P762: NONLINIEAR IMPACT OF CLINICAL FEATURES ON THE SURVIVAL OUTCOME OF MYELODYSPLASTIC SYNDROMES PATIENTS

Abstract

Background: Myelodysplastic syndromes (MDS) are a group of hematopoietic stem-cell disorders with heterogenous prognosis. The revised international prognostic scoring system (IPSS-R) (Greenberg et al., Blood, 2012) is the most widely used score for prognostication. However, the allocation of IPSS-R risk categories remains imprecise for some patients leading to unsatisfactory therapy decisions. Newer molecular prognostic models (Nazha et al., JCO, 2021 and Bernard et al., ASH, 2021) have added mutation data to improve survival predictions for MDS patients. Aims: Our aims were to investigate the nonlinear association of normalized clinical data in combination with molecular and cytogenetic data and to improve the prognostication potential of IPSS-R for patients with MDS. Methods: A multivariable fractional polynomials (MFP) algorithm was applied on clinical data of 943 MDS patients and nonlinear associations between continuous covariates and survival outcomes were efficiently modeled within Cox regression. Selected transformed variables were picked and the potential overfit was avoided by a closed test procedure for function selection (Royston et al., John Wiley & Sons, 2008). The performance of the model was evaluated in an independent set of 436 MDS patients and the accuracy was assessed using a concordance (c)index and the area under the Receiver Operating Characteristic curve (AUC) at each time point. Mutation data was obtained in a subset of 695 patients using next-generation sequencing, evaluating a panel of 81 genes at the time of diagnosis. Results: An MFP-model consisting of cytogenetics per IPSS-R and WHO subtypes, continuous forms of age and hemoglobin, and non-linear transformations of platelets and bone marrow blast percentage yielded a c-index performance of 0.82 vs 0.78 for IPSS-R and a significant AUC upgrade at all time points in the training set (Figure a). In comparison with the IPSS-R, absolute neutrophil count was not identified as an independent prognostic covariate and common features were treated in a non-linear fashion. Validation of the model in an independent set of 438 patients yielded a higher c-index (0.72 vs 0.68) with an improved AUC over time compared to the IPSS-R (Figure b). Based on MFP risk score, patients were then clustered into 5 different risk categories and compared with IPSS-R classifications. Our data showed no significant difference in time to death between IPSS-R groups for very low risk and low risk or between low risk and intermediate risk (Figure c); however, MFP classification showed 5 significantly different subgroups in predicting time to death for MDS patients (Figure d). We further investigated the effect of molecular data on disease prognostics in a subset of 695 patients sequenced using an 81 gene panel. In addition to the selected clinical covariates, occurrence of TP53, SRSF2, and ZRSR2 mutations were independently associated with lower survival, while the presence of TET2 mutation showed a depression effect on TP53 mutation towards a better outcome. As SF3B1 was not selected in the final model, we further evaluated its potential interactions and identified its correlation with cytogenetics and platelets, which predominated in the MFP model. This might be due to heterogeneity within the data set (e.g., 25% co-occurrence of SF3B1 with TP53 mutation) and needs to be investigated in larger cohorts. Image:Summary/Conclusion: We have established a nonlinear model that yields improved risk assessment compared to IPSS-R criteria.