Evidence map›Paper›PMID 40701981›Full record

ArticleScientific data2025

A highly annotated drug combination resource for catalyzing precision combinatorial therapy.

Tianyi You, Lili Wang, Jianhua Wang, Dongqing Xu, Xinran Xu, Nan Li, Mulin Jun Li, Haitao Wang, Xiaobao Dong

Abstract readDataset
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. WTAP-Mediated mAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  2. A Knowledge Graph Approach To Discovering Drug Combination Therapies Across The Phenome.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026
    Article
  3. Review
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

9 authors.

Tianyi You *Department of Bioinformatics, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China.
Lili Wang *Department of Oncology, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China.
Jianhua Wang *Department of Bioinformatics, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China.
Dongqing XuDepartment of Bioinformatics, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China.
Xinran XuDepartment of Bioinformatics, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China.
Nan LiDepartment of Oncology, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China.
Mulin Jun LiDepartment of Bioinformatics, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China. mulinli@connect.hku.hk.
Haitao WangDepartment of Oncology, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China. haitao_peterrock@outlook.com.
Xiaobao DongDepartment of Bioinformatics, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China. dongxiaobao@tmu.edu.cn.ORCID 0000-0003-1652-117X

Funding

National Natural Science Foundation of China (National Science Foundation of China) 31801122National Natural Science Foundation of China (National Science Foundation of China) 32070675
6 · The paper itself

Abstract

Combinations of cancer drugs have the potential to overcome resistance, improve the response rate of existing drugs and reduce dose-limiting toxicity associated with single agents. Existing drug combination databases only provide response data, such as synergy scores between two drugs, without important contextual information to assist oncologists in matching their patients with these combinations in an evidence-based way. To address this gap, we developed a cancer drug combination database (named as OncoDrug+) by manually collecting and integrating drug combinations and corresponding evidences from FDA databases, clinical guidelines, clinical trials, clinical case reports, patient-derived tumor xenograft models, cell line models and bioinformatics predictions. OncoDrug+ includes 7895 data entries, covering 77 cancer types, including unique 2201 drug combination therapies, involving 1200 biomarkers, 763 published reports and seven types of evidence. Unlike many previous databases only include treatment regime and drug response data, OncoDrug+ provides detailed genetic evidences, pharmacological target information and evidence scores supporting each combination strategy, making evidence-based experimental or clinical applications of cancer drug combinations be possible.

Indexed as

Antineoplastic Combined Chemotherapy ProtocolsDatabases, FactualNeoplasmsPrecision MedicineDrug CombinationsHumansDrug Combinations

Identifiers

PMID40701981
PMCPMC12287253

What OpenQuestion holds

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Registered trials

None linked

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.