Evidence map›Paper›PMID 33068406›Full record

ArticleNucleic acids research2021

Project Score database: a resource for investigating cancer cell dependencies and prioritizing therapeutic targets.

Lisa Dwane, Fiona M Behan, Emanuel Gonçalves, Howard Lightfoot, Wanjuan Yang, Dieudonne van der Meer, Rebecca Shepherd, Miguel Pignatelli, Francesco Iorio, Mathew J Garnett

Abstract read
In one paragraph

Article in Nucleic acids research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers.

0numbers the graph read from it
0cells of the map it votes in
66citing 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

66 citing papers in PubMed.

  1. Review
  2. Article
  3. Interrogating the Mechanistic Link between Neighborhood Deprivation and Colorectal Cancer Risk through Transcriptomic Analysis of Normal Colorectal Biopsies.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2026
    Article
  4. Reconstruction of human metabolic models with large language models.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. Article
  16. Article
  17. Article
  18. Molecular Basis of Breast Tumor Heterogeneity.Advances in experimental medicine and biology · 2025
    Review
  19. Article
  20. Article

6 more citing papers are in PubMed but not listed here.

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

10 authors.

Lisa DwaneWellcome Sanger Institute, Hinxton, Cambridge CB10 1SA, UK.
Fiona M BehanWellcome Sanger Institute, Hinxton, Cambridge CB10 1SA, UK.
Emanuel GonçalvesWellcome Sanger Institute, Hinxton, Cambridge CB10 1SA, UK.
Howard LightfootWellcome Sanger Institute, Hinxton, Cambridge CB10 1SA, UK.
Wanjuan YangWellcome Sanger Institute, Hinxton, Cambridge CB10 1SA, UK.
Dieudonne van der MeerWellcome Sanger Institute, Hinxton, Cambridge CB10 1SA, UK.
Rebecca ShepherdWellcome Sanger Institute, Hinxton, Cambridge CB10 1SA, UK.
Miguel PignatelliWellcome Sanger Institute, Hinxton, Cambridge CB10 1SA, UK.
Francesco IorioWellcome Sanger Institute, Hinxton, Cambridge CB10 1SA, UK.
Mathew J GarnettWellcome Sanger Institute, Hinxton, Cambridge CB10 1SA, UK.

Funding

Wellcome Trust 206194
6 · The paper itself

Abstract

CRISPR genetic screens in cancer cell models are a powerful tool to elucidate oncogenic mechanisms and to identify promising therapeutic targets. The Project Score database (https://score.depmap.sanger.ac.uk/) uses genome-wide CRISPR-Cas9 dropout screening data in hundreds of highly annotated cancer cell models to identify genes required for cell fitness and prioritize novel oncology targets. The Project Score database currently allows users to investigate the fitness effect of 18 009 genes tested across 323 cancer cell models. Through interactive interfaces, users can investigate data by selecting a specific gene, cancer cell model or tissue type, as well as browsing all gene fitness scores. Additionally, users can identify and rank candidate drug targets based on an established oncology target prioritization pipeline, incorporating genetic biomarkers and clinical datasets for each target, and including suitability for drug development based on pharmaceutical tractability. Data are freely available and downloadable. To enhance analyses, links to other key resources including Open Targets, COSMIC, the Cell Model Passports, UniProt and the Genomics of Drug Sensitivity in Cancer are provided. The Project Score database is a valuable new tool for investigating genetic dependencies in cancer cells and the identification of candidate oncology targets.

Indexed as

Databases, FactualGene Expression Regulation, NeoplasticGenome, HumanSoftwareAntineoplastic AgentsBiomarkers, TumorCarcinogenesisCell Line, TumorCRISPR-Cas SystemsGenetic FitnessHumansInternetMolecular Targeted TherapyNeoplasmsOncogenesAntineoplastic AgentsBiomarkers, Tumor

Identifiers

PMID33068406
PMCPMC7778984

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.