From morphologic to molecular: established and emerging molecular diagnostics for breast carcinoma

Portier, Bryce P.; Gruver, Aaron M.; Huba, Michael A.; Minca, Eugen C.; Cheah, Alison L.; Wang, Zhen; Tubbs, Raymond R.

doi:10.1016/j.nbt.2012.03.011

Cited by 6 publications

(9 citation statements)

References 105 publications

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“…Triple-negative breast cancer (TNBC) is diagnosed in cases where tumors are negative for ESR1, PGR, and HER2. In these breast tumors, the immunohistochemical measurement of basal markers (cytokeratin 5/6, EGFR), claudins (CLD3/4/7), cadherins (CDH1, CDH3), stem cell markers (CD44/CD24, ALDH1), apoptosis markers (BCL2, TP53), a transcription marker (YB-1) and urokinase-type plasminogen activator (uPA)/plasminogen activator inhibitor-1 (PAI-1) have also been suggested for advanced stratification (16, 17, 31, 32).…”

Section: Discussionmentioning

confidence: 99%

“…Evaluation of HER2 (ERBB2, neu) status has also been routinely used in breast cancer (PAI-1) have also been suggested for advanced stratification (16,17,31,32).…”

Section: Proteins With Significant Prognostic Powermentioning

confidence: 99%

“…Evaluation of these biomarkers is mandatory to assign patients into clinically effective treatment subtypes termed basal (receptor negative), luminal A (ESR1 and PGR positive and low MKI67), luminal B (ESR1 and PGR positive and high MKI67), and HER2-enriched (HER2 positive ESR1 negative) (15). Of note, additional markers, including androgen receptor (AR), epidermal growth factor receptor (EGFR) and cytokeratins (CK), have also been proposed for biomarker-based subtyping (16, 17).…”

Section: Introductionmentioning

confidence: 99%

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Survival analysis in breast cancer using proteomic data from four independent datasets

Õsz

Lánczky

Győrffy

2020

Preprint

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Breast cancer clinical treatment selection is based on the immunohistochemical determination of four proteins: ESR1, PGR, HER2, and MKI67. Our aim was to correlate immunohistochemical results to proteome-level technologies in measuring the expression of these markers. We also aimed to integrate available proteome-level breast cancer datasets to identify and validate new prognostic biomarker candidates.We identified protein studies involving breast cancer patient cohorts with published survival and proteomic information. Immunohistochemistry and proteomic technologies were compared using the Mann-Whitney test. Receiver operating characteristics (ROC) curves were generated to validate discriminative power. Cox regression and Kaplan-Meier survival analysiss were calculated to assess prognostic power. The false discovery rate was computed to correct for multiple hypothesis testing.The complete database contains protein expression data and survival information from four independent cohorts for 1,229 breast cancer patients. In all four studies combined, a total of 7,342 unique proteins were identified, and 1,417 of these were identified in at least three datasets. ESR1, PGR, HER2 protein expression levels determined by RPPA or LC-MS/MS methods showed a significant correlation with the levels determined by immunohistochemistry (p<0.0001). PGR and ESR1 levels showed a moderate correlation (correlation coefficient=0.17, p=0.0399). A panel of candidate proteins, including apoptosis-related proteins (BCL2,), adhesion markers (CDH1, CLDN3, CLDN7) and basal markers (cytokeratins), were validated as prognostic biomarkers. We expanded our established web tool to validate survival-associated biomarkers to include the proteomic datasets analyzed in this study (https://kmplot.com/analysis/).Large proteomic studies now provide sufficient data enabling the validation and ranking of new protein biomarkers.

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Section: Discussionmentioning

confidence: 99%

“…Evaluation of HER2 (ERBB2, neu) status has also been routinely used in breast cancer (PAI-1) have also been suggested for advanced stratification (16,17,31,32).…”

Section: Proteins With Significant Prognostic Powermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Survival analysis in breast cancer using proteomic data from four independent datasets

Õsz

Lánczky

Győrffy

2020

Preprint

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“…Im letzten Jahrzehnt wurden durch den Einsatz von RNA-Expressions-Arrays (auch cDNA-Microarrays oder Genchips genannt), die die parallele Messung relativer Expressionsunterschiede in zahlreichen Genen erlaubt, etliche sogenannte Gensignaturen gefunden, die eine Prognoseabschätzung oder die Vorhersage eines Therapieansprechens für unterschiedliche Tumoren erlauben [7]. Während die meisten dieser Daten nicht unabhängig validiert werden konnten und damit nicht Eingang in den klinischen Alltag fanden, gelang es für das Mammakarzinom Tests weiterzuentwickeln, die über die Messung der Genexpression im Tumor eine klinisch relevante Risikostratifizierung der Patientinnen erlauben.…”

Section: Mammakarzinomunclassified

Molekulare Diagnostik in der Pathologie

Penzel

Endris

Weichert

2013

Dtsch med Wochenschr

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Tissue-based molecular diagnostics is a fast growing diagnostic field, which already complements morphologic classifications in many cases. Pathology based molecular diagnosis is performed almost exclusively on paraffin embedded material and always in conjunction with histopathology. Besides the classic field of tissue based detection of pathogenic organisms such as bacteria, viruses and fungi, molecular diagnostics of tumor tissue is one of the current hot topics in oncology. In this context the detection of predictive molecular biomarkers, such as specific mutations, allows patient stratification for individually tailored treatment strategies and thereby is one of the key components of individualized patient care in oncology. The rapidly growing number of clinically relevant predictive biomarkers together with impressive technical advances, specifically the development of massive parallel sequencing, will modify the care of patients with malignant diseases. Pathology, therefore, has returned in the very center of interdisciplinary patient care.

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“…Breast cancer has been perceived as several distinct diseases characterised by intrinsic aberrations, heterogeneous behaviour and divergent clinical outcome [ 1 ]. The classification of breast cancer in discernible molecular subtypes has motivated translational researchers in the past decades towards the design of patient prognosis and the development of tailored treatments [ 2 ]. In this scenario, the analysis of breast tumours using microarray data has significantly improved the disease taxonomy and the discovery of new biomarkers for implementation in clinical practice [ 3 – 6 ].…”

Section: Introductionmentioning

confidence: 99%

The Discovery of Novel Biomarkers Improves Breast Cancer Intrinsic Subtype Prediction and Reconciles the Labels in the METABRIC Data Set

et al. 2015

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BackgroundThe prediction of breast cancer intrinsic subtypes has been introduced as a valuable strategy to determine patient diagnosis and prognosis, and therapy response. The PAM50 method, based on the expression levels of 50 genes, uses a single sample predictor model to assign subtype labels to samples. Intrinsic errors reported within this assay demonstrate the challenge of identifying and understanding the breast cancer groups. In this study, we aim to: a) identify novel biomarkers for subtype individuation by exploring the competence of a newly proposed method named CM1 score, and b) apply an ensemble learning, as opposed to the use of a single classifier, for sample subtype assignment. The overarching objective is to improve class prediction.Methods and FindingsThe microarray transcriptome data sets used in this study are: the METABRIC breast cancer data recorded for over 2000 patients, and the public integrated source from ROCK database with 1570 samples. We first computed the CM1 score to identify the probes with highly discriminative patterns of expression across samples of each intrinsic subtype. We further assessed the ability of 42 selected probes on assigning correct subtype labels using 24 different classifiers from the Weka software suite. For comparison, the same method was applied on the list of 50 genes from the PAM50 method.ConclusionsThe CM1 score portrayed 30 novel biomarkers for predicting breast cancer subtypes, with the confirmation of the role of 12 well-established genes. Intrinsic subtypes assigned using the CM1 list and the ensemble of classifiers are more consistent and homogeneous than the original PAM50 labels. The new subtypes show accurate distributions of current clinical markers ER, PR and HER2, and survival curves in the METABRIC and ROCK data sets. Remarkably, the paradoxical attribution of the original labels reinforces the limitations of employing a single sample classifiers to predict breast cancer intrinsic subtypes.

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From morphologic to molecular: established and emerging molecular diagnostics for breast carcinoma

Cited by 6 publications

References 105 publications

Survival analysis in breast cancer using proteomic data from four independent datasets

Survival analysis in breast cancer using proteomic data from four independent datasets

Molekulare Diagnostik in der Pathologie

The Discovery of Novel Biomarkers Improves Breast Cancer Intrinsic Subtype Prediction and Reconciles the Labels in the METABRIC Data Set

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