Endocrine Organs
Dissecting the role of tumor cell heterogeneity in Pancreatic Neuroendocrine Tumor progression
Group Marinoni, Perren, Sadowski Cancer is a dynamic disease; genetic and epigenetic alterations drive intra-tumoral cell heterogeneity, resulting in the selection of aggressive cell populations capable of driving progression and ultimately metastasis. Pancreatic neuroendocrine tumours (PanNETs) are tumours that arise from the islets of Langerhans. They exhibit intra-tumoral cell heterogeneity, but it is unclear how this evolves during tumour development and how it contributes to progression. Our previous data suggest that epigenetic changes are the major drivers of progression and cell heterogeneity in PanNETs. By integrating epigenetic and transcriptomic profiles, we found that cell dedifferentiation and metabolic changes characterise the progression from small PanNETs to more advanced ones. We are currently investigating the evolution of intra-tumoral heterogeneity of PanNETs through space and time. Specific cell subpopulations identified as driving progression could then be targeted to stop metastasis formation. The identification of targetable pathways that impair metastasis formation will provide a rationale for new treatments.
Design, synthesis, analysis, and optimization of novel small molecule inhibitors against prostate cancer
Group Pandey Androgens are linked to pathology of prostate cancer. Cytochrome P450 CYP17A1 and Aldo-keto reductase AKR1C3 involved in steroid metabolism are drug targets. The current anti-prostate cancer drug, abiraterone, targeting CYP17A1, is not very effective, and has side effects. We found that Abiraterone inhibits CYP21A2 and cortisol production; and a metabolite of abiraterone is a potent androgen, which ultimately defeats the treatment. With computational and medicinal chemistry groups from Denmark, Poland, Italy and Spain, we produce novel inhibitors of CYP17A1 and AKR1C3. We design and improve the compounds and test them in the laboratory. After the virtual screening, we apply machine learning and automated workflows to identify pharmacophores for structural modifications and synthesis of novel chemicals. Nanoparticle based delivery is used to enhance the efficacy. Using several cell and recombinant protein models novel inhibitors are being tested which are now working at nano molar levels.