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David Earl Hostallero

Alumni

Publications

Anticancer Monotherapy and Polytherapy Drug Response Prediction Using Deep Learning: Guidelines and Best Practices
Amin Emad
David Earl Hostallero
2024-12-31
Methods in Molecular Biology (unknown)
doi.org
Interpretable deep learning architectures for improving drug response prediction performance: myth or reality?
Yihui Li
David Earl Hostallero
Amin Emad
Motivation: Recent advances in deep learning model development have enabled more accurate prediction of drug response in cancer. However, th… (see more)e black-box nature of these models still remains a hurdle in their adoption for precision cancer medicine. Recent efforts have focused on making these models interpretable by incorporating signaling pathway information in model architecture. While these models improve interpretability, it is unclear whether this higher interpretability comes at the cost of less accurate predictions, or a prediction improvement can also be obtained. Results: In this study, we comprehensively and systematically assessed four state-of-the-art interpretable models developed for drug response prediction to answer this question using three pathway collections. Our results showed that models that explicitly incorporate pathway information in the form of a latent layer perform worse compared to models that incorporate this information implicitly. Moreover, in most evaluation setups the best performance is achieved using a simple black-box model. In addition, replacing the signaling pathways with randomly generated pathways shows a comparable performance for the majority of these interpretable models. Our results suggest that new interpretable models are necessary to improve the drug response prediction performance. In addition, the current study provides different baseline models and evaluation setups necessary for such new models to demonstrate their superior prediction performance. Availability and Implementation: Implementation of all methods are provided inhttps://github.com/Emad-COMBINE-lab/InterpretableAI_for_DRP. Generated uniform datasets are inhttps://zenodo.org/record/7101665#.YzS79HbMKUk. Contact:amin.emad@mcgill.caSupplementary Information: Online-only supplementary data is available at the journal’s website.
2022-12-31
Bioinform. (published)
doi.org
Preclinical-to-clinical Anti-cancer Drug Response Prediction and Biomarker Identification Using TINDL
David Earl Hostallero
Lixuan Wei
Liewei Wang
Junmei Cairns
Amin Emad
Prediction of the response of cancer patients to different treatments and identification of biomarkers of drug response are two major goals … (see more)of individualized medicine. Here, we developed a deep learning framework called TINDL, completely trained on preclinical cancer cell lines (CCLs), to predict the response of cancer patients to different treatments. TINDL utilizes a tissue-informed normalization to account for the tissue type and cancer type of the tumors and to reduce the statistical discrepancies between CCLs and patient tumors. Moreover, by making the deep learning black box interpretable, this model identifies a small set of genes whose expression levels are predictive of drug response in the trained model, enabling identification of biomarkers of drug response. Using data from two large databases of CCLs and cancer tumors, we showed that this model can distinguish between sensitive and resistant tumors for 10 (out of 14) drugs, outperforming various other machine learning models. In addition, our small interfering RNA (siRNA) knockdown experiments on 10 genes identified by this model for one of the drugs (tamoxifen) confirmed that tamoxifen sensitivity is substantially influenced by all of these genes in MCF7 cells, and seven of these genes in T47D cells. Furthermore, genes implicated for multiple drugs pointed to shared mechanism of action among drugs and suggested several important signaling pathways. In summary, this study provides a powerful deep learning framework for prediction of drug response and identification of biomarkers of drug response in cancer. The code can be accessed at https://github.com/ddhostallero/tindl.
2022-12-31
Genomics, Proteomics & Bioinformatics (published)
doi.org