Evidence map›Paper›PMID 29066719›Full record

ArticleNature communications2017

Gene isoforms as expression-based biomarkers predictive of drug response in vitro.

Zhaleh Safikhani, Petr Smirnov, Kelsie L Thu, Jennifer Silvester, Nehme El-Hachem, Rene Quevedo, Mathieu Lupien, Tak W Mak, David Cescon, Benjamin Haibe-Kains

Erratum issuedAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing 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

35 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Article
  6. Exploring the role of ITGB6: fibrosis, cancer, and other diseases.Apoptosis : an international journal on programmed cell death · 2024
    Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Review
  18. Machine learning in the prediction of cancer therapy.Computational and structural biotechnology journal · 2021
    Review
  19. Review
  20. Platform-integrated mRNA isoform quantification.Bioinformatics (Oxford, England) · 2020
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Zhaleh SafikhaniPrincess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON, Canada, M5G1L7.
Petr SmirnovPrincess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON, Canada, M5G1L7.ORCID http://orcid.org/0000-0001-9395-8450
Kelsie L ThuPrincess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON, Canada, M5G1L7.
Jennifer SilvesterPrincess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON, Canada, M5G1L7.
Nehme El-HachemInstitut de Recherches Cliniques de Montréal, 110 Pine Avenue West, Montreal, QC, Canada, H2W 1R7.
Rene QuevedoPrincess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON, Canada, M5G1L7.
Mathieu LupienPrincess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON, Canada, M5G1L7.
Tak W MakPrincess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON, Canada, M5G1L7.
David CesconPrincess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON, Canada, M5G1L7.
Benjamin Haibe-KainsPrincess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON, Canada, M5G1L7. bhaibeka@uhnresearch.ca.ORCID http://orcid.org/0000-0002-7684-0079

Funding

CIHR
6 · The paper itself

Abstract

Next-generation sequencing technologies have recently been used in pharmacogenomic studies to characterize large panels of cancer cell lines at the genomic and transcriptomic levels. Among these technologies, RNA-sequencing enable profiling of alternatively spliced transcripts. Given the high frequency of mRNA splicing in cancers, linking this feature to drug response will open new avenues of research in biomarker discovery. To identify robust transcriptomic biomarkers for drug response across studies, we develop a meta-analytical framework combining the pharmacological data from two large-scale drug screening datasets. We use an independent pan-cancer pharmacogenomic dataset to test the robustness of our candidate biomarkers across multiple cancer types. We further analyze two independent breast cancer datasets and find that specific isoforms of IGF2BP2, NECTIN4, ITGB6, and KLHDC9 are significantly associated with AZD6244, lapatinib, erlotinib, and paclitaxel, respectively. Our results support isoform expressions as a rich resource for biomarkers predictive of drug response.

Indexed as

Drug Screening Assays, AntitumorPharmacogeneticsAlternative SplicingAntineoplastic AgentsBenzimidazolesBiomarkersBreast NeoplasmsCarrier ProteinsCell Adhesion MoleculesChemistry, PharmaceuticalErlotinib HydrochlorideGenome, HumanHumansIntegrin beta ChainsLapatinibNectinsAntineoplastic AgentsAZD 6244BenzimidazolesBiomarkersCarrier ProteinsCell Adhesion MoleculesErlotinib HydrochlorideIGF2BP2 protein, humanintegrin beta6Integrin beta ChainsKLHDC9 protein, humanLapatinibNECTIN4 protein, humanNectinsPaclitaxelProtein IsoformsQuinazolinesRNA-Binding ProteinsRNA, Messenger

Identifiers

PMID29066719
PMCPMC5655668

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.