Evidence map›Paper›PMID 30790462›Full record

ReviewProteomics. Clinical applications2019

The Case for Proteomics and Phospho-Proteomics in Personalized Cancer Medicine.

Sophia Doll, Florian Gnad, Matthias Mann

Abstract readReview
In one paragraph

Review in Proteomics. Clinical applications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.

0numbers the graph read from it
0cells of the map it votes in
60citing 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

60 citing papers in PubMed.

  1. Article
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  14. Circulating biomarkers for diagnosis and response to therapies in cancer patients.International review of cell and molecular biology · 2025
    Review
  15. Review
  16. Article
  17. Review
  18. Special Issue "Deployment of Proteomics Approaches in Biomedical Research".International journal of molecular sciences · 2024
    Article
  19. Article
  20. 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

3 authors.

Sophia DollDepartment of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, 82152, Martinsried, Germany.
Florian GnadDepartment of Bioinformatics and Computational Biology, Cell Signaling Technology Inc, 01923, Danvers, MA, USA.
Matthias MannDepartment of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, 82152, Martinsried, Germany.

Funding

Max Planck Society for the Advancement of Science and the Novo Nordisk Foundation NNF15CC0001
6 · The paper itself

Abstract

The concept of personalized medicine is predominantly been pursued through genomic and transcriptomic technologies, leading to the identification of multiple mutations in a large variety of cancers. However, it has proven challenging to distinguish driver and passenger mutations and to deal with tumor heterogeneity and resistant clonal populations. More generally, these heterogeneous mutation patterns do not in themselves predict the tumor phenotype. Analysis of the expressed proteins in a tumor and their modification states reveals if and how these mutations are translated to the functional level. It is already known that proteomic changes including posttranslational modifications are crucial drivers of oncogenesis, but proteomics technology has only recently become comparable in depth and accuracy to RNAseq. These advances also allow the rapid and highly sensitive analysis of formalin-fixed and paraffin-embedded biobank tissues, on both the proteome and phosphoproteome levels. In this perspective, pioneering mass spectrometry-based proteomic studies are highlighted that pave the way toward clinical implementation. It is argued that proteomics and phosphoproteomics could provide the missing link to make omics analysis actionable in the clinic.

Indexed as

HumansMass SpectrometryNeoplasmsPhosphoproteinsPrecision MedicineProteomicsPhosphoproteinsclinical proteomicsmass spectrometryoncology

Identifiers

PMID30790462
PMCPMC6519247

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.