2. Academic Validation
  3. A prognostic survival model based on metabolism-related gene expression in plasma cell myeloma

A prognostic survival model based on metabolism-related gene expression in plasma cell myeloma

  • Leukemia. 2021 Nov;35(11):3212-3222. doi: 10.1038/s41375-021-01206-4.
Han-Ying Huang  # 1 2 3,  Yun Wang  # 1 2,  Wei-da Wang  # 1 2,  Xiao-Li Wei  # 1 4,  Robert Peter Gale 5,  Jin-Yuan Li 1 2,  Qian-Yi Zhang 1 2,  Ling-Ling Shu 1 2,  Liang Li 1 2,  Juan Li 6,  Huan-Xin Lin 7 8,  Yang Liang 9 10
Affiliations

Affiliations

  • 1 State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, P.R. China.
  • 2 Department of Hematologic Oncology, Sun Yat-sen University Cancer Center, Guangzhou, P.R. China.
  • 3 Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Guangzhou, P.R. China.
  • 4 Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, P.R. China.
  • 5 Department of Immunology and Inflammation, Haematology Research Centre, Imperial College London, London, UK.
  • 6 Department of Hematology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China. ljuan@mail.sysu.edu.cn.
  • 7 State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, P.R. China. linhx@sysucc.org.cn.
  • 8 Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Guangzhou, P.R. China. linhx@sysucc.org.cn.
  • 9 State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, P.R. China. liangyang@sysucc.org.cn.
  • 10 Department of Hematologic Oncology, Sun Yat-sen University Cancer Center, Guangzhou, P.R. China. liangyang@sysucc.org.cn.
  • # Contributed equally.
PMID: 33686197 DOI: 10.1038/s41375-021-01206-4
Abstract

Accurate survival prediction of persons with plasma cell myeloma (PCM) is challenging. We interrogated clinical and laboratory co-variates and RNA matrices of 1040 subjects with PCM from public datasets in the Gene Expression Omnibus database in training (N = 1) and validation (N = 2) datasets. Genes regulating plasma cell metabolism correlated with survival were identified and seven used to build a metabolic risk score using Lasso COX regression analyses. The score had robust predictive performance with 5-year survival area under the curve (AUCs): 0.71 (95% confidence interval, 0.65, 0.76), 0.88 (0.67, 1.00) and 0.64 (0.57, 0.70). Subjects in the high-risk training cohort (score > median) had worse 5-year survival compared with those in the low-risk cohort (62% [55, 68%] vs. 85% [80, 90%]; p < 0.001). This was also so for the validation cohorts. A nomogram combining metabolic risk score with Revised International Staging System (R-ISS) score increased survival prediction from an AUC = 0.63 [0.58, 0.69] to an AUC = 0.73 [0.66, 0.78]; p = 0.015. Modelling predictions were confirmed in in vitro tests with PCM cell lines. Our metabolic risk score increases survival prediction accuracy in PCM.

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