Genetic polymorphisms associated with increased risk of developing chronic myelogenous leukemia
Metrics: PDF 2222 views | HTML 1747 views | ?
Heriberto Bruzzoni-Giovanelli1,2, Juan R. González3,4,5, François Sigaux6, Bruno O. Villoutreix7, Jean Michel Cayuela8,9, Joëlle Guilhot10, Claude Preudhomme11, François Guilhot10, Jean-Luc Poyet1 and Philippe Rousselot12
1 Université Paris Diderot, Sorbonne Paris Cité UMRS 1160 INSERM, Paris, France
2 Centre d’Investigations Cliniques 9504 INSERM-AP-HP Hôpital Saint-Louis, Paris, France
3 Centre de Recerca en Epidemiologia Ambiental (CREAL), Barcelona, Spain
4 Institut Municipal d’Investigació Mèdica (IMIM), Barcelona, Spain
5 CIBER Epidemiología y Salud Pública (CIBERESP), Spain Centre de Recerca en Epidemiologia Ambiental (CREAL), Barcelona, Spain
6 Institut Universitaire d’Hématologie, Université Paris Diderot, Sorbonne Paris Cité, Paris, France
7 Université Paris Diderot, Sorbonne Paris Cité UMRS 973 Inserm, Paris, France/ Inserm, U973, Paris, France
8 Laboratoire Central d’Hématologie, Hôpital Saint Louis, Paris, France
9 EA3518, Université Paris Diderot, Sorbonne Paris Cité, Paris, France
10 Inserm CIC 0802, CHU de Poitiers, Poitiers, France
11 Laboratoire d’Hématologie, Inserm, U837, CHRU, Lille, France/Université de Lille Nord, Institut de Recherche sur le Cancer de Lille, Lille, France
12 Service d’Hématologie et d’Oncologie, Hôpital Mignot, Université Versailles, Saint-Quentin-en-Yvelines, France
Heriberto Bruzzoni-Giovanelli, email:
Juan R. González, email:
Keywords: CML, SNPs, genetic predisposition, myeloid leukemia
Received: August 12, 2015 Accepted: September 14, 2015 Published: October 12, 2015
Little is known about inherited factors associated with the risk of developing chronic myelogenous leukemia (CML). We used a dedicated DNA chip containing 16 561 single nucleotide polymorphisms (SNPs) covering 1 916 candidate genes to analyze 437 CML patients and 1 144 healthy control individuals. Single SNP association analysis identified 139 SNPs that passed multiple comparisons (1% false discovery rate). The HDAC9, AVEN, SEMA3C, IKBKB, GSTA3, RIPK1 and FGF2 genes were each represented by three SNPs, the PSM family by four SNPs and the SLC15A1 gene by six. Haplotype analysis showed that certain combinations of rare alleles of these genes increased the risk of developing CML by more than two or three-fold. A classification tree model identified five SNPs belonging to the genes PSMB10, TNFRSF10D, PSMB2, PPARD and CYP26B1, which were associated with CML predisposition. A CML-risk-allele score was created using these five SNPs. This score was accurate for discriminating CML status (AUC: 0.61, 95%CI: 0.58–0.64). Interestingly, the score was associated with age at diagnosis and the average number of risk alleles was significantly higher in younger patients. The risk-allele score showed the same distribution in the general population (HapMap CEU samples) as in our control individuals and was associated with differential gene expression patterns of two genes (VAPA and TDRKH). In conclusion, we describe haplotypes and a genetic score that are significantly associated with a predisposition to develop CML. The SNPs identified will also serve to drive fundamental research on the putative role of these genes in CML development.
All site content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 License.