Evidence map›Paper›PMID 41719584›Full record

ArticleDatabase : the journal of biological databases and curation2026

scDrugAtlas: an integrative single-cell drug response database for dissecting tumour heterogeneity in therapeutic efficacy.

Yanfei Wu, Wei Huang, Xinda Ren, Hui Liu, Ling Xu

Abstract read
In one paragraph

Article in Database : the journal of biological databases and curation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Yanfei WuCollege of Computer and Information Engineering, Nanjing Tech University, Nanjing 211816, Jiangsu, China.
Wei HuangCollege of Computer and Information Engineering, Nanjing Tech University, Nanjing 211816, Jiangsu, China.
Xinda RenCollege of Computer and Information Engineering, Nanjing Tech University, Nanjing 211816, Jiangsu, China.
Hui LiuCollege of Computer and Information Engineering, Nanjing Tech University, Nanjing 211816, Jiangsu, China.ORCID 0000-0001-7158-913X
Ling XuDepartment of Radiotherapy, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou, 213000, Jiangsu, China.

Funding

National Natural Science Foundation of China 62372229Natural Science Foundation of Jiangsu Province BK20231271
6 · The paper itself

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/.

Indexed as

Antineoplastic AgentsDrug Resistance, NeoplasmNeoplasmsSingle-Cell AnalysisBiocurationHumansAntineoplastic Agents

Identifiers

PMID41719584
PMCPMC12923164

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.