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

Research Paper

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

Xiaoxia Hu1✉*, Bianhong Wang2,3*, 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 Yu3,6✉, Jianmin Wang1✉

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.
* These authors contributed equally.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions.
Hu X, Wang B, Chen Q, Huang A, Fu W, Liu L, Zhang Y, Tang G, Cheng H, Ni X, Gao L, Chen J, Chen L, Zhang W, Yang J, Cao S, Yu L, Wang J. A clinical prediction model identifies a subgroup with inferior survival within intermediate risk acute myeloid leukemia. J Cancer 2021; 12(16):4912-4923. doi:10.7150/jca.57231. Available from https://www.jcancer.org/v12p4912.htm

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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