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Abstract

Although acquired mutations in KIT are commonly detected in various categories of mastocytosis, the methodologies applied to detect and quantify the mutant type and allele burden in various cells and tissues are poorly defined. We here propose a consensus on methodologies used to detect KIT mutations in patients with mastocytosis at diagnosis and during follow-up with sufficient precision and sensitivity in daily practice. In addition, we provide recommendations for sampling and storage of diagnostic material as well as a robust diagnostic algorithm. Using highly sensitive assays, KIT D816V can be detected in peripheral blood leukocytes from most patients with systemic mastocytosis (SM) that is a major step forward in screening and SM diagnosis. In addition, the KIT D816V allele burden can be followed quantitatively during the natural course or during therapy. Our recommendations should greatly facilitate diagnostic and follow-up investigations in SM in daily practice as well as in clinical trials. In addition, the new tools and algorithms proposed should lead to a more effective screen, early diagnosis of SM and help to avoid unnecessary referrals.

Original languageEnglish
JournalLeukemia
Volume29
Issue number6
Pages (from-to)1223-1232
Number of pages10
ISSN0887-6924
DOIs
Publication statusPublished - 09.06.2015

Funding

C Akin is a consultant in a Novartis trial. O Hermine and P Dubreuil receive research funding and honorarium from AB Science. NCP Cross receives honoraria and research funding from Novartis. K Hartmann is a consultant in a Novartis trial and received research grants from Novartis. J Gotlib is a consultant in a Novartis trial and receives research funding from Novartis. A Reiter is a consultant in a Novartis trial and receives honoraria and travel support. P Valent receives honoraria and a research grant from Novartis and is Consultant in a Global Novartis trial. The other authors declare no conflict of interest. We were unable to cite all the original papers because of reference limits and apologize for not citing all relevant original papers. M Arock is supported by Fondation de France; P Dubreuil is supported by La Ligue Nationale Contre le Cancer (équipe labellisée) and INCa; A Garcia-Montero and A Orfao are supported by grants from the Instituto de Salud Carlos III, Ministry of Economy and Competitivity, Madrid, Spain (Grant Numbers RD12/0036/0048 and PI11/02399, FEDER) and from Fundacion Ramon Areces, Madrid, Spain (Grant Number CIVP16A1806); DD Metcalfe is supported in part by the Division of Intramural Research, NIAID; P Valent is supported by Austrian Science Funds (FWF) Project SFB F4611 and SFB F4704-B20.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

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