering enables novel synthetic effector states escaping canonical exhaustion in tumor-rejecting CD8(+) T cells.

Corria-Osorio J, Carmona SJ, Stefanidis E, (...), Luther SA, Irving M, Coukos G

Nature immunology – 2023 Apr 20

A CD4(+) T cell reference map delineates subtype-specific adaptation during acute and chronic viral infections.

Andreatta M, Tjitropranoto A, Sherman Z, (...), Kelly MC, Ciucci T, Carmona SJ

eLife – 2022 Jul 13

scGate: marker-based purification of cell types from heterogeneous single-cell RNA-seq datasets.

Andreatta M, Berenstein AJ, Carmona SJ

Bioinformatics (Oxford, England) – 2022 Apr 28

SPICA: Swiss portal for immune cell analysis.

Andreatta M, David FPA, Iseli C, Guex N, Carmona SJ

Nucleic acids research – 2022 Jan 7

Low-Dose Radiotherapy Reverses Tumor Immune Desertification and Resistance to Immunotherapy.

Herrera FG, Ronet C, Ochoa de Olza M, (...), Dangaj Laniti D, Irving M, Coukos G

Cancer discovery – 2021 Sep 3

UCell: Robust and scalable single-cell gene signature scoring.

Andreatta M, Carmona SJ

Computational and structural biotechnology journal – 2021 Jun 30

Interpretation of T cell states from single-cell transcriptomics data using reference atlases.

Andreatta M, Corria-Osorio J, Müller S, (...), Cubas R, Coukos G, Carmona SJ

Nature communications – 2021 May 20

STACAS: Sub-Type Anchor Correction for Alignment in Seurat to integrate single-cell RNA-seq data.

Andreatta M, Carmona SJ

Bioinformatics (Oxford, England) – 2021 May 5

Deciphering the transcriptomic landscape of tumor-infiltrating CD8 lymphocytes in B16 melanoma tumors with single-cell RNA-Seq.

Carmona SJ, Siddiqui I, Bilous M, Held W, Gfeller D

Oncoimmunology – 2020 Mar 12

Single-cell transcriptome analysis of fish immune cells provides insight into the evolution of vertebrate immune cell types.

Carmona SJ, Teichmann SA, Ferreira L, (...), Stubbington MJ, Cvejic A, Gfeller D

Genome research – 2017 Jan 13

Related news


AGORA Single-cell omics meetings


AGORA PRS | December 12th


Visiting scientist seminar, Dr. Kieran McGourty | December 8th


AGORA Single-cell omics meetings


AGORA Single-cell omics meetings