The evolving field of prognostication and risk stratification in MDS: Recent developments and future directions.

YBLRE-00397; No of Pages 10 Blood Reviews xxx (2015) xxx–xxx

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REVIEW

The evolving field of prognostication and risk stratification in MDS: Recent developments and future directions Eun-Ju Lee, Nikolai Podoltsev, Steven D. Gore, Amer M. Zeidan ⁎ Section of Hematology, Department of Internal Medicine, Yale University, New Haven, CT, USA

a r t i c l e

i n f o

Available online xxxx Keywords: Risk stratification Prognostication Prognosis Myelodysplastic syndromes (MDS) Hypomethylating agents Biomarkers Mutations Genome sequencing

a b s t r a c t The clinical course of patients with myelodysplastic syndromes (MDS) is characterized by wide variability reflecting the underlying genetic and biological heterogeneity of the disease. Accurate prediction of outcomes for individual patients is an integral part of the evidence-based risk/benefit calculations that are necessary for tailoring the aggressiveness of therapeutic interventions. While several prognostication tools have been developed and validated for risk stratification, each of these systems has limitations. The recent progress in genomic sequencing techniques has led to discoveries of recurrent molecular mutations in MDS patients with independent impact on relevant clinical outcomes. Reliable assays of these mutations have already entered the clinic and efforts are currently ongoing to formally incorporate mutational analysis into the existing clinicopathologic risk stratification tools. Additionally, mutational analysis holds promise for going beyond prognostication to therapeutic selection and individualized treatment-specific prediction of outcomes; abilities that would revolutionize MDS patient care. Despite these exciting developments, the best way of incorporating molecular testing for use in prognostication and prediction of outcomes in clinical practice remains undefined and further research is warranted. © 2015 Published by Elsevier Ltd.

1. Introduction Myelodysplastic syndromes (MDS) represent a diverse group of clonal hematopoietic cell neoplastic disorders characterized clinically by dysplasia, peripheral blood [PB] cytopenias, and a variably increased risk of progression to acute myeloid leukemia (AML) [1,2]. The majority of patients are older than 65 at time of diagnosis. The age-adjusted incidence of MDS in the United States is estimated at 3.4 per 100,000 people, but underdetection and underreporting have likely underestimated the true incidence of MDS [1–3]. Outcomes of patients with myelodysplastic syndromes (MDS), including the propensity for progressive disease vary considerably between individuals [3,4]. Reflecting the heterogeneous subtypes comprising MDS, the natural history and tempo of disease ranges from life expectancy of a few months to several years [4,5]. On the therapeutic front, there are currently 3 agents specifically approved for the treatment of MDS. These are the DNA methyltransferase inhibitors (DNMTis) azacitidine and decitabine and the oral thalidomide–congener lenalidomide [6]. These agents, in conjunction with supportive care (transfusions, hematopoietic growth factors, iron chelation when indicated), immunosuppressive therapies, allogeneic ⁎ Corresponding author at: Section of Hematology, Department of Internal Medicine, Yale University, 333 Cedar Street, PO Box 208028, New Haven, CT 06520-8028, USA. Tel.: +1 203 737 7103; fax: +1 203 785 7232. E-mail address: [email protected] (A.M. Zeidan).

hematopoietic cell transplantation (alloHCT), and occasionally cytotoxic chemotherapy, comprise therapies commonly used for MDS. Treatment options are utilized in a risk-adapted manner balancing the risk/benefit of the proposed intervention with the patient's predicted survival and probability of leukemic progression in order to avoid undue harm [6–8]. Accurate risk stratification is paramount in aiding predictions of survival and for guiding individual treatment decisions. Prediction of clinical benefit from specific MDS therapies is also a significant area of ongoing research. In this article, we review the commonly-used clinicopathologic prognostication tools in MDS, discuss the improvements in cytogenetic prognostic classification, introduce recently identified molecular mutations with prognostic importance, describe efforts at integrating these molecular biomarkers in risk stratification tools, and delineate clinical areas of unmet needs and future directions of research. 2. The role of prognostication in MDS Given the significant variation in disease course and natural history among patients with MDS, prognostication tools use clinical and pathologic characteristics of an individual patient's disease to clarify the probability of disease progression and survival. This information is used to counsel patients and in clinical decision-making [6]. Formerly, MDS was broadly divided into lower-risk (LR) and higher-risk (HR) categories based on International Prognostic Scoring System (IPSS) categorization [5,9]. The treatment goals for LR-MDS often focus on symptom management and preservation of quality of life. In this group of patients,

http://dx.doi.org/10.1016/j.blre.2015.06.004 0268-960X/© 2015 Published by Elsevier Ltd.

Please cite this article as: Lee E-J, et al, The evolving field of prognostication and risk stratification in MDS: Recent developments and future directions, Blood Rev (2015), http://dx.doi.org/10.1016/j.blre.2015.06.004

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E.-J. Lee et al. / Blood Reviews xxx (2015) xxx–xxx

initial therapy [6,12,13]. Those with HR-MDS, on the other hand, have poor outcomes with a median survival of less than one year without treatment [5]. For HR patients who are not candidates for alloHCT, treatment with azacitidine improves survival [6,14]. Several commonly used

hematopoietic growth factors, immunosuppressive therapies, and supportive therapy with transfusions comprise the backbone of management [6,10,11]. Subsequently, treatment with DNMTis or alloHCT is considered in patients who develop worsening cytopenias despite

Table 1 Commonly used prognostic scoring systems for myelodysplastic syndromes (MDS). Adapted from ref. [9]. IPSS: International Prognostic Scoring System (ref. [5]), IPSS-R: revised IPSS (ref. [26]), WPSS: World Health Organization Classification-Based Prognostic Scoring System (ref. [20]), MPSS: MD Anderson Global Prognostic Scoring System (ref. [36]), LR-PSS: MD Anderson Lower-Risk Prognostic Scoring System (ref. [24]), T-PSS: Treatment-related MDS Prognostic Scoring System (ref. [41]), and FPSS: French Prognostic Scoring System (ref. [105]). IPSS, 1997

IPSS-R, 2012

Parameter

Score

BM blasts % b5% 5–10% 11–20% 21–30% Cytogenetics* Good Intermediate Poor

Parameter

Score

BM blasts % ≤2% N2 to b5% 5–10% N10% Cytogenetics** Very good Good Intermediate' Poor Very poor

0 0.5 1.5 2 0 0.5 1

Cytopenias† 0 or 1 2 or 3

WPSS, 2007

0 0.5

Hb (g/dL) ≥ 10 8 to b10 b8 ANC (×10^9/L) ≥ 0.8 b0.8 Platelets (×10^9/L) ≥ 100 50 to b100 b50

Outcomes (median OS)

0 1 2 3 0 1 2 3 4 0 1 1.5 0 0.5 0 0.5 1

MPSS, 2008

Parameter

Score

WHO category RA, RARS, del5q MDS RCMD, RCMD-RS RAEB-I RAEB-II Cytogenetics* Good Intermediate Poor

0 1 2 3 0 1 2

RBC transfusion dependence †† Yes No


1 0

Score

OS (Y)

Risk group

Score

OS (Y)

Risk group

Score

Low INT-1 INT-2 High

0 0.5–1 1.5–2.0 ≥2.5

5.7 3.5 1.2 0.4

Very low Low INT High Very high

≤1.5 N1.5–3 N3–4.5 N4.5–6 N6

8.8 5.3 3.0 1.6 0.8

Very low Low INT High Very high

0 1 2 3–4 5–6

T-PSS¥, 2014

Parameter

Score

Age (years) b60 ≥60 Cytogenetics Favorable (diploid or del5q) Unfavorable (all others) BM blasts% ≥4%

0 2 0 1

1

Platelets (×10^9/L) b50 50–200

2 1

Hb (g/dL) b10

1


Score

Age (years) 60–64 ≥65

1 2

ECOG PS ≥ 2 Cytogenetics Chr 7 abnormality or complex (≥3)

2 3

BM blasts % 5–10% 11–29%

1 2

Platelets (×10^9/L) b30 30–49 50–199 WBC (×10^9/L) N 20 Hb b 12 g/dL Prior RBC transfusion — Yes

3 2 1 2 2 1


Risk group

LR-PSS+, 2008

Parameter

Outcomes (median OS) OS (M) 141 66 48 26 9

Risk group

Score

OS (M)

Low INT-1 INT-2 High

0–4 5–6 7–8 ≥9

54 25 14 6

FPSSŦ, 2011

Parameter

Score

Parameter

Score

Age (years) ≥ 65

1

ECOG PS ≥ 2

1

ECOG PS ≥ 2 WHO class RARS, RAEB-I, RAEB-2 Others

1

Presence of PB blasts — Yes Cytogenetics* Good Intermediate Poor

1

1 0

Cytogenetics Unfavorable (−7, complex [≥3]) All others

1 0

Hb (g/dL) b 11

1

Platelets (×10^9/L) b 50

1

Prior RBC transfusion — Yes

1


RBC transfusion dependence††† Yes No

0 1 2

1 0

Outcomes

Risk group

Score

OS (M)

Risk group

Score

OS (M)

Risk group

Score

OS (M)

Cat-1 Cat-2 Cat-3

0–2 3–4 ≥5

80.3 26.6 14.2

Good INT Poor

0–2 3–4 5–7

34 12 5

Low INT High

0 1–3 4–5

32.1 15.0 6.1

+: LR-PSS applies to IPSS low and INT-1 disease, ¥: TPSS was designed for treatment-related MDS, Ŧ: FPSS applies to IPSS INT-2 and high risk MDS patients on Azacitidine therapy, BM: bone marrow, PS: performance status, RA: refractory anemia, RARS: refractory anemia with ring sideroblasts, RCMD: Refractory cytopenia with multilineage dysplasia, RAEB: Refractory anemia with excess blasts, 5q-: interstitial deletion of long arm of chromosome 5, PB: peripheral blood, *Good: Normal, −Y, 5q-, 20q-; Poor: complex (≥3 abnormalities) or chromosome 7 abnormalities. Intermediate: Other karyotypic abnormalities, ANC: absolute neutrophil count, g/dL: gram/deciliter, L: liter, †: Hb b 10 g/dL; ANC b 1800/μL; platelets b 100,000/μL, **: Very good: −Y, del(11q), Good: normal, del(5q),del(12p), del(20q), double including del(5q), Intermediate: del(7q), +8, +10, i(17q), any other single or double independent clones, Poor: −7, inv.(3)/t(3q)/del(3q), double including −7/del(7q), complex: 3 abnormalities, Very poor: complex N 3 abnormalities, ††: ≥1 RBC transfusion every 8 weeks over a period of 4 months or Hb (g/dL) b 9 in males or b8 in females, †††: ≥4 RBC transfusion over 8 weeks or Hb (g/dL) b 11, OS: overall survival, Y: year, M: month.



prognostic schemes divide patients into risk groups [Table 1], though each of these tools has inherent limitations. These prognostic schemes were not designed to predict response to any specific treatment [3,7,9, 15]. Medical comorbidities have not been incorporated into these schemata; such concomitant conditions may limit feasibility of and tolerance to intensive therapies in the elderly MDS population [16,17]. Most importantly, median survival predictions apply to populations of patients and not to individuals [16]. 3. Current prognostication schemes and classification systems

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risk categories of the IPSS-R. 27% of patients with IPSS LR-MDS will be upstaged in the IPSS-R; 18% of patients with IPSS HR-MDS will be downstaged. Although the IPSS-R was intended for use only at diagnosis, prognostic utility of the IPSS-R at various points in the disease course, including after treatment with DNMTis and alloHCT, has been demonstrated [28–33]. However, further study is needed to clarify the utility of the IPSS-R in making clinical decisions especially for the intermediate risk group [31]. While the IPSS-R addresses many of the shortcomings of the IPSS, some limitations remain. Patients with 20–30% BM blasts were included in the IPSS-R, whereas those with t-MDS and proliferative CMML were not.

3.1. International Prognostic Scoring System (IPSS) The International Prognostic Scoring System (IPSS) was published in 1997 and continues to be used widely despite more accurate prediction models [5]. The IPSS was developed from an international database of 816 patients with MDS, most of whom were treated with only supportive measures. In the IPSS, patients are divided into one of four prognostic risk categories: low, intermediate-1 (INT-1), intermediate 2 (INT-2), and high [Table 1] [5]. For practical clinical purposes, the IPSS low and INT-1 risk groups are often combined into LR-MDS whereas HR-MDS consists of the IPSS INT-2 and high-risk groups. Although commonly used given its relative simplicity and ease of use, there are several important limitations to the IPSS. Patients with proliferative chronic myelomonocytic leukemia (CMML) and those with treatment-related (t)-MDS were excluded from the initial tool derivation. Conversely, patients with 20–30% BM blasts, now reclassified as AML with myelodysplasia-related changes can be classified using IPSS [18,19]. The IPSS stratifies for the development of 30% blasts (so-called AML transformation at the time) better than for survival, hence potentially overweighing the blast percentage while undervaluing the impact of cytogenetic risk groups [20–22]. The IPSS was designed for use only at the time of diagnosis and was not formulated as a dynamic prognostic tool for use throughout the disease course [20]. A significant number of patients classified as having LR-MDS according to the IPSS have an aggressive course with poorer than predicted prognosis. This underestimation of risk is concerning given the potential delay in initiation of disease-modifying agents [23–25]. The degree of cytopenias and severity and extent of dysplasia are not considered in the IPSS. 3.2. Revised IPSS (IPSS-R) A large international database of 7012 patients with de novo, untreated MDS was compiled with the goal of refining the IPSS by incorporating updated data regarding prognostic importance of cytogenetics and accounting for severity of cytopenias [26]. Similar to the IPSS, the backbone of the IPSS-R remains bone marrow (BM) blast percentage, cytogenetics, and cytopenias. However, revisions were made to each of these categories. For BM blast percentage, the b5% group is divided into ≤2% and N2 b 5%, acknowledging the adverse risk of even a low percentage of blasts. The three cytogenetic risk groups from the IPSS were expanded into five and cytogenetic risk group bears heavier weighting in the IPSS-R [27]. Cytopenias were divided based on degree rather than simply the number of affected lines. Based on the total point score obtained using five variables at time of diagnosis, patients are assigned to one of five risk categories [Table 1]. BM fibrosis was not found to carry independent prognostic impact and was not included in the IPSS-R. Age, performance status, serum lactate dehydrogenase (LDH) level, serum ferritin level, and serum β2microglobulin level had some impact on survival, but none added independently to estimates of disease progression and were not included in the IPSS-R. A formula was derived to incorporate the effect of age on the IPSS-R score. However, given its complexity, it is unclear how commonly this adjustment for age is used in real-life practice. The IPSS-R appears to have a greater prognostic precision compared to the IPSS, separating patients in the IPSS INT-1 and INT-2 across all the

3.3. World Health Organization (WHO) classification-based Prognostic Scoring System (WPSS) The goal with the WPSS was to create a dynamic tool able to provide estimates of survival and disease progression throughout the disease course [20]. The original variables in the WPSS are the WHO pathologic subgroups, cytogenetics per the original IPSS classification, and red blood cell (RBC) transfusion requirement. With equal weight assigned to each prognostic factor, patients are classified into one of five risk groups [Table 1]. The WPSS was designed to be a time-dependent system and has the advantage of use at diagnosis as well as in follow-up, but it has not been validated in patients with t-MDS, CMML or MDS/ myeloproliferative neoplasms (MPN) overlap disorders [9]. Another criticism is the subjective nature of determining RBC transfusion dependence [34]. To address this concern, the WPSS was revised to use severe anemia defined as Hb b 9 g/dL in men and b8 g/dL in women, in place of transfusion requirement [35]. Additional limitations include the absence of platelet and white blood cell levels in the scoring system, the requirement of pathologic expertise to ensure reproducibility which can be limited in the community setting, and the inclusion of the less comprehensive original IPSS cytogenetic categories [9]. 3.4. The MD Anderson Prognostic Scoring Systems Multivariate analyses were performed on a database of 1915 patients including those with secondary MDS, CMML, MDS/MPN overlap disorders and those who received prior therapy to formulate the MD Anderson Global Prognostic Scoring System (MPSS) [36]. Based on 8 factors, patients were assigned to one of four prognostic risk groups [Table 1]. The MPSS has been externally validated and found to improve on the prognostic value of the IPSS [37]. However, this system is not widely used due to its relative complexity with 8 included factors, the simplification of cytogenetics with inclusion of only two abnormal karyotypes, and uncertainty in regard to best treatment options for patients with upstaged risk category after reassessment by the MPSS [9,38–40]. The presence of a subset of patients with aggressive disease and poor prognosis despite LR categorization by the IPSS led to the creation and subsequent validation of the MD Anderson Lower-Risk Prognostic Scoring System (LR-PSS) [24,25]. Proper identification of this group impacts clinical decision-making since these patients may benefit from early consideration of disease modifying therapies or clinical trial enrollment. A total of 856 patients with IPSS low and INT-1 disease were included in multivariate analyses and 5 prognostic factors were determined [Table 1]. Based on the total score, patients are placed into one of 3 risk groups: category 1, category 2 and category 3 with median survivals of 80.3 months, 26.6 months, and 14.2 months, respectively. The change in prognosis from potentially several years for patients with LR disease per the IPSS to as short as a little over a year for patients reclassified to category 3 of the LR-PSS is substantial; however the best therapeutic approach for these reclassified patients is unclear. A third prognostic model was developed by the MD Anderson group (the treatment-related MDS Prognostic Scoring System [T-PSS]) specifically for patients with t-MDS using data from 281 patients with t-MDS, defined as those who have received chemotherapy or radiation for


4


treatment of malignancy [41]. The 2008 WHO classification includes this group of patients under the entity of therapy-related myeloid neoplasm (t-MN) [19]. Based on 7 variables, patients are divided into three prognostic groups [Table 1]. Creating a scoring system specifically for tMDS is important since this population was excluded from development of the IPSS, IPSS-R, and WPPS, and may be less responsive to standard therapies compared to those with de novo MDS [41,42]. This prognostic score nonetheless requires further validation. Another prognostic scoring system designed specifically for hypoplastic MDS was published by the MD Anderson Group in 2012 [38]. The development of clinicopathologic prognostic tools appears to have reached a plateau in prognostic utility and incorporation of molecular data derived from newer technology is likely required for any improvement in this domain. 4. Genetic alterations and prognosis in patients with MDS 4.1. Cytogenetics and prognosis in MDS Cytogenetic abnormalities are the most important variable in determining prognosis, contribute to the phenotypic heterogeneity of MDS [43,44] and accordingly have been included in all prognostic scoring systems. Using traditional karyotyping, approximately 50% of patients with de novo MDS and up to 80% of t-MDS patients have abnormalities in conventional cytogenetics. In contrast to de novo AML, the chromosomal aberrations in MDS largely consist of structural or numerical deletions with very few balanced abnormalities such as translocations. Many of the cytogenetic abnormalities in MDS represent secondary genetic events resulting from genomic instability rather than primary pathogenetic driver mutations with important exceptions such as isolated del5q. In an Austro-German database of 2072 patients, the overall frequency of detectable cytogenetic aberrations was 52.3% [44]. In de novo MDS, 50% of patients bore cytogenetic abnormalities; in t-MDS, the incidence was 68.5%. The investigators identified 684 different cytogenetic profiles. Among patients treated solely with supportive measures, the prognostic relevance of the original IPSS cytogenetic profiles was validated for both good and poor risk karyotypes. In contrast, translocations involving 7q, considered poor-risk in the IPSS, had a more favorable prognosis with a median OS of 35 months. In this large dataset, the researchers identified rare chromosomal abnormalities with good (+ 1/+1q, t(1q), t(7q), del9q, del12p, chromosome 15 anomalies, t(17q), −21, trisomy 21, and −X), intermediate (del11q, chromosome 19 anomalies), and poor (t(5q)) prognostic value. They also found an inverse correlation between number of abnormalities and median survival. Those with 3 abnormalities had a median survival of 17.1 months compared to 9 months for those with 4–6 abnormalities and 5 months for those with 6 or greater abnormalities [44]. The same group collaborated with investigators from MD Anderson to compare the prognostic impact of cytogenetics to BM blast percentage [22]. The authors found that the IPSS poor-risk cytogenetic group had a similarly unfavorable impact on OS as BM blast percentage N20%. Outcomes with complex karyotype involving chromosome 5 and/or 7 abnormalities were even more unfavorable. The negative impact of poor risk cytogenetics remained true independent of MDS treatment [22]. These findings highlighted the underestimation of cytogenetic risk on prognosis relative to BM blast percentage in the IPSS as well as the need to refine the cytogenetic classification itself. Using a large international cohort of 2902 patients with de novo MDS, Schanz et al. devised a new comprehensive cytogenetic scoring system with 5 cytogenetic prognostic groups [Table 2] [27]. The large population allowed for inclusion of rarer cytogenetic abnormalities into analyses of prognosis. As with prior studies, increasing number of cytogenetic abnormalities correlated with worse OS with distinction seen between 3 and ≥4 abnormalities. While the IPSS considered chromosome 7 abnormalities as poor risk, in this analysis, isolated del7q fell in the

Table 2 The new 5-group comprehensive cytogenetic prognostic system for MDS developed by Schanz et al. Adapted from ref. [27]. NA: not available, OS: overall survival, NR: not reached. Very good Cytogenetic abnormality Single Double Complex

Good

Intermediate

Poor

Very poor

−Y, 11q- Normal, 5q-, 7q-, +8, i(17q), inv(3), t(3q), NA 12p-, 20q+19, any other 3q-, −7 NA Including 5q- Any other Including −7 NA or 7qNA NA NA 3 N3

Outcomes Median OS 60.8 (months) Median time to NR leukeumic progression (months)

48.6

26

15.8

5.9

NR

78

21

8.2

intermediate cytogenetic risk group while monosomy 7 fell into the poor risk group [27]. Other groups validated this new comprehensive cytogenetic score at various times in the disease course and it subsequently was incorporated into the IPSS-R [26,45,46]. The impact of monosomal karyotypes on prognosis remains uncertain. While some studies showed an independent negative prognostic impact of monosomal karyotypes, others did not [47,48]. Factors contributing to this discrepancy may include the confounding effect of association of monosomal karyotypes with higher numbers of cytogenetic abnormalities and coexisting TP53 mutations, both of which are associated with inferior outcomes [48,49]. With this controversial data, we do not use the presence of monosomal karyotype for additional risk stratification. While the favorable prognostic impact of del5q in MDS has been accepted for many years, emerging evidence suggests that additional cytogenetic abnormalities, coexisting molecular mutations (e.g. TP53 mutations, see below), and clonal evolution may have a role in refining prognostic predictions among these patients. In a large study, the number of chromosomal abnormalities among patients with del5q defined three different risk groups for disease progression (isolated del5q, del5q with one abnormality, and del5q with ≥ 2 abnormalities) [50]. Those with del5q or del5q with one abnormality had a median OS of 58 months compared to 6.8 months for those with del5q and ≥2 abnormalities [50]. Additionally, the emergence of new cytogenetic abnormalities during the course of disease for patients with LR-MDS (i.e. clonal evolution) has been shown to correlate with inferior outcomes. This suggests that reassessment of the cytogenetic profile in patients with LR-MDS when there is suspicion of disease progression (e.g. worsening of blood counts) could be helpful for risk stratification [51]. 4.2. Fluorescence in situ hybridization (FISH), comparative genomic hybridization, and single-nucleotide polymorphism arrays and prognostication in MDS Fluorescence in situ hybridization (FISH) is another commonly used method to detect chromosomal abnormalities in MDS. Advantages compared to traditional G-band karyotyping are that FISH can be performed using non-dividing cells and has higher resolution with the ability to detect more subtle aberrations in chromosomes. On the other hand, FISH analysis is limited to the areas targeted by pre-defined probes whereas banding techniques allow for a more comprehensive chromosomal evaluation [52–54]. While studies do not support use of FISH alone, FISH in combination with traditional karyotyping has added prognostic utility in MDS. For example, FISH can detect chromosomal abnormalities in cases of karyotype failure due to lack of metaphase cells or a poor preparation [55–57]. Furthermore, a recent report validated the use of



FISH on peripheral blood CD34+ cells in assigning cytogenetic risk classification per IPSS and IPSS-R risk groups, suggesting a role for FISH when chromosomal banding analysis of bone marrow samples is not possible [54]. Some studies illustrate discovery of abnormalities by FISH having association with inferior prognosis in a significant proportion of patients with normal metaphase cytogenetics (MC) [55,58] compared to other studies demonstrating much lower yield [56,57]. For now, in cases of adequate metaphase cells, FISH likely has little to add and perhaps should not be routinely ordered on every bone marrow sample [59]. Although cytogenetics is the most important prognostic factor in MDS, approximately 50% of de novo MDS patients do not have detectable aberrations or have inadequate karyotyping using MC [44]. About three-quarters of patients fall into the ‘good’ prognostic subgroups of the IPSS and IPSS-R, respectively, largely due to the high prevalence of normal cytogenetics [26]. These observations emphasize the fact that genetic derangements undetected by MC drive the course of disease and highlight the need for methods to better refine genetic risk stratification in MDS [60]. With recent technological advances, this is becoming a reality. Array-based comparative genomic hybridization (aCGH), singlenucleotide polymorphism arrays (SNP-A), and next-generation sequencing (NGS) techniques have revolutionized the understanding of the pathogenesis of MDS and enabled discoveries of recurrent genetic alterations with prognostic impact in the vast majority of MDS patients [61–63]. Importantly, aCGH detects DNA copy number alternations (CNAs) but not uniparental disomy (UPD). Using aCGH, clonal genomic aberrations are identified in up to 87% of patients with MDS [64]. In a study of 107 MDS patients with normal cytogenetics by MC, 39% harbored clonal genomic imbalances and those patients had inferior survival in comparison to those that did not [63]. In another investigation utilizing aCGH on CD34 + cells from 44 patients with LR-MDS, 36% exhibited karyotypic abnormalities [65]. Clonal CNAs were detected in 82% and maintenance of genomic integrity correlated with improved OS and lower probability of disease progression [65]. Table 3 Frequency and impact of identified genetic mutations in myelodysplastic syndrome (MDS). 5q-: interstitial deletion of long arm of chromosome 5, OS: overall survival, alloSCT: allogeneic stem cell transplant, BM: bone marrow, CMML: chronic myelomonocytic leukemia, AZA: azacitidine. Data from references [25,43,72–73,76–83]. Gene Prognostic Estimated mutation impact frequency in MDS

Additional findings

SF3B1

Favorable

20–35%

TP53

Poor

8% (found in 20% of 5q-)

EZH2

Poor

5%

Frequently seen in MDS with ringed sideroblasts, occurrence with JAK2, MPL, or CALR mutations is associated with thrombocytosis Decreased response to lenalidomide in 5q-, poor outcome post alloSCT, associated with complex karyotype, higher BM blasts, and thrombocytopenia Identifies lower risk MDS with aggressive course

ETV6 RUNX1 ASXL1

Poor Poor Poor

NRAS DNMT3A SRSF2

Poor Poor Poor

2–3% 9–10% 15% (found in 40% of CMML) 4–8% 10–15% 12%

PTPN11 U2AF1 TET2

Poor Poor Unclear

1% 7–10% 20–30%

Poor outcome post alloSCT Occurrence with TET2 mutation is associated with monocytosis

5

SNP-A, unlike aCGH, can detect copy-neutral acquired loss of heterozygosity (CN-LOH) including somatic UPD [66–68]. Studies comparing SNP-A to MC show that SNP-A can identify clonal genomic aberrations undetected by MC in roughly 40–80% of patients [69–71]. The combination of SNP-A and MC resulted in a higher diagnostic yield of chromosomal defects compared to MC alone, including discovery of novel aberrations in over half of the patients with normal or noninformative MC [67]. There are conflicting reports regarding the prognostic significance of these additional clonal abnormalities found by SNP-A [67,71]. 5. Molecular mutations in prognostication Targeted and genome-wide NGS have identified new somatic mutations with prognostic significance in MDS [25,72]. Recent large studies report recurrent genetic mutations in more than 45 genes in greater than 85–90% of MDS patients, including those with normal karyotype [72,73]. These mutations are found in genes involved in pathways such as DNA methylation (DNMT3A, TET2, IDH1, IDH2), posttranslational chromatin modification (EZH2, ASXL1), transcriptional regulation (TP53, RUNX1, GATA2), RNA splicing (SF3B1, U2AF1, SRSF2, ZRSR2), cohesin complex (STAG2), and signal transduction (JAK2, KRAS, CBL) [15,25,43, 72–75]. Table 3 lists some of the recurrent molecular mutations with prognostic significance in MDS. Accumulation of these mutations portends worse clinical outcomes and presence of mutations in even subclonal populations retains this impact on survival [73]. TP53, EZH2, ETV6, RUNX1, SRSF2 and ASXL1 gene mutations are negatively associated with OS [43,73,76,77] whereas SF3B1 mutations are associated with better prognosis [78,79]. The discovery of genetic mutations with implications on clinical course is significant, but how to integrate this data with other clinical features for use in patient care remains unclear. Bejar et al. combined genotyping and sequencing techniques in samples from 439 patients to investigate recurrent mutations and their prognostic significance [43]. At least one mutation was found in 51.5% of patients, including 52% with normal cytogenetics. Mutations in TP53 strongly correlated with complex karyotype, thrombocytopenia, higher BM blast percentage, and were more often found in HR-MDS. Even after adjusting for IPSS classification, which accounts for these poor risk features, TP53 mutation retained strong association with decreased survival. Mutations in EZH2, ETV6, RUNX1, and ASXL1, present in one-third of the patients, were also found to have independent negative impact on prognosis. Demonstrating utility in addition to the IPSS, having at least one mutation in these specific genes upstaged IPSS classification such that the prognosis became similar to the next highest risk group [43]. In another landmark paper, Papaemmanuil et al. investigated the prognostic impact of genetic mutations in 738 patients with MDS of whom 78% had at least one mutation [73]. Increased number of mutations correlated with leukemic progression and decrease in survival. However, a prognostic model including mutational data was not significantly different when compared to a model of standard clinical risk factors with updated data on cytogenetic abnormalities, degree of cytopenias and BM blast percentage [73]. Recently, Haferlach et al. found at least one genetic mutation in 90% of cases in a large population of 944 patients with MDS including genes which had not been previously identified [72]. The authors devised a novel prognostic model combining mutational status of 14 genes with conventional risk models including variables used in the IPSS. This novel model better predicted OS compared to a model composed solely of genetic mutations and the IPSS-R [72]. However, this model still requires independent validation. 5.1. Molecular mutations in MDS subgroups

Improved response to AZA, poor outcome post alloSCT, occurrence with SRSF2 mutation is associated with monocytosis

There have also been efforts to look at significance of mutations in particular MDS subgroups. Mutations in SF3B1, a gene encoding part of the RNA splicing machinery, are found mainly in the WHO subgroups


6


of MDS with ring sideroblasts. In these patients, presence of mutated SF3B1 is associated with improved OS. The favorable impact of SF3B1 overcomes the negative impact of multilineage dysplasia and was found to add to the prognostic power to the IPSS [78,79]. Mutations in ASXL1, found in 40% of patients with CMML, are independently associated with a negative impact on survival [80] and were incorporated into a clinicopathologic prognostic model for CMML [80]. Mutation analysis also impacts prognosis in patients with LR-MDS [25]. Mutation number appears to increase with age, but there are no specific mutations associated with advancing age. Mutations in EZH2, RUNX1, TP53, and ASXL1 were associated with decreased OS but only EZH2 maintained a negative prognostic value in multivariable analyses. The role of the other mutations may be accounted for via existing variables of the LR-PSS. These findings suggest a benefit in combining LRPSS with EZH2 mutation status to more accurately identify LR-MDS patients with aggressive courses who may benefit from early therapy [25]. LR-MDS with isolated del5q is a subgroup traditionally associated with a more favorable prognosis with a relatively low risk of transformation to AML [5,81]. However, presence of TP53 mutation has been reported in 18% of this patient population and even at a subclonal level was associated with a significant risk of leukemic evolution [82,83]. Evaluation of TP53 mutations in these patients could be useful in guiding monitoring for lenalidomide failure and disease progression leading potentially to earlier use of aggressive interventions. 6. Comorbidity-based prognostic models in MDS As MDS is generally a disease of the elderly, concurrent medical conditions are prevalent in this patient population [17,84]. The commonly used prognostic tools for MDS generally do not account for important host-related factors save for the inclusion of age and performance status in some models. Multiple groups have tried to develop new standardized tools or modify existing tools from other settings to formally account for the prognostic impact of medical comorbidities in MDS. For example, the Charlson Comorbidity Index, hematopoietic cell transplantation-specific comorbidity index (HCT-CI), and the Adult Comorbidity Evaluation-27 scale (ACE-27) were all prognostic for survival and risk of non-leukemic mortality in MDS patients even after accounting for age and the IPSS, especially in patients with LR-MDS [85–89]. An Italian group analyzed a large cohort of MDS patients and found that hepatic, cardiac, renal, and pulmonary diseases or a prior diagnosis of a solid malignancy independently predicted probability of a nonleukemic death [90]. Using these findings, a dynamic comorbidity index was developed specifically for MDS, the MDS-CI, which discriminated three risk groups of patients [90]. Although the MDS-CI was able to refine predictions of life expectancy for MDS patients stratified according to the WPSS, especially in those with LR disease [88–91], comorbidity-based prognostic tools for MDS are not widely used. Integration of disease-based and host-based risk factors into a single tool would facilitate comprehensive patient evaluation. MD Anderson investigators developed a hybrid prognostic tool that included age, IPSS, and the ACE-27 scale to discriminate three prognostic groups [87]. Frailty and presence of geriatric syndromes (e.g. frequent falls and incontinence) are potential prognostic factors that are not accounted for in existing tools [92,93]. Multidimensional geriatric assessment tools have been also proposed for use as secondary classifiers [93]. The optimal way to account for the prognostic effects of comorbidities in MDS remains undefined. 7. Other factors that influence prognosis in MDS Although various other clinical, pathologic, and laboratory factors have prognostic value among patients with MDS [Table 4], when evaluated with traditional prognostic factors in multivariable models the independent effect of many is not clear. Multiple immunologic changes have been proposed to be of prognostic importance in MDS. For

example, expansion of effector memory regulatory T-lymphocytes (hypothesized to facilitate immune escape of leukemic cells) had a negative prognostic impact in patients with LR-MDS in multivariable analysis [94]. Aberrant expression of myeloid markers on flow cytometric analyses was also shown to have diagnostic and prognostic value in MDS [95–97]. While flow cytometric prognostic tools have been developed and validated, they are not widely used pending resolution of issues regarding standardization, validation of independent value for prognostication and clarification of how to use the data to help guide clinical decisions [98–100]. RBC transfusion dependence and markers of iron overload such as serum ferritin have negative prognostic impact on survival especially in patients with LR-MDS and in the alloHCT setting [101]. In multivariable analyses, low serum albumin was found to have an independent negative prognostic impact on survival [102]. Other identified negative prognostic factors include significant BM fibrosis and presence of CD34 + cell clusters [103]. However, while BM fibrosis upstaged patients classified per the IPSS and WPSS, it was not independently prognostic in the IPSS-R cohort [26,103]. 8. Predictors of response to treatment 8.1. Clinical predictors: the French Prognostic Scoring System (FPSS) Azacitidine prolongs OS in HR-MDS patients compared to conventional care — defined as supportive care, low-dose cytarabine, or intensive chemotherapy, in patients ineligible for alloHCT [14]. Unfortunately, only about one-half of patients respond to azacitidine [14,104]. Clinical responses to DNMTis are not immediate and may take months to become apparent. Thus, without existing markers able to predict response, many patients may remain on these therapies for months without benefit and with subsequent delay in starting alternative therapies [8,9]. The Groupe Francophone des Myelodysplasies evaluated a cohort of 282 patients with HR-MDS treated with azacitidine in search of predictive factors for response and OS. BM blast percentage b15%, normal karyotype and no prior treatment with low-dose cytarabine each predicted improved response to azacitidine. In multivariate analyses, ECOG performance status ≥2, IPSS intermediate and poor cytogenetic risk groups, circulating blasts and RBC transfusion dependence of ≥ 4 units per 8 weeks were independent prognostic factors for decreased OS. These 4 factors were used to devise a scoring system, the FPSS, which divides patients into 3 risk groups with significantly different OS [Table 1] [105, 106]. The FPSS was validated among those patients randomized to the azacitidine arm in the AZA-001 trial as well as in two European single institution studies by Breccia et al. of 60 patients including both de novo and t-MDS and by van der Helm et al. of 90 patients [14,107,108]. In a North American population of 150 patients treated with azacitidine as a single agent or in combination with the histone deacetylase inhibitor entinostat, both the FPSS and the IPSS-R predicted OS but neither predicted response to azacitidine [30]. There was no additional prognostic utility for the FPSS over the IPSS-R in these Table 4 Selected factors reported in the literature to impact prognosis in MDS. LDH: Lactate dehydrogenase, BM: bone marrow, RBC: red blood cell. Data from references [94–97,101–103]. Category

Proposed variables

Comorbidities Cardiac disease, liver disease, pulmonary disease, renal disease. Smoking, age, performance status, frailty. Prior history of solid malignancy Laboratory Serum albumin, ferritin, LDH, β2-microglobulin. Number and severity of cytopenias Pathology BM fibrosis, BM blast percentage, Degree of dysplasia, CD34+ cell clusters Genetic Cytogenetics, molecular mutations, clonal evolution Immunology Effector memory regulatory T cell expansion, aberrant myeloid marker expression Other RBC transfusion dependence



azacitidine-treated HR-MDS patients [30]. Further studies are needed to clarify the role, if any, of the FPSS in identifying patients with HR-MDS who are less likely to benefit from azacitidine therapy. In addition to the FPSS, certain clinical features appear to predict responsiveness to erythropoiesis stimulating agents (ESAs) (e.g. erythropoietin level) or to immunosuppressive therapy (e.g. younger age, normal karyotype or trisomy 8, HLA-DR15 allele, and presence of paroxysmal nocturnal hemoglobinuria [PNH] clone) among anemic LR-MDS patients [7]. Another potential approach could be to assess short-term trackable variation in these factors, such as changes in blood counts or dynamic methylation changes, at 2 to 4 weeks of initiation of HMAs to predict the long-term clinical benefit of these agents. To illustrate this point, platelet count doubling after the first cycle of azacitidine has been shown to predict eventual responsiveness and survival benefit from azacitidine therapy [108,109]. 8.2. Genetic predictors: molecular mutations as markers for treatment response In a recent publication by Bejar et al., BM or PB samples from 213 patients with MDS prior to treatment with azacitidine or decitabine were sequenced to identify molecular mutations associated with response to DNMTi therapy and OS. Only TP53 and PTPN11 mutations were associated with negative OS, but neither affected response to DNMTi therapy. Mutations in TET2, if present at a mutant allele frequency of ≥ 10%, were associated with a better response to DNMTis but had no significant impact on OS. There was no mutation identified that predicted inferior response to DNMTi treatment [110]. Previous report illustrates a similar finding of improved overall response to azacitidine in those with mutated TET2 but again without influence on OS [111]. Mutations in TET2 as well as DNMT3A independently predicted an improved overall response to DNMTis in a study of 92 patients with MDS, MDS/MPN or secondary AML treated with DNTMis [112]. TET2 mutations are found in around 20% of patients with MDS [113,114]. TP53 is another mutation that has been shown to have possible implications on treatment choices. TP53 mutations in patients with LRMDS with del5q have been shown to be associated with shorter survival and decreased response to lenalidomide [82,83]. Given the clinical significance of TP53 mutation in this group of patients, the European Leukemia Net recommends accounting for its presence when making treatment decisions in this population [115]. In a study of clinical outcome after alloHCT, patients with mutations in TP53, DNMT3A, and TET2 had poor survival. This was especially pronounced in TP53 mutations, which was the strongest independent predictor of death after alloHCT, with median survival of only 4.6 months [116]. 9. Other therapy-specific outcome predictors Two treatment-specific biopredictors of outcome have entered the clinic and are widely used in the community: serum erythropoietin level to predict responsiveness to ESAs in anemic LR-MDS patients and the presence of del5q to predict responsiveness to lenalidomide among anemic LR-MDS patients. Haploinsufficiency for the casein kinase 1A1 gene (CSNK1A1) as well as a polymorphism in the gene that codes for the ubiquitin ligase cereblon appear to enhance sensitivity to lenalidomide among patients with del5q LR-MDS while TP53 mutations and additional cytogenetic abnormalities predict for worse outcomes [117]. A recent paper suggested that the level of expression of one of the metabolizing enzymes of azacitidine (UCK1) is a potential biomarker for survival advantage with azacitidine therapy, but validation in larger cohorts of patients is needed [118]. Methylation biomarkers have generally been disappointing in selecting patients who might benefit from DNMTis although recent data in CMML suggest that this could be due to the focus on promoter methylation rather than assessment of methylation at other parts of the genome [119]. It remains to be seen whether

7

we will discover a reliable biomarker(s) that can consistently select patients who are likely or unlikely to gain clinical benefit from DNMTi therapy [120]. 9.1. Areas of future research Accurate prognostication and risk stratification are vital for delivery of evidence-based risk-adapted therapies. As such, there are ongoing efforts to refine and supplement the currently used clinicopathologic prognostic tools with molecular data in order to better guide clinical decisions. As new therapies emerge for MDS and our understanding of both the mechanisms of pathogenesis of disease as well as potential mechanisms of action of current and future MDS therapies increase, emphasis on treatment-specific outcome prediction tools will continue to occur. Genomic data are already showing promise in helping establish the diagnosis of MDS, risk stratifying patients, and suggesting predictive biomarkers. Still, much work is needed to standardize the performance and interpretation of molecular tests and to determine how best to integrate the results into clinical practice. 10. Practice points – Multiple well-validated clinicopathologic prognostic tools are available for risk stratification in MDS patients, but each has limitations that should be considered when used in making clinical decisions. – While several recurrent molecular mutations hold independent prognostic impact in MDS patients, the best ways to incorporate molecular testing into clinical practice are not yet defined. – TET2 mutations appear to predict responsiveness to DNMTis in patients with HR-MDS, but they do not predict for a survival advantage, and there is currently no predictive molecular profile that precludes the use of DNMTis in these patients. – TP53 mutations appear to carry a very negative prognostic impact among patients with HR-MDS that does not seem to be abrogated by the use of HMAs or alloHCT. As a result, such patients should be considered early for clinical trials. – TP53 mutations also carry a negative prognostic impact among LRMDS with del5q and predict for poor outcomes with lenalidomide therapy. – Large, well-annotated cohorts of MDS patients with serial and prospective comprehensive genetic, epigenetic and immunologic assessments are needed to further define the roles of biomarkers for prognostication and prediction of therapy-specific outcomes in MDS.

11. Research agenda – Development of updated prognostic scoring systems that incorporate the impact of molecular mutations on overall survival and leukemic progression in MDS – Discovery of clinical features and/or biomarkers that can reliably predict clinical and survival benefit from DNMTi and other MDS therapies – Clarification of the impact of recurrent molecular mutations on response to specific MDS treatments

Conflict of interest None of the authors declare any relevant conflicts of interest. References [1] Jadersten M, Hellstrom-Lindberg E. Myelodysplastic syndromes: biology and treatment. J Intern Med 2009;265(3):307–28.


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