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Artificial Intelligence

A compendium of next-generation patient-derived models for diverse cancers

| Source: Nature

Abstract The development of new therapeutics and the validation of pathogenetic cancer mechanisms require representative laboratory models 1,2 . However, existing collections represent only a fraction of the diversity observed in human cancer 2–4 . Recent technologies have enabled efficient in vitro model derivation (for example, tumour organoids) 5 . However, whether these maintain essential properties of patient tumours during long-term expansion has not been systematically investigated. Here

Abstract The development of new therapeutics and the validation of pathogenetic cancer mechanisms require representative laboratory models 1,2 . However, existing collections represent only a fraction of the diversity observed in human cancer 2–4 . Recent technologies have enabled efficient in vitro model derivation (for example, tumour organoids) 5 . However, whether these maintain essential properties of patient tumours during long-term expansion has not been systematically investigated. Here we present results of a large-scale international programme—the Human Cancer Models Initiative—which involved the generation of a resource of 665 next-generation models from 2,780 donors with 25 cancer types and integrated tumour–model whole genome, exome, methylome and transcriptome analyses. The resource provides 522 models with comprehensive clinical data, 153 models of rare cancers and 71 models from participants with non-European ancestry. Analyses of 421 matched tumour–model pairs reveal high genetic (97.8%) and epigenetic (95%) concordance and define correlates of model discordance. Single-nucleus RNA sequencing of tumour–model pairs reveals subsets of models in which culture conditions significantly influence cell states. Finally, we characterize model preservation of extrachromosomal DNA and post-treatment mutational signatures to provide opportunities to study therapeutic resistance. This model repository is being made available to the community—including multimodal molecular profiling, clinical information and integrative software tools—thus providing a valuable resource for preclinical investigation of cancer pathogenesis and treatment response.

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