The development of new therapeutics and the validation of pathogenetic cancer mechanisms require representative laboratory models1,2. However, existing collections represent only a fraction of the diversity observed in human cancer2-4. 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.
Dina Elharouni, Mushriq Al-Jazrawe, Seongmin Choi et al.· Nature· 2 citations
The authors apply ARACNe and metaVIPER to published scRNA-seq datasets to characterize pancreatic cancer subtypes, identifying six distinct cell states as well as their mechanistic determinants.
P. Laise, Mikko M. Turunen, Álvaro Curiel-García et al.· Nature Genetics· 0 citations
A multi-omics framework that infers context-specific protein activities from transcriptomic, phosphoproteomic, and protein correlation-based protein-protein interaction data is introduced, integrating modality-specific algorithms via network diffusion.
George A. Rosenberger, Peng Xue, Isabell Bludau et al.· Molecular Systems Biology· 0 citations
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