Evidence map›Paper›PMID 25350482›Full record

ArticleJournal of proteome research2015

Comprehensive quantitative analysis of ovarian and breast cancer tumor peptidomes.

Zhe Xu, Chaochao Wu, Fang Xie, Gordon W Slysz, Nikola Tolic, Matthew E Monroe, Vladislav A Petyuk, Samuel H Payne, Grant M Fujimoto, Ronald J Moore and 13 more

Abstract read
In one paragraph

Article in Journal of proteome research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–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

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Limitations of Mass Spectrometry-Based Peptidomic Approaches.Journal of the American Society for Mass Spectrometry · 2015
    Pooled it
  2. Article
  3. Surveying the sequence diversity of model prebiotic peptides by mass spectrometry.Proceedings of the National Academy of Sciences of the United States of America · 2017
    Article
  4. Article
  5. Review
  6. Article
  7. An Optimized Informatics Pipeline for Mass Spectrometry-Based Peptidomics.Journal of the American Society for Mass Spectrometry · 2015
    Article
  8. Review
  9. 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

23 authors.

Zhe XuBiological Sciences Division and ‡Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory , Richland, Washington 99354, United States.
Chaochao Wu
Fang Xie
Gordon W Slysz
Nikola Tolic
Matthew E Monroe
Vladislav A Petyuk
Samuel H Payne
Grant M Fujimoto
Ronald J Moore
Thomas L Fillmore
Athena A Schepmoes
Douglas A Levine
R Reid Townsend
Sherri R Davies
Shunqiang Li
Matthew Ellis
Emily Boja
Robert Rivers
Henry Rodriguez
Karin D Rodland
Tao Liu
Richard D Smith

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Nicholas D Socci · 1985 to 2026
$347.4M
Washington University Center for Cellular ImagingP30CA091842 · NCI · WASHINGTON UNIVERSITY · PI TIMOTHY J. EBERLEIN · 2001 to 2026
$128.0M
Washington University Institute of Clinical and Translational SciencesUL1TR000448 · NCATS · WASHINGTON UNIVERSITY · PI EVANOFF, BRADLEY A · 2012 to 2016
$41.4M
Equipment Supplement Request for a Research Resource for Ultra-sensitive High Throughput ProteomicsP41GM103493 · NIGMS · BATTELLE PACIFIC NORTHWEST LABORATORIES · PI SMITH, RICHARD D · 2012 to 2022
$22.6M
PNNL Clinical Proteome Characterization CenterU24CA160019 · NCI · BATTELLE PACIFIC NORTHWEST LABORATORIES · PI RODLAND, KARIN D, SMITH, RICHARD D · 2011 to 2016
$15.7M
Cancer Proteome Center at Washington Univ, Univ of North Carolina & Boise StateU24CA160035 · NCI · WASHINGTON UNIVERSITY · PI CHEN, XIAN, ELLIS, MATTHEW J · 2011 to 2016
$11.8M
NCATS NIH HHS UL1 TR000448NCI NIH HHS P30 CA008748NCI NIH HHS P30 CA091842NCI NIH HHS U24 CA160019NCI NIH HHS U24CA160019NCI NIH HHS U24 CA160035NIGMS NIH HHS P41 GM103493NIGMS NIH HHS P41GM103493
6 · The paper itself

Abstract

Aberrant degradation of proteins is associated with many pathological states, including cancers. Mass spectrometric analysis of tumor peptidomes, the intracellular and intercellular products of protein degradation, has the potential to provide biological insights on proteolytic processing in cancer. However, attempts to use the information on these smaller protein degradation products from tumors for biomarker discovery and cancer biology studies have been fairly limited to date, largely due to the lack of effective approaches for robust peptidomics identification and quantification and the prevalence of confounding factors and biases associated with sample handling and processing. Herein, we have developed an effective and robust analytical platform for comprehensive analyses of tissue peptidomes, which is suitable for high-throughput quantitative studies. The reproducibility and coverage of the platform, as well as the suitability of clinical ovarian tumor and patient-derived breast tumor xenograft samples with postexcision delay of up to 60 min before freezing for peptidomics analysis, have been demonstrated. Moreover, our data also show that the peptidomics profiles can effectively separate breast cancer subtypes, reflecting tumor-associated protease activities. Peptidomics complements results obtainable from conventional bottom-up proteomics and provides insights not readily obtainable from such approaches.

Indexed as

Breast NeoplasmsChromatography, LiquidFemaleGene Expression Regulation, NeoplasticHumansOvarian NeoplasmsPeptidesProteomeProteomicsTandem Mass SpectrometryTime FactorsPeptidesProteomebreast cancerischemiaovarian cancerpeptidomicsproteasesprotein degradationtumor

Identifiers

PMID25350482
PMCPMC4286152

What OpenQuestion holds

Textmetadata
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.