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MULTIPLE MYELOMA GAMMOPATHIES

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

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.

References

  1. 1.

    Gerecke C, Fuhrmann S, Strifler S, Schmidt-Hieber M, Einsele H, Knop S. The diagnosis and treatment of multiple myeloma. Dtsch Arztebl Int. 2016;113:470–6.

    PubMed  PubMed Central  Google Scholar 

  2. 2.

    Hu Y, Chen W, Wang J. Progress in the identification of gene mutations involved in multiple myeloma. Onco Targets Ther. 2019;12:4075–80.

    CAS  Article  Google Scholar 

  3. 3.

    Flynt E, Bisht K, Sridharan V, Ortiz M, Towfic F, Thakurta A. Prognosis, biology, and targeting of TP53 dysregulation in multiple myeloma. Cells. 2020;9:287.

    CAS  Article  Google Scholar 

  4. 4.

    Greipp PR, San Miguel J, Durie BG, Crowley JJ, Barlogie B, Blade J, et al. International staging system for multiple myeloma. J Clin Oncol. 2005;23:3412–20.

    Article  Google Scholar 

  5. 5.

    Palumbo A, Avet-Loiseau H, Oliva S, Lokhorst HM, Goldschmidt H, Rosinol L, et al. Revised International Staging System for multiple myeloma: a report from International Myeloma Working Group. J Clin Oncol. 2015;33:2863–9.

    CAS  Article  Google Scholar 

  6. 6.

    Cho H, Yoon DH, Lee JB, Kim SY, Moon JH, Do YR, et al. Comprehensive evaluation of the revised international staging system in multiple myeloma patients treated with novel agents as a primary therapy. Am J Hematol. 2017;92:1280–6.

    CAS  Article  Google Scholar 

  7. 7.

    Xia B, Wang X, Yang R, Mengzhen L, Yang K, Ren L, et al. Epstein-Barr virus infection is associated with clinical characteristics and poor prognosis of multiple myeloma. Biosci Rep. 2019;39:BSR20190284.

    CAS  Article  Google Scholar 

  8. 8.

    Gonsalves WI, Jevremovic D, Nandakumar B, Dispenzieri A, Buadi FK, Dingli D, et al. Enhancing the R-ISS classification of newly diagnosed multiple myeloma by quantifying circulating clonal plasma cells. Am J Hematol. 2020;95:310–5.

    CAS  Article  Google Scholar 

  9. 9.

    Al Saleh AS, Sidiqi MH, Dispenzieri A, Kapoor P, Muchtar E, Buadi FK, et al. Hematopoietic score predicts outcomes in newly diagnosed multiple myeloma patients. Am J Hematol. 2020;95:4–9.

    Article  Google Scholar 

  10. 10.

    Jung SH, Kwon SY, Min JJ, Bom HS, Ahn SY, Jung SY, et al. F-FDG PET/CT is useful for determining survival outcomes of patients with multiple myeloma classified as stage II and III with the Revised International Staging System. Eur J Nucl Med Mol Imaging. 2019;46:107–15.

    Article  Google Scholar 

  11. 11.

    Bolli N, Genuardi E, Ziccheddu B, Martello M, Oliva S, Terragna C. Next-generation sequencing for clinical management of multiple myeloma: ready for prime time? Front Oncol. 2020;10:189.

    Article  Google Scholar 

  12. 12.

    EI Arfani C, De Veirman K, Maes K, De Bruyne E, Menu E. Metabolic features of multiple myeloma. Int J Mol Sci. 2018;19:1200.

    Article  Google Scholar 

  13. 13.

    Janker L, Mayer RL, Bileck A, Kreutz D, Mader JC, Utpatel K, et al. Metabolic, anti-apoptotic and immune evasion strategies of primary human myeloma cells indicate adaptations to hypoxia. Mol Cell Proteom. 2019;18:936–53.

    CAS  Article  Google Scholar 

  14. 14.

    Maiso P, Huynh D, Moschetta M, Sacco A, Aljawai Y, Mishima Y, et al. Metabolic signature identifies novel targets for drug resistance in multiple myeloma. Cancer Res. 2015;75:2071–82.

    CAS  Article  Google Scholar 

  15. 15.

    Hsu HC, Tong S, Zhou Y, Elmes MW, Yan S, Kaczocha M, et al. The antinociceptive agent SBFI-26 binds to anandamide transporters FABP5 and FABP7 at two different sites. Biochemistry. 2017;56:3454–62.

    CAS  Article  Google Scholar 

  16. 16.

    Higgins MJ, Graves PR, Graves LM. Regulation of human cytidine triphosphate synthetase 1 by glycogen synthase kinase 3. J Biol Chem. 2007;282:29493–503.

    CAS  Article  Google Scholar 

  17. 17.

    Helliwell SB, Karkare S, Bergdoll M, Rahier A, Leighton-Davis JR, Fioretto C, et al. FR171456 is a specific inhibitor of mammalian NSDHL and yeast Erg26p. Nat Commun. 2015;6:8613.

    CAS  Article  Google Scholar 

  18. 18.

    Liu XP, Yin XH, Meng XY, Yan XH, Wang F, He L. Development and validation of a 9-gene prognostic signature in patients with multiple myeloma. Front Oncol. 2018;8:615.

    Article  Google Scholar 

  19. 19.

    Berger WT, Ralph BP, Kaczocha M, Sun J, Balius TE, Rizzo RC, et al. Targeting fatty acid binding protein (FABP) anandamide transporters—a novel strategy for development of anti-inflammatory and anti-nociceptive drugs. PLoS One. 2012;7:e50968.

    CAS  Article  Google Scholar 

  20. 20.

    Kim DG, Cho S, Lee KY, Cheon SH, Yoon HJ, Lee JY, et al. Crystal structures of human NSDHL and development of its novel inhibitor with the potential to suppress EGFR activity. Cell Mol Life Sci. 2020;78:207–25.

    Article  Google Scholar 

  21. 21.

    Li X, Yan X, Wang F, Yang Q, Luo X, Kong J, et al. Down-regulated lncRNA SLC25A5-AS1 facilitates cell growth and inhibits apoptosis via miR-19a-3p/PTEN/PI3K/AKT signalling pathway in gastric cancer. J Cell Mol Med. 2019;23:2920–32.

    CAS  Article  Google Scholar 

  22. 22.

    Rawat A, Gopal G, Selvaluxmy G, Rajkumar T. Inhibition of ubiquitin conjugating enzyme UBE2C reduces proliferation and sensitizes breast cancer cells to radiation, doxorubicin, tamoxifen and letrozole. Cell Oncol (Dordr). 2013;36:459–67.

    CAS  Article  Google Scholar 

  23. 23.

    Shafat MS, Oellerich T, Mohr S, Robinson SD, Edwards DR, Marlein CR, et al. Leukemic blasts program bone marrow adipocytes to generate a protumoral microenvironment. Blood 2017;129:1320–32.

    CAS  Article  Google Scholar 

  24. 24.

    Martin E, Minet N, Boschat AC, Sanquer S, Sobrino S, Lenoir C, et al. Impaired lymphocyte function and differentiation in CTPS1-deficient patients result from a hypomorphic homozygous mutation. JCI Insight. 2020;5:e133880.

    Article  Google Scholar 

  25. 25.

    De Braekeleer E, Douet-Guilbert N, Morel F, Le Bris MJ, Meyer C, Marschalek R, et al. FLNA, a new partner gene fused to MLL in a patient with acute myelomonoblastic leukaemia. Br J Haematol. 2009;146:693–5.

    Article  Google Scholar 

  26. 26.

    Palmer DC, Guittard GC, Franco Z, Crompton JG, Eil RL, Patel SJ, et al. Cish actively silences TCR signaling in CD8+ T cells to maintain tumor tolerance. J Exp Med. 2015;212:2095–113.

    CAS  Article  Google Scholar 

Download references

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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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.

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

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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 (2021). https://doi.org/10.1038/s41375-021-01206-4

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