Raymond Dalgleish

The WHO Classification of Tumours is used widely by histopathologists and other disciplines as it provides an internationally agreed taxonomy of neoplastic disorders. Now published online and in print, it has often included genetic tumour syndromes (GTS) relevant to particular organs, as suggested by the editorial board. This highlighted the lack of a systematic classification of GTS, providing international standards for their diagnosis and a basis for research. The need for this was established by a series of meetings, leading to the adoption of a hierarchical classification of GTS based on the cellular mechanism affected, the molecular pathway involved, the clinical syndrome and the gene defects present. The major cellular mechanisms affected were identified as: growth factor receptors and related pathways; oxidative stress response and metabolism; cell cycle and apoptosis; DNA repair and genomic stability; telomere maintenance; epigenetic drivers and chromatin; RNA regulation and protein regulation. Assembly of the classification emphasised current gaps in knowledge, together with the need for further research. The new classification highlights the relationships between some genetic syndromes and provides an internationally agreed framework for the incorporation of new information as it becomes available.

A paper entitled “A novel splice-altering frameshift variant in the COL1A1 gene underlies osteogenesis imperfecta type I: molecular characterization

A paper published in Orphanet Journal of Rare Diseases proposes a new classification of osteogenesis imperfecta (OI) based upon underlying pathological mechanisms. The proposed numbering of OI types conflicts with the currently used numbering and is likely to lead to confusion. In addition, classification of OI according to underlying pathogenic mechanisms is not novel.

Summary: The Global Alliance for Genomics and Health (GA4GH) Phenopacket Schema was released in 2022 and approved by ISO as a standard for sharing clinical and genomic information about an individual, including phenotypic descriptions, numerical measurements, genetic information, diagnoses, and treatments. A phenopacket can be used as an input file for software that supports phenotype-driven genomic diagnostics and for algorithms that facilitate patient classification and stratification for identifying new diseases and treatments. There has been a great need for a collection of phenopackets to test software pipelines and algorithms. Here, we present phenopacket-store. Version 0.1.12 of phenopacket-store includes 4916 phenopackets representing 277 Mendelian and chromosomal diseases associated with 236 genes, and 2872 unique pathogenic alleles curated from 605 different publications. This represents the first large-scale collection of case-level, standardized phenotypic information derived from case reports in the literature with detailed descriptions of the clinical data and will be useful for many purposes, including the development and testing of software for prioritizing genes and diseases in diagnostic genomics, machine learning analysis of clinical phenotype data, patient stratification, and genotype-phenotype correlations. This corpus also provides best-practice examples for curating literature-derived data using the GA4GH Phenopacket Schema.