Preclinical-to-clinical Anti-cancer Drug Response Prediction and Biomarker Identification Using TINDL

Genomics Proteomics Bioinformatics. 2023 Feb 10:S1672-0229(23)00032-3. doi: 10.1016/j.gpb.2023.01.006. Online ahead of print.ABSTRACTPrediction of the response of cancer patients to different treatments and identification of biomarkers of drug response are two major goals 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 messenger RNA (mRNA) 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 significantly influenced by all of these genes for MCF7 cells, and seven of these genes for T47D cells. Furthermore, genes implicated for multiple drugs pointed to ...
Source: Genomics Proteomics ... - Category: Genetics & Stem Cells Authors: Source Type: research