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• W4295872780 abstract "Abstract Cell lines and patient-derived xenografts are essential to cancer research, however, the results derived from such models often lack clinical translatability, as these models do not fully recapitulate the complex cancer biology. It is critically important to better understand the systematic differences between cell lines, xenografts and clinical tumors, and to be able to identify pre-clinical models that sufficiently resemble the biological characteristics of clinical tumors across different cancers. On another side, direct comparison of transcriptional profiles from pre-clinical models and clinical tumors is infeasible due to the mixture of technical artifacts and inherent biological signals. To address these challenges, we developed MOBER, M ulti- O rigin B atch E ffect R emover method, to simultaneously extract biologically meaningful embeddings and remove batch effects from transcriptomic datasets of different origin. MOBER consists of two neural networks: conditional variational autoencoder and source discriminator neural network that is trained in adversarial fashion. We applied MOBER on transcriptional profiles from 932 cancer cell lines, 434 patient-derived tumor xenografts and 11’159 clinical tumors and identified pre-clinical models with greatest transcriptional fidelity to clinical tumors, and models that are transcriptionally unrepresentative of their respective clinical tumors. MOBER can conserve the biological signals from the original datasets, while generating embeddings that do not encode confounder information. In addition, it allows for transformation of transcriptional profiles of pre-clinical models to resemble the ones of clinical tumors, and therefore can be used to improve the clinical translation of insights gained from pre-clinical models. As a batch effect removal method, MOBER can be applied widely to transcriptomics datasets of different origin, allowing for integration of multiple datasets simultaneously." @default.
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• W4295872780 date "2022-09-09" @default.
• W4295872780 modified "2023-10-02" @default.
• W4295872780 title "Biologically relevant integration of transcriptomics profiles from cancer cell lines, patient-derived xenografts and clinical tumors using deep learning" @default.
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• W4295872780 doi "https://doi.org/10.1101/2022.09.07.506964" @default.
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