Evidence map›Paper›PMID 29075496›Full record

ArticleBiomarker research2017

Assessing biological and technological variability in protein levels measured in pre-diagnostic plasma samples of women with breast cancer.

Christine Y Yeh, Ravali Adusumilli, Majlinda Kullolli, Parag Mallick, Esther M John, Sharon J Pitteri

Open access · goldAbstract read
In one paragraph

Article in Biomarker research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
0.9field-weighted citation impact, top 29% 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

10 citing papers in PubMed, 15 citations in OpenAlex.

  1. Trial
  2. Protein Variability Patterns in Ovarian Serous Carcinoma.International journal of molecular sciences · 2026
    Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Finding a Needle in a Haystack: Proteomics in Heart Failure.JACC. Basic to translational science · 2020
    Review
  10. 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

6 authors at 3 institutions in 1 country.

Christine Y Yeh *Department of Biomedical Informatics, Stanford University School of Medicine, Stanford, CA 93405 USA.
Ravali Adusumilli *Department of Radiology, Canary Center at Stanford for Cancer Early Detection, Stanford University School of Medicine, Palo Alto, CA 94304 USA.
Majlinda KullolliDepartment of Radiology, Canary Center at Stanford for Cancer Early Detection, Stanford University School of Medicine, Palo Alto, CA 94304 USA.
Parag MallickDepartment of Radiology, Canary Center at Stanford for Cancer Early Detection, Stanford University School of Medicine, Palo Alto, CA 94304 USA.
Esther M JohnStanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94305 USA.
Sharon J PitteriDepartment of Radiology, Canary Center at Stanford for Cancer Early Detection, Stanford University School of Medicine, Palo Alto, CA 94304 USA.ORCID 0000-0002-3119-873X
Stanford University · USCancer Prevention Institute of California · USStanford Medicine · US

Funding

Breast Cancer Family Registry CohortUM1CA164920 · NCI · CANCER PREVENTION INSTIT OF CALIFORNIA · PI ANDRULIS, IRENE, BUYS, SAUNDRA S · 2012 to 2017
$12.7M
NCI NIH HHS UM1 CA164920
6 · The paper itself

Abstract

backgroundQuantitative proteomics allows for the discovery and functional investigation of blood-based pre-diagnostic biomarkers for early cancer detection. However, a major limitation of proteomic investigations in biomarker studies remains the biological and technical variability in the analysis of complex clinical samples. Moreover, unlike 'omics analogues such as genomics and transcriptomics, proteomics has yet to achieve reproducibility and long-term stability on a unified technological platform. Few studies have thoroughly investigated protein variability in pre-diagnostic samples of cancer patients across multiple platforms.

methodsWe obtained ten blood plasma "case" samples collected up to 2 years prior to breast cancer diagnosis. Each case sample was paired with a matched control plasma from a full biological sister without breast cancer. We measured protein levels using both mass-spectrometry and antibody-based technologies to: (1) assess the technical considerations in different protein assays when analyzing limited clinical samples, and (2) evaluate the statistical power of potential diagnostic analytes.

resultsAlthough we found inherent technical variation in the three assays used, we detected protein dependent biological signal from the limited samples. The three assay types yielded 32 proteins with statistically significantly (

conclusionsTechnical, practical, and study design considerations are essential to maximize information obtained in limited pre-diagnostic samples of cancer patients. This study provides a framework that estimates biological effect sizes critical for consideration in designing studies for pre-diagnostic blood-based biomarker detection.

Indexed as

Blood plasmaBreast cancerImmunoassayMass spectrometryProtein

Identifiers

PMID29075496
PMCPMC5645980
OpenAlexW2766809519

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.