Evidence map›Paper›PMID 24886063›Full record

ArticleJournal of translational medicine2014

Mining the pre-diagnostic antibody repertoire of TgMMTV-neu mice to identify autoantibodies useful for the early detection of human breast cancer.

Jianning Mao, Jon Ladd, Ekram Gad, Lauren Rastetter, Melissa M Johnson, Edmond Marzbani, Jennifer S Childs, Hailing Lu, Yushe Dang, Elizabeth Broussard and 3 more

Open access · goldAbstract read
In one paragraph

Article in Journal of translational medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
0.7field-weighted citation impact, top 31% 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

16 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.

  1. Pooled it
  2. ATRA upregulates OTUD6B to recruit CD8Cell death & disease · 2025
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  6. Breast cancer vaccines for treatment and prevention.Breast cancer research and treatment · 2022
    Review
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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

13 authors at 4 institutions in 1 country.

Jianning Mao
Jon Ladd
Ekram Gad
Lauren Rastetter
Melissa M Johnson
Edmond Marzbani
Jennifer S Childs
Hailing Lu
Yushe Dang
Elizabeth Broussard
Sasha E Stanton
Sam M Hanash
Mary L DisisTumor Vaccine Group, Center for Translational Medicine in Women's Health, University of Washington, 850 Republican Street, Box 358050, Seattle, WA 98109, USA. ndisis@uw.edu.
Institute of Translational Health Sciences · USUniversity of Washington · USFred Hutch Cancer Center · USThe University of Texas MD Anderson Cancer Center · US

Funding

TRAINING IN CANCER BIOLOGY &TRANSPLANTATIONT32CA009515 · NCI · UNIVERSITY OF WASHINGTON · PI NANCY ELLEN DAVIDSON, Effie W Petersdorf · 1985 to 2026
$16.2M
Immune diagnostics and vaccine targets: breast cancer antigens from mice to womenU01CA141539 · NCI · UNIVERSITY OF WASHINGTON · PI DISIS, MARY L. · 2009 to 2013
$3.7M
Preventive Interventions Leadership U01U01CA141550 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI HANASH, SAMIR M · 2009 to 2013
$2.0M
NCI NIH HHS T32 CA009515NCI NIH HHS U01CA141539NCI NIH HHS U01 CA141550
6 · The paper itself

Abstract

backgroundThe use of autoantibodies for the early detection of breast cancer has generated much interest as antibodies can be readily assayed in serum when antigen levels are low. Ideally, diagnostic autoantibodies would be identified in individuals who harbored pre-invasive disease/high risk lesions leading to malignancy. Prospectively collected human serum samples from these individuals are rare and not often available for biomarker discovery. We questioned whether transgenic animals could be used to identify cancer-associated autoantibodies present at the earliest stages of the malignant transformation of breast cancer.

methodsWe collected sera from transgenic mice (TgMMTV-neu) from the time of birth to death by spontaneous mammary tumors. Using sera from a time point prior to the development of tumor, i.e. "pre-diagnostic", we probed cDNA libraries derived from syngeneic tumors to identify proteins recognized by IgG antibodies. Once antigens were identified, selected proteins were evaluated via protein arrays, for autoantibody responses using plasma from women obtained prior to the development of breast cancer and matched controls. The ability of the antigens to discriminate cases from controls was assessed using receiver-operating-characteristic curve analyses and estimates of the area under the curve.

resultsWe identified 6 autoantibodies that were present in mice prior to the development of mammary cancer: Pdhx, Otud6b, Stk39, Zpf238, Lgals8, and Vps35. In rodent validation cohorts, detecting both IgM and IgG antibody responses against a subset of the identified proteins could discriminate pre-diagnostic sera from non-transgenic control sera with an AUC of 0.924. IgG and IgM autoantibodies, specific for a subset of the identified antigens, could discriminate the samples of women who eventually developed breast cancer from case-matched controls who did not develop disease. The discriminatory potential of the pre-diagnostic autoantibodies was enhanced if plasma samples were collected greater than 5 months prior to a breast cancer diagnosis (AUC 0.68; CI 0.565-0.787, p=0.0025).

conclusionGenetically engineered mouse models of cancer may provide a facile discovery tool for identifying autoantibodies useful for human cancer diagnostics.

Indexed as

Genes, erbB-2AnimalsAutoantibodiesBiomarkers, TumorBreast NeoplasmsEarly DiagnosisFemaleHumansMiceMice, TransgenicAutoantibodiesBiomarkers, Tumor

Identifiers

PMID24886063
PMCPMC4022541
OpenAlexW2077219444

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.