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
Drug resistance is a major challenge in cancer therapy. Cancer cells with pre-existing or acquired mutations that confer resistance to a given drug treatment outgrow the susceptible cell population and cause cancer recurrence after an initial successful treatment response. Knowledge about resistance mutations before they occur in the clinic could prevent unnecessary patient treatment with ineffective drugs, in clinical trials as well as clinical practice, or potentially speed up the development of follow-up compounds. Here, we focused on on-target amino acid mutations that confer resistance to an inhibitor compound with a known binding mode. We evaluated whether a combination of physics-based free energy perturbation (FEP) affinity estimates and protein language model-based protein fitness estimates could improve the in silico identification of resistance mutations. Validation was done with data from deep mutational scanning (DMS) experiments that tested for resistance to single amino acid mutations. A public data set testing ERK2 resistance against the inhibitor SCH772984 and an internal data set testing resistance of an EGFR_exon20 mutant against a Bayer small molecule inhibitor were used. Our results show that protein fitness estimates can facilitate the identification of resistance mutations by filtering mutations with a low estimated fitness. Even though FEP has flagged such mutations as affinity-decreasing and thus potentially resistant, they were not resistant according to the DMS experiment and therefore correctly filtered out. This indicates that protein language model-based protein fitness estimates could be a computationally efficient method to filter mutations without having to model the negative impact of mutations on native function or protein stability, which is error-prone and computationally expensive.
Dominik Schwarz, Sven H. Giese, Akansha A. Gupta et al.· Journal of Chemical Informat...· 0 citations
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