Evidence map›Paper›PMID 28716134›Full record

ArticleGenome medicine2017

Detecting protein variants by mass spectrometry: a comprehensive study in cancer cell-lines.

Javier A Alfaro, Alexandr Ignatchenko, Vladimir Ignatchenko, Ankit Sinha, Paul C Boutros, Thomas Kislinger

Open access · goldAbstract read
In one paragraph

Article in Genome medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
3.9field-weighted citation impact, top 6% of its field
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

23 citing papers in PubMed, 60 citations in OpenAlex.

  1. Article
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  6. Multi-omic stratification of the missense variant cysteinome.bioRxiv : the preprint server for biology · 2023
    Article
  7. Review
  8. Statistical Methodology for Ribosomal Frameshift Detection.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2022
    Article
  9. Review
  10. Article
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  12. Cancer Conformational Landscape Shapes Tumorigenesis.Journal of proteome research · 2022
    Article
  13. Article
  14. Article
  15. Review
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  18. Article
  19. PrecisionMolecular & cellular proteomics : MCP · 2019
    Article
  20. Clinical potential of mass spectrometry-based proteogenomics.Nature reviews. Clinical oncology · 2019
    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

6 authors at 4 institutions in 1 country.

Javier A AlfaroInformatics Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada.
Alexandr IgnatchenkoPrincess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
Vladimir IgnatchenkoPrincess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
Ankit SinhaDepartment of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
Paul C BoutrosInformatics Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada. Paul.Boutros@oicr.on.ca.
Thomas KislingerDepartment of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada. thomas.kislinger@utoronto.ca.
Princess Margaret Cancer Centre · CAUniversity of Toronto · CAOntario Institute for Cancer Research · CAUniversity Health Network · CA

Funding

Development of Protein Biomarkers in Post-DRE Urine for use in Liquid Biopsy of Prostate CancerU01CA214194 · NCI · EASTERN VIRGINIA MEDICAL SCHOOL · PI BOUTROS, PAUL CHRISTOPHER, KISLINGER, THOMAS · 2016 to 2021
$2.5M
NCI NIH HHS U01 CA214194
6 · The paper itself

Abstract

backgroundOnco-proteogenomics aims to understand how changes in a cancer's genome influences its proteome. One challenge in integrating these molecular data is the identification of aberrant protein products from mass-spectrometry (MS) datasets, as traditional proteomic analyses only identify proteins from a reference sequence database.

methodsWe established proteomic workflows to detect peptide variants within MS datasets. We used a combination of publicly available population variants (dbSNP and UniProt) and somatic variations in cancer (COSMIC) along with sample-specific genomic and transcriptomic data to examine proteome variation within and across 59 cancer cell-lines.

resultsWe developed a set of recommendations for the detection of variants using three search algorithms, a split target-decoy approach for FDR estimation, and multiple post-search filters. We examined 7.3 million unique variant tryptic peptides not found within any reference proteome and identified 4771 mutations corresponding to somatic and germline deviations from reference proteomes in 2200 genes among the NCI60 cell-line proteomes.

conclusionsWe discuss in detail the technical and computational challenges in identifying variant peptides by MS and show that uncovering these variants allows the identification of druggable mutations within important cancer genes.

Indexed as

MutationPolymorphism, GeneticAlgorithmsCell Line, TumorHumansMass SpectrometryNeoplasm ProteinsProtein IsoformsProteomicsWorkflowNeoplasm ProteinsProtein IsoformsIntegrative –omicsMass-spectrometry-based mutant detectionPersonalized proteomicsProtein mutant detectionProtein search databasesProteoformsProteogenomicsProteomics

Identifiers

PMID28716134
PMCPMC5514513
OpenAlexW2732144023

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.