Abstract
Myelodysplastic syndromes (MDS) are clonal stem cell neoplasms affecting patients usually over 60 years old that typically present into the clinic with common symptoms including cytopenias, recurrent infections, bleeding and bruising. Approximately 20-30% of MDS patients progress to acute myeloid leukemia (AML) and are associated with inferior survival1. Diagnosis of MDS relies on findings from peripheral blood counts, examination of bone marrow morphology and evaluation of cytogenetic profiles for chromosomal aberrations. Using the WHO 2008 criteria, the proportion of blasts in the bone marrow, the number of cell lineages affected and the presence of del(5q) are collectively evaluated to classify patients into one of the five MDS categories [refractory anemia, refractory anemia with ring sideroblasts, refractory cytopenia multilineage dysplasia, refractory anemia with excess blasts, MDS with del(5q)]. The International Prognostication Scoring System (IPSS & IPSS-R) is the most widely used prognostic system in MDS. IPSS utilizes morphological variables to assign patients into low, intermediate or high-risk groups2. Accurate classification into one of these prognostic categories is critical as it determines selection of therapy regimes. Recent systematic profiling screens of MDS genomes have unraveled a complex network of cellular pathways that are causally implicated in MDS pathogenesis. Mutations have now been characterized in a number of key components of the spliceosome machinery (SF3B1, SRSF2, U2AF1, U2AF2, ZRSR2), regulators of DNA methylation (DNMT3A, IDH1, IDH2, TET2), chromatin modification (ASXL1, EZH2), transcription (EVI1, RUNX1, GATA2), signal transduction (NRAS, JAK2, KRAS, CBL) and cell cycle control (TP53)3-9. Collectively, more than 40 genes are significantly mutated in MDS; these mutations account for nearly 90% of MDS patients. The majority of patients present with two or more oncogenic mutations at diagnosis, and significant patterns of gene-gene interactions and mutual exclusivity have been reported10,11. Systematic integration of mutation data with large and well-annotated clinical datasets offers an unprecedented opportunity to decipher both the diagnostic as well as prognostic potential of these mutations as clinical biomarkers. However, the underlying genetic heterogeneity imposes significant challenges and important considerations that need to be accounted for when interpreting observed correlations between genotype, morphology and patient outcome. To unravel the interlocking genetic heterogeneity in MDS, Bejar et al., Papaemmanuil et al., and Haferlach et al. have studied the prevalence of acquired gene mutations in MDS and closely related chronic myeloid neoplasms in ~ 2100 MDS patients with well-annotated diagnostic and clinical outcome variables10-12. Univariate analysis has identified more than 10 genes to be significantly correlated with clinical outcome, including SF3B1, SRSF2, ASXL1, RUNX1, TP53, BCOR, RUNX1, EZH2, IDH2, ZRSR2, U2AF1 and CUX1. The total number of oncogenic mutations identified in each patient is selected as one of the most significant genetic predictors of outcome. Mutations in gene components of the spliceosome machinery are observed in approximately 50% of MDS patients, identifying pre-mRNA splicing as the most frequently altered biological process in MDS. Additionally, clonal relationship analysis of these mutations identifies that mutations in splicing genes occur early, followed by mutations in preferred partner genes, and mutations in different genes of the spliceosome machinery are associated with distinct morphological classification groups. The present talk will provide an overview of our current understanding of the underlying molecular mechanisms that underpin MDS biology. It will also evaluate how the genetic architecture of MDS can be incorporated in developing reliable and informative patient classification as well as outcome prediction models that can support clinical decision making in the future.
References:
1. Tefferi A, Vardiman JW. Myelodysplastic syndromes. N Engl J Med. 2009;361(19):1872-1885.
2. Greenberg PL, Tuechler H, Schanz J, et al. Revised International Prognostic Scoring System (IPSS-R) for myelodysplastic syndromes. Blood. 2012.
3. Yoshida K, Sanada M, Shiraishi Y, et al. Frequent pathway mutations of splicing machinery in myelodysplasia. Nature. 2011;478(7367):64-69.
4. Graubert TA, Shen D, Ding L, et al. Recurrent mutations in the U2AF1 splicing factor in myelodysplastic syndromes. Nat Genet. 2012;44(1):53-57.
5. Ernst T, Chase AJ, Score J, et al. Inactivating mutations of the histone methyltransferase gene EZH2 in myeloid disorders. Nat Genet. 2010;42(8):722-726.
6. Ley TJ, Ding L, Walter MJ, et al. DNMT3A mutations in acute myeloid leukemia. N Engl J Med. 2010;363(25):2424-2433.
7. Mardis ER, Ding L, Dooling DJ, et al. Recurring mutations found by sequencing an acute myeloid leukemia genome. N Engl J Med. 2009;361(11):1058-1066.
8. Gelsi-Boyer V, Trouplin V, Adelaide J, et al. Mutations of polycomb-associated gene ASXL1 in myelodysplastic syndromes and chronic myelomonocytic leukaemia. Br J Haematol. 2009;145(6):788-800.
9. Shih AH, Levine RL. Molecular biology of myelodysplastic syndromes. Semin Oncol. 2011;38(5):613-620.
10. Haferlach T, Nagata Y, Grossmann V, et al. Landscape of genetic lesions in 944 patients with myelodysplastic syndromes. Leukemia. 2014;28(2):241-247.
11. Papaemmanuil E, Gerstung M, Malcovati L, et al. Clinical and biological implications of driver mutations in myelodysplastic syndromes. Blood. 2013;122(22):3616-3627; quiz 3699.
12. Bejar R, Stevenson K, Abdel-Wahab O, et al. Clinical effect of point mutations in myelodysplastic syndromes. N Engl J Med. 2011;364(26):2496-2506.
No relevant conflicts of interest to declare.
Author notes
Asterisk with author names denotes non-ASH members.
