Figure S5. Unsupervised hierarchical clustering of the global gene expression profile from 323 patients with APL. Genes and samples are clustered using the ward.D method, and distances are calculated on the basis of the Pearson's correlation coefficient. Columns indicate patients with APL and rows indicate genes.
Funding
National High-tech Research and Development Program
Shanghai Jiao Tong University
Regenerative Medicine and Stem Cell Research
National Natural Science Foundation of China
National Key Research and Development Program of China
Shanghai Municipal Education Commission
ARTICLE ABSTRACT
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.
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).
Through combining NRAS mutations, APL9 score, and WBC, 321 cases can be stratified into two groups with significantly different outcomes. The estimated 5-year overall (P = 0.00031), event-free (P < 0.0001), and disease-free (P = 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.
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.
