LAUSR

Adult acute lymphoblastic leukemia (ALL) is heterogeneous both biologically and clinically. The outcomes of ALL have been improved with the application of children-like regimens and novel agents including immune therapy in young adults. The refractory to therapy and relapse of ALL have occurred in most adult cases. Factors affecting the prognosis of ALL include age and white blood cell (WBC) count at diagnosis. The clinical implications of genetic biomarkers, including chromosome translocation and gene mutation, have been explored in ALL. The interactions of these factors on the prediction of prognosis have not been evaluated in adult ALL. A prognostic model based on clinical and genetic abnormalities is necessary for clinical practice in the management of adult ALL. The newly diagnosed adult ALL patients were divided into the training and the validation cohort at 7:3 ratio. Factors associated with overall survival (OS) were assessed by univariate/multivariate Cox regression analyses and a signature score was assigned to each independent factor. A nomogram based on the signature score was developed and validated. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to assess the performance of the nomogram model. This study included a total of 229 newly diagnosed ALL patients. Five independent variables including age, WBC, bone marrow (BM) blasts, MLL rearrangement, and ICT gene mutations (carried any positive mutation of IKZF1, CREBBP and TP53) were identified as independent adverse factors for OS evaluated by the univariate, Kaplan-Meier survival and multivariate Cox regression analyses. A prognostic nomogram was built based on these factors. The areas under the ROC curve and calibration curve showed good accuracy between the predicted and observed values. The DCA curve showed that the performance of our model was superior to current risk factors. A nomogram was developed and validated based on the clinical and laboratory factors in newly diagnosed ALL patients. This model is effective to predict the overall survival of adult ALL. It is a simple and easy-to-use model that could efficiently predict the prognosis of adult ALL and is useful for decision making of treatment.