Evidence map›Paper›PMID 30117066›Full record

SynthesisBreast cancer research and treatment2018

Co-expressed genes enhance precision of receptor status identification in breast cancer patients.

Michael Kenn, Dan Cacsire Castillo-Tong, Christian F Singer, Michael Cibena, Heinz Kölbl, Wolfgang Schreiner

Open access · hybridAbstract readMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Breast cancer research and treatment, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.5field-weighted citation impact, top 37% 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

5 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. 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.

Michael KennSection of Biosimulation and Bioinformatics, Center for Medical Statistics, Informatics and Intelligent Systems (CeMSIIS), Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria.
Dan Cacsire Castillo-TongTranslational Gynecology Group, Department of Obstetrics and Gynecology, Comprehensive Cancer Center, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
Christian F SingerTranslational Gynecology Group, Department of Obstetrics and Gynecology, Comprehensive Cancer Center, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
Michael CibenaSection of Biosimulation and Bioinformatics, Center for Medical Statistics, Informatics and Intelligent Systems (CeMSIIS), Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria.
Heinz KölblDepartment of General Gynecology and Gynecologic Oncology, and Comprehensive Cancer Center, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
Wolfgang SchreinerSection of Biosimulation and Bioinformatics, Center for Medical Statistics, Informatics and Intelligent Systems (CeMSIIS), Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria. wolfgang.schreiner@meduniwien.ac.at.ORCID http://orcid.org/0000-0003-1910-5788
Comprehensive Cancer Center Vienna · ATMedical University of Vienna · ATStatistics Austria · AT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTherapeutic decisions in breast cancer patients crucially depend on the status of estrogen receptor, progesterone receptor and HER2, obtained by immunohistochemistry (IHC). These are known to be inaccurate sometimes, and we demonstrate how to use gene-expression to increase precision of receptor status.

methodsWe downloaded data from 3241 breast cancer patients out of 36 clinical studies. For each receptor, we modelled the mRNA expression of the receptor gene and a co-gene by logistic regression. For each patient, predictions from logistic regression were merged with information from IHC on a probabilistic basis to arrive at a fused prediction result.

resultsWe introduce Sankey diagrams to visualize the step by step increase of precision as information is added from gene expression: IHC-estimates are qualified as 'confirmed', 'rejected' or 'corrected'. Additionally, we introduce the category 'inconclusive' to spot those patients in need for additional assessments so as to increase diagnostic precision and safety.

conclusionsWe demonstrate a sound mathematical basis for the fusion of information, even if partly contradictive. The concept is extendable to more than three sources of information, as particularly important for OMICS data. The overall number of undecidable cases is reduced as well as those assessed falsely. We outline how decision rules may be extended to also weigh consequences, being different in severity for false-positive and false-negative assessments, respectively. The possible benefit is demonstrated by comparing the disease free survival between patients whose IHC could be confirmed versus those for which it was corrected.

Indexed as

Biomarkers, TumorBreast NeoplasmsCarboxylic Ester HydrolasesDisease-Free SurvivalErb-b2 Receptor Tyrosine KinasesEstrogen Receptor alphaFemaleGene Expression Regulation, NeoplasticHumansPrecision MedicineReceptors, Cell SurfaceReceptors, ProgesteroneBiomarkers, TumorCarboxylic Ester HydrolasesERBB2 protein, humanErb-b2 Receptor Tyrosine KinasesESR1 protein, humanEstrogen Receptor alphaPGAP3 protein, humanReceptors, Cell SurfaceReceptors, ProgesteroneBreast cancerData scienceGene expressionMathematical oncologyPrecision medicineReceptor status

Identifiers

PMID30117066
PMCPMC6208909
OpenAlexW2885492917

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.