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MULTIPLE MYELOMA GAMMOPATHIES

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

Leukemia volume 35, pages 3212–3222 (2021)Cite this article

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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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Fig. 1: Construction of the metabolic model.
Fig. 2: Validation of the metabolic risk-scoring model.
Fig. 3: Building the combined nomogram to predict the overall survival (OS) of patients with multiple myeloma (MM).
Fig. 4: Pathways with significant enrichment in each dataset.
Fig. 5: External validation using online databases.
Fig. 6: In vitro studies of SBFI-26, miltefosine or trilostane combined with bortezomib.

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Acknowledgements

YL is supported, in part, by Sun Yat-sen University Start-up Funding, grant no. 201603, the Program for Guangdong Introducing Innovative and Entrepreneurial Teams (2017ZT07S096) and the National Natural Science Foundation of China (Grant No. 81873428). H-xL is supported by the National Natural Science Foundation of China (Grant No. 81773103) and the Natural Science Foundation of Guangdong Province (2017A030313617). JL is supported by Sun Yat-sen University Medical Clinical Trial ‘5010 Plan’ (2017005). RPG acknowledges support from the National Institute of Health Research (NIHR) Biomedical Research Centre funding scheme.

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Author notes

  1. These author contributed equally: Han-ying Huang, Yun Wang, Wei-da Wang, Xiao-li Wei

Authors and 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

    Han-ying Huang, Yun Wang, Wei-da Wang, Xiao-li Wei, Jin-yuan Li, Qian-yi Zhang, Ling-ling Shu, Liang Li, Huan-xin Lin & Yang Liang

  2. Department of Hematologic Oncology, Sun Yat-sen University Cancer Center, Guangzhou, P.R. China

    Han-ying Huang, Yun Wang, Wei-da Wang, Jin-yuan Li, Qian-yi Zhang, Ling-ling Shu, Liang Li & Yang Liang

  3. Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Guangzhou, P.R. China

    Han-ying Huang & Huan-xin Lin

  4. Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, P.R. China

    Xiao-li Wei

  5. Department of Immunology and Inflammation, Haematology Research Centre, Imperial College London, London, UK

    Robert Peter Gale

  6. Department of Hematology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China

    Juan Li

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  1. Han-ying Huang
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Contributions

H-yH and YW: preparing the typescript; YL, RPG, JL, H-yH and YW: reviewing and editing; W-dW and J-yL: software; X-lW and L-lS: methodology; Q-yZ and LL: conceptualisation; YL, H-xL and JL: design research, project administration and funding acquisition.

Corresponding authors

Correspondence to Juan Li, Huan-xin Lin or Yang Liang.

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Conflict of interest

RPG is a paid Consultant to BeiGene Ltd., CStone Pharmaceuticals and Kite Pharmma; a Consultant to Fusion Pharma LLC, LaJolla NanoMedical Inc. and Mingsight Parmaceuticals Inc.; an Advisory Board member for Antegene Biotech LLC and StemRad Ltd; Medical Director at FFF Enterprises Inc; Partner in AZACA Inc; and on the Board of Directors of the Russian Foundation for Cancer Research Support. All other authors declare no competing interests.

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Huang, Hy., Wang, Y., Wang, Wd. et al. A prognostic survival model based on metabolism-related gene expression in plasma cell myeloma. Leukemia 35, 3212–3222 (2021). https://doi.org/10.1038/s41375-021-01206-4

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