Evidence map›Paper›PMID 38924463›Full record

ArticleCancer research2024

Proteomics Meets Dose Response: A New Paradigm for Deciphering Drug Effects.

Chenwei Wang, Bing Zhang

Abstract read
In one paragraph

Article in Cancer research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Chenwei WangLester and Sue Smith Breast Center, Baylor College of Medicine, Houston, Texas.ORCID 0000-0002-0920-8639
Bing ZhangLester and Sue Smith Breast Center, Baylor College of Medicine, Houston, Texas.ORCID 0000-0001-8676-2425

Funding

Translating Pediatric Cancer Proteogenomic Data into Biological and Clinical InsightsU24CA271076 · NCI · BAYLOR COLLEGE OF MEDICINE · PI Bing Zhang · 2022 to 2026
$5.0M
NCI NIH HHS U24 CA271076
6 · The paper itself

Abstract

In a groundbreaking study, Eckert and colleagues introduce DecryptE, an innovative approach to dose-resolved proteomics that significantly advances our understanding of drug effects at the proteomic level. This method integrates cutting edge sample preparation and mass spectrometry technologies, establishing a robust platform for high-throughput proteome analysis. DecryptE enables the quantification of more than 7,000 proteins per hour and was employed to study 144 clinical drugs and research compounds, generating more than 1 million dose-response curves using Jurkat acute T-cell leukemia cells as a model system. The platform demonstrates outstanding reproducibility, ensuring reliable and consistent results across multiple experiments. By providing detailed information on drug potency and efficacy, DecryptE allows the identification of subtle changes in protein expression and facilitates the clustering of drugs based on their proteomic profiles. This study not only reveals novel drug mechanisms but also creates a comprehensive resource that can be utilized by the broader research community. Furthermore, it highlights the potential of integrating proteomics-and potentially other omics modalities in the future-with dose-response analysis to advance pharmacological research and improve therapeutic strategies.

Indexed as

Dose-Response Relationship, DrugProteomicsAntineoplastic AgentsHumansJurkat CellsMass SpectrometryProteomeAntineoplastic AgentsProteome

Identifiers

PMID38924463
PMCPMC12010437

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.