Evidence map›Paper›PMID 28446242›Full record

ArticleGenome medicine2017

Activity of distinct growth factor receptor network components in breast tumors uncovers two biologically relevant subtypes.

Mumtahena Rahman, Shelley M MacNeil, David F Jenkins, Gajendra Shrestha, Sydney R Wyatt, Jasmine A McQuerry, Stephen R Piccolo, Laura M Heiser, Joe W Gray, W Evan Johnson and 1 more

Open access · goldAbstract read
In one paragraph

Article in Genome medicine, 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 23% 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, 19 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

11 authors at 4 institutions in 1 country.

Mumtahena RahmanDepartment of Pharmacology and Toxicology, University of Utah, 30 S 2000 E, Salt Lake City, UT, 84108, USA.
Shelley M MacNeilDepartment of Pharmacology and Toxicology, University of Utah, 30 S 2000 E, Salt Lake City, UT, 84108, USA.
David F JenkinsDivision of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA.
Gajendra ShresthaDepartment of Pharmacology and Toxicology, University of Utah, 30 S 2000 E, Salt Lake City, UT, 84108, USA.
Sydney R WyattDepartment of Pharmacology and Toxicology, University of Utah, 30 S 2000 E, Salt Lake City, UT, 84108, USA.
Jasmine A McQuerryDepartment of Pharmacology and Toxicology, University of Utah, 30 S 2000 E, Salt Lake City, UT, 84108, USA.
Stephen R PiccoloDepartment of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
Laura M HeiserDepartment of Biomedical Engineering, Center for Spatial Systems Biomedicine, Knight Cancer Institute, Oregon Health and Sciences University, Portland, OR, USA.
Joe W GrayDepartment of Biomedical Engineering, Center for Spatial Systems Biomedicine, Knight Cancer Institute, Oregon Health and Sciences University, Portland, OR, USA.
W Evan JohnsonDepartment of Oncological Sciences, University of Utah, Salt Lake City, UT, USA.
Andrea H BildDepartment of Pharmacology and Toxicology, University of Utah, 30 S 2000 E, Salt Lake City, UT, 84108, USA. andreab@genetics.utah.edu.
University of Utah · USBoston University · USOregon Health & Science University · USBrigham Young University · US

Funding

UNIVERSITY OF UTAH MEDICAL INFORMATICS TRAININGT15LM007124 · NLM · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · PI Karen Louise Eilbeck · 1997 to 2026
$22.0M
Project 2: Targeting Cooperative Phenotypes Common in Spatial HeterogeneityU54CA209978 · NCI · UNIVERSITY OF UTAH · PI BILD, ANDREA HOPE · 2017 to 2021
$10.5M
Integrative signaling models to decipher complex cancer phenotypesU01CA164720 · NCI · UNIVERSITY OF UTAH · PI BILD, ANDREA HOPE, GRAY, JOE W. · 2012 to 2016
$2.9M
Removing batch effects in high-throughput biomedical studiesR01GM127430 · NIGMS · RUTGERS BIOMEDICAL AND HEALTH SCIENCES · PI William Evan Johnson · 2018 to 2026
$2.4M
Integrative analyses of reference epigenomic maps and applicationsR01ES025002 · NIEHS · BOSTON UNIVERSITY MEDICAL CAMPUS · PI JOHNSON, WILLIAM EVAN · 2014 to 2015
$659k
NCI NIH HHS U01 CA164720NCI NIH HHS U54 CA209978NIEHS NIH HHS R01 ES025002NIGMS NIH HHS R01 GM127430NLM NIH HHS T15 LM007124
6 · The paper itself

Abstract

backgroundThe growth factor receptor network (GFRN) plays a significant role in driving key oncogenic processes. However, assessment of global GFRN activity is challenging due to complex crosstalk among GFRN components, or pathways, and the inability to study complex signaling networks in patient tumors. Here, pathway-specific genomic signatures were used to interrogate GFRN activity in breast tumors and the consequent phenotypic impact of GRFN activity patterns.

methodsNovel pathway signatures were generated in human primary mammary epithelial cells by overexpressing key genes from GFRN pathways (HER2, IGF1R, AKT1, EGFR, KRAS (G12V), RAF1, BAD). The pathway analysis toolkit Adaptive Signature Selection and InteGratioN (ASSIGN) was used to estimate pathway activity for GFRN components in 1119 breast tumors from The Cancer Genome Atlas (TCGA) and across 55 breast cancer cell lines from the Integrative Cancer Biology Program (ICBP43). These signatures were investigated for their relationship to pro- and anti-apoptotic protein expression and drug response in breast cancer cell lines.

resultsApplication of these signatures to breast tumor gene expression data identified two novel discrete phenotypes characterized by concordant, aberrant activation of either the HER2, IGF1R, and AKT pathways ("the survival phenotype") or the EGFR, KRAS (G12V), RAF1, and BAD pathways ("the growth phenotype"). These phenotypes described a significant amount of the variability in the total expression data across breast cancer tumors and characterized distinctive patterns in apoptosis evasion and drug response. The growth phenotype expressed lower levels of BIM and higher levels of MCL-1 proteins. Further, the growth phenotype was more sensitive to common chemotherapies and targeted therapies directed at EGFR and MEK. Alternatively, the survival phenotype was more sensitive to drugs inhibiting HER2, PI3K, AKT, and mTOR, but more resistant to chemotherapies.

conclusionsGene expression profiling revealed a bifurcation pattern in GFRN activity represented by two discrete phenotypes. These phenotypes correlate to unique mechanisms of apoptosis and drug response and have the potential of pinpointing targetable aberration(s) for more effective breast cancer treatments.

Indexed as

Gene Expression Regulation, NeoplasticGenes, NeoplasmReceptors, Growth FactorSignal TransductionBreast NeoplasmsFemaleGene Expression ProfilingHumansReceptors, Growth FactorBreast cancerCancer phenotypesGene expression signaturesGenomicsGrowth factor receptor networkTargeted therapy

Identifiers

PMID28446242
PMCPMC5406893
OpenAlexW2608769745

What OpenQuestion holds

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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.