Suzanne Pasmans

Erasmus MC

In short, Susanne Pasmans's research entails the following:

AIM-CMN = New insights in melanoma in congenital nevi using AI-Morpholomics

Congenital Melanocytic Nevi (CMN), are rare birthmarks that significantly impact patients' lives due to their lifelong abnormal appearance physical discomfort and the risk of developing a melanoma. These nevi can reach sizes over 60 cm (projected-adult-size) and, according to literature, have up to a 14% chance of becoming malignant, often occurring at a younger age but also in adulthood, with a low survival rate. When this occurs, patients will die within weeks. We do not know who among these patients is at risk of developing a melanoma and how to treat them.

CMN appear the same in children and adults. The precise mechanism of transformation in melanoma and the accompanied immune responses is still not understood. If possible, also after (partial) surgical removal of these nevi, these patients still can develop life-threatening melanoma. To date, there are no reliable methods to predict which CMN patient is at high risk, nor are there effective treatments to prevent or address malignant transformation in large CMN.

We believe, the innovative morpholomics platform developed by Deepcell combines with artificial intelligence enable real-time morphological characterization and cell isolation. Erasmus MC is the first site worldwide to implement this system clinically. Morphology, a fundamental cell property, for identifying cell identity, state, function, and disease. The morpholomics technology at Erasmus MC enables pathologists to detect and identify the differences between affected and no affected cells using morphometric features such as cell diameter, nucleation, granulation, vacuolation, and concavity. 

The AIM-CMN project aims to validate the Dutch CMN risk classification proposed in the national guideline (to implement it internationally) while optimizing it for individual patient screening. Additionally, the project seeks to identify personalized therapeutic targets by integrating clinical knowledge, DNA, RNA, protein data, Immuno-histo-chemistry and morphology data from the Deepcell platform. For the first time, AIM-CMN will demonstrate how immune responses, cell counts, and morphological changes in peripheral blood cells correlate with melanoma risk in individual CMN patients.