Pediatric patients with high-risk extra-cranial solid tumors face a 5-year survival rate below 50%. As molecular profiling alone is insufficient to guide treatment at relapse, complementary strategies like drug screening are urgently needed. We evaluated short-term drug screening as a rapid, reliable method to assess drug sensitivities in pediatric solid tumors using ex vivo cultures from previously established patient-derived xenograft (PDX) models. Ex vivo drug screening was performed within 14 days of receipt across two institutes, testing 77-224 compounds depending on cell availability. Drug responses were consistent across institutes (n = 6), and effective compounds were reproducibly identified in a replicate model. Tumor type-specific responses were observed. In neuroblastoma, ALK-mutation status did not correlate with ALK-inhibitor response, whereas correlations with transcriptomic changes were observed. Timepoint-specific drug sensitivities were observed in serial Ewing sarcoma models. Overall, drug hits were identified in 94% of screens (n = 63), broadening treatment options for 88% of cases without targetable alterations (n = 11). In case of a targetable event, drug screening refined compound choice. Ex vivo drug screening is a fast and feasible method, providing insights into compound efficacy and enabling quick identification of functional treatment suggestions. Ex vivo drug screening should be integrated into a future next-generation diagnostic platform for pediatric solid tumors, combined with genomics and transcriptomics.
Imaging-based spatial transcriptomics enables high-resolution spatial mapping of RNA species. A key challenge in imaging-based spatial transcriptomics is accurate cell segmentation to assign each RNA molecule to the right cell. Here, we present RNA2seg, a novel segmentation algorithm trained on over 4 million cells from MERFISH and CosMx datasets across seven organs using a teacher-student training scheme. RNA2seg integrates RNA point clouds and all available membrane and nuclear stainings. Validation on manually annotated data shows superior performance including in zero-shot and few-shot settings.
