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Gene expression-based prognostic and predictive tools in breast cancer

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Abstract

Genomic assays measuring the expression of multiple genes have made their way into clinical practice and their utilization is now recommended by major international guidelines. A basic property of these tests is their capability to sub-divide patients into high- and low-risk cohorts thereby providing prognostic, and in certain settings, predictive decision support. Here, we summarize commercially available assays for breast cancer including RT-PCR and gene chip-based tests. Given the relative uncertainty in cancer treatment, multigene tests have the potential for a significant cost reduction as they can pinpoint those patients for whom chemotherapy proves to be unnecessary. However, concordance of risk assessment for an individual patient is still far from optimal. Additionally, emerging multigene approaches focus on predicting therapy response, which is a black spot of current tests. Promising techniques include the homologous recombination deficiency score, utilization of massive parallel sequencing to identify driver genes, employment of internet-based meta-analysis tools and investigation of miRNA expression signatures. Combination of multiple simultaneous analyses at diagnosis, including classical histopathological diagnostics, monogenic markers, genomic signatures and clinical parameters will most likely bring maximal benefit for patients. As the main driving force behind such genomic tests is the power to achieve cost reduction due to avoiding unnecessary systemic treatment, the future is most likely to hold a further proliferation of such assays.

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Abbreviations

BC:

Breast cancer

CNA:

Copy number alterations

ER:

Estrogen receptor

FDA:

US Food and Drug Administration

FFPE:

Formalin-fixed, paraffin-embedded

FISH:

Fluorescence in situ hybridization

GGI:

Genomic grade index

HER2:

Human epidermal growth factor receptor 2

HRD:

Homologous recombination deficiency

IHC:

Immunohistochemistry

miRNA:

Micro RNA

NCCN:

National Comprehensive Cancer Network

NGS:

Next (second) generation sequencing

PgR:

Progesterone receptor

ROR:

Risk of recurrence

TCGA:

The cancer genome atlas

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Acknowledgments

This study was supported by the OTKA K108655 grant. A. M. S. was supported by the European Union and the State of Hungary, co-financed by the European Social Fund in the framework of TÁMOP-4.2.4.A/2-11-1-2012-0001 ‘National Excellence Program’.

Conflict of interest

The authors declare that they have no conflict of interest.

Author information

Authors and Affiliations

  1. MTA-SE Pediatrics and Nephrology Research Group, Semmelweis University, Bókay u. 53, Budapest, 1083, Hungary

    Gyöngyi Munkácsy

  2. 2nd Department of Pathology, Semmelweis University, Üllői út 93, Budapest, 1091, Hungary

    Marcell A. Szász

  3. MTA TTK Lendület Cancer Biomarker Research Group, Magyar tudósok körútja 2, Budapest, 1117, Hungary

    Otilia Menyhárt

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  1. Gyöngyi Munkácsy
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  2. Marcell A. Szász
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Correspondence to Gyöngyi Munkácsy.

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Munkácsy, G., Szász, M.A. & Menyhárt, O. Gene expression-based prognostic and predictive tools in breast cancer. Breast Cancer 22, 245–252 (2015). https://doi.org/10.1007/s12282-015-0594-y

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