TY - JOUR T1 - DeepGeni: Deep generalized interpretable autoencoder elucidates gut microbiota for better cancer immunotherapy JF - bioRxiv DO - 10.1101/2021.05.06.443032 SP - 2021.05.06.443032 AU - Min Oh AU - Liqing Zhang Y1 - 2021/01/01 UR - http://biorxiv.org/content/early/2021/05/07/2021.05.06.443032.abstract N2 - Recent studies revealed that gut microbiota modulates the response to cancer immunotherapy and fecal microbiota transplantation has clinical benefit in melanoma patients during the treatment. Understanding microbiota affecting individual response is crucial to advance precision oncology. However, it is challenging to identify the key microbial taxa with limited data as statistical and machine learning models often lose their generalizability. In this study, DeepGeni, a deep generalized interpretable autoencoder, is proposed to improve the generalizability and interpretability of microbiome profiles by augmenting data and by introducing interpretable links in the autoencoder. DeepGeni-based machine learning classifier outperforms state-of-the-art classifier in the microbiome-driven prediction of responsiveness of melanoma patients treated with immune checkpoint inhibitors. DeepGeni-based machine learning classifier outperforms state-of-the-art classifier in the microbiome-driven responsiveness prediction of melanoma patients treated with immune checkpoint inhibitors. Also, the interpretable links of DeepGeni elucidate the most informative microbiota associated with cancer immunotherapy response.Competing Interest StatementThe authors have declared no competing interest.ICIImmune checkpoint inhibitorFMTfecal microbiota transplantationmOTUmarker gene-based operational taxonomic unitGANgenerative adversarial networkSVMsupport vector machineRFrandom forestNNfeedforward neural networkAUCArea under the receiver operating characteristics curveROCReceiver operating characteristics ER -