Synergistic Interactions of Molecular and Clinical Advances for Characterizing the Myelodysplastic Syndromes

Author: Peter L. Greenberg MD
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The myelodysplastic syndromes (MDS) are a heterogeneous spectrum of myeloid clonal hemopathies that provide a clinical model for evaluating the potential evolution of a relatively benign group of hematologic malignancies into one that is frankly aggressive (acute myeloid leukemia [AML]). Clinical and biological complexity has become apparent in this spectrum of disorders. To help to more clearly define the clinical status, prognosis, and therapeutic strategies for these patients, several clinical risk-based classification systems have been developed. In addition, investigations using next-generation molecular technology have permitted clarification of the exomic gene mutational landscape of marrow cells from these patients, describing critical biologic derangements that contribute to the patients' clinical phenotypes. Proposals have been generated to attempt to effectively incorporate these developments into current management strategies. Given these features, the current NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for MDS have been modified to reflect these relevant advances.1 This article focuses on describing a synthesis of recently reported clinical features and underlying molecular pathogenetic findings intrinsic to the aberrant marrow hematopoiesis generating patient outcomes in MDS.

To aid clinical characterization of patients with MDS, the standard risk-based International Prognostic Scoring System (IPSS) was recently refined and superseded by the Revised IPSS (IPSS-R),2 as reviewed in the current NCCN Guidelines for MDS.1 Advances within the IPSS-R methodology include 5, rather than the previous 3, cytogenetic prognostic subgroups, with specific new classification of several less-common cytogenetic subsets, a split of the low marrow blast percentage value, and scores based on depth rather than the number of cytopenias. This model provides 5 major prognostic categories rather than the 4 that are represented in the IPSS. Various other differentiating features in the IPSS-R are additive to the 5 major parameters for predicting survival, albeit not for AML evolution: age, performance status, serum ferritin levels, and lactate dehydrogenase levels. Compared with the IPSS, the IPSS-R has shown improved predictive prognostic power. The IPSS-R has been extensively validated,3 and has also been shown to be useful for predicting clinical outcomes in patients with secondary MDS and after various forms of therapy (chemotherapy or hematopoietic stem cell transplantation).46 The WHO classification–based Prognostic Scoring System (WPSS), which uses WHO morphologic categories in addition to other features of the IPSS-R, has also been updated and shown to have comparable prognostic efficacy.7

Pathogenetic mechanisms contributing to the patients' clinical phenotypes include the impact of a disparate group of somatic gene mutations. Recent investigations have provided major molecular insights into specific mutations within hematopoietic cells that play a critical role in clinical outcomes. Point mutations have been identified in more than 90% of patients with MDS, including most patients with a normal karyotype.810 These mutational features encompass abnormalities involving genes engaged in molecular signaling and differentiation, regulation of cell cycle progression, apoptosis, transcriptional RNA splicing, translation, and epigenetic changes. Some of these mutations are mutually exclusive, whereas others are coexpressed, suggesting that disease status is codependent on these abnormalities. Although approximately 50 to 100 genes are frequently mutated in MDS, consistent mutations exist in only approximately 10% of patients.

Spliceosome mutations are those most commonly found in MDS, and are present in approximately 50% of patients.11 Few (usually 1–3) driver mutations are found at disease presentation, along with numerous passenger mutations that generally increase with disease evolution. Analysis of the clonal architecture of MDS has shown that genetic evolution, and disease progression to AML is a dynamic process shaped by multiple cycles of mutation acquisition and clonal selection.12

These molecular findings have also provided prognostic information. An inverse correlation exists between the number of mutations and overall and leukemia-free survival.9 At least 25 mutations have been associated with poor prognosis in MDS, with good reproducibility for at least 5 specific mutations (TP53, EZH2, ETV6, RUNX1, ASXL1) that are present in 3% to 14% of patients.810 TP53 mutations have major negative prognostic implications for patients with MDS, particularly in those with del(5q) cytogenetic abnormalities, complex cytogenetics, or secondary forms of MDS.1315 In contrast, SF3B1 mutations, present in virtually all patients with ring sideroblasts, are associated with a good prognosis.16

Studies have recently defined molecular abnormalities in chronic myelomonocytic leukemia, with more than 90% of patients with this disease having 1 of 9 specific mutations (TET2, SRSF2, ASXL1, RAS, RUNX, CBL, EZH2, JAK2, IDH1/2).17 The SRSF2 mutation was generally associated with a good prognosis, whereas poor outcomes were found in patients with U2AF1 and DNMT3A mutations.18

However, in addition to somatic mutations occurring in patients with MDS and other myeloid malignancies, recent studies have also shown these lesions in individuals without hematologic abnormalities, particularly as they age.19,20 An increased proportion of such individuals (ie, with clonal hematopoiesis of indeterminate prognosis) may subsequently develop a myeloid malignancy.20,21

Given these findings, major issues have arisen as to the use and timing of molecular testing for clinical evaluation. Because no mutation is specific for MDS, and not all patients with MDS have mutations, these molecular features are not diagnostic of the disease. Thus, although these findings can establish somatic (acquired) versus congenital (germline) abnormalities, information obtained from molecular studies should be used in the appropriate clinical context—after clinical and cytogenetic features have established the diagnosis of MDS. After the diagnosis has been made, these molecular findings provide additive prognostic information. The previously noted genes associated with poor prognosis have been shown to alter clinical risk associations by shifting patients into a higher IPSS or IPSS-R clinical risk category.8 Also, germline abnormalities may exist in certain individuals, particularly those who are relatively young or have familial hematologic disorders.22 Thus, evaluating nonhematopoietic tissue (eg, skin, buccal smears) for the mutations is important in these circumstances. In addition, more consistency in analysis is needed because differing results may be reported when nonuniform molecular platforms have been used.

Despite these molecular advances, much work is needed to clarify the roles of many of these disparate mutational features, particularly for those lesions that are less commonly present. In addition, the sequence and coexpression of such aberrancies and their relation to either AML progression or marrow failure remains to be defined. To further clarify such issues, a global multi-institutional collaborative project (the International Working Group for Prognosis in MDS-Molecular Project, coordinated under the aegis of the MDS Foundation) is currently obtaining and analyzing molecular abnormalities from marrow samples in a large group of patients with MDS to define the clearer implications of these findings.

In addition to exomic mutational data, transcriptional studies using microarray platforms,2325 and more recently with RNA sequencing,26 have shown alterations of gene expression patterns from MDS CD34+ marrow cells related to their clinical stages and outcomes, with significant and distinctive differences in gene expression between MDS and normal marrow. Specific clustering of the differentially expressed genes showed alterations in functional pathways and biologic processes that are highly relevant for MDS. These transcriptomic data provide valuable information complementary to exomic gene mutational findings that contribute to further understanding of the biologic mechanisms underlying MDS.

These recent advances that demonstrate methods for characterization of patients with MDS are harbingers of future MDS classifications that will incorporate both molecular and clinical features to refine the diagnostic and prognostic status of these patients. This approach will permit a molecularly based classification within this heterogeneous disease spectrum. Further, as the pathogenetic features related to molecular mutations underlying MDS are more clearly discerned, this “next-generation comprehension” should lead to the discovery of valuable novel therapeutic targets for these patients.

References

  • 1.

    Greenberg PL, Stone RM, Bejar R. NCCN Clinical Practice Guidelines in Oncology: Myelodysplastic Syndromes, Version 1.2015. Available at: NCCN.org. Accessed June 15, 2015.

    • Search Google Scholar
    • Export Citation
  • 2.

    Greenberg P, Tuechler H, Schanz J. Revised International Prognostic Scoring System for myelodysplastic syndromes. Blood 2012;120:24542465.

  • 3.

    Voso MT, Fenu S, Latagliata R. Revised International Prognostic Scoring System predicts survival and leukemic evolution of myelodysplastic syndromes significantly better than IPSS and WHO Prognostic Scoring System: validation by the Gruppo Romano Mielodisplasie Italian Regional Database. J Clin Oncol 2013;31:26712677.

    • Search Google Scholar
    • Export Citation
  • 4.

    Ok CY, Hasserjian RP, Fox PS. Application of the International Prognostic Scoring System-Revised in therapy-related myelodysplastic syndromes and oligoblastic acute myeloid leukemia. Leukemia 2014;28:185189.

    • Search Google Scholar
    • Export Citation
  • 5.

    Zeidan AM, Lee JW, Prebet T. Comparison of the prognostic utility of the Revised International Prognostic Scoring System and the French Prognostic Scoring System in azacitidine-treated patients with myelodysplastic syndromes. Brit J Haematol 2014;166:352359.

    • Search Google Scholar
    • Export Citation
  • 6.

    Della Porta MG, Alessandrino EP, Bacigalupo A. Predictive factors for the outcome of allogeneic transplantation in patients with MDS stratified according to the revised IPSS-R. Blood 2014;123:23332342.

    • Search Google Scholar
    • Export Citation
  • 7.

    Della Porta MG, Tuechler H, Malcovati L. Validation of WHO classification-based Prognostic Scoring System (WPSS) for myelodysplastic syndromes and comparison with the revised International Prognostic Scoring System (IPSS-R). A study of the International Working Group for Prognosis in Myelodysplasia (IWG-PM) [published online ahead of print February 27, 2015]. Leukemia, doi: 10.1038/leu.2015.55.

    • Search Google Scholar
    • Export Citation
  • 8.

    Bejar R, Stevenson K, Abdel-Wahab O. Clinical effect of point mutations in myelodysplastic syndromes. N Engl J Med 2011;364:24962506.

  • 9.

    Papaemmanuil E, Gerstung M, Malcovati L. Clinical and biological implications of driver mutations in myelodysplastic syndromes. Blood 2013;122:36163627.

    • Search Google Scholar
    • Export Citation
  • 10.

    Haferlach T, Nagata Y, Grossmann V. Landscape of genetic lesions in 944 patients with myelodysplastic syndromes. Leukemia 2014;28:241247.

  • 11.

    Yoshida K, Sanada M, Shiraishi Y. Frequent pathway mutations of splicing machinery in myelodysplasia. Nature 2011;478:6469.

  • 12.

    Walter MJ, Shen D, Ding L. Clonal architecture of secondary acute myeloid leukemia. N Engl J Med 2012;366:10901098.

  • 13.

    Kulasekararaj AG, Smith AE, Mian SA. TP53 mutations in myelodysplastic syndrome are strongly correlated with aberrations of chromosome 5, and correlate with adverse prognosis. Br J Haematol 2013;160:660672.

    • Search Google Scholar
    • Export Citation
  • 14.

    Jädersten M, Saft L, Smith A. TP53 mutations in low-risk myelodysplastic syndromes with del(5q) predict disease progression. J Clin Oncol 2011;29:19711979.

    • Search Google Scholar
    • Export Citation
  • 15.

    Bejar R, Papaemmanuil E, Haferlach T. TP53 mutation status divides MDS patients with complex karyotypes into distinct prognostic risk groups: analysis of combined datasets from the International Working Group for MDS-Molecular Prognosis Committee (IWG-PM) [abstract]. Blood 2014;124(Suppl 21):Abstract 532.

    • Search Google Scholar
    • Export Citation
  • 16.

    Malcovati L, Karimi M, Papaemmanuil E. SF3B1 mutation identifies a distinct subset of myelodysplastic syndrome with ring sideroblasts [published online ahead of print May 8, 2015]. Blood, pii: blood-2015-03-633537.

    • Search Google Scholar
    • Export Citation
  • 17.

    Meggendorfer M, Roller A, Haferlach T. SRSF2 mutations in 275 cases with chronic myelomonocytic leukemia (CMML). Blood 2012;120:30803088.

  • 18.

    Abu Kar S, Jankowska A, Makishima H. Spliceosomal gene mutations are frequent events in the diverse mutational spectrum of chronic myelomonocytic leukemia but largely absent in juvenile myelomonocytic leukemia. Haematologica 2013;98:107113.

    • Search Google Scholar
    • Export Citation
  • 19.

    Jaiswal S, Fontanillas P, Flannick J. Age-related clonal hematopoiesis associated with adverse outcomes. N Engl J Med 2014;371:24882498.

  • 20.

    Genovese G, Jaiswal S, Ebert BL, McCarroll SA. Clonal hematopoiesis and blood-cancer risk. N Engl J Med 2015;372:10711072.

  • 21.

    Steensma DP, Bejar R, Jaiswal S. Clonal hematopoiesis of indeterminate potential and its distinction from myelodysplastic syndromes [published online ahead of print April 30, 2015]. Blood, pii: blood-2015-03-631747.

    • Search Google Scholar
    • Export Citation
  • 22.

    West AH, Godley LA, Churpek JE. Familial myelodysplastic syndrome/acute leukemia syndromes: a review and utility for translational investigations. Ann N Y Acad Sci 2014;1310:111118.

    • Search Google Scholar
    • Export Citation
  • 23.

    Pellagatti A, Cazzola M, Giagounidis AA. Gene expression profiles of CD34+ cells in myelodysplastic syndromes: involvement of interferon-stimulated genes and correlation to FAB subtype and karyotype. Blood 2006;108:337345.

    • Search Google Scholar
    • Export Citation
  • 24.

    Sridhar K, Ross D, Tibshirani R. Relationship of differential gene expression profiles in CD34+ myelodysplastic syndrome marrow cells to disease subtype and progression. Blood 2009;114:48474858.

    • Search Google Scholar
    • Export Citation
  • 25.

    Pellagatti A, Benner A, Mills KI. Identification of gene expression-based prognostic markers in the hematopoietic stem cells of patients with myelodysplastic syndromes. J Clin Oncol 2013;31:35573564.

    • Search Google Scholar
    • Export Citation
  • 26.

    Im H, Rao V, Sridhar K. Transcriptomic evaluation of CD34+ marrow cells from myelodysplastic syndrome patients [abstract]. Blood 2014;124(Suppl 21):Abstract 1894.

    • Search Google Scholar
    • Export Citation

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Peter L. Greenberg, MD, is Professor of Medicine in the Division of Hematology and Director of the Myelodysplastic Syndromes (MDS) Center at Stanford University Cancer Institute. He is Chair of the NCCN MDS Guidelines Panel, serves as a member of the Leukemia Core Committee of ECOG, and is a member of the Board of Directors of the MDS Foundation. He is coordinator of the International Working Group for Prognosis in MDS (IWG-PM).

The ideas and viewpoints expressed in this editorial are those of the author and do not necessarily represent any policy, position, or program of NCCN.

  • 1.

    Greenberg PL, Stone RM, Bejar R. NCCN Clinical Practice Guidelines in Oncology: Myelodysplastic Syndromes, Version 1.2015. Available at: NCCN.org. Accessed June 15, 2015.

    • Search Google Scholar
    • Export Citation
  • 2.

    Greenberg P, Tuechler H, Schanz J. Revised International Prognostic Scoring System for myelodysplastic syndromes. Blood 2012;120:24542465.

  • 3.

    Voso MT, Fenu S, Latagliata R. Revised International Prognostic Scoring System predicts survival and leukemic evolution of myelodysplastic syndromes significantly better than IPSS and WHO Prognostic Scoring System: validation by the Gruppo Romano Mielodisplasie Italian Regional Database. J Clin Oncol 2013;31:26712677.

    • Search Google Scholar
    • Export Citation
  • 4.

    Ok CY, Hasserjian RP, Fox PS. Application of the International Prognostic Scoring System-Revised in therapy-related myelodysplastic syndromes and oligoblastic acute myeloid leukemia. Leukemia 2014;28:185189.

    • Search Google Scholar
    • Export Citation
  • 5.

    Zeidan AM, Lee JW, Prebet T. Comparison of the prognostic utility of the Revised International Prognostic Scoring System and the French Prognostic Scoring System in azacitidine-treated patients with myelodysplastic syndromes. Brit J Haematol 2014;166:352359.

    • Search Google Scholar
    • Export Citation
  • 6.

    Della Porta MG, Alessandrino EP, Bacigalupo A. Predictive factors for the outcome of allogeneic transplantation in patients with MDS stratified according to the revised IPSS-R. Blood 2014;123:23332342.

    • Search Google Scholar
    • Export Citation
  • 7.

    Della Porta MG, Tuechler H, Malcovati L. Validation of WHO classification-based Prognostic Scoring System (WPSS) for myelodysplastic syndromes and comparison with the revised International Prognostic Scoring System (IPSS-R). A study of the International Working Group for Prognosis in Myelodysplasia (IWG-PM) [published online ahead of print February 27, 2015]. Leukemia, doi: 10.1038/leu.2015.55.

    • Search Google Scholar
    • Export Citation
  • 8.

    Bejar R, Stevenson K, Abdel-Wahab O. Clinical effect of point mutations in myelodysplastic syndromes. N Engl J Med 2011;364:24962506.

  • 9.

    Papaemmanuil E, Gerstung M, Malcovati L. Clinical and biological implications of driver mutations in myelodysplastic syndromes. Blood 2013;122:36163627.

    • Search Google Scholar
    • Export Citation
  • 10.

    Haferlach T, Nagata Y, Grossmann V. Landscape of genetic lesions in 944 patients with myelodysplastic syndromes. Leukemia 2014;28:241247.

  • 11.

    Yoshida K, Sanada M, Shiraishi Y. Frequent pathway mutations of splicing machinery in myelodysplasia. Nature 2011;478:6469.

  • 12.

    Walter MJ, Shen D, Ding L. Clonal architecture of secondary acute myeloid leukemia. N Engl J Med 2012;366:10901098.

  • 13.

    Kulasekararaj AG, Smith AE, Mian SA. TP53 mutations in myelodysplastic syndrome are strongly correlated with aberrations of chromosome 5, and correlate with adverse prognosis. Br J Haematol 2013;160:660672.

    • Search Google Scholar
    • Export Citation
  • 14.

    Jädersten M, Saft L, Smith A. TP53 mutations in low-risk myelodysplastic syndromes with del(5q) predict disease progression. J Clin Oncol 2011;29:19711979.

    • Search Google Scholar
    • Export Citation
  • 15.

    Bejar R, Papaemmanuil E, Haferlach T. TP53 mutation status divides MDS patients with complex karyotypes into distinct prognostic risk groups: analysis of combined datasets from the International Working Group for MDS-Molecular Prognosis Committee (IWG-PM) [abstract]. Blood 2014;124(Suppl 21):Abstract 532.

    • Search Google Scholar
    • Export Citation
  • 16.

    Malcovati L, Karimi M, Papaemmanuil E. SF3B1 mutation identifies a distinct subset of myelodysplastic syndrome with ring sideroblasts [published online ahead of print May 8, 2015]. Blood, pii: blood-2015-03-633537.

    • Search Google Scholar
    • Export Citation
  • 17.

    Meggendorfer M, Roller A, Haferlach T. SRSF2 mutations in 275 cases with chronic myelomonocytic leukemia (CMML). Blood 2012;120:30803088.

  • 18.

    Abu Kar S, Jankowska A, Makishima H. Spliceosomal gene mutations are frequent events in the diverse mutational spectrum of chronic myelomonocytic leukemia but largely absent in juvenile myelomonocytic leukemia. Haematologica 2013;98:107113.

    • Search Google Scholar
    • Export Citation
  • 19.

    Jaiswal S, Fontanillas P, Flannick J. Age-related clonal hematopoiesis associated with adverse outcomes. N Engl J Med 2014;371:24882498.

  • 20.

    Genovese G, Jaiswal S, Ebert BL, McCarroll SA. Clonal hematopoiesis and blood-cancer risk. N Engl J Med 2015;372:10711072.

  • 21.

    Steensma DP, Bejar R, Jaiswal S. Clonal hematopoiesis of indeterminate potential and its distinction from myelodysplastic syndromes [published online ahead of print April 30, 2015]. Blood, pii: blood-2015-03-631747.

    • Search Google Scholar
    • Export Citation
  • 22.

    West AH, Godley LA, Churpek JE. Familial myelodysplastic syndrome/acute leukemia syndromes: a review and utility for translational investigations. Ann N Y Acad Sci 2014;1310:111118.

    • Search Google Scholar
    • Export Citation
  • 23.

    Pellagatti A, Cazzola M, Giagounidis AA. Gene expression profiles of CD34+ cells in myelodysplastic syndromes: involvement of interferon-stimulated genes and correlation to FAB subtype and karyotype. Blood 2006;108:337345.

    • Search Google Scholar
    • Export Citation
  • 24.

    Sridhar K, Ross D, Tibshirani R. Relationship of differential gene expression profiles in CD34+ myelodysplastic syndrome marrow cells to disease subtype and progression. Blood 2009;114:48474858.

    • Search Google Scholar
    • Export Citation
  • 25.

    Pellagatti A, Benner A, Mills KI. Identification of gene expression-based prognostic markers in the hematopoietic stem cells of patients with myelodysplastic syndromes. J Clin Oncol 2013;31:35573564.

    • Search Google Scholar
    • Export Citation
  • 26.

    Im H, Rao V, Sridhar K. Transcriptomic evaluation of CD34+ marrow cells from myelodysplastic syndrome patients [abstract]. Blood 2014;124(Suppl 21):Abstract 1894.

    • Search Google Scholar
    • Export Citation
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