Innovations Teams
Computational Cancer Genomics Team (CCG)

Starting date: July 2021

Work Programme

Leveraging state-of-the-art technologies and innovative statistical and computational methods, the Computational Cancer Genomics Team (CCG) investigates the molecular mechanisms that drive cancer initiation, rapid early-stage progression, and aggressive disease in cancers with poor prognosis. CCG focuses on lung cancer (including small cell lung cancer), pancreatic cancer, and other cancers of strategic relevance to IARC and international cancer prevention priorities (e.g. G7 Cancer), such as oesophageal cancer. By integrating molecular, pathological, clinical, and epidemiological data, CCG aims to identify early biological changes and tumour ecosystem features that can inform prevention, early detection, tumour classification, and open, reusable research resources.

To achieve its aim, CCG follows different approaches:

• performing integrative multi-omics, single-cell and spatial molecular analyses of large biorepositories with good quality of samples and detailed pathological, clinical, and epidemiological annotations;
• integrating big data generated from multiple large-scale genomics initiatives to expedite the translation of this research to the classification of tumours;
• identifying morphological features by using artificial intelligence (AI) on whole-slide images and integrating them with the molecular data; and
• using state-of-the-art in vitro organoid models and 3D printing technology to study cancer initiation and progression (through external collaborators).

CCG is strongly committed to open science and makes available all the resources needed to reproduce the analyses, including raw and processed data, interactive computational notebooks, user-friendly tumour maps that anyone can explore in a web browser, R packages, and bioinformatics pipelines.

Current cancers of interest and studies led by CCG:

• small cell lung cancer (GENESIS-SCLC)
• pancreatic cancer (GENESIS-PDAC)
• explainability of AI whole-slide image analysis and multimodal data integration
• evolution of cancer ecosystems (ECE)
• lung neuroendocrine tumours (lungNENomics)
• malignant pleural mesothelioma (MESOMICS)

External website: https://www.computationalcancergenomics.com

Team Composition

Team Leaders: Dr Lynnette Fernandez-Cuesta, Dr Matthieu Foll, and Dr Nicolas Alcala, Genomic Epidemiology Branch (GEM), IARC
Emails: FernandezCuestaL@iarc.who.int; FollM@iarc.who.int; AlcalaN@iarc.who.int

Team members:
Dr Alexandra Sexton-Oates (Scientist, GEM)
Dr Catherine Voegele (Bioinformatician, GEM)
Dr Lisa Bonheme (Postdoctoral Scientist, GEM)
Ms Lipika Kalson (Doctoral Student, GEM)
Ms Laurane Mangé (Doctoral Student, GEM)
Ms Yuliya Lim (Doctoral Student, GEM)
Ms Gabrielle Drevet (Doctoral Student, GEM)
Dr Tiffany Delhomme (Consultant, GEM)

Key networks: European Neuroendocrine Tumor Society (ENETS), International Association for the Study of Lung Cancer (IASLC), LYriCAN+ (https://cancer-lyricanplus.fr/), COALA (https://www.coala-lung.org)

Key funding: Neuroendocrine Tumor Research Foundation (NETRF), United States Department of Defense (DOD), Worldwide Cancer Research (WCR), Institut national du Cancer (INCa), Ligue nationale contre le Cancer (LNCC), Stanford University, Agence nationale de la Recherche (ANR)

Key publications

1. Sexton-Oates A, Mathian E, Candeli N, Lim Y, Voegele C, Di Genova A, et al. (2026). Deep molecular profiling of lung neuroendocrine tumours and supra-carcinoids. Mol Cancer.(Forthcoming)
2. Kalson L, Sexton-Oates A, Mathian E, Voegele C, Di Genova A, Li Z, et al. (2026). A multi-omic, spatial, and whole-slide image dataset of lung neuroendocrine tumours from the lungNENomics cohort. (Forthcoming)
3. Beasley MB, Yatabe Y, Papotti M, Cooper WA, Derks JL, Fernandez-Cuesta L, et al. (2026). IASLC update on classification of pulmonary neuroendocrine neoplasms. J Thorac Oncol. 103977. https://doi.org/10.1016/j.jtho.2026.103977 PMID:42251892
4. Drevet G, Kalson L, Mangé L, Galateau-Salle F, Scherpereel A, Chalabreysse L, et al. (2026). Moving beyond morphology: toward a morpho-molecular classification of pleural mesothelioma. J Thorac Oncol. 21(5):103551. https://doi.org/10.1016/j.jtho.2026.01.003 PMID:41718511
5. Mathian É, Drouet Y, Sexton-Oates A, Papotti MG, Pelosi G, Vignaud JM, et al. (2024). Assessment of the current and emerging criteria for the histopathological classification of lung neuroendocrine tumours in the lungNENomics project. ESMO Open. 9(6):103591. https://doi.org/10.1016/j.esmoop.2024.103591 PMID:38878324
6. Dayton TL, Alcala N, Moonen L, den Hartigh L, Geurts V, Mangiante L, et al. (2023). Druggable growth dependencies and tumor evolution analysis in patient-derived organoids of neuroendocrine neoplasms from multiple body sites. Cancer Cell. 41(12):2083–2099.e9. https://doi.org/10.1016/j.ccell.2023.11.007 PMID:38086335
7. Mangiante L, Alcala N, Sexton-Oates A, Di Genova A, Gonzalez-Perez A, Khandekar A, et al. (2023). Multiomic analysis of malignant pleural mesothelioma identifies molecular axes and specialized tumor profiles driving intertumor heterogeneity. Nat Genet. 55(4):607–18. https://doi.org/10.1038/s41588-023-01321-1 PMID:36928603
8. Mathian E, Liu H, Fernandez-Cuesta L, Samaras D, Foll M, Chen L (2023). HaloAE: a local transformer auto-encoder for anomaly detection and localization based on HaloNet. In: Radeva P, Farinella GM, Bouatouch K, editors. Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023), Lisbon, Portugal, 19–21 February 2023. Volume 5: VISAPP. SciTePress; pp. 325–337. https://doi.org/10.5220/0011865900003417
9. Alcala N, Mangiante L, Le-Stang N, Gustafson CE, Boyault S, Damiola F, et al. (2019). Redefining malignant pleural mesothelioma types as a continuum uncovers immune-vascular interactions. EBioMedicine. 48:191–202. https://doi.org/10.1016/j.ebiom.2019.09.003 PMID:31648983
10. Alcala N, Leblay N, Gabriel AAG, Mangiante L, Hervas D, Giffon T, et al. (2019). Integrative and comparative genomic analyses identify clinically relevant pulmonary carcinoid groups and unveil the supra-carcinoids. Nat Commun. 10(1):3407. https://doi.org/10.1038/s41467-019-11276-9 PMID:31431620