KRAS is the most frequently mutated oncogene. The incidence of specifi c KRAS alleles varies between cancers from different sites, but it is unclear whether allelic selection results from biological selection for specifi c mutant KRAS proteins. We used a crossdisciplinary approach to compare KRAS G12D , a common mutant form, and KRAS A146T , a mutant that occurs only in selected cancers. Biochemical and structural studies demonstrated that KRAS A146T exhibits a marked extension of switch 1 away from the protein body and nucleotide binding site, which activates KRAS by promoting a high rate of intrinsic and guanine nucleotide exchange factorinduced nucleotide exchange. Using mice genetically engineered to express either allele, we found that KRAS G12D and KRAS A146T exhibit distinct tissue-specifi c effects on homeostasis that mirror mutational frequencies in human cancers. These tissue-specifi c phenotypes result from allele-specifi c signaling properties, demonstrating that context-dependent variations in signaling downstream of different KRAS mutants drive the KRAS mutational pattern seen in cancer. SIGNIFICANCE: Although epidemiologic and clinical studies have suggested allele-specifi c behaviors for KRAS , experimental evidence for allele-specifi c biological properties is limited. We combined structural biology, mass spectrometry, and mouse modeling to demonstrate that the selection for specifi c KRAS mutants in human cancers from different tissues is due to their distinct signaling properties.
The high failure rate of therapeutics showing promise in mouse models to translate to patients is a pressing challenge in biomedical science. Though retrospective studies have examined the fidelity of mouse models to their respective human conditions, approaches for prospective translation of insights from mouse models to patients remain relatively unexplored. Here, we develop a semi-supervised learning approach for inference of disease-associated human differentially expressed genes and pathways from mouse model experiments. We examined 36 transcriptomic case studies where comparable phenotypes were available for mouse and human inflammatory diseases and assessed multiple computational approaches for inferring human biology from mouse datasets. We found that semi-supervised training of a neural network identified significantly more true human biological associations than interpreting mouse experiments directly. Evaluating the experimental design of mouse experiments where our model was most successful revealed principles of experimental design that may improve translational performance. Our study shows that when prospectively evaluating biological associations in mouse studies, semi-supervised learning approaches, combining mouse and human data for biological inference, provide the most accurate assessment of human in vivo disease processes. Finally, we proffer a delineation of four categories of model system-to-human “Translation Problems” defined by the resolution and coverage of the datasets available for molecular insight translation and suggest that the task of translating insights from model systems to human disease contexts may be better accomplished by a combination of translation-minded experimental design and computational approaches.
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