Adding genomic data to new prognostic model improves predictors of post-HCT outcomes in patients with MDS

Researchers have developed a new personalized hematopoietic cell transplantation (HCT) outcomes prediction model for patients with myelodysplastic syndromes (MDS) that incorporates both genomic and clinical data. Findings from the new model may help physicians better identify potential risks and benefits for patients for whom HCT may be a treatment option.

The model was created and tested with 2,302 patients with MDS. Patients were divided into two groups; 1,471 were included in the training cohort and 831 in the validation cohort. In the training cohort, the median age was 71 years (range: 19-99), 230 patients (16%) progressed to acute myeloid leukemia (AML), 156 (11%) had secondary/therapy-related MDS, and 130 (9%) underwent HCT.

Researchers conducted several feature extraction analyses to identify the most important variables that impacted the outcome and produced the best prediction. The top variables impacting overall survival (OS) were age, the presence of TP53 and white blood cell count. The highest variables influencing relapse were TP53, conditioning regimen and disease status. 

The new model (c-index .74, .81) outperformed the current internal prognostic scoring system (IPSS) models, IPSS (c-index .66, .73) and IPSS-R (.67, .73) for OS and AML transformation, respectively. The model outperformed analysis with mutations only (c-index .64, .72), mutations + cytogenetics (c-index .68, .74), and mutations + cytogenetics + age (c-index .69, .75) for OS and AML transformation, respectively. 

Similar findings were found when applied to patients in the validation cohort with c-index results for OS and AML transformation of .80 and .78 respectively. 

Dr. Aziz Nazha from the Cleveland Clinic, Cleveland, OH presented the model on behalf of the CIBMTR® (Center for International Blood and Marrow Transplant Research®) Chronic Leukemia Committee at the 2018 American Society of Hematology (ASH) Annual Meeting. Dr. Nazha concluded by noting, “The new model provides probabilities of survival and relapse at different time points that may aid in treatment decisions.”   

(Nazha A, et al. 2018 ASH Annual Meeting)