Variable but consistent pattern of Meningioma 1 gene (MN1) expression in different genetic subsets of acute myelogenous leukaemia and its potential use as a marker for minimal residual disease detection

Sonia Carturan, Jessica Petiti, Valentina Rosso, Chiara Calabrese, Elisabetta Signorino, Giada Bot-Sartor, Paolo Nicoli, Daniela Gallo, Enrico Bracco, Alessandro Morotti, Cristina Panuzzo, Enrico Gottardi, Francesco Frassoni, Giuseppe Saglio, Daniela Cilloni

Research output: Contribution to journalArticlepeer-review

Abstract

Meningioma 1 (MN1) gene overexpression has been reported in acute myeloid leukaemia (AML) patients and identified as a negative prognostic factor. In order to characterize patients presenting gene overexpression and to verify if MN1 transcript could be a useful marker for minimal residual disease detection, MN1 was quantified in 136 AML patients with different cytogenetic risk and in 50 normal controls. In 20 patients bearing a fusion gene transcript suitable for minimal residual disease quantitative assessment and in 8 patients with NPM1 mutation, we performed a simultaneous analysis of MN1 and the fusion-gene transcript or NPM1 mutation during follow-up. Sequential MN1 and WT1 analysis was also performed in 13 AML patients lacking other molecular markers. The data obtained show that normal cells consistently express low levels of MN1 transcript. In contrast, high levels of MN1 expression are present in 47% of patients with normal karyotype and in all cases with inv(16). MN1 levels during follow-up were found to follow the pattern of other molecular markers (fusion gene transcripts, NPM1 and WT1). Increased MN1 expression in the BM during follow up was always found to be predictive of an impending hematological relapse.

Original languageEnglish
Pages (from-to)74082-74096
Number of pages15
JournalOncotarget
Volume7
Issue number45
Publication statusPublished - 2016

Keywords

  • Acute leukemias
  • Meningioma 1 gene
  • Minimal residual disease
  • Molecular marker

ASJC Scopus subject areas

  • Oncology

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