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scDrugAtlas: an integrative single-cell drug response database for dissecting tumour heterogeneity in therapeutic
Yanfei Wu1, Wei Huang1, Xinda Ren1
1College of Computer and Information Engineering, Nanjing Tech University, Nanjing 211816, Jiangsu, China.
Abstract:
Tumour heterogeneity often leads to substantial differences in responses to same drug treatment. The presence of pre-existing or acquired drug-resistant cell subpopulations within a tumour survive and proliferate, ultimately resulting in tumour relapse and metastasis. The drug resistance is the leading cause of failure in clinical tumour therapy. Therefore, accurate identification of drug-resistant tumour cell subpopulations could greatly facilitate the precision medicine and novel drug development. However, the scarcity of single-cell drug response data significantly hinders the exploration of tumour cell resistance mechanisms and the development of computational predictive methods. In this paper, we propose scDrugAtlas, a comprehensive database devoted to integrating the drug response data at single-cell level. We manually compiled more than 100 datasets containing single-cell drug responses from various public resources. The current version comprises large-scale single-cell transcriptional profiles and drug response labels from 1023 samples, across 77 unique drugs and 31 major cancer types. Particularly, we assigned a confidence level to each response label based on the tissue source (primary or relapse/metastasis), drug exposure time, and drug-induced cell phenotype. We believe scDrugAtlas could greatly facilitate the Bioinformatics community for developing computational models and biologists for identifying drug-resistant tumour cells and underlying molecular mechanism. Database URL: http://drug.hliulab.tech/scDrugAtlas/.
Insights
scDrugAtlas is a new database integrating single-cell drug response data to combat tumor drug resistance. This resource aids researchers in developing predictive models and identifying resistant cancer cells.
Area of Science:
- Bioinformatics
- Cancer Research
- Computational Biology
Background:
- Tumor heterogeneity causes varied drug responses, with resistant cells leading to relapse and metastasis.
- Drug resistance is a major obstacle in cancer therapy, hindering treatment efficacy.
- Limited single-cell drug response data impedes understanding resistance mechanisms and developing predictive computational methods.
Purpose of the Study:
- To introduce scDrugAtlas, a comprehensive database for single-cell level drug response data.
- To provide a valuable resource for researchers studying tumor cell drug resistance.
- To facilitate the development of computational models and identify drug-resistant cancer cells.
Main Methods:
- Manually compiled over 100 datasets of single-cell drug responses from public resources.
- Integrated large-scale single-cell transcriptional profiles and drug response labels.
- Assigned confidence levels to response labels based on tissue source, drug exposure, and cell phenotype.
Main Results:
- The database contains data from 1023 samples, 77 drugs, and 31 cancer types.
- Includes single-cell transcriptional profiles and associated drug response labels.
- Provides confidence scores for drug response labels to enhance data reliability.
Conclusions:
- scDrugAtlas serves as a crucial resource for the bioinformatics and cancer research communities.
- Enables the development of advanced computational models for predicting drug response.
- Supports biologists in identifying drug-resistant tumor cells and their underlying molecular mechanisms.
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