Evidence map›Paper›PMID 22110952›Full record

ArticleInternational journal of proteomics2011

Proteomic-based biosignatures in breast cancer classification and prediction of therapeutic response.

Jianbo He, Stephen A Whelan, Ming Lu, Dejun Shen, Debra U Chung, Romaine E Saxton, Kym F Faull, Julian P Whitelegge, Helena R Chang

Open access · hybridAbstract read
In one paragraph

Article in International journal of proteomics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
1.1field-weighted citation impact, top 24% 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

20 citing papers in PubMed, 34 citations in OpenAlex.

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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

9 authors at 3 institutions in 1 country.

Jianbo HeGonda/UCLA Breast Cancer Research Laboratory, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095, USA.
Stephen A Whelan
Ming Lu
Dejun Shen
Debra U Chung
Romaine E Saxton
Kym F Faull
Julian P Whitelegge
Helena R Chang
University of California, Los Angeles · USBeth Israel Deaconess Medical Center · USBoston University · US

Funding

Ethnicity-based proteomic biomarkers in breast cancerR01CA093736 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI CHANG, HELENA R · 2002 to 2004
$1.5M
NCI NIH HHS R01 CA093736
6 · The paper itself

Abstract

Protein-based markers that classify tumor subtypes and predict therapeutic response would be clinically useful in guiding patient treatment. We investigated the LC-MS/MS-identified protein biosignatures in 39 baseline breast cancer specimens including 28 HER2-positive and 11 triple-negative (TNBC) tumors. Twenty proteins were found to correctly classify all HER2 positive and 7 of the 11 TNBC tumors. Among them, galectin-3-binding protein and ALDH1A1 were found preferentially elevated in TNBC, whereas CK19, transferrin, transketolase, and thymosin β4 and β10 were elevated in HER2-positive cancers. In addition, several proteins such as enolase, vimentin, peroxiredoxin 5, Hsp 70, periostin precursor, RhoA, cathepsin D preproprotein, and annexin 1 were found to be associated with the tumor responses to treatment within each subtype. The MS-based proteomic findings appear promising in guiding tumor classification and predicting response. When sufficiently validated, some of these candidate protein markers could have great potential in improving breast cancer treatment.

Identifiers

PMID22110952
PMCPMC3202144
OpenAlexW2086526592

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Registered trials

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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.