Innovations Teams
Computational Cancer Genomics Team (CCG)

Starting date: July 2021

Work Programme

Rare cancers (i.e. those with incidence rates of < 6 per 100 000 people per year) collectively account for 24% of all cancer cases and 25% of cancer deaths, representing a substantial burden of disease. Despite the high collective occurrence of rare cancers, basic biological and clinical knowledge is lacking. The Computational Cancer Genomics Team (CCG) aims to shed light on the molecular characteristics of rare cancers, to understand their etiology and carcinogenesis processes, and to ultimately improve the clinical management and prognosis of the patients.

To achieve its aim, CCG follows different approaches:

• performing integrative multi-omics 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;
• reviewing and identifying new morphological characteristics using image-based deep learning and integrating them with the molecular data; and
• using state-of-the-art in vitro organoid models 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, and bioinformatics pipelines.

Current cancers of interest and studies led by CCG:

• neuroendocrine neoplasms (lungNENomics and panNENomics)
• malignant pleural mesothelioma (MESOMICS)
• soft tissue sarcomas and carcinosarcomas (SARCOMICS)

External website: www.rarecancersgenomics.com

Team Composition

Team Leaders: Dr Lynnette Fernandez-Cuesta and Dr Matthieu Foll, Genomic Epidemiology Branch (GEM), IARC
Emails: [email protected]; [email protected]

Team members:
Dr Nicolas Alcala (Scientist, GEM)
Dr Catherine Voegele (Bioinformatician, GEM)
Dr Alexandra Sexton-Oates (Postdoctoral Scientist, GEM)
Ms Emilie Mathian (Doctoral Student, GEM)
Ms Gabrielle Drevet (Doctoral Student, GEM)
Ms Laurane Mangé (Doctoral Student, GEM)
Ms Eleonora Lauricella (Visitor from Policlinico di Bari, Italy)
Ms Lipika Lipika (Visitor from Medical University of Graz, Austria)
Ms Maike Morrison (Visitor from Stanford University, USA)

Key networks: European Neuroendocrine Tumor Society (ENETS), European Reference Network on Rare Adult Solid Cancers (EURACAN), European Prospective Investigation into Cancer and Nutrition (EPIC), French MESOBANK

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), Danish Cancer Society (DCS), National Institutes of Health (NIH), Stanford University, Agence nationale de la Recherche (ANR)

Key publications

1. 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
2. Alcala N, Fernandez-Cuesta L (2023). Lifting the curtain on molecular differences between malignant pleural mesotheliomas. Nat Genet. 55(4):540–1. https://doi.org/10.1038/s41588-023-01322-0 PMID:36928604
3. 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
4. Di Genova A, Mangiante L, Sexton-Oates A, Voegele C, Fernandez-Cuesta L, Alcala N, et al. (2022). A molecular phenotypic map of malignant pleural mesothelioma. Gigascience. 12:giac128. https://doi.org/10.1093/gigascience/giac128 PMID:36705549
5. Dayton TL, Alcala N, Moonen L, den Hartigh L, Mangiante L, Lap L, et al. (2022). Druggable growth dependencies and tumor evolution analysis in patient-derived organoids of neuroendocrine cancer. bioRxiv. https://doi.org/10.1101/2022.10.31.514549
6. Gabriel AAG, Mathian E, Mangiante L, Voegele C, Cahais V, Ghantous A, et al. (2020). A molecular map of lung neuroendocrine neoplasms. Gigascience. 9(11):giaa112. https://doi.org/10.1093/gigascience/giaa112 PMID:33124659
7. 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
8. 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
9. Leblay N, Leprêtre F, Le Stang N, Gautier-Stein A, Villeneuve L, Isaac S, et al. (2017). BAP1 is altered by copy number loss, mutation, and/or loss of protein expression in more than 70% of malignant peritoneal mesotheliomas. J Thorac Oncol. 12(4):724–33. https://doi.org/10.1016/j.jtho.2016.12.019 PMID:28034829