Abstract 1695

Introduction:

CMML is a genetically and clinically heterogeneous malignancy characterized by peripheral monocytosis, cytopenias, and a propensity for AML transformation. Several prognostic models attempt to stratify patients into subcategories that are predictive for overall survival (OS), six models of which are specific to CMML. However, these models have either never been externally validated in the context of CMML or were externally validated prior to the use of hypomethylating agents. We externally validate and perform a detailed statistical comparison between the International Prognostic Scoring System (IPSS), MD Anderson Scoring System (MDASC), MD Anderson Prognostic Score (MDAPS), Dusseldorf Score (DS), and Spanish Scoring Systems (SS) in a large, single institution cohort.

Methods:

Data were collected retrospectively from the Moffitt Cancer Center (MCC) CMML database and charts were reviewed of patients that satisfied the WHO criteria for the diagnosis of CMML. The primary objective of the study was to validate the above prognostic models calculated at the time of initial presentation to MCC. All prognostic models were calculated as previously published. All analyses were conducted using SPSS version 15.0 (SPSS Inc, Chicago, IL). The Kaplan–Meier (KM) method was used to estimate median overall survival and the log rank test was used to compare KM survival estimates between two groups.

Results:

Between January 2000 and February 2012, 123 patients were captured by the MCC CMML database. The median age at diagnosis was 69 (30–90) years and the majority of patients were male (69%). By the WHO classification, the majority of patients had CMML-1 (84% vs. 16%) and most patients were subcategorized as MPN-CMML (59%) versus MDS-CMML (39%) by the FAB CMML criteria. The median overall survival of the entire cohort was 30 months and the rate of AML transformation was 44% (54). Twenty-two patients (18%) were treated with decitabine and 66 (54%) patients were treated with 5-azacitidine. Risk group stratification according to specific prognostic model is summarized in Table 1. The IPSS, MDASC, DS, and SS all predicted OS (p<0.05) while the MDASP could not be validated (p=0.924). When only patients who were treated with 5-azacitadine were considered, the MDASC, DS, and SS continued to predict OS (p<0.05) while the IPSS (p=0.15) and MDASP (p=0.239) did not. Previous reports have demonstrated that the MDASC provides further discrimination to refine stratification by the IPSS in Myelodysplastic Syndromes (MDS). Except for the low-risk DS patients, we grouped patients in our CMML cohort into lower and higher risk disease with each prognostic score and attempted to further stratify patients by the MDASC using KM and the log rank test. The MDASC was able to further risk stratify patients in each group for all prognostic models except those in the higher risk groups by the SS (p=0.07) and DS (P=0.45). When a similar statistical analysis was applied to each prognostic scoring system, only the MDASC was consistently able to further stratify the majority of risk groups as described in Table 2. The Dusseldorf scoring system was able to further stratify all lower risk groups regardless of model but was not able to do so in higher risk disease.

Conclusions:

This represents the first external validation of existing CMML prognostic models in the era of hypomethylating agent therapy. Except for the MDASP, we were able to validate the prognostic value all models tested. The MDASC represents the most robust model as it consistently refined the stratification of other models tested and remained predictive of OS in 5-azacitidine treated patients. Multi-institution collaboration is needed to construct a robust CMML specific prognostic model. Comparison to the IPSS-R is in progress.

Table 1.
MDASCMDASPDSIPSSSS
RISK GROUPPercentPercentPercentPercentPercent
low 25 24 13 22 57 
int-I 32 41 67 53 
int-II 25 30 20 
high 18 20 43 
MDASCMDASPDSIPSSSS
RISK GROUPPercentPercentPercentPercentPercent
low 25 24 13 22 57 
int-I 32 41 67 53 
int-II 25 30 20 
high 18 20 43 
Table 2.
Initial Staging<– Refining Models–>
MDASCMDASCDSIPSSSS
Lower Risk  p<0.05 p=0.41 p<0.05 
Higher Risk  p=0.24 p=0.22 p=0.79 
DS     
Lower Risk p<0.05  p=0.39 p=0.14 
Higher Risk p=0.46  p=0.99 p=0.20 
IPSS     
Lower Risk p<0.05 p<0.05  p<0.05 
Higher Risk p<0.05 p=0.17  p=0.73 
SS     
Lower Risk p<0.05 p<0.05 p<0.05  
Higher Risk p=0.07 p=0.10 p=0.58  
Initial Staging<– Refining Models–>
MDASCMDASCDSIPSSSS
Lower Risk  p<0.05 p=0.41 p<0.05 
Higher Risk  p=0.24 p=0.22 p=0.79 
DS     
Lower Risk p<0.05  p=0.39 p=0.14 
Higher Risk p=0.46  p=0.99 p=0.20 
IPSS     
Lower Risk p<0.05 p<0.05  p<0.05 
Higher Risk p<0.05 p=0.17  p=0.73 
SS     
Lower Risk p<0.05 p<0.05 p<0.05  
Higher Risk p=0.07 p=0.10 p=0.58  
Disclosures:

No relevant conflicts of interest to declare.

Author notes

*

Asterisk with author names denotes non-ASH members.

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