Validation of the IHC4 Breast Cancer Prognostic Algorithm Using Multiple Approaches on the Multinational TEAM Clinical Trial

Bartlett, John M.S.; Christiansen, Jason; Gustavson, Mark; Rimm, David L.; Piper, Tammy; Velde, Cornelis J.H. van de; Hasenburg, Annette; Kieback, Dirk G.; Putter, Hein; Markopoulos, Christos; Dirix, Luc; Seynaeve, Caroline; Rea, Daniel

doi:10.5858/arpa.2014-0599-oa

Cited by 35 publications

(37 citation statements)

References 35 publications

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“…In this respect, it is interesting that a similar score, namely the immunohistochemical 4 (IHC4) score, with comparable prognostic value was generated by using just the four IHC markers HER2, ER, PgR, and Ki67 [39, 40]. However, insufficient standardization and considerable interlaboratory variability of IHC suggest that the IHC4 algorithm cannot easily be transferred to other laboratories, although it was successfully validated in an independent study cohort [41]. On the basis of data obtained in this study, MammaTyper® could provide a highly reproducible and reliable assessment of these four markers.…”

Section: Discussionmentioning

confidence: 99%

An international reproducibility study validating quantitative determination of ERBB2, ESR1, PGR, and MKI67 mRNA in breast cancer using MammaTyper®

Varga

Lebeau²,

et al. 2017

Breast Cancer Res

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BackgroundAccurate determination of the predictive markers human epidermal growth factor receptor 2 (HER2/ERBB2), estrogen receptor (ER/ESR1), progesterone receptor (PgR/PGR), and marker of proliferation Ki67 (MKI67) is indispensable for therapeutic decision making in early breast cancer. In this multicenter prospective study, we addressed the issue of inter- and intrasite reproducibility using the recently developed reverse transcription-quantitative real-time polymerase chain reaction-based MammaTyper® test.MethodsTen international pathology institutions participated in this study and determined messenger RNA expression levels of ERBB2, ESR1, PGR, and MKI67 in both centrally and locally extracted RNA from formalin-fixed, paraffin-embedded breast cancer specimens with the MammaTyper® test. Samples were measured repeatedly on different days within the local laboratories, and reproducibility was assessed by means of variance component analysis, Fleiss’ kappa statistics, and interclass correlation coefficients (ICCs).ResultsTotal variations in measurements of centrally and locally prepared RNA extracts were comparable; therefore, statistical analyses were performed on the complete dataset. Intersite reproducibility showed total SDs between 0.21 and 0.44 for the quantitative single-marker assessments, resulting in ICC values of 0.980–0.998, demonstrating excellent agreement of quantitative measurements. Also, the reproducibility of binary single-marker results (positive/negative), as well as the molecular subtype agreement, was almost perfect with kappa values ranging from 0.90 to 1.00.ConclusionsOn the basis of these data, the MammaTyper® has the potential to substantially improve the current standards of breast cancer diagnostics by providing a highly precise and reproducible quantitative assessment of the established breast cancer biomarkers and molecular subtypes in a decentralized workup.Electronic supplementary materialThe online version of this article (doi:10.1186/s13058-017-0848-z) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

An international reproducibility study validating quantitative determination of ERBB2, ESR1, PGR, and MKI67 mRNA in breast cancer using MammaTyper®

Varga

Lebeau²,

et al. 2017

Breast Cancer Res

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“…This panel was termed IHC4 and the algorithm has been validated as a predictor of risk of distant recurrence in breast cancer [17,18].…”

Section: Clinical Biomarkers In Breast Cancermentioning

confidence: 99%

Molecular signatures in breast cancer

Lal

Reed

Luca

et al. 2017

Methods

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The use of molecular signatures to add value to standard clinical and pathological parameters has impacted clinical practice in many cancer types, but perhaps most notably in the breast cancer field. This is, in part, due to the considerable complexity of the disease at the clinical, morphological and molecular levels. The adoption of molecular profiling of DNA, RNA and protein continues to reveal important differences in the intrinsic biology between molecular subtypes and has begun to impact the way patients are managed. Several bioinformatic tools have been developed using DNA or RNA-based signatures to stratify the disease into biologically and/or clinically meaningful subgroups. Here, we review the approaches that have been used to develop gene expression signatures into currently available diagnostic assays (e.g. OncotypeDX® and Mammaprint®), plus we describe the latest work on genome sequencing, the methodologies used in the discovery process of mutational signatures, and the potential of these signatures to impact the clinic.

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“…To benchmark SIMMS's PI3K modules signature against current clinicallyvalidated approaches, we compared its performance to a clinically-used proteinbased residual risk predictor, IHC4 34 . IHC4 was assessed using quantitative IHC measurements of ER, PgR, Ki67 and HER2 35…”

Section: Pik3ca Signaling Modules Outperform Existing Markersmentioning

confidence: 99%

Network-Based Biomarkers Enable Cross-Disease Biomarker Discovery

Haider

Sabine

et al. 2018

Preprint

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Biomarkers lie at the heart of precision medicine, biodiversity monitoring, agricultural pathogen detection, amongst others. Surprisingly, while rapid genomic profiling is becoming ubiquitous, the development of biomarkers almost always involves the application of bespoke techniques that cannot be directly applied to other datasets. There is an urgent need for a systematic methodology to create biologically-interpretable molecular models that robustly predict key phenotypes. We therefore created SIMMS: an algorithm that fragments pathways into functional modules and uses these to predict phenotypes. We applied SIMMS to multiple data-types across four diseases, and in each it reproducibly identified subtypes, made superior predictions to the best bespoke approaches, and identified known and novel signaling nodes. To demonstrate its ability on a new dataset, we measured 33 genes/nodes of the PIK3CA pathway in 1,734 FFPE breast tumours and created a four-subnetwork prediction model. This model significantly out-performed existing clinically-used molecular tests in an independent 1,742-patient validation cohort. SIMMS is generic and can work with any molecular data or biological network, and is freely available at: https://cran.r-project.org/web/packages/SIMMS.

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Validation of the IHC4 Breast Cancer Prognostic Algorithm Using Multiple Approaches on the Multinational TEAM Clinical Trial

Abstract: The use of the IHC4 algorithm was validated on the TEAM trial for predicting residual risk in patients with breast cancer. These data support the use of the IHC4 algorithm clinically, but quantitative and standardized approaches need to be used.

Cited by 35 publications

References 35 publications

An international reproducibility study validating quantitative determination of ERBB2, ESR1, PGR, and MKI67 mRNA in breast cancer using MammaTyper®

An international reproducibility study validating quantitative determination of ERBB2, ESR1, PGR, and MKI67 mRNA in breast cancer using MammaTyper®

Molecular signatures in breast cancer

Network-Based Biomarkers Enable Cross-Disease Biomarker Discovery

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