A Clinical Prediction Model Identifies a Subgroup with Inferior Survival Within Intermediate Risk Acute Myeloid Leukemia

Overview

Journal J Cancer

Specialty Oncology

Date 2021 Jul 8

PMID 34234861

Citations 6

Authors

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

Affiliations

Soon will be listed here.

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 ≥10×10/L at diagnosis, mutated 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% chemotherapy: 8.6±5.8%, = 0.010). The prediction model can help identify patients with an unfavorable prognosis and refine risk-adapted therapy for intermediate risk AML patients.

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References

Gong Y, Zhao M, Yang W, Gao A, Yin X, Hu L . Megakaryocyte-derived excessive transforming growth factor β1 inhibits proliferation of normal hematopoietic stem cells in acute myeloid leukemia. Exp Hematol. 2018; 60:40-46.e2. DOI: 10.1016/j.exphem.2017.12.010. View

Prassek V, Rothenberg-Thurley M, Sauerland M, Herold T, Janke H, Ksienzyk B . Genetics of acute myeloid leukemia in the elderly: mutation spectrum and clinical impact in intensively treated patients aged 75 years or older. Haematologica. 2018; 103(11):1853-1861. PMC: 6278991. DOI: 10.3324/haematol.2018.191536. View

Ley T, Ding L, Walter M, McLellan M, Lamprecht T, Larson D . DNMT3A mutations in acute myeloid leukemia. N Engl J Med. 2010; 363(25):2424-33. PMC: 3201818. DOI: 10.1056/NEJMoa1005143. View

Zhang Y, Gu H, Chen Q, Zhang Y, Cheng H, Yang J . Low Platelet Counts at Diagnosis Predict Better Survival for Patients with Intermediate-Risk Acute Myeloid Leukemia. Acta Haematol. 2019; 143(1):9-18. DOI: 10.1159/000500230. View

Zhang Y, Zhang Y, Chen Q, Tang G, Zhang W, Yang J . Allogeneic hematopoietic stem cells transplantation improves the survival of intermediate-risk acute myeloid leukemia patients aged less than 60 years. Ann Hematol. 2019; 98(4):997-1007. DOI: 10.1007/s00277-018-3584-2. View

Zhang W, Wang Z, Hu X, Chen J, Yang D, Song X . Preconditioning with fludarabine, busulfan and cytarabine versus standard BuCy2 for patients with acute myeloid leukemia: a prospective, randomized phase II study. Bone Marrow Transplant. 2018; 54(6):894-902. DOI: 10.1038/s41409-018-0356-5. View

Ley T, Miller C, Ding L, Raphael B, Mungall A, Robertson A . Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N Engl J Med. 2013; 368(22):2059-74. PMC: 3767041. DOI: 10.1056/NEJMoa1301689. View

Rauch P, Ellegast J, Widmer C, Fritsch K, Goede J, Valk P . MPL expression on AML blasts predicts peripheral blood neutropenia and thrombocytopenia. Blood. 2016; 128(18):2253-2257. DOI: 10.1182/blood-2016-04-711986. View

Bullinger L, Dohner K, Bair E, Frohling S, Schlenk R, Tibshirani R . Use of gene-expression profiling to identify prognostic subclasses in adult acute myeloid leukemia. N Engl J Med. 2004; 350(16):1605-16. DOI: 10.1056/NEJMoa031046. View

10.

Li Z, Herold T, He C, Valk P, Chen P, Jurinovic V . Identification of a 24-gene prognostic signature that improves the European LeukemiaNet risk classification of acute myeloid leukemia: an international collaborative study. J Clin Oncol. 2013; 31(9):1172-81. PMC: 3595425. DOI: 10.1200/JCO.2012.44.3184. View

11.

Zhou F, Zhou F, Du M, Liu L, Guo T, Xia L . Comprehensive prognostic scoring systems could improve the prognosis of adult acute myeloid leukemia patients. Int J Hematol. 2019; 110(5):575-583. DOI: 10.1007/s12185-019-02721-y. View

12.

Wilop S, Chou W, Jost E, Crysandt M, Panse J, Chuang M . A three-gene expression-based risk score can refine the European LeukemiaNet AML classification. J Hematol Oncol. 2016; 9(1):78. PMC: 5009640. DOI: 10.1186/s13045-016-0308-8. View

13.

Greif P, Dufour A, Konstandin N, Ksienzyk B, Zellmeier E, Tizazu B . GATA2 zinc finger 1 mutations associated with biallelic CEBPA mutations define a unique genetic entity of acute myeloid leukemia. Blood. 2012; 120(2):395-403. DOI: 10.1182/blood-2012-01-403220. View

14.

Xu J, Wang Y, Dai Y, Zhang W, Zhang W, Xiong S . DNMT3A Arg882 mutation drives chronic myelomonocytic leukemia through disturbing gene expression/DNA methylation in hematopoietic cells. Proc Natl Acad Sci U S A. 2014; 111(7):2620-5. PMC: 3932885. DOI: 10.1073/pnas.1400150111. View

15.

Konstandin N, Pastore F, Herold T, Dufour A, Rothenberg-Thurley M, Hinrichsen T . Genetic heterogeneity of cytogenetically normal AML with mutations of . Blood Adv. 2018; 2(20):2724-2731. PMC: 6199648. DOI: 10.1182/bloodadvances.2018016840. View

16.

Lin D . Non-parametric inference for cumulative incidence functions in competing risks studies. Stat Med. 1997; 16(8):901-10. DOI: 10.1002/(sici)1097-0258(19970430)16:8<901::aid-sim543>3.0.co;2-m. View

17.

Cheng H, Sun G, Cheng T . Hematopoiesis and microenvironment in hematological malignancies. Cell Regen. 2019; 7(1):22-26. PMC: 6326248. DOI: 10.1016/j.cr.2018.08.002. View

18.

Takahashi S . Current findings for recurring mutations in acute myeloid leukemia. J Hematol Oncol. 2011; 4:36. PMC: 3180439. DOI: 10.1186/1756-8722-4-36. View

19.

Metzeler K, Hummel M, Bloomfield C, Spiekermann K, Braess J, Sauerland M . An 86-probe-set gene-expression signature predicts survival in cytogenetically normal acute myeloid leukemia. Blood. 2008; 112(10):4193-201. PMC: 2954679. DOI: 10.1182/blood-2008-02-134411. View

20.

Hamilton B, Visconte V, Jia X, Tabarroki A, Makishima H, Hasrouni E . Impact of allogeneic hematopoietic cell transplant in patients with myeloid neoplasms carrying spliceosomal mutations. Am J Hematol. 2016; 91(4):406-9. DOI: 10.1002/ajh.24306. View