Project title: Reconciling lung carcinoids histopathological and molecular classifications

Description

This research project aims to improve the diagnosis and treatment of lung NETs, using a multidisciplinary approach to reconcile the histopathological and molecular classifications of these tumors and generate the knowledge to allow the translation of the molecular classification into the clinical setting.

What critical NET problem/question will researchers try to answer?

Refined classifications for pulmonary NETs have been proposed in a specific area of research (histopathology, molecular biology, or nuclear medicine) independently of the others. Dr. Foll and his colleagues will explore whether they can unify these findings into a new classification framework and determine which morphological criteria and biomarkers should be used.

Why is this important?

Lung NETs mostly tend to grow slowly, but a significant number of tumors are more aggressive and spread to nearby tissue or the regional lymph nodes, reducing the 5-year overall survival rate to 50%. The current diagnosis criteria are imperfect and don’t accurately identify these more aggressive tumors that would benefit from better follow-up and tailored treatments.

What will the researchers do?

Dr. Foll and his colleague will conduct a multidisciplinary study to understand and improve the diagnosis and treatment of NETs. Using a combination of innovative technology such as Artificial Intelligence (AI), molecular imaging, and spatial omics data to reconcile the histopathological and molecular classifications of these tumors, this project is expected to generate the knowledge to overcome existing barriers to integrating molecular findings into the classification of tumors. 

How might this improve treatment of NETs?

The biologically informed classification of lung NETs generated in this project will lead to a better stratification of patients into more homogeneous groups in terms of prognosis and response to therapy. The data generated by this project may also lead to the identification of targets that influence treatment decisions regarding standard options and follow-up.

What is the next step?

Dr. Foll’s project is expected to pave the way to a more robust and prognostic-based lung NET classification and provide the opportunity to design future clinical trials to evaluate innovative therapies. Larger studies will be needed to evaluate the reproducibility and robustness of the nuclear imaging biomarkers, as well as alternative follow-up strategies based on the proposed classification.

« Back to all funded research projects