A clinical prediction model identifies a subgroup with inferior survival within intermediate risk acute myeloid leukemia

Xiaoxia Hu; Bianhong Wang; Qi Chen; Aijie Huang; Weijia Fu; Lixia Liu; Ying Zhang; Gusheng Tang; Hui Cheng; Xiong Ni; Lei Gao; Jie Chen; Li Chen; Weiping Zhang; Jianmin Yang; Shanbo Cao; Li Yu; Jianmin Wang

doi:10.7150/jca.57231

A clinical prediction model identifies a subgroup with inferior survival within intermediate risk acute myeloid leukemia

J Cancer. 2021 Jun 11;12(16):4912-4923. doi: 10.7150/jca.57231. eCollection 2021.

Authors

Xiaoxia Hu¹, Bianhong Wang^{2

3}, Qi Chen⁴, Aijie Huang¹, Weijia Fu¹, Lixia Liu⁵, Ying Zhang¹, Gusheng Tang¹, Hui Cheng¹, Xiong Ni¹, Lei Gao¹, Jie Chen¹, Li Chen¹, Weiping Zhang¹, Jianmin Yang¹, Shanbo Cao⁵, Li Yu^{3

6}, Jianmin Wang¹

Affiliations

¹ Department of Hematology, Institute of Hematology, Changhai Hospital, Shanghai 200433, China.
² Department of Hematology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 102218, China.
³ Department of Hematology, Chinese PLA General Hospital, Beijing, 100853, China.
⁴ Department of Health Statistics, Second Military Medical University, Shanghai 200433, China.
⁵ Acornmed Biotechnology Co., Ltd. Beijing, 100176, China.
⁶ Department of Hematology and Oncology, Shenzhen University General Hospital; Shenzhen University International Cancer Center, Shenzhen University Health Science Center, Shenzhen, 518000, China.

Abstract

Intermediate risk acute myeloid leukemia (AML) comprises around 50% of AML patients and is featured with heterogeneous clinical outcomes. The study aimed to generate a prediction model to identify intermediate risk AML patients with an inferior survival. We performed targeted next generation sequencing analysis for 121 patients with 2017 European LeukemiaNet-defined intermediate risk AML, revealing 122 mutated genes, with 24 genes mutated in > 10% of patients. A prognostic nomogram characterized by white blood cell count ≥10×10⁹/L at diagnosis, mutated DNMT3A and genes involved in signaling pathways was developed for 110 patients who were with clinical outcomes. Two subgroups were identified: intermediate low risk (ILR; 43.6%, 48/110) and intermediate high risk (IHR; 56.4%, 62/110). The model was prognostic of overall survival (OS) and relapse-free survival (RFS) (OS: Concordance index [C-index]: 0.703, 95%CI: 0.643-0.763; RFS: C-index: 0.681, 95%CI 0.620-0.741), and was successfully validated with two independent cohorts. Allogeneic hematopoietic stem cell transplantation (alloHSCT) reduced the relapse risk of IHR patients (3-year RFS: alloHSCT: 40.0±12.8% vs. chemotherapy: 8.6±5.8%, P= 0.010). The prediction model can help identify patients with an unfavorable prognosis and refine risk-adapted therapy for intermediate risk AML patients.

Keywords: Acute myeloid leukemia; Intermediate risk; Nomogram; Prediction model; allogenic hematopoietic stem cell transplantation.