Oslo Cancer Cluster is an innovation environment in Oslo that accelerates the development of new cancer treatments. The cluster brings together researchers, clinicians and industry partners to advance...

Cell Death Discovery - Cell cycle regulator MYBL2 is a distinct vulnerability in acute myeloid leukemia

This course aims to synthesize the state-of-the-art and the therapeutic perspectives related to harnessing children's immune system to target pediatric tumors. Encompassing topics from biological char...

A comprehensive single-nucleus RNA-seq atlas of >367,000 nuclei from male and female Aedes aegypti mosquitoes reveals sexual dimorphism in sensory systems and brain cell types and widespread co-expres...

Mesothelioma is a highly lethal and poorly biologically understood disease which presents diagnostic challenges due to its morphological complexity. This study uses self-supervised AI (Artificial Intelligence) to map the histomorphological landscape of the disease. The resulting atlas consists of recurrent patterns identified from 3446 Hematoxylin and Eosin (H&E) stained images scanned from resected tumour slides. These patterns generate highly interpretable predictions, achieving state-of-the-art performance with 0.65 concordance index (c-index) for outcomes and 88% AUC in subtyping. Their clinical relevance is endorsed by comprehensive human pathological assessment. Furthermore, we characterise the molecular underpinnings of these diverse, meaningful, predictive patterns. Our approach both improves diagnosis and deepens our understanding of mesothelioma biology, highlighting the power of this self-learning method in clinical applications and scientific discovery. Mesothelioma is