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

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

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Prediction 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 sta-tistical 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 predic-tive 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.

Drug responseDeep learningExplainable AICancerGene knockdown experiment

David Earl Hostallero、Lixuan Wei、Liewei Wang、Junmei Cairns、Amin Emad

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Department of Electrical and Computer Engineering,McGill University,Montreal,QC H3A 0E9,Canada

Mila-Quebec Artificial Intelligence Institute,Montreal,QC H2S 3H1,Canada

Department of Molecular Pharmacology and Experimental Therapeutics,Mayo Clinic,Rochester,MN 55905,USA

The Rosalind and Morris Goodman Cancer Institute,McGill University,Montreal,QC H3A 1A3,Canada

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New Frontiers in Research Fund(NFRF)of Government of CanadaNatural Sciences and Engineering Research Council of Canada(NSERC)McGill Initiative in Computational Medicine(MiCM)Génome QuébecMinistère de l'Economie et de l'Innovation du QuébecInstitut de Valorisation des Données(IVADO)Canada First Research Excellence FundOncopoleMerck Canada Inc.,and the Fonds de Recherche du Québec-SantéCalcul QuébecCompute Canada

NFRFE-2019-01290RGPIN-2019-04460

2023

10.1016/j.gpb.2023.01.006

基因组蛋白质组与生物信息学报(英文版)
中国科学院北京基因组研究所

基因组蛋白质组与生物信息学报(英文版)

CSTPCDCSCD
影响因子：0.495
ISSN：1672-0229
年,卷(期)：2023.21(3)
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