Evidence map›Paper›PMID 25710561›Full record

ArticlePloS one2015

mRNA profiling reveals determinants of trastuzumab efficiency in HER2-positive breast cancer.

Silvia von der Heyde, Steve Wagner, Alexander Czerny, Manuel Nietert, Fabian Ludewig, Gabriela Salinas-Riester, Dorit Arlt, Tim Beißbarth

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
2.6field-weighted citation impact, top 9% 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

26 citing papers in PubMed, 48 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

8 authors at 3 institutions in 1 country.

Silvia von der HeydeStatistical Bioinformatics, Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany; IndivuTest GmbH, Hamburg, Germany.
Steve WagnerDivision of Stem Cells and Cancer, German Cancer Research Center, Heidelberg, Germany; Division of Molecular Genome Analysis, German Cancer Research Center, Heidelberg, Germany.
Alexander CzernyStatistical Bioinformatics, Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.
Manuel NietertStatistical Bioinformatics, Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.
Fabian LudewigDNA Microarray and Deep-Sequencing Facility Göttingen, Department of Developmental Biochemistry, University of Göttingen, Göttingen, Germany.
Gabriela Salinas-RiesterDNA Microarray and Deep-Sequencing Facility Göttingen, Department of Developmental Biochemistry, University of Göttingen, Göttingen, Germany.
Dorit ArltDivision of Stem Cells and Cancer, German Cancer Research Center, Heidelberg, Germany; Division of Molecular Genome Analysis, German Cancer Research Center, Heidelberg, Germany.
Tim BeißbarthStatistical Bioinformatics, Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.
University of Göttingen · DEGerman Cancer Research Center · DEUniversitätsmedizin Göttingen · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Intrinsic and acquired resistance to the monoclonal antibody drug trastuzumab is a major problem in the treatment of HER2-positive breast cancer. A deeper understanding of the underlying mechanisms could help to develop new agents. Our intention was to detect genes and single nucleotide polymorphisms (SNPs) affecting trastuzumab efficiency in cell culture. Three HER2-positive breast cancer cell lines with different resistance phenotypes were analyzed. We chose BT474 as model of trastuzumab sensitivity, HCC1954 as model of intrinsic resistance, and BTR50, derived from BT474, as model of acquired resistance. Based on RNA-Seq data, we performed differential expression analyses on these cell lines with and without trastuzumab treatment. Differentially expressed genes between the resistant cell lines and BT474 are expected to contribute to resistance. Differentially expressed genes between untreated and trastuzumab treated BT474 are expected to contribute to drug efficacy. To exclude false positives from the candidate gene set, we removed genes that were also differentially expressed between untreated and trastuzumab treated BTR50. We further searched for SNPs in the untreated cell lines which could contribute to trastuzumab resistance. The analysis resulted in 54 differentially expressed candidate genes that might be connected to trastuzumab efficiency. 90% of 40 selected candidates were validated by RT-qPCR. ALPP, CALCOCO1, CAV1, CYP1A2 and IGFBP3 were significantly higher expressed in the trastuzumab treated than in the untreated BT474 cell line. GDF15, IL8, LCN2, PTGS2 and 20 other genes were significantly higher expressed in HCC1954 than in BT474, while NCAM2, COLEC12, AFF3, TFF3, NRCAM, GREB1 and TFF1 were significantly lower expressed. Additionally, we inferred SNPs in HCC1954 for CAV1, PTGS2, IL8 and IGFBP3. The latter also had a variation in BTR50. 20% of the validated subset have already been mentioned in literature. For half of them we called and analyzed SNPs. These results contribute to a better understanding of trastuzumab action and resistance mechanisms.

Indexed as

Antibodies, Monoclonal, HumanizedAntineoplastic AgentsBreast NeoplasmsCaveolin 1Cell Line, TumorCell SurvivalDrug Resistance, NeoplasmErb-b2 Receptor Tyrosine KinasesFemaleHumansPolymorphism, Single NucleotidePrincipal Component AnalysisReal-Time Polymerase Chain ReactionRNA, MessengerSequence Analysis, RNATrastuzumabAntibodies, Monoclonal, HumanizedAntineoplastic AgentsCAV1 protein, humanCaveolin 1Erb-b2 Receptor Tyrosine KinasesRNA, MessengerTrastuzumab

Identifiers

PMID25710561
PMCPMC4339844
OpenAlexW1968057579

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.