Project title: Reconciling lung carcinoids histopathological and molecular classifications

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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.

Outcomes:

This research project focused on improving our understanding and management of lung neuroendocrine tumors. The main goal of our work was to better classify these tumors and to identify features that could help doctors predict how aggressive a tumor may be to better adapt the follow up.

Over the course of the project, we developed new computer-based methods that can analyse tumor images and help identify areas that show signs of faster growth or more aggressive behavior. We used these methods to test how well current and emerging diagnostic criteria work and found that relying on tumor appearance alone is often not enough to clearly identify patients at higher risk. This showed that new approaches are needed.

We then helped identify a small set of proteins that can be detected in routine hospital tests and that allow lung neuroendocrine tumors to be divided into distinct biological groups. These groups are linked to differences in tumor behavior, risk of recurrence, and potential treatment options. This is an important step toward more personalized care, where treatment choices are better matched to each patient’s tumor.

In addition, we also worked on other related studies that strengthen the field as a whole. These included creating laboratory models grown directly from patient tumors to better study treatment responses, identifying a marker linked to particularly aggressive disease, and building a large European database to support research into rare cancers. We also contributed to international clinical guidelines aimed at improving consistency in how patients with neuroendocrine tumours are treated.

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