A Prognostic Model of Therapy-Related Myelodysplastic Syndrome for Predicting Survival and Transformation to Acute Myeloid Leukemia
Authors: QuintÀs-Cardama Alfonso , Kim Hawk , Shan Jianqin , Jabbour Elias , Faderl Stefan , Wierda William G. , Ravandi Farhad , Kadia Tapan , Wang Sa A. , Pierce Sherry , Kantarjian Hagop M , Garcia-Manero Guillermo 
Published: October 2019

Abstract 967

A PROGNOSTIC MODEL OF THERAPY-RELATED MYELODYSPLASTIC SYNDROME FOR PREDICTING SURVIVAL AND TRANSFORMATION TO ACUTE MYELOID LEUKEMIA

Alfonso Quintás-Cardama, Hawk Kim, Elias Jabbour, Stefan Faderl, William Wierda, Farhad Ravandi, Tapan Kadia, Sa Wang, Sherry Pierce, Jianqin Shan, Hagop Kantarjian, Guillermo Garcia-Manero

Background:

A significant fraction of patients with MDS have a prior history of an antecedent malignancy treated with chemotherapy and/or radiotherapy. Therapy related MDS (t-MDS) differs from de novo MDS in its high frequency of chromosomal abnormalities (typically in the context of complex karyotypes), high rate of transformation to acute myeloid leukemia (AML), and high resistance to standard MDS therapy. MDS prognostic models (e.g., IPSS, WPSS) have been developed based primarily on cohorts of patients with de novo MDS. We evaluated the characteristics of a large cohort of patients with t-MDS and created a specific t-MDS prognostic model.

Patients and methods:

From 1998 to 2007, we identified 1950 patients with MDS of which 438 (22%) (RAEB-T by FAB were excluded) had a history of one or more prior malignancies and treatment for their malignancies prior to a diagnosis of MDS. Of those, 279 (64%) had received prior chemotherapy and/or radiotherapy, and therefore were categorized as t-MDS. Potential prognostic factors were determined by univariate analyses and validated by multivariate analysis. The final prognostic factors were incorporated into a novel prognostic model.

Results:

Univariate analysis identified significant factors in association with overall survival. They included hepatomegaly (no vs. yes; p=0.02), hemoglobin (<9.9 vs. 10.0–11.9 vs. ≥ 12.0; p<0.001), platelet (<30 vs. 30–49 vs. 50–199 vs. ≥ 200; p<0.001), marrow blast% (<5, 5–10 and 11–19; p <0.001), cytogenetics (5q-, 20q-, Y-, normal vs. others vs. 7- and/or complex; p<0.001), types of MDS by WHO classification (RA, RCMD, MDSu vs. others; p<0.001), time from treatment to MDS (≤5 vs. >5 years; p=0.06), number of lines of therapy (1 vs. ≥2; p=0.06), serum albumin (≥4 vs. <4g/dL; p=0.01), serum β-2 microglobulin (≤3 vs. >3mg/L; p=0.05), ECOG performance status (0–1 vs. ≥2; p<0.001), and prior transfusion (p<0.001). When incorporated into the multivariate model, we identified 7 factors that independently predicted survival: age (≥65yrs vs <65yrs; HR=1.63), ECOG performance status (2–4 vs. 0–1; HR=1.86), cytogenetics (−7 and/or complex vs others; HR=2.47), WHO MDS subtype (RARs, RAEB-1/2 vs others; HR=1.92), hemoglobin (<11g/dL vs ≥11.0 g/dL; HR=2.24), platelets (<50 vs ≥50; HR=2.01), and transfusion dependency (yes vs no; HR=1.59). These factors were then used to create a prognostic model that segregates patients into 3 discreet prognostic groups: good (n=57, 21%; 0–2 risk factors; median survival 34 months), intermediate (n=154, 57%; 3–4 risk factors; median survival 12 months) and poor (n=61, 22%; 5–7 risk factors; median survival 5 months) (Figure 1A). This model also predicted 1-year leukemia free survival (good: 96%, intermediate: 84%, and poor: 72%; p=0.001). This model was subsequently validated in a test group of 189 patients with t-MDS diagnosed between 2008 and 2010. The median survival rates for low, intermediate, and poor risk patients in this group were: 26, 13, and 7 months (p<0.001) (Figure 1B).

Conclusion:

We propose a prognostic model specific for patients with t-MDS that predicts overall and leukemia-free survivals. This model may facilitate the development of risk-adapted therapeutic strategies.

Disclosures:

No relevant conflicts of interest to declare.