Integration of Genomic and Transcriptomic Markers Improves the Prognosis Prediction of Acute Promyelocytic Leukemia
Xiao-Jing Lin, Niu Qiao, Yang Shen, Hai Fang, Qing Xue, Bowen Cui, Li Chen, Hongming Zhu, Sujiang Zhang, Yu Chen, Lu Jiang, Shengyue Wang, Junmin Li, Bingshun Wang, Bing Chen, Zhu Chen, Sai‐Juan Chen
Abstract
PURPOSE: The current stratification system for acute promyelocytic leukemia (APL) is based on the white blood cell (WBC) and the platelet counts (i.e., Sanz score) over the past two decades. However, the borderlines among different risk groups are sometimes ambiguous, and for some patients, early death and relapse remained challenges. Besides, with the evolving of the treatment strategy from all-trans-retinoic acid (ATRA) and chemotherapy to ATRA-arsenic trioxide-based synergistic targeted therapy, the precise risk stratification with molecular markers is needed. EXPERIMENTAL DESIGN: This study performed a systematic analysis of APL genomics and transcriptomics to identify genetic abnormalities in 348 patients mainly from the APL2012 trial (NCT01987297) to illustrate the potential molecular background of Sanz score and further optimize it. The least absolute shrinkage and selection operator algorithm was used to analyze the gene expression in 323 cases to establish a scoring system (i.e., APL9 score). RESULTS: = 0.001) survival rates in the revised standard-risk group (95.6%, 93.8%, and 98.1%, respectively) were significantly better than those in the revised high-risk group (82.9%, 77.4%, and 88.4%, respectively), which could be validated using The Cancer Genome Atlas dataset. CONCLUSIONS: We have proposed a two-category system for improving prognosis in patients with APL. Molecular markers identified in this study may also provide genomic insights into the disease mechanism for improved therapy.