Evidence map›Paper›PMID 28193205›Full record

ArticleBMC cancer2017

Comparison of immunohistochemistry with PCR for assessment of ER, PR, and Ki-67 and prediction of pathological complete response in breast cancer.

Hans-Peter Sinn, Andreas Schneeweiss, Marius Keller, Kornelia Schlombs, Mark Laible, Julia Seitz, Sotirios Lakis, Elke Veltrup, Peter Altevogt, Sebastian Eidt and 2 more

Open access · goldAbstract readComparative Study
In one paragraph

Article in BMC cancer, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.

0numbers the graph read from it
0cells of the map it votes in
51citing papers in PubMed
3.3field-weighted citation impact, top 7% 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

51 citing papers in PubMed, 88 citations in OpenAlex.

  1. Article
  2. Review
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  8. SCGAN: Sparse CounterGAN for Counterfactual Explanations in Breast Cancer Prediction.IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society · 2024
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  12. Extrahepatic Bile Duct Organoids as a Model to Study Ischemia/Reperfusion Injury During Liver Transplantation.Transplant international : official journal of the European Society for Organ Transplantation · 2024
    Article
  13. Article
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  15. Article
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  17. Review
  18. XPERTEcancermedicalscience · 2023
    Article
  19. Generating Counterfactual Explanations For Causal Inference in Breast Cancer Treatment Response.IEEE International Conference on Automation Science and Engineering (CASE) : [proceedings]. IEEE Conference on Automation Science and Engineering · 2022
    Article
  20. 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

12 authors at 5 institutions in 1 country.

Hans-Peter SinnInstitute of Pathology, University of Heidelberg, Im Neuenheimer Feld 220-221, 69120, Heidelberg, Germany. peter.sinn@med.uni-heidelberg.de.
Andreas SchneeweissNational Center for Tumor Diseases, University-Hospital Heidelberg, Im Neuenheimer Feld 460, 69120, Heidelberg, Germany.
Marius KellerInstitute of Pathology, University of Heidelberg, Im Neuenheimer Feld 220-221, 69120, Heidelberg, Germany.
Kornelia SchlombsBioNTech Diagnostics GmbH, 55131, Mainz, Germany.
Mark LaibleBioNTech Diagnostics GmbH, 55131, Mainz, Germany.
Julia SeitzNational Center for Tumor Diseases, University-Hospital Heidelberg, Im Neuenheimer Feld 460, 69120, Heidelberg, Germany.
Sotirios LakisSTRATIFYER Molecular Pathology GmbH, Werthmannstr. 1c, 50935, Köln, Germany.
Elke VeltrupSTRATIFYER Molecular Pathology GmbH, Werthmannstr. 1c, 50935, Köln, Germany.
Peter AltevogtGerman Cancer Research Center, Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.
Sebastian EidtDepartment of Pathology, St. Elisabeth-Krankenhaus, Werthmannstr. 1c, 50935, Köln, Germany.
Ralph M WirtzSTRATIFYER Molecular Pathology GmbH, Werthmannstr. 1c, 50935, Köln, Germany.
Frederik MarméNational Center for Tumor Diseases, University-Hospital Heidelberg, Im Neuenheimer Feld 460, 69120, Heidelberg, Germany.
National Center for Tumor Diseases · DEBioNTech (Germany) · DEHeidelberg University · DEKrankenhaus St. Elisabeth und St. Barbara · DEGerman Cancer Research Center · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundProliferation may predict response to neoadjuvant therapy of breast cancer and is commonly assessed by manual scoring of slides stained by immunohistochemistry (IHC) for Ki-67 similar to ER and PgR. This method carries significant intra- and inter-observer variability. Automatic scoring of Ki-67 with digital image analysis (qIHC) or assessment of MKI67 gene expression with RT-qPCR may improve diagnostic accuracy.

methodsKi-67 IHC visual assessment was compared to the IHC nuclear tool (AperioTM) on core biopsies from a randomized neoadjuvant clinical trial. Expression of ESR1, PGR and MKI67 by RT-qPCR was performed on RNA extracted from the same formalin-fixed paraffin-embedded tissue. Concordance between the three methods (vIHC, qIHC and RT-qPCR) was assessed for all 3 markers. The potential of Ki-67 IHC and RT-qPCR to predict pathological complete response (pCR) was evaluated using ROC analysis and non-parametric Mann-Whitney Test.

resultsCorrelation between methods (qIHC versus RT-qPCR) was high for ER and PgR (spearman´s r = 0.82, p < 0.0001 and r = 0.86, p < 0.0001, respectively) resulting in high levels of concordance using predefined cut-offs. When comparing qIHC of ER and PgR with RT-qPCR of ESR1 and PGR the overall agreement was 96.6 and 91.4%, respectively, while overall agreement of visual IHC with RT-qPCR was slightly lower for ER/ESR1 and PR/PGR (91.2 and 92.9%, respectively). In contrast, only a moderate correlation was observed between qIHC and RT-qPCR continuous data for Ki-67/MKI67 (Spearman's r = 0.50, p = 0.0001). Up to now no predictive cut-off for Ki-67 assessment by IHC has been established to predict response to neoadjuvant chemotherapy. Setting the desired sensitivity at 100%, specificity for the prediction of pCR (ypT0ypN0) was significantly higher for mRNA than for protein (68.9% vs. 22.2%). Moreover, the proliferation levels in patients achieving a pCR versus not differed significantly using MKI67 RNA expression (Mann-Whitney p = 0.002), but not with qIHC of Ki-67 (Mann-Whitney p = 0.097) or vIHC of Ki-67 (p = 0.131).

conclusionDigital image analysis can successfully be implemented for assessing ER, PR and Ki-67. IHC for ER and PR reveals high concordance with RT-qPCR. However, RT-qPCR displays a broader dynamic range and higher sensitivity than IHC. Moreover, correlation between Ki-67 qIHC and RT-qPCR is only moderate and RT-qPCR with MammaTyper® outperforms qIHC in predicting pCR. Both methods yield improvements to error-prone manual scoring of Ki-67. However, RT-qPCR was significantly more specific.

Indexed as

Antineoplastic Combined Chemotherapy ProtocolsBreast NeoplasmsCarcinoma, Ductal, BreastCarcinoma, LobularClinical Trials, Phase II as TopicErb-b2 Receptor Tyrosine KinasesFemaleHumansImmunoenzyme TechniquesKi-67 AntigenNeoadjuvant TherapyNeoplasm GradingNeoplasm StagingPrognosisRandomized Controlled Trials as TopicReal-Time Polymerase Chain ReactionErb-b2 Receptor Tyrosine KinasesKi-67 AntigenReceptors, EstrogenReceptors, ProgesteroneRNA, MessengerBreast cancerImage analysisImmunohistochemistry (IHC)Ki67MammaTyper®mRNAneoadjuvantPathologic complete responsePredictionRT-qPCR

Identifiers

PMID28193205
PMCPMC5307758
OpenAlexW2588028576

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LicenceCC BY
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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.