Moving towards a uniform risk stratification system in CMML - How far are we?

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Moving towards a uniform risk stratification system in CMML How far are we? Onyee Chan, Eric Padron∗ Division of Malignant Hematology, Moffitt Cancer Center, Tampa, FL, USA

A R T IC LE I N F O

ABS TRA CT

Keywords: Chronic myelomonocytic leukemia CMML prognostic scores CMML risk assessment CMML mutations

Many prognostic scoring systems have been developed for chronic myelomonocytic leukemia (CMML). Although these efforts have been informative, no single model has been considered the consensus for CMML prognostication and all models are only moderately prognostic. CMML clinical models utilize mainly hematology and morphology parameters to estimate risk. A better understanding of cytogenetics and the genomic landscape of CMML have resulted in integrated risk models such as CMML Prognostic Scoring System (CPSS)-Mol and Mayo Molecular that may provide better prognostic accuracy for an individual patient. For example, frameshift/nonsense ASXL1 mutations have been consistently shown to confer inferior outcomes leading to its incorporation into some of the major risk classification systems. Prognostication in the setting of therapeutic interventions such as hypomethylating agents and allogeneic hematopoietic cell transplantation have also garnered considerable interest. Despite having many validated risk models available, not a single system is universally adopted. Herein, we will provide an overview of how these systems evolved and progress toward a uniform system.

Introduction Chronic myelomonocytic leukemia (CMML) is a clonal hematopoietic malignancy characterized by a classical monocytosis with overlapping features of both myelodysplastic syndrome (MDS) and myeloproliferative neoplasm (MPN) [1]. It is a rare condition estimated to occur in about 4 out of a million people in the United States each year with a propensity for elderly men [2]. CMML classification has undergone a number of changes over the years in part because of its rarity and heterogeneity, leading to the underrecognition of its unique biological properties. Since the inception of the French-American-British (FAB) classification system in 1976, CMML, an entity that was not well understood at the time, had been categorized as a subtype of MDS [3]. It is not until 2001 when the World Health Organization (WHO) reclassified CMML as a MDS/MPN overlap syndrome because of the increasing evidence that clinical myeloproliferative features distinguish CMML from MDS [4]. In the latest iteration of WHO classification schema in 2017, a key diagnostic criteria for CMML include persistent monocytosis (> 1 × 109/L) in peripheral blood accounting for ≥ 10% of the leukocytes and either dysplasia involving ≥ 1 myeloid lineages or the presence of an acquired, clonal cytogenetic/molecular genetic abnormality in hematopoietic cells [1]. About one-quarter of these patients will transform into acute myeloid leukemia (AML), a significant source of mortality [5]. Despite heightened awareness and therapeutic advances, the outcomes for these patients remain dismal with a median overall survival (OS) less than 1.5 years and an estimated 5-year OS less than 20% according to a recent analysis from the Surveillance Epidemiology and End Results (SEER) database [2]. Risk stratification for CMML has evolved from using MDS based models such as the International Prognostic Scoring System (IPSS) and revised-IPSS (R–IPSS) to more disease∗

Corresponding author. Division of Malignant Hematology, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, FL, 33612, USA. E-mail address: Eric.Padron@moffitt.org (E. Padron).

https://doi.org/10.1016/j.beha.2019.101131 Received 25 November 2019; Received in revised form 29 November 2019; Accepted 2 December 2019 1521-6926/ © 2019 Elsevier Ltd. All rights reserved.

Please cite this article as: Onyee Chan and Eric Padron, Best Practice & Research Clinical Haematology, https://doi.org/10.1016/j.beha.2019.101131

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Fig. 1. CMML prognostic scoring systems over time.

specific models like the CMML Prognostic Scoring System (CPSS) [6–9]. A better understanding of molecular pathogenesis helps further refine risk assessment by accounting for certain mutations that have prognostic values [10,11]. There are altogether 7 commonly used models with more still being developed [12–14]. Several of them are externally validated including CPSS, Groupe Francophone des Myelodysplasies (GFM) model, and the Mayo model, but no single model has been universally adopted [6,14,15]. In this review, we will examine the different risk stratification systems for CMML over time (Fig. 1) and some of the progresses and challenges toward a uniform system. Risks by hematologic features and morphology FAB and WHO classification Even though the FAB classification and its subsequent modifications consider CMML as a subtype of MDS, it separates the disease into myelodysplastic (MD)-CMML or myeloproliferative (MP)-CMML depending on the amount of white blood cell (WBC) in the peripheral blood, < 13 × 109/L and > 13 × 109/L, respectively [3,16]. Eventually, the WHO 2001 and 2008 classification, recognizing CMML as a distinct entity under MDS/MPN, subdivide it into 2 categories based on percentages of blasts/promonocytes in peripheral blood (PB) and bone marrow (BM) [4,17]. Specifically, patients with CMML-1 have blasts/promonocytes < 5% in PB and < 10% in BM whereas those with CMML-2 have 5%–19% in PB or 10%–19% in BM. This scheme was changed again in WHO 2016 after evidence suggests a different cut-off of blasts count and the original FAB subtypes affect outcomes. Schuler et al. performed a retrospective study on a cohort (n = 386) of patients with CMML and found that a 3-tiered system (CMML-0 with < 5% blasts in BM, CMML-1 with 5–9%, and CMML-2 with 10–19%) has the advantage of identifying a group, CMML-0, that has a significantly better prognosis than CMML-1 or CMML-2 (median OS of 31 months vs. 19 and 13 months, respectively), which can impact treatment decision making [18]. Furthermore, patients with MD-CMML appear to have better outcomes than MP-CMML across all groups. Currently, the WHO 2017 define CMML-0 as PB < 2% and BM < 5% blasts, CMML-1 as PB 2–4% or BM 5–9% blasts, and CMML-2 as PB 5–19% or BM 10–19% blasts, and/or when any Auer rods are present [1]. To validate this classification scheme, a recent study by Loghavi et al. assessed 629 CMML patients to see whether the 3-tiered blast-based groupings provide superior predictive value compared to the WHO 2001 system [5]. They only found a marginal difference in OS between CMML-0 and CMML-1 under WHO 2017, suggesting the prognostic strength might not be as strong as previously thought. Also, WHO subgroups of either iteration were associated with AML-free survival. In agreement with prior literature, patients with MP-CMML showed a shorter OS compared to MDCMML with a hazard ratio (HR) of 1.89 and 95% confidence interval (CI) of 1.54–2.38 (p < 0.0001) supporting the prognostic utility of differentiating the two FAB subtypes [5]. Early prognostic scores for CMML While the FAB and WHO classifications provide some indications of clinical outcomes, they are crude measures when used in isolation and better served as methods of categorization rather than comprehensive tools for prognostication. One of the first CMML specific prognostic scoring systems is the MD Anderson Prognostic Score (MDAPS), which was proposed in 2002 based on a retrospective analysis of clinical and laboratory variables of 213 patients [19,20]. It has 4 risk groups depending on the sum of the following risk factors: Hemoglobin (Hgb) < 12 g/dL, absolute lymphocyte count (ALC) > 2.5 × 109/L, PB immature myeloid cells > 0%, and BM blasts > 10%. Median OS ranges from 26 months for low risk to 5 months for high-risk patients [19]. MDAPS was later validated in a separate cohort of 250 patients by the same institution [21]. In that study, investigators also suggested a modification to the original MDAPS (known as MDAPS-M1) by replacing the presence of circulating blasts as a risk factor with elevated lactate dehydrogenase (LDH) > 700 U/L which yielded similar median OS for each risk groups. A German group proposed a slightly different system in 2004 based on 288 CMML patients included in the Dusseldorf MDS Registry (DUSS) [22]. Besides low Hgb and high LDH, they identified BM blasts ≥ 5% instead of PB blasts and platelets < 100 × 109/L to be risk factors. 2

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Chromosomal abnormalities and prognosis Clonal cytogenetic abnormalities are found in about 20–30% of patients with CMML [19]. The Spanish group examined 414 patients, and 27% of them had abnormal karyotype [23]. The most frequent cytogenetic aberrations were trisomy 8 (27%) and loss of Y chromosome (16%). Outcome analysis demonstrated 3 cytogenetic risk groups including low (normal karyotype or -Y as a single anomaly), high (+8, abnormalities of chromosome 7, or complex karyotype), and intermediate (not low or high risk). The 5-years OS for low, intermediate, and high risks were 35%, 26%, and 4%, respectively. Multivariate analysis confirmed that this risk stratification was an independent prognostic factor for OS (p = 0.001). It was also validated by the MD Anderson group using a cohort of 417 CMML patients effectively confirming the prognostic impact of cytogenetic abnormalities on OS [24]. Interestingly, they observed reassigning 8 + to intermediate risk group and making a distinction between 3 vs. > 3 cytogenetic abnormalities may provide a better separation of OS. Similarly, the Mayo Clinic-French Consortium also performed a study with one of the objectives being correlating prognosis with cytogenetics abnormalities in CMML patients (n = 409) [25]. Their survival analysis resulted in the following risk categories: low (normal karyotype, sole -Y, or sole der(3q); median OS of 41 months), high (monosomal or complex cytogenetics; median OS of 3 months), and intermediate (not low or high risk; median OS of 20 months). Collectively, these findings support incorporating cytogenetics in risk models to improve prognostic assessment. Such et al. proposed the CPSS model in 2013 which includes CMML-specific cytogenetics, FAB, WHO, and red blood cell (RBC) transfusion dependence as parameters to predict OS and AML evolution [6]. It consists of 4 risk groups, namely low, intermediate-1, intermediate-2, and high with median OS of 72, 31, 13, and 5 months, respectively. It was developed based on a large cohort (n = 558) of CMML patients compared to the other models, and it was externally validated by 274 patients from the Germany group. Like the MDAPS-M1, minor revision had been proposed for CPSS to include platelets < 100 × 109/L (known as CPSS-P) to the initial model after more analysis using a different cohort of patients was done [26]. Notably, all existing CMML models are established using both patients who are eligible and ineligible for allogeneic hematopoietic cell transplantation (HCT), the only potentially curative treatment for CMML at this time. One group took interest to see if some of the commonly used models (MDAPS, DUSS, CPSS, Mayo) and CMML-specific cytogenetics can successfully predict outcomes in this population [27]. In a retrospective study of 38 patients with CMML who underwent HCT, authors found only CMML-specific cytogenetics risk classification can predict 3-years OS (low, intermediate, and high at 56.7%, 12.5%, and 0%, respectively; p = 0.01). A large, international study (n = 1823) comparing prognostic scoring systems for CMML including IPSS, R–IPSS, Global MD Anderson Scoring System, MDAPS, DUSS, Mayo, CPSS in both HCT and non-HCT setting found all of them to be valid and will be further discussed below [28].

Molecular lesions inform outcomes While cytogenetic abnormalities are present in relatively low frequencies in CMML, gene mutations occur in over 90% of all cases with an average of 2 recurrent oncogene mutations in each case [29]. Mutations can be grouped into different functional classes such as epigenetic modifiers (DNMT3A, TET2, ASXL1, IDH1, IDH2, EZH2), spliceosome complex (SRSF2, U2AF1, SF3B1, ZRSR2), and signaling molecules (NRAS, KRAS, CBL, JAK2) [30]. TET2, SRSF2, and ASXL1 are especially prevalent in CMML and occur in about 30–50% of patients, respectively. Fig. 2 depicts the top 20 recurrent oncogenes and their associated frequencies [31]. In particular, concurrent TET2 and SRSF2 is highly predictive of CMML and other myeloid neoplasm characterized by myelodysplasia and monocytosis [32]. Despite being a clinically diverse disease, CMML has a rather homogeneous molecular fingerprint, and so molecular analysis may help to support this diagnosis in ambiguous cases if CMML-related mutations are found [29,33]. ASXL1 (additional sex combs like 1), located on the chromosome region 20q11, encodes for a polycomb chromatin-binding protein that is involved in epigenetic regulation of gene expression via repressive histone marks [34]. Some have reported a poor prognostic impact of ASXL1 on patients with CMML, both in terms of survival and transformation to AML [35–37]. Others have found DNMT3A, SRSF2, NRAS, or cooccurrence of ASXL1 and EZH2 to be prognostically detrimental [38–41].

Original Mayo model and GFM model As next-generation sequencing (NGS) become more commonplace and increase recognition of its predictive role in many myeloid malignancies, the Mayo group developed its first prognostic model for CMML in 2013 based on 226 patients [15]. Selected mutational status including SRSF2, SF3B1, U2AF35, and ASXL1 was available for analysis. ASXL1 mutations (any nucleotide variation) were associated with a higher absolute monocyte count (AMC) and proliferative phenotype. However, only AMC > 10 × 109/L, blasts in PB, Hgb < 10 g/dL, and platelets < 100 × 109/L were found to be significant in predicting OS in multivariable analysis and therefore included in the final model, which was validated by an independent cohort of patients (n = 268) at Moffitt. The Mayo model has 3 risk categories: low (0 risk factor), intermediate (1 risk factor), and high (more than 1 risk factors) conferring median OS of 32, 18.5, and 10 months, respectively [15]. Shortly after, the French group published the GFM model, their version of prognostic scoring system for CMML, after retrospectively reviewing 312 patients [14]. The major differences from the original Mayo model is that ASXL1 (nonsense or frameshift but not missense) was shown to be an adverse prognostic factor for OS and AML-free survival in multivariate analysis (p < 0.0001). Risk factors in the final model include WBC > 15 × 109/L (3 points), age > 65 years (2 point), anemia (2 point), platelets < 100 × 109/L (2 point), and ASXL1 mutation (2 point). It delineates low (0–4 points), intermediate (5–7 points), and high risk (8–12 points), with respective median OS of not reached (NR), 38.5, and 14.4 months [14]. 3

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Fig. 2. Top 20 gene mutations in CMML by frequency and functional class. TET2, SRSF2, and ASXL1 are highly prevalent in CMML. FLT3 and IDH1 mutations are infrequent but targetable. NPM1 is also infrequent but may help rule out AML. Data derived from Itzykson et al. [31].

More CMML models with molecular integration In light of emerging data regarding the prognostic impact of somatic mutations in ASXL1, Mayo updated its prognostic system (now called Mayo Molecular) in 2014 to include frameshift or nonsense ASXL1 mutation as a risk factor after an updated analysis, in collaboration with the French group to increase patient cohort size (n = 466), targeting these specific mutational variations were shown to produce inferior outcomes [10]. Patients with mutated ASXL1 (frameshift or nonsense) had a shorter median OS of 24 months compared to 44 months for those with wildtype ASXL1 (p < 0.0001). SETBP1, not significantly affecting outcomes in this study, was also scrutinized because there have been reports suggesting its negative prognostic impact [42]. Altogether, the 5 risk factors delineated a 4-tiered model with low (0 risk), intermediate-1 (1 risk), intermediate-2 (2 risks), and high (more than 2 risks) associated with median OS of 16, 31, 59, and 97 months, respectively [10]. Subsequent studies from other institutions also confirmed the prognostic relevance of frameshift and nonsense ASXL1 mutations in this population [43]. In an attempt to unify the numerous prognostic systems for CMML, the Moffitt group conducted a large, international study with contributions from 8 centers across the globe (n = 1832) [28]. All of the aforementioned models (except GFM which was not included in their study) were found to be valid but prone to upstaging. Analysis of genetic data showed ASXL1 (frameshift or nonsense) (p < 0.0001), RUNX1 (p = 00001), and CBL (p = 00001) have similar adverse prognostic significance. However, only ASXL1 (p = 0.0114) and CBL (p = 0.003) were found to be independent prognostic factors after accounting for Hgb, PB blasts, platelets, and cytogenetics [28]. This study emphasized the need for better prognostication strategies in which incorporation of biomarkers such as molecular to clinical variables is important given the limited prognostic power of clinical variables alone. In fact, there is evidence to suggest other biomarkers such as inflammatory cytokines and clonal plasmacytoid dendritic cells may have prognostic implications as well [44,45]. More recently, CPSS also included molecular modifications, the CPSS-Mol, to further improve risk stratification. Multivariate analysis of 214 CMML patients with external validation (n = 260) showed mutation RUNX1, NRAS, SETBP1, and ASXL1 were all independently associated with inferior OS [11]. These mutations were integrated into the genetic risk group parameter that assigns a score of 0 for low risk cytogenetics (as previously defined in CPSS) and absence of aforementioned mutations, a score of 1 for intermediate risk cytogenetics and mutations involving ASXL1/SETBP1/NRAS, and a score of 2 for high risk cytogenetics and RUNX1 mutations. The genetic risk group has 4 categories depending on the total scores, and it replaces the CMML-specific cytogenetics from the original CPSS model. Under CPSS-Mol, FAB and WHO types were also replaced by BM blasts and WBC counts. This results in an enhanced, robust prognostic tool for CMML that consists of 4 risk groups with markedly different median OS from NR down to 18 months and AML evolution from 0% up to 48% [11,46]. Table 1 summarizes the different prognostic scoring systems for CMML.

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Table 1 CMML-specific risk stratification models. Modified from Patnaik et al. [12]. Model

MDAPS

DUSS

CPSS

GFM

Mayo

Mayo Molecular CPSS-Mol

Hellenic Registry

Risk factors

Median OS (months)

1.) 2.) 3.) 4.) 1.) 2.) 3.) 4.) 1.) 2.) 3.) 4.) 1.) 2.) 3.) 4.) 5.) 1.) 2.) 3.) 4.) 1.) 1.) 2.) 3.) 4.)

Hgb < 12 g/dL ALC > 2.5 × 10∧9/L Peripheral immature myeloid cells Bone marrow blasts > 10% Hgb ≤ 9 g/dL Bone marrow blasts ≥ 5% Platelets ≤ 100 × 10∧9/L LDH > 200 U/L RBC transfusion dependency CMML FAB type CMML WHO type CMML-specific cytogenetics Anemia WBC > 15 × 10∧9/L Platelets ≤ 100 × 10∧9/L Age > 65 years ASXL1 mutation Hgb < 10 g/dL AMC > 10 × 10∧9/L Peripheral immature myeloid cells Platelets ≤ 100 × 10∧9/L Risk factors from Mayo above 2.) Frameshift and nonsense ASXL1 RBC transfusion dependency WBC ≥ 13 × 10∧9/L Bone marrow blasts ≥ 5% Genetic risk groups as defined by CPSS cytogenetics risk stratification and ASXL1, NRAS, SETBP1, and RUNX1 1.) Peripheral immature myeloid cells 2.) Platelets ≤ 100 × 10∧9/L 3.) Elevated ferritin level

Reference

Low risk

Int-1 risk

Int-2 risk

High risk

24

15

8

5

[19]

93

26

–

11

[22]

72

31

13

5

[6]

NR

38.5

–

14.4

[14]

32

18.5

–

10

[15]

97 NR

59 64

31 37

18 18

[10] [11]

44.1

29.7

–

16.4

[13]

ALC: absolute lymphocyte count; AMC: absolute monocyte count; CMML: chronic myelomonocytic leukemia; CPSS: CMML Prognostic Scoring System; DUSS: Dusseldorf score for CMML; FAB: French American British; GFM: Groupe Francophone des Myelodysplasies; Hgb: hemoglobin; LDH: lactate dehydrogenase; MDAPS: MD Anderson Prognostic Scoring System; RBC: red blood cell count; WBC: white blood cell count; WHO: World Health Organization.

The five recommended prognostic systems The European LeukemiaNet (ELN) and European Hematology Association (EHA) together published its first consensus-based guidelines in December 2018 outlining recommendations for diagnosis and treatment of CMML [31]. Cytogenetics and at the minimum 4 genetic mutations analysis including ASXL1, RUNX1, NRAS, and SETBP1 should be tested. If possible, other mutations such as FLT3 and IDH1/2 would be helpful as well as these may have practical therapeutic implications. Among the multiple existing prognostic models, they recommend using one of the following 5 systems: MDAPS, CPSS, GFM, Mayo Molecular, or CPSS-Mol. A comparison and contrast of these models are illustrated in Fig. 3. Having international collaborative networks and now the first consensus document by renown associations and experts are major milestones toward a more uniform approach to this rare, heterogeneous disease. Ultimately, translational studies that can shed light on the biological pathogenesis of CMML is key to develop novel therapies, better refine risks, and improve survival. Predictive factors in the setting of hypomethylating agents Hypomethylating agents (HMAs) such as azacytidine (AZA) and decitabine (DAC) have been used for the treatment of CMML since the Food and Drug Administration (FDA) approved these drugs in 2004 based on a phase 3, randomized study that is mainly focused on MDS [47,48]. Subsequent smaller studies have demonstrated modest objective response rates (ORRs) of about 25% (14% complete response (CR) and 11% partial response (PR)) in CMML patients [47,49,50]. In a recent prospective, multicenter, phase 2 clinical trial, Santini et al. studied 43 CMML patients (> 50% with high risk disease) and their response to DAC [51]. The overall response rate after 6 cycles of DAC was 47.6% with 7 patients (16.5%) achieved CR. The median OS was 17 months with responders having a significantly better outcome than non-responders (> 20 months vs. < 6 months, p = 0.02). Given HMAs are the only FDA approved therapy for CMML, there is a good reason to see if any clinical variables or mutations are prognostic in this subgroup of patients who had only received HMA. Early attempts did not find any molecular predictors of response to DAC in CMML [52]. However, Meldi et al. found differentially methylated regions (DMRs) of DNA performed using NGS are able to distinguish DAC 5

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Fig. 3. Compare and contrast of the 5 CMML risk stratification systems recommended by ELN/EHA. Each circle represents 1 model and each samecolored-text represents one of the parameters used in the model. In general, hemoglobin levels, white counts, and blasts burden are common risk factors in these systems. Three of the later scoring systems include ASXL1 mutation (frameshift or nonsense) as an adverse prognostic factor.

responsiveness in 40 CMML patients [53]. Responders tend to have upregulated genes associated with cell cycles while non-responders have overexpressed CXCL4 and CXCL7. These findings help elucidate the pathophysiology of DAC resistance in this population. In a recent retrospective study that included 174 CMML patients from multiple centers who had received AZA or DAC, investigators found ASXL1 mutation to be a predictor of lower ORR (Odd ratio (OR) = 0.85, p = 0.037) [54]. In contrast, those with mutated TET2 and wildtype ASXL1 (TET2mut/ASXL1wt) have a significantly higher CR rate (OR = 1.18, p = 0.011) with a better OS (HR = 0.60, p = 0.05). Furthermore, multivariate analysis showed WBC (log10 transformed) (HR = 2.30, p = 0.005) and mutation with RUNX1 or CBL compared to their wildtype counterparts to have inferior outcomes (HR 2.00 with CI 1.17–3.42 and HR 1.90 with CI 1.06–3.40, respectively). It is unclear if HMA changes the underlying disease biology and therefore yielding some of the different prognostic factors observed. One recent study argues against that after finding 29% of CMML patients (n = 121) achieved CR with HMA but still progressed to AML [55]. In addition, TET2mut/ASXL1wt was previously found to have a favorable prognosis even in a cohort of patients with heterogenous treatments [56,57]. The Greece group recently proposed a new risk stratification system specifically for CMML patients (n = 88) who were treated with azacytidine [13]. High serum ferritin levels at diagnosis, PB blasts, and platelets < 100 × 109/L were selected to be part of the 3-tiered model with low, intermediate, and high risk conferred median OS of 44.1, 29.7, and 16.4 months, respectively (Table 1). The use of ferritin might introduce bias, given that it is an acute-phase reactant; however, c-reactive protein (CRP) was also measured to help minimize this bias. Of note, no molecular information was available for analysis in this study.

Summary There are numerous prognostication systems for CMML, and each is composed of a slightly different set of risk factors. The advent of molecular testing has led to an increased understanding of the disease and incorporation of mutational status into these systems. Overall, there has been incremental progress with each new model or iteration of existing risk classification providing a more refined estimate of survival for patients with CMML. For a disease that was almost unknown a few decades ago, the advent of multiple models is both a positive advance and a challenge. Identifying a consensus model, either from existing models or a novel model, remains a critical knowledge gap in this disease. The ELN/EHA consensus report recommending 5 preferred systems of which 3 require molecular analysis is a step in the right direction. The major challenge toward a uniform system is the rare, heterogeneous nature of the disease which makes it very difficult to study prospectively. Large dataset will be required to resolve the prognostic challenge in this disease so that consensus can be reached on the best performing prognostic model. Effective therapeutics may potentially homogenize the outcome of disease by affecting the underlying biological pathogenesis. Prognostic tools in this setting can be helpful and will evolve along with drug discovery for CMML.

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Practice points

• Cytogenetic analysis and molecular testing at a minimal for 4 genes including ASXL1, NRAS, RUNX1, and SETBP1 are highly recommended at the time of CMML diagnosis. • While many prognostic scoring systems for CMML are validated, the following are preferred whenever possible: CPSS-Mol (require molecular and cytogenetic testing), Mayo Molecular (require testing for ASXL1), GFM (require testing for ASXL1), CPSS (require cytogenetics), and MDAPS (only require hematologic and morphologic information).

Research agenda

• CMML is a clinically diverse but relatively molecularly homogenous disease and efforts to find novel prognostic biomarkers outside of the molecular realm is a worthwhile pursuit. • Having well-organized, collaborative networks of institutions is especially critical in the study of a rare disease such as CMML since a lot of knowledge is learned from retrospective studies.

Declaration of competing interest The authors have no relevant conflicts of interest to disclose. Acknowledgement No relevant sources of funding for this review. References [1] Arber DA, Orazi A, Hasserjian R, Thiele J, Borowitz MJ, Le Beau MM, et al. The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. Blood 2016;127(20):2391–405. [2] Guru Murthy GS, Dhakal I, Mehta P. Incidence and survival outcomes of chronic myelomonocytic leukemia in the United States. Leuk Lymphoma 2017;58(7):1648–54. [3] Bennett JM, Catovsky D, Daniel MT, Flandrin G, Galton DA, Gralnick HR, et al. Proposals for the classification of the acute leukaemias. French-American-British (FAB) co-operative group. Br J Haematol 1976;33(4):451–8. [4] Vardiman JW, Harris NL, Brunning RD. The World Health Organization (WHO) classification of the myeloid neoplasms. Blood 2002;100(7):2292–302. [5] Loghavi S, Sui D, Wei P, Garcia-Manero G, Pierce S, Routbort MJ, et al. Validation of the 2017 revision of the WHO chronic myelomonocytic leukemia categories. Blood Adv 2018;2(15):1807–16. [6] Such E, Germing U, Malcovati L, Cervera J, Kuendgen A, Della Porta MG, et al. Development and validation of a prognostic scoring system for patients with chronic myelomonocytic leukemia. Blood 2013;121(15):3005–15. [7] Greenberg P, Cox C, LeBeau MM, Fenaux P, Morel P, Sanz G, et al. International scoring system for evaluating prognosis in myelodysplastic syndromes. Blood 1997;89(6):2079–88. [8] Greenberg PL, Tuechler H, Schanz J, Sanz G, Garcia-Manero G, Sole F, et al. Revised international prognostic scoring system for myelodysplastic syndromes. Blood 2012;120(12):2454–65. [9] Kantarjian H, O'Brien S, Ravandi F, Cortes J, Shan J, Bennett JM, et al. Proposal for a new risk model in myelodysplastic syndrome that accounts for events not considered in the original international prognostic scoring system. Cancer 2008;113(6):1351–61. [10] Patnaik MM, Itzykson R, Lasho TL, Kosmider O, Finke CM, Hanson CA, et al. ASXL1 and SETBP1 mutations and their prognostic contribution in chronic myelomonocytic leukemia: a two-center study of 466 patients. Leukemia 2014;28(11):2206–12. [11] Elena C, Galli A, Such E, Meggendorfer M, Germing U, Rizzo E, et al. Integrating clinical features and genetic lesions in the risk assessment of patients with chronic myelomonocytic leukemia. Blood 2016;128(10):1408–17. [12] Patnaik MM, Tefferi A. Chronic myelomonocytic leukemia: 2018 update on diagnosis, risk stratification and management. Am J Hematol 2018;93(6):824–40. [13] Diamantopoulos PT, Kotsianidis I, Symeonidis A, Pappa V, Galanopoulos A, Gogos D, et al. Chronic myelomonocytic leukemia treated with 5-azacytidine - results from the Hellenic 5-Azacytidine Registry: proposal of a new risk stratification system. Leuk Lymphoma 2019;60(7):1721–30. [14] Itzykson R, Kosmider O, Renneville A, Gelsi-Boyer V, Meggendorfer M, Morabito M, et al. Prognostic score including gene mutations in chronic myelomonocytic leukemia. J Clin Oncol 2013;31(19):2428–36. [15] Patnaik MM, Padron E, LaBorde RR, Lasho TL, Finke CM, Hanson CA, et al. Mayo prognostic model for WHO-defined chronic myelomonocytic leukemia: ASXL1 and spliceosome component mutations and outcomes. Leukemia 2013;27(7):1504–10. [16] Bennett JM, Catovsky D, Daniel MT, Flandrin G, Galton DA, Gralnick H, et al. The chronic myeloid leukaemias: guidelines for distinguishing chronic granulocytic, atypical chronic myeloid, and chronic myelomonocytic leukaemia. Proposals by the French-American-British Cooperative Leukaemia Group. Br J Haematol 1994;87(4):746–54. [17] Vardiman JW, Thiele J, Arber DA, Brunning RD, Borowitz MJ, Porwit A, et al. The 2008 revision of the World Health Organization (WHO) classification of myeloid neoplasms and acute leukemia: rationale and important changes. Blood 2009;114(5):937–51. [18] Schuler E, Schroeder M, Neukirchen J, Strupp C, Xicoy B, Kundgen A, et al. Refined medullary blast and white blood cell count based classification of chronic myelomonocytic leukemias. Leuk Res 2014;38(12):1413–9. [19] Onida F, Kantarjian HM, Smith TL, Ball G, Keating MJ, Estey EH, et al. Prognostic factors and scoring systems in chronic myelomonocytic leukemia: a retrospective analysis of 213 patients. Blood 2002;99(3):840–9. [20] Germing U, Strupp C, Aivado M, Gattermann N. New prognostic parameters for chronic myelomonocytic leukemia. Blood 2002;100(2):731–2. author reply 2-3. [21] Beran M, Wen S, Shen Y, Onida F, Jelinek J, Cortes J, et al. Prognostic factors and risk assessment in chronic myelomonocytic leukemia: validation study of the M.D. anderson prognostic scoring system. Leuk Lymphoma 2007;48(6):1150–60. [22] Germing U, Kundgen A, Gattermann N. Risk assessment in chronic myelomonocytic leukemia (CMML). Leuk Lymphoma 2004;45(7):1311–8. [23] Such E, Cervera J, Costa D, Sole F, Vallespi T, Luno E, et al. Cytogenetic risk stratification in chronic myelomonocytic leukemia. Haematologica 2011;96(3):375–83. [24] Tang G, Zhang L, Fu B, Hu J, Lu X, Hu S, et al. Cytogenetic risk stratification of 417 patients with chronic myelomonocytic leukemia from a single institution. Am J Hematol 2014;89(8):813–8. [25] Wassie EA, Itzykson R, Lasho TL, Kosmider O, Finke CM, Hanson CA, et al. Molecular and prognostic correlates of cytogenetic abnormalities in chronic

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O. Chan and E. Padron

myelomonocytic leukemia: a mayo clinic-french consortium study. Am J Hematol 2014;89(12):1111–5. [26] Calvo X, Nomdedeu M, Santacruz R, Martinez N, Costa D, Pereira A, et al. Comparison of three prognostic scoring systems in a series of 146 cases of chronic myelomonocytic leukemia (CMML): MD Anderson prognostic score (MDAPS), CMML-specific prognostic scoring system (CPSS) and Mayo prognostic model. A detailed review of prognostic factors in CMML. Leuk Res November 2015;39(11):1146–53https://www.ncbi.nlm.nih.gov/pubmed/26354683. [27] Motohashi K, Fujisawa S, Doki N, Kobayashi T, Mori T, Usuki K, et al. Cytogenetic risk stratification may predict allogeneic hematopoietic stem cell transplantation outcomes for chronic myelomonocytic leukemia. Leuk Lymphoma 2018;59(6):1332–7. [28] Padron E, Garcia-Manero G, Patnaik MM, Itzykson R, Lasho T, Nazha A, et al. An international data set for CMML validates prognostic scoring systems and demonstrates a need for novel prognostication strategies. Blood Canc J 2015;5:e333. [29] Itzykson R, Duchmann M, Lucas N, Solary E. CMML: clinical and molecular aspects. Int J Hematol 2017;105(6):711–9. [30] Patnaik MM, Tefferi A. Cytogenetic and molecular abnormalities in chronic myelomonocytic leukemia. Blood Canc J 2016;6:e393. [31] Itzykson R, Fenaux P, Bowen D, Cross NCP, Cortes J, Witte TD, et al. Diagnosis and treatment of chronic myelomonocytic leukemias in adults: recommendations from the European hematology association and the European LeukemiaNet. HemaSphere 2018;2(6). [32] Malcovati L, Papaemmanuil E, Ambaglio I, Elena C, Galli A, Della Porta MG, et al. Driver somatic mutations identify distinct disease entities within myeloid neoplasms with myelodysplasia. Blood 2014;124(9):1513–21. [33] Ball M, List AF, Padron E. When clinical heterogeneity exceeds genetic heterogeneity: thinking outside the genomic box in chronic myelomonocytic leukemia. Blood 2016;128(20):2381–7. [34] Gelsi-Boyer V, Brecqueville M, Devillier R, Murati A, Mozziconacci MJ, Birnbaum D. Mutations in ASXL1 are associated with poor prognosis across the spectrum of malignant myeloid diseases. J Hematol Oncol 2012;5:12. [35] Gelsi-Boyer V, Trouplin V, Adelaide J, Bonansea J, Cervera N, Carbuccia N, et al. Mutations of polycomb-associated gene ASXL1 in myelodysplastic syndromes and chronic myelomonocytic leukaemia. Br J Haematol 2009;145(6):788–800. [36] Gelsi-Boyer V, Trouplin V, Roquain J, Adelaide J, Carbuccia N, Esterni B, et al. ASXL1 mutation is associated with poor prognosis and acute transformation in chronic myelomonocytic leukaemia. Br J Haematol 2010;151(4):365–75. [37] Patnaik MM, Wassie EA, Padron E, Onida F, Itzykson R, Lasho TL, et al. Chronic myelomonocytic leukemia in younger patients: molecular and cytogenetic predictors of survival and treatment outcome. Blood Canc J 2015;5:e280. [38] Patnaik MM, Vallapureddy R, Lasho TL, Hoversten KP, Finke CM, Ketterling R, et al. EZH2 mutations in chronic myelomonocytic leukemia cluster with ASXL1 mutations and their co-occurrence is prognostically detrimental. Blood Canc J 2018;8(1):12. [39] Patnaik MM, Barraco D, Lasho TL, Finke CM, Hanson CA, Ketterling RP, et al. DNMT3A mutations are associated with inferior overall and leukemia-free survival in chronic myelomonocytic leukemia. Am J Hematol 2017;92(1):56–61. [40] Palomo L, Garcia O, Arnan M, Xicoy B, Fuster F, Cabezon M, et al. Targeted deep sequencing improves outcome stratification in chronic myelomonocytic leukemia with low risk cytogenetic features. Oncotarget 2016;7(35):57021–35. [41] Grossmann V, Kohlmann A, Eder C, Haferlach C, Kern W, Cross NC, et al. Molecular profiling of chronic myelomonocytic leukemia reveals diverse mutations in > 80% of patients with TET2 and EZH2 being of high prognostic relevance. Leukemia 2011;25(5):877–9. [42] Damm F, Itzykson R, Kosmider O, Droin N, Renneville A, Chesnais V, et al. SETBP1 mutations in 658 patients with myelodysplastic syndromes, chronic myelomonocytic leukemia and secondary acute myeloid leukemias. Leukemia 2013;27(6):1401–3. [43] Sallman DA, Komrokji R, Cluzeau T, Vaupel C, Al Ali NH, Lancet J, et al. ASXL1 frameshift mutations drive inferior outcomes in CMML without negative impact in MDS. Blood Canc J 2017;7(12):633. [44] Niyongere S, Lucas N, Zhou JM, Sansil S, Pomicter AD, Balasis ME, et al. Heterogeneous expression of cytokines accounts for clinical diversity and refines prognostication in CMML. Leukemia 2019;33(1):205–16. [45] Lucas N, Duchmann M, Rameau P, Noel F, Michea P, Saada V, et al. Biology and prognostic impact of clonal plasmacytoid dendritic cells in chronic myelomonocytic leukemia. Leukemia 2019;33(10):2466–80. [46] Steensma DP. Putting it all together in CMML risk stratification. Blood 2016;128(10):1318–9. [47] Silverman LR, Demakos EP, Peterson BL, Kornblith AB, Holland JC, Odchimar-Reissig R, et al. Randomized controlled trial of azacitidine in patients with the myelodysplastic syndrome: a study of the cancer and leukemia group B. J Clin Oncol 2002;20(10):2429–40. [48] Hunter AM, Zhang L, Padron E. Current management and recent advances in the treatment of chronic myelomonocytic leukemia. Curr Treat Options Oncol 2018;19(12):67. [49] Kantarjian H, Oki Y, Garcia-Manero G, Huang X, O'Brien S, Cortes J, et al. Results of a randomized study of 3 schedules of low-dose decitabine in higher-risk myelodysplastic syndrome and chronic myelomonocytic leukemia. Blood 2007;109(1):52–7. [50] Wijermans PW, Ruter B, Baer MR, Slack JL, Saba HI, Lubbert M. Efficacy of decitabine in the treatment of patients with chronic myelomonocytic leukemia (CMML). Leuk Res 2008;32(4):587–91. [51] Santini V, Allione B, Zini G, Gioia D, Lunghi M, Poloni A, et al. A phase II, multicentre trial of decitabine in higher-risk chronic myelomonocytic leukemia. Leukemia 2018;32(2):413–8. [52] Braun T, Itzykson R, Renneville A, de Renzis B, Dreyfus F, Laribi K, et al. Molecular predictors of response to decitabine in advanced chronic myelomonocytic leukemia: a phase 2 trial. Blood 2011;118(14):3824–31. [53] Meldi K, Qin T, Buchi F, Droin N, Sotzen J, Micol JB, et al. Specific molecular signatures predict decitabine response in chronic myelomonocytic leukemia. J Clin Investig 2015;125(5):1857–72. [54] Duchmann M, Yalniz FF, Sanna A, Sallman D, Coombs CC, Renneville A, et al. Prognostic role of gene mutations in chronic myelomonocytic leukemia patients treated with hypomethylating agents. EBioMedicine 2018;31:174–81. [55] Coston T, Pophali P, Vallapureddy R, Lasho TL, Finke CM, Ketterling RP, et al. Suboptimal response rates to hypomethylating agent therapy in chronic myelomonocytic leukemia; a single institutional study of 121 patients. Am J Hematol 2019;94(7):767–79. [56] Patnaik MM, Lasho TL, Vijayvargiya P, Finke CM, Hanson CA, Ketterling RP, et al. Prognostic interaction between ASXL1 and TET2 mutations in chronic myelomonocytic leukemia. Blood Canc J 2016;6:e385. [57] Patnaik MM, Zahid MF, Lasho TL, Finke C, Ketterling RL, Gangat N, et al. Number and type of TET2 mutations in chronic myelomonocytic leukemia and their clinical relevance. Blood Canc J 2016;6(9):e472.

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