Research

MammaPrint Pre-screen Algorithm (MPA) reduces chemotherapy in patients with early-stage breast cancer

Kathleen A Grant, Justus P Apffelstaedt, Colleen A Wright, Ettienne Myburgh, Rika Pienaar, Manie de Klerk, Maritha J Kotze

Abstract


Background. Clinical and pathological parameters may overestimate the need for chemotherapy in patients with early-stage breast cancer. More accurate determination of the risk of distant recurrence is now possible with use of genetic tests, such as the 70-gene MammaPrint profile.

Objectives. A health technology assessment performed by a medical insurer in 2009 introduced a set of test eligibility criteria – the MammaPrint Pre-screen Algorithm (MPA) – applied in this study to determine the clinical usefulness of a pathology-supported genetic testing strategy, aimed at the reduction of healthcare costs.

Methods. An implementation study was designed to take advantage of the fact that the 70-gene profile excludes analysis of hormone receptor and human epidermal growth factor receptor 2 (HER2) status, which form part of the MPA based partly on immunohistochemistry routinely performed in all breast cancer patients. The study population consisted of 104 South African women with early-stage breast carcinoma referred for MammaPrint. For the MammaPrint test, RNA was extracted from 60 fresh tumours (in 58 patients) and 46 formalin-fixed, paraffin-embedded (FFPE) tissue samples.

Results. When applying the MPA for selection of patients eligible for MammaPrint testing, 95 of the 104 patients qualified. In this subgroup 62% (59/95) were classified as low risk. Similar distribution patterns for risk classification were obtained for RNA extracted from fresh tumours v. FFPE tissue samples.

Conclusions. The 70-gene profile classifies approximately 40% of early-stage breast cancer patients as low-risk compared with 15% using conventional criteria. In comparison, more than 60% were shown to be low risk with use of the MPA validated in this study as an appropriate strategy to prevent chemotherapy overtreatment in patients with early-stage breast cancer. 


Authors' affiliations

Kathleen A Grant, Department of Biomedical Sciences, Faculty of Health and Wellness, Cape Peninsula University of Technology, Cape Town, South Africa; Division of Anatomical Pathology, Department of Pathology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa

Justus P Apffelstaedt, Department of Surgery, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa

Colleen A Wright, Division of Anatomical Pathology, Department of Pathology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa; National Health Laboratory Service, Port Elizabeth, South Africa

Ettienne Myburgh, Department of Surgery, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa; Panorama Medi-Clinic, Cape Town, South Africa

Rika Pienaar, GVI Oncology, Panorama Medi-Clinic, Cape Town, South Africa

Manie de Klerk, Metropolitan Health Group and University of Stellenbosch Business School, Cape Town, South Africa

Maritha J Kotze, Division of Anatomical Pathology, Department of Pathology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa

Full Text

PDF (164KB) HTML

Keywords

algorithm; breast cancer; gene profiling; genomics; microarray; MammaPrint

Cite this article

South African Medical Journal 2013;103(8):522-526. DOI:10.7196/SAMJ.7223

Article History

Date submitted: 2013-07-02
Date published: 2013-07-03

Article Views

Abstract views: 2587
Full text views: 4577

Comments on this article

*Read our policy for posting comments here