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American Society of Clinical Oncology, JCO Clinical Cancer Informatics, 5, p. 221-230, 2021

DOI: 10.1200/cci.20.00108

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OncoTree: A Cancer Classification System for Precision Oncology

Journal article published in 2021 by Ritika Kundra ORCID, Hongxin Zhang ORCID, Robert Sheridan, Sahussapont Joseph Sirintrapun ORCID, Avery Wang, Angelica Ochoa ORCID, Manda Wilson, Benjamin Gross, Yichao Sun ORCID, Ramyasree Madupuri, Baby A. Satravada, Dalicia Reales, Efsevia Vakiani, Hikmat A. Al-Ahmadie ORCID, Ahmet Dogan ORCID and other authors.
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

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.