Abstract
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 Statement
The authors have declared no competing interest.
List of abbreviations
- ICI
- Immune checkpoint inhibitor
- FMT
- fecal microbiota transplantation
- mOTU
- marker gene-based operational taxonomic unit
- GAN
- generative adversarial network
- SVM
- support vector machine
- RF
- random forest
- NN
- feedforward neural network
- AUC
- Area under the receiver operating characteristics curve
- ROC
- Receiver operating characteristics
