PURPOSE: Cancer classification is foundational for patient care and oncology research. Systems such as International Classification of Diseases for Oncology (ICD-O), Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT), and National Cancer Institute Thesaurus (NCIt) provide large sets of cancer classification terminologies but they lack a dynamic modernized cancer classification platform that addresses the fast-evolving needs in clinical reporting of genomic sequencing results and associated oncology research. METHODS: To meet these needs, we have developed OncoTree, an open-source cancer classification system. It is maintained by a cross-institutional committee of oncologists, pathologists, scientists, and engineers, accessible via an open-source Web user interface and an application programming interface. RESULTS: OncoTree currently includes 868 tumor types across 32 organ sites. OncoTree has been adopted as the tumor classification system for American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE), a large genomic and clinical data-sharing consortium, and for clinical molecular testing efforts at Memorial Sloan Kettering Cancer Center and Dana-Farber Cancer Institute. It is also used by precision oncology tools such as OncoKB and cBioPortal for Cancer Genomics. CONCLUSION: OncoTree is a dynamic and flexible community-driven cancer classification platform encompassing rare and common cancers that provides clinically relevant and appropriately granular cancer classification for clinical decision support systems and oncology research.

PURPOSE: In 2014, data from a comprehensive multiplatform analysis of 496 adult papillary thyroid cancer samples reported by The Cancer Genome Atlas project suggested that reclassification of thyroid cancer into molecular subtypes, RAS-like and BRAF-like, better reflects clinical behavior than sole reliance on pathologic classification. The aim of this study was to categorize the common oncogenic variants in pediatric differentiated thyroid cancer (DTC) and investigate whether mutation subtype classification correlated with the risk of metastasis and response to initial therapy in pediatric DTC. METHODS: Somatic cancer gene panel analysis was completed on DTC from 131 pediatric patients. DTC were categorized into RAS-mutant ( H-K-NRAS), BRAF-mutant ( BRAF p.V600E), and RET/ NTRK fusion ( RET, NTRK1, and NTRK3 fusions) to determine differences between subtype classification in regard to pathologic data (American Joint Committee on Cancer TNM) as well as response to therapy 1 year after initial treatment had been completed. RESULTS: Mutation-based subtype categories were significant in most variables, including age at diagnosis, metastatic behavior, and the likelihood of remission at 1 year. Patients with RET/ NTRK fusions were significantly more likely to have advanced lymph node and distant metastasis and less likely to achieve remission at 1 year than patients within RAS- or BRAF-mut subgroups. CONCLUSION: Our data support that genetic subtyping of pediatric DTC more accurately reflects clinical behavior than sole reliance on pathologic classification with patients with RET/ NTRK fusions having worse outcomes than those with BRAF-mutant disease. Future trials should consider inclusion of molecular subtype into risk stratification.

High-risk neuroblastoma, a leading cause of pediatric cancer mortality, exhibits substantial intratumoral heterogeneity, contributing to therapeutic resistance. To understand tumor microenvironment evolution during therapy, we longitudinally profiled 22 patients with high-risk neuroblastoma before and after induction chemotherapy using single-nucleus RNA and ATAC sequencing and whole-genome sequencing. This revealed profound shifts in tumor and immune cell subpopulations after therapy and identified enhancer-driven transcriptional regulators of neuroblastoma neoplastic states. Poor outcome correlated with proliferative and metabolically active neoplastic states, whereas more differentiated neuronal-like states predicted better prognosis. Proportions of mesenchymal neoplastic cells increased after therapy and a high proportion correlated with a poorer chemotherapy response. Macrophages significantly expanded towards pro-angiogenic, immunosuppressive and metabolic phenotypes. We identified paracrine signaling networks and validated the HB-EGF–ERBB4 axis between macrophage and neoplastic subsets, which promoted tumor growth through the induction of ERK signaling. These findings collectively reveal intrinsic and extrinsic regulators of therapy response in high-risk neuroblastoma.