Comparison of Existing Prognostic Models in Chronic Myelomonocytic Leukemia

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Comparison of Existing Prognostic Models in Chronic Myelomonocytic Leukemia

Chronic myelomonocytic leukemia (CMML) is a rare hematologic malignancy that exhibits characteristics of both myelodysplastic syndromes (MDS) and myeloproliferative neoplasms (MPNs). Due to its low incidence, establishing standardized diagnostic criteria and specific treatment paradigms has been challenging. Accurate prognostic scoring systems are essential for guiding appropriate treatment strategies. This article provides a comprehensive comparison of existing prognostic models for CMML, evaluating their strengths, limitations, and clinical applicability.

Overview of CMML

CMML is characterized by persistent monocytosis in the peripheral blood, along with dysplastic features in the bone marrow. The disease can manifest as either a myelodysplastic or myeloproliferative phenotype, leading to significant variability in clinical presentation and outcomes. The rarity of CMML has made it difficult to develop universally accepted diagnostic and prognostic criteria, necessitating the use of multiple prognostic models to guide clinical decision-making.

Existing Prognostic Models

Several prognostic models have been developed to predict outcomes in CMML patients. These models are based on large patient cohorts (ranging from 213 to 578 cases) and incorporate a combination of clinical, laboratory, and molecular features. The most widely used prognostic models include:

  1. CMML-Specific Prognostic Scores (CPSS): This model focuses on cytogenetic abnormalities and their impact on overall survival (OS) and the risk of transformation to acute myeloid leukemia (AML).

  2. CPSS Model (CPSS-MOL): An enhanced version of CPSS that incorporates mutations in genes such as RUNX1, NRAS, SETBP1, and ASXL1 to improve prognostic accuracy.

  3. MD Anderson Prognostic Scoring System (MDAPS): This model includes age, white blood cell count (WBC), hemoglobin (Hgb), platelet count (PLT), and bone marrow blast percentage as key prognostic factors.

  4. Global MDAPS (G-MDAPS): A modified version of MDAPS that includes additional clinical and laboratory parameters.

  5. Groupe Francophone des Myélodysplasies (GFM): This model incorporates anemia, leukocytosis, thrombocytopenia, age over 65 years, and ASXL1 mutations to predict inferior OS.

  6. Mayo Molecular Model (MMM): This model integrates absolute monocyte count, presence of circulating immature myeloid cells, Hgb, and PLT without incorporating somatic mutations. A refined version of MMM includes ASXL1 mutations to improve prognostic accuracy.

Study Design and Patient Characteristics

A retrospective study was conducted on 45 CMML patients admitted to a hospital between 2008 and 2019. The patients’ medical records were reviewed, and routine laboratory tests, including WBC, Hgb, PLT, and lactate dehydrogenase (LDH), were performed. Bone marrow biopsies, spleen biopsies, and hematoxylin-eosin staining for skin infiltration or myelofibrosis were also conducted. CMML-specific molecular risks were assessed to identify prognostic factors.

The study population had a median age of 65 years (range: 18–83 years). Among the 44 patients with complete data, 16 were diagnosed with CMML-0, eight with CMML-1, and 20 with CMML-2. The median OS was 265 days (range: 20–1565 days). Laboratory findings revealed a median WBC of 30.16 x 10^9/L, Hgb of 80 g/L, PLT of 59 x 10^9/L, and LDH of 345 U/L. Splenomegaly was observed in 16 patients (35.6%), skin infiltration in two patients (4.4%), and myelofibrosis in one patient (2.2%). Treatment regimens included decitabine in 13 patients (28.9%) and conventional chemotherapy in 23 patients (51.5%). Eleven patients (24.4%) transformed to AML during follow-up, and more than half of the patients (23/45, 55.1%) died within one year.

Prognostic Factors and Model Evaluation

The study identified several independent adverse prognostic factors, including elevated WBC (P = 0.001), French-American-British (FAB) subtype (P = 0.004), elevated LDH (P = 0.013), and CMML-specific cytogenetic risk (P = 0.002). Multivariate analysis confirmed that WBC (P = 0.002), LDH (P = 0.016), and CMML-specific cytogenetic risk (P = 0.005) were significantly associated with unfavorable OS.

The prognostic models CPSS (AUC = 0.668) and CPSS-MOL (AUC = 0.625) were effective in predicting the risk of AML transformation, while G-MDAPS (AUC = 0.749) was better at identifying the risk of death. However, there was no significant correlation between extramedullary infiltration and prognostic scores, although the World Health Organization (WHO) subgroup showed a weak correlation (Spearman correlation coefficient: 0.327, P = 0.028).

Extramedullary Infiltration and Fibrosis

Extramedullary infiltration, such as splenomegaly, skin infiltration, and meningeal involvement, is a common feature of “proliferative” CMML. Splenomegaly is associated with inferior OS, particularly in patients receiving hypomethylating agents. Skin infiltration, diagnosed via biopsy, may increase the risk of AML transformation. Meningeal infiltration can present with symptoms such as headache, ocular disorders, and facial numbness, and may resolve with remission.

Limitations and Future Directions

The study highlighted several limitations of existing prognostic models. First, there is variability in the ability of different models to predict OS and AML transformation. Second, further research is needed to understand the prognostic relevance of specific mutations. Third, it remains unclear whether existing models are applicable to special CMML subtypes, such as those with fibrosis or extramedullary diseases.

Despite these limitations, the study underscores the importance of prognostic models in guiding CMML management. The integration of molecular data into prognostic models has improved their accuracy, but there is still a need for a unified, comprehensive model that can overcome the limitations of current systems.

Conclusion

Accurate prognostic models are essential for managing CMML, a rare and complex hematologic malignancy. Existing models, such as CPSS, CPSS-MOL, MDAPS, G-MDAPS, GFM, and MMM, provide valuable insights into patient outcomes but have limitations that need to be addressed. Future research should focus on refining these models by incorporating additional molecular data and validating their applicability to special CMML subtypes. The development of a unified prognostic model will enhance our ability to predict outcomes and tailor treatment strategies for CMML patients.

doi.org/10.1097/CM9.0000000000000637

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