IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-022-33215-x.html
   My bibliography  Save this article

DNA methylation as a pharmacodynamic marker of glucocorticoid response and glioma survival

Author

Listed:
  • J. K. Wiencke

    (University of California San Francisco)

  • Annette M. Molinaro

    (University of California San Francisco)

  • Gayathri Warrier

    (University of California San Francisco)

  • Terri Rice

    (University of California San Francisco)

  • Jennifer Clarke

    (University of California San Francisco
    University of California San Francisco)

  • Jennie W. Taylor

    (University of California San Francisco
    University of California San Francisco)

  • Margaret Wrensch

    (University of California San Francisco)

  • Helen Hansen

    (University of California San Francisco)

  • Lucie McCoy

    (University of California San Francisco)

  • Emily Tang

    (University of California San Francisco)

  • Stan J. Tamaki

    (University of California San Francisco)

  • Courtney M. Tamaki

    (University of California San Francisco)

  • Emily Nissen

    (University of Kansas Medical Center)

  • Paige Bracci

    (University of California San Francisco)

  • Lucas A. Salas

    (Dartmouth College)

  • Devin C. Koestler

    (University of Kansas Medical Center)

  • Brock C. Christensen

    (Dartmouth College
    Dartmouth College
    Dartmouth College)

  • Ze Zhang

    (Dartmouth College)

  • Karl T. Kelsey

    (Brown University
    Brown University)

Abstract

Assessing individual responses to glucocorticoid drug therapies that compromise immune status and affect survival outcomes in neuro-oncology is a great challenge. Here we introduce a blood-based neutrophil dexamethasone methylation index (NDMI) that provides a measure of the epigenetic response of subjects to dexamethasone. This marker outperforms conventional approaches based on leukocyte composition as a marker of glucocorticoid response. The NDMI is associated with low CD4 T cells and the accumulation of monocytic myeloid-derived suppressor cells and also serves as prognostic factor in glioma survival. In a non-glioma population, the NDMI increases with a history of prednisone use. Therefore, it may also be informative in other conditions where glucocorticoids are employed. We conclude that DNA methylation remodeling within the peripheral immune compartment is a rich source of clinically relevant markers of glucocorticoid response.

Suggested Citation

  • J. K. Wiencke & Annette M. Molinaro & Gayathri Warrier & Terri Rice & Jennifer Clarke & Jennie W. Taylor & Margaret Wrensch & Helen Hansen & Lucie McCoy & Emily Tang & Stan J. Tamaki & Courtney M. Tam, 2022. "DNA methylation as a pharmacodynamic marker of glucocorticoid response and glioma survival," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-33215-x
    DOI: 10.1038/s41467-022-33215-x
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-33215-x
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-022-33215-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    2. Derin B. Keskin & Annabelle J. Anandappa & Jing Sun & Itay Tirosh & Nathan D. Mathewson & Shuqiang Li & Giacomo Oliveira & Anita Giobbie-Hurder & Kristen Felt & Evisa Gjini & Sachet A. Shukla & Zhutin, 2019. "Neoantigen vaccine generates intratumoral T cell responses in phase Ib glioblastoma trial," Nature, Nature, vol. 565(7738), pages 234-239, January.
    3. Vincenzo Bronte & Sven Brandau & Shu-Hsia Chen & Mario P. Colombo & Alan B. Frey & Tim F. Greten & Susanna Mandruzzato & Peter J. Murray & Augusto Ochoa & Suzanne Ostrand-Rosenberg & Paulo C. Rodrigue, 2016. "Recommendations for myeloid-derived suppressor cell nomenclature and characterization standards," Nature Communications, Nature, vol. 7(1), pages 1-10, November.
    4. Lucas A. Salas & Ze Zhang & Devin C. Koestler & Rondi A. Butler & Helen M. Hansen & Annette M. Molinaro & John K. Wiencke & Karl T. Kelsey & Brock C. Christensen, 2022. "Enhanced cell deconvolution of peripheral blood using DNA methylation for high-resolution immune profiling," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    5. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    6. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jacob Bergstedt & Sadoune Ait Kaci Azzou & Kristin Tsuo & Anthony Jaquaniello & Alejandra Urrutia & Maxime Rotival & David T. S. Lin & Julia L. MacIsaac & Michael S. Kobor & Matthew L. Albert & Darrag, 2022. "The immune factors driving DNA methylation variation in human blood," Nature Communications, Nature, vol. 13(1), pages 1-20, December.
    2. Tutz, Gerhard & Pößnecker, Wolfgang & Uhlmann, Lorenz, 2015. "Variable selection in general multinomial logit models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 207-222.
    3. Mkhadri, Abdallah & Ouhourane, Mohamed, 2013. "An extended variable inclusion and shrinkage algorithm for correlated variables," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 631-644.
    4. Chuliá, Helena & Garrón, Ignacio & Uribe, Jorge M., 2024. "Daily growth at risk: Financial or real drivers? The answer is not always the same," International Journal of Forecasting, Elsevier, vol. 40(2), pages 762-776.
    5. Christopher J Greenwood & George J Youssef & Primrose Letcher & Jacqui A Macdonald & Lauryn J Hagg & Ann Sanson & Jenn Mcintosh & Delyse M Hutchinson & John W Toumbourou & Matthew Fuller-Tyszkiewicz &, 2020. "A comparison of penalised regression methods for informing the selection of predictive markers," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
    6. Gustavo A. Alonso-Silverio & Víctor Francisco-García & Iris P. Guzmán-Guzmán & Elías Ventura-Molina & Antonio Alarcón-Paredes, 2021. "Toward Non-Invasive Estimation of Blood Glucose Concentration: A Comparative Performance," Mathematics, MDPI, vol. 9(20), pages 1-13, October.
    7. Christopher Kath & Florian Ziel, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Papers 1811.08604, arXiv.org.
    8. Gurgul Henryk & Machno Artur, 2017. "Trade Pattern on Warsaw Stock Exchange and Prediction of Number of Trades," Statistics in Transition New Series, Statistics Poland, vol. 18(1), pages 91-114, March.
    9. Michael Funke & Kadri Männasoo & Helery Tasane, 2023. "Regional Economic Impacts of the Øresund Cross-Border Fixed Link: Cui Bono?," CESifo Working Paper Series 10557, CESifo.
    10. Zichen Zhang & Ye Eun Bae & Jonathan R. Bradley & Lang Wu & Chong Wu, 2022. "SUMMIT: An integrative approach for better transcriptomic data imputation improves causal gene identification," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    11. Štefan Lyócsa & Petra Vašaničová & Branka Hadji Misheva & Marko Dávid Vateha, 2022. "Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-21, December.
    12. Peter Bühlmann & Jacopo Mandozzi, 2014. "High-dimensional variable screening and bias in subsequent inference, with an empirical comparison," Computational Statistics, Springer, vol. 29(3), pages 407-430, June.
    13. Capanu, Marinela & Giurcanu, Mihai & Begg, Colin B. & Gönen, Mithat, 2023. "Subsampling based variable selection for generalized linear models," Computational Statistics & Data Analysis, Elsevier, vol. 184(C).
    14. Abhinav Kaushik & Diane Dunham & Xiaorui Han & Evan Do & Sandra Andorf & Sheena Gupta & Andrea Fernandes & Laurie Elizabeth Kost & Sayantani B. Sindher & Wong Yu & Mindy Tsai & Robert Tibshirani & Sco, 2022. "CD8+ T cell differentiation status correlates with the feasibility of sustained unresponsiveness following oral immunotherapy," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    15. Tomáš Plíhal, 2021. "Scheduled macroeconomic news announcements and Forex volatility forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1379-1397, December.
    16. Michele Lalla & Patrizio Frederic, 2020. "Tertiary education decisions of immigrants and non-immigrants in Italy: an empirical approach," Department of Economics 0168, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
    17. Loann David Denis Desboulets, 2018. "A Review on Variable Selection in Regression Analysis," Econometrics, MDPI, vol. 6(4), pages 1-27, November.
    18. Foutzopoulos, Giorgos & Pandis, Nikolaos & Tsagris, Michail, 2024. "Predicting full retirement attainment of NBA players," MPRA Paper 121540, University Library of Munich, Germany.
    19. repec:hum:wpaper:sfb649dp2013-037 is not listed on IDEAS
    20. Zeyu Bian & Erica E. M. Moodie & Susan M. Shortreed & Sahir Bhatnagar, 2023. "Variable selection in regression‐based estimation of dynamic treatment regimes," Biometrics, The International Biometric Society, vol. 79(2), pages 988-999, June.
    21. Alexis Comber & Paul Harris, 2018. "Geographically weighted elastic net logistic regression," Journal of Geographical Systems, Springer, vol. 20(4), pages 317-341, October.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-33215-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.