Drug Repositioning and Subgroup Discovery for Precision Medicine Implementation in Triple Negative Breast Cancer

Al-Taie, Zainab; Hannink, Mark; Mitchem, Jonathan B.; Papageorgiou, Christos; Shyu, Chi‐Ren

doi:10.3390/cancers13246278

Cited by 7 publications

(8 citation statements)

References 136 publications

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“… 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.…”

Section: Discussionmentioning

confidence: 99%

“…Al-Taie et al dissected TNBC patients into five subgroups based on genotypic data and clinical information including age, race and neoplasm subdivision, and discovered appropriate therapeutic candidates separately through drug repurposing. 29 In particular, this research team built a drug repositioning knowledge network which employed a gene-centric schema including relations between genes, pathways, GO domains, disease and drugs. With the curated knowledge base, the researchers repurposed drugs for each subgroup based on the gene expression patterns and the relationship between genes and other biomedical entities.…”

Section: Gene-connection-based Scanningmentioning

confidence: 99%

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Informatics on Drug Repurposing for Breast Cancer

Zhou¹,

Liu²,

Yu³

et al. 2023

DDDT

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Moving a new drug from bench to bedside is a long and arduous process. The tactic of drug repurposing, which solves "new" diseases with "old" existing drugs, is more efficient and economical than conventional ab-initio way for drug development. Information technology has dramatically changed the paradigm of biomedical research in the new century, and drug repurposing studies have been significantly accelerated by implementing informatics techniques related to genomics, systems biology and biophysics during the past few years. A series of remarkable achievements in this field comes with the practical applications of in silico approaches including transcriptomic signature matching, gene-connection-based scanning, and simulated structure docking in repositioning drug therapies against breast cancer. In this review, we systematically curated these impressive accomplishments with summarization of the main findings on potentially repurposable drugs, and provide our insights into the current issues as well as future directions of the field. With the prospective improvement in reliability, the computer-assisted repurposing strategy will play a more critical role in drug research and development.

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Section: Discussionmentioning

confidence: 99%

Section: Gene-connection-based Scanningmentioning

confidence: 99%

Informatics on Drug Repurposing for Breast Cancer

Zhou¹,

Liu²,

Yu³

et al. 2023

DDDT

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“…TNBC, the most malignant subtype of breast cancer, was previously reported to be intrinsically sensitive to ferroptosis due to its non-apoptotic characteristics. The therapeutic effects of a number of types of ferroptosis inducers on TNBC have been previously evaluated (12,23). Erastin, the most widely used ferroptosis inducer, increases the sensitivity of TNBC cells to ferroptosis by upregulating mitochondrial ROS (24).…”

Section: Discussionmentioning

confidence: 99%

Identification of TFR2 as a novel ferroptosis‑related gene that serves an important role in prognosis and progression of triple‑negative breast cancer

Yang,

Du,

Huang

et al. 2023

Oncol Lett

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Effective targeted therapeutic strategies for triple-negative breast cancer (TNBC), the most malignant subtype of breast cancer, are currently lacking. Ferroptosis has been reported to be associated with the onset and advancement of various cancer types, including TNBC. However, there are limited studies on the correlation between TNBC and ferroptosis-related genes. In addition, the potential biomarkers of ferroptosis in TNBC need further investigation. The present study aimed to assess the prognostic role of a novel ferroptosis-related gene signature in the context of TNBC. The signature was established utilizing The Cancer Genome Atlas dataset. This three-gene model [transferrin receptor 2 (TFR2), regulator of G protein signaling 4 and zinc finger protein 36] was developed utilizing least absolute shrinkage and selection operator regression analysis and demonstrated satisfactory predictive performance in TNBC. The area under the curve values of the receiver operating characteristic curves in this model concerning the 1-, 2-and 3-year survival prediction were 0.721, 0.840 and 0.856, respectively. The predictive performance of the model was verified using the TNBC dataset GSE25307. Gene set enrichment analysis (GSEA) demonstrated the enrichment of genes in the low-risk group in a number of important metabolic pathways. Single-sample GSEA demonstrated a variation in the expression levels of immune checkpoint molecules between the high-and low-risk groups. The inhibitory impact of TFR2 knockdown on the proliferative capacity of TNBC cells was verified through in vitro experiments. The data also demonstrated that TFR2 knockdown facilitated the ferroptosis of TNBC cells.Additional assessments indicated that the effects of TFR2 knockdown were partially reversed upon treatment with the ferroptosis inhibitor ferrostatin-1. In conclusion, in the present study, a novel and accurate ferroptosis-related predictive signature was established for TNBC with potential future clinical applications. To the best of our knowledge, the present study is the first to report that TFR2 regulated ferroptosis in TNBC cells in vitro.

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“…Therefore, the developed data-driven approach was implemented in order to dissect TNBC heterogeneity as much as possible and to discover druggable molecular targets that may enhance the chemotherapy benefit by blocking chemoresistance pathways and, thus, cancer recurrence in TNBC patients. This section has been published in the Cancers journal [66]…”

Section: Breast Cancer Analysis Resultsmentioning

confidence: 99%

“…The ranking is not only based on the genes connected to the drugs and their significance in the network, but also on the gene expression perturbation a drug can cause in human cells and the number of gene patterns affected by the drug within the subgroup's network. This section has been published in the Cancers journal [66] 35…”

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confidence: 99%

Explainable artificial intelligence for patient stratification and drug repositioning

Al-Taie¹

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Enabling precision medicine requires developing robust patient stratification methods as well as drugs tailored to homogeneous subgroups of patients from a heterogeneous population. Developing de novo drugs is expensive and time consuming with an ultimately low FDA approval rate. These limitations make developing new drugs for a small portion of a disease population unfeasible. Therefore, drug repositioning is an essential alternative for developing new drugs for a disease subpopulation. There is a crucial need to develop data-driven approaches that find druggable homogeneous subgroups within the disease population and reposition the drugs for these subgroups. In this study, we developed an explainable AI approach for patient stratification and drug repositioning. Exploratory mining mimicking the trial recruitment process as well as network analysis were used to discover homogeneous subgroups within a disease population. For each subgroup, a biomedical network analysis was done to find the drugs that are most relevant to a given subgroup of patients. The set of candidate drugs for each subgroup was ranked using an aggregated drug score assigned to each drug. The method represents a human-in-the-loop framework, where medical experts use data-driven results to generate hypotheses and obtain insights into potential therapeutic candidates for patients who belong to a subgroup. To examine the validity of our method, we implemented our method on individual cancer types and on pan-cancer data to consider the inter- and intra-heterogeneity within a cancer type and among cancer types. Patients' phenotypic and genotypic data was utilized with a heterogeneous knowledge base because it gives a multi-view perspective for finding new indications for drugs outside of their original use. Our analysis of the top candidate drugs for the subgroups showed that most of these drugs are FDA-approved drugs for cancer, and others are non-cancer related, but have the potential to be repurposed for cancer. We have discovered novel cancer-related mechanisms that these drugs can target in different cancer types to reduce cancer treatment costs and improve patient survival. Further wet lab experiments to validate these findings are required prior to initiating clinical trials using these repurposed therapies.

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Drug Repositioning and Subgroup Discovery for Precision Medicine Implementation in Triple Negative Breast Cancer

Cited by 7 publications

References 136 publications

Informatics on Drug Repurposing for Breast Cancer

Informatics on Drug Repurposing for Breast Cancer

Identification of TFR2 as a novel ferroptosis‑related gene that serves an important role in prognosis and progression of triple‑negative breast cancer

Explainable artificial intelligence for patient stratification and drug repositioning

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