Developing a classification of hematologic neoplasms in the era of precision medicine

Abstract

AbstractThe recently developed International Consensus (IC) classification of hematologic neoplasms is primarily based on input from clinical advisory committees composed of pathologists, hematologists, oncologists, and genomic scientists. Morphology continues to represent a fundamental element in the definition of hematologic neoplasms. Acknowledging that the abnormal morphology is a result of dysregulated hematopoiesis driven by somatic gene mutations or altered expression, the IC classification considers genomic features more extensively. Defining nosologic entities based on underlying molecular mechanism(s) of disease is fundamental for enabling the development of precision treatments. Because translational and clinical research continuously advance the field, the classification of hematologic neoplasms will need to be regularly refined and updated; the basic question is what mechanism should be used for this purpose. Scientific hematopathology societies, in collaboration with hematology societies, should be primarily responsible for establishing a standing International Working Group, which would in turn collaborate with the World Health Organization (WHO)/International Agency for Research on Cancer (IARC) to realize and disseminate the classification. The current classification, with its strong morphology component, represents a basis for refinement. Through data sharing, the creation of large comprehensive patient data sets will allow the use of methods of inference, including statistical analyses and machine learning models, aimed at further identifying distinct disease subgroups. A collaborative clinico-pathologic review process will provide a mechanism for updating pathologic and genomic criteria within a clinical context. An interactive Web-based portal would make the classification more immediately available to the scientific community, while providing accessory features that enable the practical application of diagnostic, prognostic, and predictive information.