Itcc-p4: genomic profiling and analyses of pediatric patient tumor and patient-derived xenograft (pdx) models for high throughput in vivo testing.

Abstract Advancements in state-of-the-art molecular profiling techniques has resulted in better understanding of pediatric cancers and their drivers. Conversely, it also became apparent that pediatric cancers are much more heterogeneous than previously thought. Many new types and subtypes of pediatric cancers have been identified with distinct molecular and clinical characteristics. However, for most newly recognized entities there is no specific treatment available yet. The ITCC-P4 consortium is a collaboration between many academic centers across Europe and several pharmaceutical companies involved in preclinical testing, with the overall aim to establish a sustainable platform of >400 molecularly well-characterized PDX models of high-risk pediatric cancers and to use them for in vivo testing of novel mechanism-of-action based treatments. Currently, 340 models are fully established, including 87 brain and 253 non-brain tumor models, together representing different tumor types both from primary (113) and relapsed (92)/metastatic disease (42). 252 of these models have been fully molecularly characterized, representing 18 pediatric cancer entities and 43 different subtypes. Using low coverage whole-genome and whole exome sequencing, somatic mutation calling, DNA copy number, transcriptome analysis and methylation profiling we have observed that the molecular profile of most PDX models closely mimics their original tumors. Clonal evolution of somatic variants was only observed in some PDX-tumor pairs or so between disease states. Somatic copy number variant analysis highlights specific alterations for instance MYB, MYC, MYCN, NTRK3, PTEN loss differently distributed between PDX-patient tumor pairs in high-grade gliomas. Overall, our results show that we have established >250 PDX models of solid pediatric cancers, that well represents the disease spectrum and that is currently being used for in vivo testing of standard of care drugs and targeted small molecules. Treatment responses will be directly linked to molecular data to identify potential biomarkers for prioritization or deprioritization of individual, patient-specific specific drugs.