ASGARD is A Single-cell Guided Pipeline to Aid Repurposing of Drugs

Published in

Nature Communications(2023)

External Links:

TL;DR

A drug recommendation system ASGARD, which predicts drugs by considering cell clusters to address the intercellular heterogeneity within each patient, is proposed, which shows significantly better average accuracy on single-drug therapy compared to two bulk-cell-based drug repurposing methods.

Abstract

Single-cell RNA sequencing technology has enabled in-depth analysis of intercellular heterogeneity in various diseases. However, its full potential for precision medicine has yet to be reached. Towards this, we propose A Single-cell Guided Pipeline to Aid Repurposing of Drugs (ASGARD) that defines a drug score to recommend drugs by considering all cell clusters to address the intercellular heterogeneity within each patient. ASGARD shows significantly better average accuracy on single-drug therapy compared to two bulk-cell-based drug repurposing methods. We also demonstrated that it performs considerably better than other cell cluster-level predicting methods. In addition, we validate ASGARD using the drug response prediction method TRANSACT with Triple-Negative-Breast-Cancer patient samples. We find that many top-ranked drugs are either approved by the Food and Drug Administration or in clinical trials treating corresponding diseases. In conclusion, ASGARD is a promising drug repurposing recommendation tool guided by single-cell RNA-seq for personalized medicine. ASGARD is free for educational use at https://github.com/lanagarmire/ASGARD . The full potential of single-cell RNA-sequencing applied to precision medicine has yet to be reached. Here, we propose a drug recommendation system ASGARD, which predicts drugs by considering cell clusters to address the intercellular heterogeneity within each patient.

Authors

Yuheng Du

2 papers

Bing He

1 papers

Yao Xiao

1 papers

References86 items

Predicting patient response with models trained on cell lines and patient-derived xenografts by nonlinear transfer learning

Albendazole and Mebendazole as Anti-Parasitic and Anti-Cancer Agents: an Update

A single‐cell RNA expression atlas of normal, preneoplastic and tumorigenic states in the human breast

Powerful p-value combination methods to detect incomplete association

Confronting false discoveries in single-cell differential expression

ASGARD is A Single-cell Guided Pipeline to Aid Repurposing of Drugs

Published in

Nature Communications(2023)

External Links:

Generate Graph DownloadPDF

TL;DR

Abstract

Authors

Yuheng Du

2 papers

Bing He

1 papers

Yao Xiao

1 papers

References86 items

Predicting patient response with models trained on cell lines and patient-derived xenografts by nonlinear transfer learning

Albendazole and Mebendazole as Anti-Parasitic and Anti-Cancer Agents: an Update

A single‐cell RNA expression atlas of normal, preneoplastic and tumorigenic states in the human breast

Powerful p-value combination methods to detect incomplete association

Confronting false discoveries in single-cell differential expression

Haodong Liang

1 papers

Qianhui Huang

1 papers

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Barcoding reveals complex clonal behavior in patient-derived xenografts of metastatic triple negative breast cancer

Breaking the paradigm: Dr Insight empowers signature-free, enhanced drug repurposing

Heterogeneity of Alzheimer’s disease: consequence for drug trials?

Heterogeneity in Colorectal Cancer: A Challenge for Personalized Medicine?

Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage

Comprehensive Integration of Single-Cell Data

Drug repurposing: progress, challenges and recommendations

Profiling human breast epithelial cells using single cell RNA sequencing identifies cell diversity

Vorinostat and quinacrine have synergistic effects in T-cell acute lymphoblastic leukemia through reactive oxygen species increase and mitophagy inhibition

TL;DR

Abstract

Authors

References86 items

Predicting patient response with models trained on cell lines and patient-derived xenografts by nonlinear transfer learning

Albendazole and Mebendazole as Anti-Parasitic and Anti-Cancer Agents: an Update

A single‐cell RNA expression atlas of normal, preneoplastic and tumorigenic states in the human breast

Powerful p-value combination methods to detect incomplete association

Confronting false discoveries in single-cell differential expression

TL;DR

Abstract

Authors

References86 items

Predicting patient response with models trained on cell lines and patient-derived xenografts by nonlinear transfer learning

Albendazole and Mebendazole as Anti-Parasitic and Anti-Cancer Agents: an Update

A single‐cell RNA expression atlas of normal, preneoplastic and tumorigenic states in the human breast

Powerful p-value combination methods to detect incomplete association

Confronting false discoveries in single-cell differential expression

Causes of death and comorbidities in hospitalized patients with COVID-19

COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas

Natural killer cells associated with SARS-CoV-2 viral RNA shedding, antibody response and mortality in COVID-19 patients

OCTAD: an open workspace for virtually screening therapeutics targeting precise cancer patient groups using gene expression features

Evaluation of Cell Type Annotation R Packages on Single-cell RNA-seq Data

T cells in COVID-19 — united in diversity

Uncontrolled Innate and Impaired Adaptive Immune Responses in Patients with COVID-19 Acute Respiratory Distress Syndrome

Intercellular signaling dynamics from a single cell atlas of the biomaterials response

Neratinib Plus Capecitabine Versus Lapatinib Plus Capecitabine in HER2-Positive Metastatic Breast Cancer Previously Treated With ≥ 2 HER2-Directed Regimens: Phase III NALA Trial

Monocyte-driven atypical cytokine storm and aberrant neutrophil activation as key mediators of COVID-19 disease severity

Prediction of repurposed drugs for treating lung injury in COVID-19

Identification of Repurposal Drugs and Adverse Drug Reactions for Various Courses of Coronavirus Disease 2019 (COVID-19) Based on Single-cell RNA Sequencing Data

Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19

Repurposing Didanosine as a Potential Treatment for COVID-19 Using Single-Cell RNA Sequencing Data

One size does not fit all – Patterns of vulnerability and resilience in the COVID-19 pandemic and why heterogeneity of disease matters

Eleven grand challenges in single-cell data science

The potential and controversy of targeting STAT family members in cancer.

Phase 1 study of the Aurora kinase A inhibitor alisertib (MLN8237) combined with the histone deacetylase inhibitor vorinostat in lymphoid malignancies

Intratumoral heterogeneity and clonal evolution in liver cancer

Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma

Neratinib for breast cancer

Single-cell analysis of childhood leukemia reveals a link between developmental states and ribosomal protein expression as a source of intra-individual heterogeneity

Fostamatinib for the treatment of chronic immune thrombocytopenia.

Spleen Tyrosine Kinase-Mediated Autophagy Is Required for Epithelial-Mesenchymal Plasticity and Metastasis in Breast Cancer.

Cerebrovascular Disease: Primary and Secondary Stroke Prevention.

Barcoding reveals complex clonal behavior in patient-derived xenografts of metastatic triple negative breast cancer

Breaking the paradigm: Dr Insight empowers signature-free, enhanced drug repurposing

Heterogeneity of Alzheimer’s disease: consequence for drug trials?

Heterogeneity in Colorectal Cancer: A Challenge for Personalized Medicine?

Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage

Comprehensive Integration of Single-Cell Data

Drug repurposing: progress, challenges and recommendations

Profiling human breast epithelial cells using single cell RNA sequencing identifies cell diversity

Vorinostat and quinacrine have synergistic effects in T-cell acute lymphoblastic leukemia through reactive oxygen species increase and mitophagy inhibition

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