Gene expression and risk of leukemic transformation in myelodysplasia.

Myelodysplastic syndromes (MDSs) are a heterogeneous group of clonal hematopoietic disorders with a highly variable prognosis. To identify a gene expression-based classification of myelodysplasia with biological and clinical relevance, we performed a comprehensive transcriptomic analysis of myeloid neoplasms with dysplasia using transcriptome sequencing. Unsupervised clustering of gene expression data of bone marrow CD34+ cells from 100 patients identified 2 subgroups. The first subtype was characterized by increased expression of genes related to erythroid/megakaryocytic (EMK) lineages, whereas the second subtype showed upregulation of genes related to immature progenitor (IMP) cells. Compared with the first so-called EMK subtype, the IMP subtype showed upregulation of many signaling pathways and downregulation of several pathways related to metabolism and DNA repair. The IMP subgroup was associated with a significantly shorter survival in both univariate (hazard ratio [HR], 5.0; 95% confidence interval [CI], 1.8-14; P = .002) and multivariate analysis (HR, 4.9; 95% CI, 1.3-19; P = .02). Leukemic transformation was limited to the IMP subgroup. The prognostic significance of our classification was validated in an independent cohort of 183 patients. We also constructed a model to predict the subgroups using gene expression profiles of unfractionated bone marrow mononuclear cells (BMMNCs). The model successfully predicted clinical outcomes in a test set of 114 patients with BMMNC samples. The addition of our classification to the clinical model improved prediction of patient outcomes. These results indicated biological and clinical relevance of our gene expression-based classification, which will improve risk prediction and treatment stratification of MDS.

[1]  W. Evans,et al.  A subtype of childhood acute lymphoblastic leukaemia with poor treatment outcome: a genome-wide classification study. , 2009, The Lancet. Oncology.

[2]  M. Stratton,et al.  Clinical and biological implications of driver mutations in myelodysplastic syndromes. , 2013, Blood.

[3]  P. Campbell,et al.  Combining gene mutation with gene expression data improves outcome prediction in myelodysplastic syndromes , 2015, Nature Communications.

[4]  S. Gabriel,et al.  Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. , 2010, Cancer cell.

[5]  Claude Preudhomme,et al.  A 17-gene stemness score for rapid determination of risk in acute leukaemia , 2016, Nature.

[6]  H. Kantarjian,et al.  A prognostic score for patients with lower risk myelodysplastic syndrome , 2008, Leukemia.

[7]  J. Melo,et al.  The polycomb group BMI1 gene is a molecular marker for predicting prognosis of chronic myeloid leukemia. , 2007, Blood.

[8]  R. Tibshirani,et al.  Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[9]  M. Lichtman Does a diagnosis of myelogenous leukemia require 20% marrow myeloblasts, and does <5% marrow myeloblasts represent a remission? The history and ambiguity of arbitrary diagnostic boundaries in the understanding of myelodysplasia. , 2013, The oncologist.

[10]  H. Preisler,et al.  Apoptosis in bone marrow biopsy samples involving stromal and hematopoietic cells in 50 patients with myelodysplastic syndromes. , 1995, Blood.

[11]  A. Schambach,et al.  Prognostic significance of combined MN1, ERG, BAALC, and EVI1 (MEBE) expression in patients with myelodysplastic syndromes , 2012, Annals of Hematology.

[12]  Nathan C Boles,et al.  Less is more: unveiling the functional core of hematopoietic stem cells through knockout mice. , 2012, Cell stem cell.

[13]  M. Cazzola,et al.  The genetic basis of myelodysplasia and its clinical relevance. , 2013, Blood.

[14]  H. Preisler,et al.  Simultaneous assessment of cell kinetics and programmed cell death in bone marrow biopsies of myelodysplastics reveals extensive apoptosis as the probable basis for ineffective hematopoiesis , 1995, American journal of hematology.

[15]  M. Cazzola,et al.  Time-dependent prognostic scoring system for predicting survival and leukemic evolution in myelodysplastic syndromes. , 2007, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[16]  D. Neuberg,et al.  Clinical effect of point mutations in myelodysplastic syndromes. , 2011, The New England journal of medicine.

[17]  Angelo J. Canty,et al.  Stem cell gene expression programs influence clinical outcome in human leukemia , 2011, Nature Medicine.

[18]  M. Caligiuri,et al.  Prognostic importance of MN1 transcript levels, and biologic insights from MN1-associated gene and microRNA expression signatures in cytogenetically normal acute myeloid leukemia: a cancer and leukemia group B study. , 2009, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[19]  Stephen L. Abrams,et al.  Targeting survival cascades induced by activation of Ras/Raf/MEK/ERK, PI3K/PTEN/Akt/mTOR and Jak/STAT pathways for effective leukemia therapy , 2008, Leukemia.

[20]  T. Naoe,et al.  Biologic and clinical significance of the FLT3 transcript level in acute myeloid leukemia. , 2004, Blood.

[21]  Mario Cazzola,et al.  The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. , 2016, Blood.

[22]  M. Odero,et al.  SETBP1 overexpression is a novel leukemogenic mechanism that predicts adverse outcome in elderly patients with acute myeloid leukemia. , 2010, Blood.

[23]  Christian A. Rees,et al.  Molecular portraits of human breast tumours , 2000, Nature.

[24]  T Hamblin,et al.  International scoring system for evaluating prognosis in myelodysplastic syndromes. , 1997, Blood.

[25]  Robert Tibshirani,et al.  Relationship of differential gene expression profiles in CD34+ myelodysplastic syndrome marrow cells to disease subtype and progression. , 2009, Blood.

[26]  M. Cazzola,et al.  Molecular and clinical features of refractory anemia with ringed sideroblasts associated with marked thrombocytosis. , 2009, Blood.

[27]  Torsten Haferlach,et al.  Microarray-based classifiers and prognosis models identify subgroups with distinct clinical outcomes and high risk of AML transformation of myelodysplastic syndrome. , 2009, Blood.

[28]  J. Issa,et al.  Proposal for a new risk model in myelodysplastic syndrome that accounts for events not considered in the original International Prognostic Scoring System , 2008, Cancer.

[29]  T. Giese,et al.  Expression of CDKN1C in the bone marrow of patients with myelodysplastic syndrome and secondary acute myeloid leukemia is associated with poor survival after conventional chemotherapy , 2016, International journal of cancer.

[30]  Rafael A. Irizarry,et al.  A Model-Based Background Adjustment for Oligonucleotide Expression Arrays , 2004 .

[31]  Ash A. Alizadeh,et al.  Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling , 2000, Nature.

[32]  S. Miyano,et al.  Dynamics of clonal evolution in myelodysplastic syndromes , 2016, Nature Genetics.

[33]  Randy J. Read,et al.  Transcriptional diversity during lineage commitment of human blood progenitors , 2014, Science.

[34]  Benjamin L Ebert,et al.  Molecular pathophysiology of myelodysplastic syndromes. , 2013, Annual review of pathology.

[35]  Ali H. Brivanlou,et al.  Signaling Pathways in Cancer and Embryonic Stem Cells , 2007, Stem Cell Reviews.

[36]  J. Kutok,et al.  MSI2 protein expression predicts unfavorable outcome in acute myeloid leukemia. , 2011, Blood.

[37]  Robert Gray,et al.  A Proportional Hazards Model for the Subdistribution of a Competing Risk , 1999 .

[38]  E Mardis,et al.  Clonal diversity of recurrently mutated genes in myelodysplastic syndromes , 2013, Leukemia.

[39]  M. Cazzola,et al.  Identification of Gene Expression Based Prognostic Markers in the Hematopoietic Stem Cells of Patients with Myelodysplastic Syndromes , 2012 .

[40]  Benjamin J. Raphael,et al.  Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. , 2013, The New England journal of medicine.

[41]  M. Cazzola,et al.  Deregulated Gene Expression Pathways in Myelodysplastic Syndrome Hematopoietic Stem Cells. , 2009 .

[42]  Brian P. Brunk,et al.  Comparative analysis of RNA-Seq alignment algorithms and the RNA-Seq unified mapper (RUM) , 2011, Bioinform..

[43]  C Haferlach,et al.  Landscape of genetic lesions in 944 patients with myelodysplastic syndromes , 2013, Leukemia.

[44]  Luca Malcovati,et al.  Prognostic factors and life expectancy in myelodysplastic syndromes classified according to WHO criteria: a basis for clinical decision making. , 2005, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[45]  Bob Löwenberg,et al.  High EVI1 expression predicts poor survival in acute myeloid leukemia: a study of 319 de novo AML patients. , 2003, Blood.

[46]  E. Vellenga,et al.  Signaling pathways in self-renewing hematopoietic and leukemic stem cells: do all stem cells need a niche? , 2006, Human molecular genetics.

[47]  H. Aburatani,et al.  Integrated molecular analysis of clear-cell renal cell carcinoma , 2013, Nature Genetics.

[48]  R. Eils,et al.  Identification of regulatory networks in HSCs and their immediate progeny via integrated proteome, transcriptome, and DNA methylome analysis. , 2014, Cell stem cell.

[49]  Mark D. Robinson,et al.  edgeR: a Bioconductor package for differential expression analysis of digital gene expression data , 2009, Bioinform..

[50]  Frequent Pathway Mutations of Splicing Machinery in Myelodysplasia , 2011 .

[51]  M. Caligiuri,et al.  BAALC and ERG expression levels are associated with outcome and distinct gene and microRNA expression profiles in older patients with de novo cytogenetically normal acute myeloid leukemia: a Cancer and Leukemia Group B study. , 2010, Blood.

[52]  Luca Malcovati,et al.  Revised international prognostic scoring system for myelodysplastic syndromes. , 2012, Blood.

[53]  R. Deberardinis,et al.  Ascorbate regulates haematopoietic stem cell function and leukaemogenesis , 2017, Nature.

[54]  Di Wu,et al.  ROAST: rotation gene set tests for complex microarray experiments , 2010, Bioinform..

[55]  Zoltan Szallasi,et al.  Jetset: selecting the optimal microarray probe set to represent a gene , 2011, BMC Bioinformatics.