Computational
Cancer
Genomics

Multidisciplinary and multi-omics molecular
characterization of cancers

Computational
Cancer
Genomics

Multidisciplinary and multi-omics molecular
characterisation of cancers

Aim and Research Approach

Computational Cancer Genomics

Leveraging state-of-the-art technologies and innovative statistical and computational methods, previously applied to rare cancers, the Computational Cancer Genomics Team (CCG) aims to understand the rapid progression of common cancers with very poor survival, including lung and pancreas, as well as cancers identified as being of interest by the G7 Cancer International Partnership, such as oesophageal cancer. By addressing critical questions related to cancer initiation and progression, the team strives to advance our understanding of these lethal diseases and provide insights for prevention and early detection.

To shed light on the molecular characteristics of cancers, understand their etiology and carcinogenesis processes, and to ultimately improve their clinical management and consequently, patient’s prognosis, we follow different approaches:

1

Perform integrative multi-omics multi-region bulk and single-cell sequencing analyses as well as spatial profiling of large bio-repositories with good quality of samples and detailed pathological, clinical and epidemiological annotations.

(led by Drs Lynnette Fernandez-Cuesta and Matthieu Foll)

2

Review and identify new morphological characteristics using image-based AI and integrate them with the molecular data.

(led by Drs Lynnette Fernandez-Cuesta and Matthieu Foll – read more)

3

Study cancer ecology and evolution to identify and understand carcinogenic processes.

(led by Dr Nicolas Alcala – read more)

4

Use state-of-the-art in vitro organoid models to study the cancer initiation and progression.

(led by Dr Talya Dayton – read more)

Open Science Effort

Computational Cancer Genomics

The team works with a strong commitment to open science, reproducibility, and capacity building: best-practices pipelines have been set-up to analyze WGS, RNAseq, and methylation data, as well as supervised and unsupervised methods to perform multi-omic data integration. All the necessary resources to reproduce the initiative’s analyses are available, and the bioinformatics pipelines are continuously updated and improved, with detailed documentation to ensure that they are reproducible and effectively reusable by others. Similar single-cell RNA- seq, ATAC-seq, and spatial RNA-seq data processing workflows following best practices (Andrews, 2021) are currently being developed.

Projects

Computational Cancer Genomics

lungNENomics
MESOMICS
Image-based AI
Cancer Ecology and Evolution

lungNENomics

Pulmonary carcinoids, including the low-grade typical carcinoids and the intermediate-grade atypical carcinoids, belong to the group of lung neuroendocrine neoplasms that also includes the high-grade large-cell neuroendocrine lung carcinomas (LCNEC) and small-cell lung cancers (SCLC). It has been widely accepted that well-differentiated pulmonary carcinoids have unique clinico-histopathological traits with no causative relationship or genetic, epidemiologic, or clinical traits in common with poorly-differentiated, high-grade LCNECs and SCLCs. However, several recent studies suggest that a molecular link might exist between these diseases, especially between atypical carcinoids and LCNEC.

MESOMICS

Malignant pleural mesothelioma is a rare, understudied cancer associated with exposure to carcinogenic mineral fibers, jointly known as “asbestos”. Most patients die within two years after diagnosis, mainly due to the limited available therapeutic and early detection opportunities. One of the reasons is the existence of only few molecular studies. Despite the ban of asbestos in many developed countries, the long latency of the disease together with the aging of the population, the increased environmental exposure, and the ongoing use of asbestos mostly in developing countries, among other factors, translates in malignant mesothelioma being an ongoing health problem.

Image-based AI

The aim of this transversal project is to translate our multi-omics tumor profiling into the clinical setting without the need to generate costly and complex-to-analyze molecular data. To do this, we are exploring how different deep-learning computer vision algorithms can detect morphological features that pathologists will be able to recognize, with the ultimate goal to improve the diagnosis and treatment of rare cancers.

Cancer Ecology and Evolution

The cancer ecology and evolution project of the rare cancers genomics team is a transversal research program led by Dr. Nicolas Alcala that aims to build a theoretical and analytical framework of cancer formation and development. The project makes use of existing mathematical and computational models, as well as development of new methods, applying them to the multi-omic data generated within other team projects (lungNENomics, panNENomics, MESOMICS, and SARCOMICS).