Neural-crest derived catecholamine producing
tumours: implementation of molecular and metabolic fingerprints
to predict disease progression and therapeutic response

Catecholamine-producing tumours derived from neural crest cells (NCCP), such as neuroblastomas, pheochromocytomas and paragangliomas, are characterised by marked biological heterogeneity and variable aggressiveness. This project investigates the hypothesis that different tumour cell subpopulations and their interaction with the tumour microenvironment – especially under pseudohypoxic conditions – significantly influence tumour behaviour, metastatic potential and response to therapy.
SnRNA sequencing data, established tumour cell models, and 2D and 3D co-culture systems are used to analyse intratumoural heterogeneity. Building on previous funding periods, molecular and metabolic marker profiles are further validated and, in collaboration with the service projects, translated into robust diagnostic and prognostic tools. By integrating machine learning models into clinical decision support systems, the aim is to develop digital, regulatory-compliant tools for improved diagnostics, risk stratification and early identification of aggressive NCCP tumours.

Aims

(I) Establish mechanistic and functional links between intratumoural heterogeneity and tumour progression in NCCP tumours
(II) Implement molecular and metabolic fingerprints of specific NCCP tumour subtypes to stratify disease aggressiveness and predict treatment response
(III) Develop a ML-based software CDSS according to medical device standards that will facilitate prediction of metastatic disease and enable translation of (pre-) and clinical findings into routine clinical practice