Rationale & Aims

Rare cancers account for ~25–30% of all cancer diagnoses and 25% of cancer deaths, representing a substantial burden of disease. However, basic science research, clinical trials and approval of new therapies for rare cancers are lacking. This translates into a worse prognosis for patients with a rare cancer than for those with common cancers. With the number of rare cancers increasing, finding more appropriate solutions for diagnosing, managing and studying rare cancers is essential.

Our Cancer Projects
Rare Cancers Genomics
  • 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.
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.
Our Transversal Projects
Rare Cancers Genomics
  • Image-based AI
  • Cancer Ecology and Evolution
  • Computational Cancer Genomics
Image-based AI
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
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).
Computational Cancer Genomics
Computational Cancer Genomics
Meet the multidisciplinary team
Techniques and analytical tools
  • Whole-genome sequencing
  • RNA-seq
  • Single-cell sequencing
  • ATAC-sequencing
  • Spatial transcriptomics
  • Spatial proteomics
  • Digital pathology
  • Artificial Intelligence
  • Dimension reduction
  • Integrative analyses
  • Multi-region sequencing
  • Organoid models
Latest News
Rare Cancers Genomics

Award from Worldwide Cancer Research

Nicolas Alcala received a grant from Worldwide Cancer Research (WCR) with collaborator Jaehee Kim from ...

The RCG team hosts Prof Alex Di Genova for a week

We had the pleasure to have the visit of Prof Alex Di Genova, former RCG ...

Computational Biology of Cancer 2024 conference

Matthieu Foll was a keynote speaker at BioSyl Computational Biology of Cancer 2024 conference, Grenoble ...


Rare Cancer Genomics

iMig 2023

ASCO 2023 – lungNENomics – part 1

ASCO 2023 – lungNENomics – part 2

ASCO 2023 – MESOMICS – part 1

ASCO 2023 – MESOMICS – part 2

NETRF 2022

RCG Initiative 2020