Spatial Transcriptomics in Melanoma (HECTOR)

Characterization of the Intratumoral Heterogeneity of Melanoma by Spatially Resolved Molecular and AI-based Analyses (Hector Foundation, Germany)

The project is a collaboration with the Clinical Cooperation Unit Dermato-Oncology of DKFZ (Jochen Utikal). It focuses on understanding spatial intra-tumoral heterogeneity in malignant melanoma and its impact on patient prognosis. By studying both transcriptional and morphological heterogeneity, we aim to investigate the relationship between the different levels of heterogeneity, their correlation with prognosis, and potentially train an algorithm to identify relevant segments for potential use as digital biomarkers.

At the gene expression level, Xenium in-situ analysis will be employed to investigate transcriptional heterogeneity. Selected genes from mesenchymal and melanocytic signatures will be examined. Histologically, heterogeneous segments will be characterized using pathological annotation and AI-based clustering to further investigate correlations between molecular and histological properties. A Deep-Learning-based segmentation algorithm will be trained on fusion segments resulting from overlaps between the two categories to annotate spatial heterogeneity on new melanoma sections. If a correlation between the extent of heterogeneity and prognosis is found, the algorithm can be used to train a risk classifier as a digital prognostic biomarker to predict recurrence or mortality risk.