Informatics on Drug Repurposing for Breast Cancer

Figures & data

Figure 1 Illustration of four informatics-driven approaches for breast cancer drug repositioning described in this review. (A) Transcriptomic signature matching. (B) Gene-connection-based scanning. (C) Simulated structure docking. (D) Artificial intelligence.

Table 1 Brief Introduction and Candidate Drugs of Each Research Collected in This Review

Strategy	Ref.	Brief Introduction	Candidate Drugs for Repurposing*
Transcriptomic signature matching	[9]	A 57-gene “estrogenic signature” downregulated by conventional ERα antagonists was constructed and CMap was utilized for forward matching.	Mitoxantrone, thioridazine, menadione.
	[11]	A reported signature containing 25 upregulated and 14 downregulated genes of breast cancer stem cell was used and CMap was utilized for reverse matching.	Fasudil, pivmecillinam, ursolic acid, 16,16-dimethylprostaglandin E2, lovastatin.
	[12]	DeCoST, a framework based on the Control System Theory was proposed and tissue-level expression profiles were utilized in the framework.	Erbitux, flutamide, medrysone et al for ERα+ BC; Daunorubicin and donepezil for ERα- BC.
	[14]	Dr Insight, an automated tool eliminating the need for subjective selection of query transcriptomic signatures was proposed.	15-delta prostaglandin J2, trichostatin A (TSA), LY-294002, wortmannin, trifluoperazine
	[15]	A pathway-based method for BC subtyping was proposed and contextual prioritization of pathways and drugs was generated.	Individualized drug regimens for two patients with basal subtype
	[19]	ASGARD, a drug repurposing recommendation tool inspired by single-cell RNA data was proposed and compounds which significantly reversed the expression pattern in a single cell cluster or multiple clusters were ranked.	Fostamatinib and colchicine for advanced metastatic BC; Mebendazole and crizotinib for TNBC.
Gene-connection -based scanning	[21]	Four cluster detection algorithms were utilized to systematically investigated the cluster of PPI network of BC cell line MCF-7 and compared in the performance for drug target prediction.	Fulvestrant, tanespimycin, geldanamycin et al
	[22]	An integrated omics approach with transcriptome and interactome data was presented to identify active PPI networks in TNBC patients.	Selumetinib, trametinib, wortmannin et al
	[24]	A drug-disease network was built by considering all interactions between drug targets and disease-related genes in the context of all KEGG human signaling pathways.	Captopril, glibenclamide, fluorometholone et al
	[25]	Copy number variation and aberration data were employed in the development of a network-based integration approach.	Ruxolitinib, bromocriptine, deferiprone et al
	[26]	A maximum flow-based PPI network was constructed and new drug targets were identified from the targets of the FDA drugs and their associated drugs through the network.	Guanidine, phenethyl isothiocyanate, caffeine et al
	[27]	Key hub genes involved in driving neoadjuvant chemotherapy resistance were obtained via gene co-expression network analysis and Multiple Correlation Clustering.	Carfilzomib, bortezomib, Ixazomib citrate et al
	[28]	An R-based package named PriorCD for cancer drug repurposing was developed by first considering the drug functional similarities at the pathway level.	Amythiamicin A, zorubicin, daunorubicin et al
	[29]	A knowledge network based on a gene-centric schema including relations between genes and other biomedical entities was built.	Different targeted drugs were suggested for different BC subgroups
Simulated structure docking	[31]	FDA-approved drugs which were structurally similar to two well-known inhibitors of EGFR and HER2 were investigated through molecular docking, molecular dynamics simulation and MM-GBSA binding free energy prediction.	Terazosin, alfuzosin, prazosin, irinotecan, quinacrine.
	[32]	Drugs from the library of 1293 FDA-approved drugs to block all three target proteins including IR, ITGB1 and CD36 were investigated through the same series of in silico methods in.^Citation31	Capmatinib, ponatinib, naldemedine, pimozide.
	[34]	Virtual screening for γ-secretase inhibitors was performed via molecular docking and molecular dynamics simulation.	Venetoclax.
	[35]	Virtual screening for RBPJ inhibitors was performed via molecular docking, molecular dynamics simulation and the MM-PBSA binding free energy prediction.	Fidaxomicin, schaftoside, acarbose.
	[36]	Virtual screening for HDAC7 inhibitors was performed via molecular docking, molecular dynamics simulation and cell viability assay.	Ibotenic acid.
Artificial intelligence	[38]	A feature-based method to predict unknown drug-target interaction called PsePDC-DTIs was developed.	Erlotinib, caffeine, afatinib et al
	[39]	A LASSO (least absolute shrinkage and selection operator)-based deep learning method to predict drug-target interaction was developed.	Hypericin, ingenol mebutate, D-Myo-Inositol-Hexasulphate, hyperforin, carboxyatractyl oside
	[40]	A graph neural network model called GraphRepur was proposed.	Selinexor, mycophenolic acid, pitavastatin et al
	[41]	A recommender system named ANTENNA for prediction of new drug-gene-disease associations by mining a large scale of chemogenomics and disease association data was developed.	Diazoxide.
	[42]	Word2Vec, which conducted Literature-Wide Association Studies (LWAS) based on the text mining results from millions of cancer-related PubMed abstracts was developed.	Cytarabine, dacarbazine, hydroxyurea et al

Note: *Drugs with in vitro evidence are underlined.

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