An Immune Risk Score Predicts Survival of Patients with Acute Myeloid Leukemia Receiving Chemotherapy
| Autor: | Run-Cong Nie, Jinyuan Li, Pei-dong Chi, Qian-yi Zhang, Tobias Herold, Wolfgang Hiddemann, Yun Wang, Yong-qing Wang, Xiong Zhang, Yan-Yu Cai, Yu Zhang, Ze-lin Liu, Yang Liang, San-bin Wang, Robert Peter Gale, Yong-zhong Su, Xin Du, Klaus H. Metzeler, Tong-hua Yang, Qing Zhang, Zhe-Yuan Qin, Yun Zeng, Xin-mei Zhang, Yuan-bin Wu, Na Zhong, Jian-wei Wu, Bei Zhang, Zhi-jun Wuxiao, Xue-Yi Pan, Qifa Liu, Zhi-wei Liang, Shun-Qing Wang, Jing-bo Xu, Si-Liang Chen, Bingyi Wu, Ruo-zhi Xiao |
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| Rok vydání: | 2021 |
| Předmět: |
Male
0301 basic medicine Oncology Cancer Research medicine.medical_specialty T-Lymphocytes medicine.medical_treatment Datasets as Topic Risk Assessment 03 medical and health sciences 0302 clinical medicine Text mining Immune system Predictive Value of Tests Risk Factors hemic and lymphatic diseases Internal medicine Tumor Microenvironment medicine Humans RNA-Seq Chemotherapy Framingham Risk Score Gene Expression Regulation Leukemic business.industry Proportional hazards model Myeloid leukemia Middle Aged Flow Cytometry Prognosis Survival Rate Leukemia Myeloid Acute 030104 developmental biology ROC Curve 030220 oncology & carcinogenesis Female business Selection operator Predictive modelling |
| Zdroj: | Clinical Cancer Research. 27:255-266 |
| ISSN: | 1557-3265 1078-0432 |
| DOI: | 10.1158/1078-0432.ccr-20-3417 |
| Popis: | Purpose: Prediction models for acute myeloid leukemia (AML) are useful, but have considerable inaccuracy and imprecision. No current model includes covariates related to immune cells in the AML microenvironment. Here, an immune risk score was explored to predict the survival of patients with AML. Experimental Design: We evaluated the predictive accuracy of several in silico algorithms for immune composition in AML based on a reference of multi-parameter flow cytometry. CIBERSORTx was chosen to enumerate immune cells from public datasets and develop an immune risk score for survival in a training cohort using least absolute shrinkage and selection operator Cox regression model. Results: Six flow cytometry–validated immune cell features were informative. The model had high predictive accuracy in the training and four external validation cohorts. Subjects in the training cohort with low scores had prolonged survival compared with subjects with high scores, with 5-year survival rates of 46% versus 19% (P < 0.001). Parallel survival rates in validation cohorts-1, -2, -3, and -4 were 46% versus 6% (P < 0.001), 44% versus 18% (P = 0.041), 44% versus 24% (P = 0.004), and 62% versus 32% (P < 0.001). Gene set enrichment analysis indicated significant enrichment of immune relation pathways in the low-score cohort. In multivariable analyses, high-risk score independently predicted shorter survival with HRs of 1.45 (P = 0.005), 2.12 (P = 0.004), 2.02 (P = 0.034), 1.66 (P = 0.019), and 1.59 (P = 0.001) in the training and validation cohorts, respectively. Conclusions: Our immune risk score complements current AML prediction models. |
| Databáze: | OpenAIRE |
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