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Cancer Drug Resist 2022;5:[Accepted].10.20517/cdr.2021.145© The Author(s) 2022
Accepted Manuscript
Open AccessOriginal Article

Multicellular biomarkers of drug resistance as promising targets for glioma precision medicine and predictors of patient survival 

Correspondence Address: Prof./Dr. Yongzhao Shao, Departments of Population Health and Environmental Medicine, New York University Grossman School of Medicine, 180 Madison Avenue, New York, NY 10016, USA. E-mail: Yongzhao.Shao@nyulangone.org

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© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Abstract

Aim: This paper is to translate a known drug-resistance mechanism of long-term CSF1R inhibition into multicellular biomarkers that can serve as potential therapeutic targets as well as predictive markers for survival of glioma patients.

Methods: Using existing data from a published mouse study of drug resistance in an immunotherapy for glioma, we identified multicellular differentially expressed genes (DEGs) between drug-sensitive and drug-resistant mice and translated the DEGs in mouse genome to human homolog. We constructed correlation gene networks for drug resistance in mouse and in glioma patients, and selected candidate genes via concordance analysis of human with mouse gene networks. Markers of drug resistance and an associated predictive signature for patient survival were developed using regularized Cox models with data of glioma patients from The Cancer Genome Atlas (TCGA) database. Predictive performance of the identified predictive signature was evaluated using an independent human dataset from the Chinese Glioma Genome Atlas (CGGA) database.

Results: Fourteen genes (CCL22, ADCY2, PDK1, ZFP36, CP, CD2, PLAUR, ACAP1, COL5A1, FAM83D, PBK, FANCA, ANXA7, TACC3) were identified as genetic biomarkers that were all associated with pathways in glioma progression and drug resistance. Five of the 14 genes (CCL22, ADCY2, PDK1, CD2, COL5A1) were used to construct a signature that are predictive of patient survival in the proneural subtype GBM patients with an AUC under the time-dependent ROC of 2-year survival equal to 0.89. This signature also shows promising predictive accuracy for survival of LGG patients but not for non-proneural type GBMs.


Conclusion: Our translational approach can utilize gene correlation networks from multiple type of cells in tumor microenvironment of animals. The identified biomarkers of drug resistance have good power to predict patient survival in some major subtypes of gliomas (the proneural subtype of GBM and LGG). The expression levels of the biomarkers of drug resistance may be modified for the development of personalized immunotherapies to prolong survival for a large portion of glioma patients.

Cite This Article

Lu Y, Shao Y. Multicellular biomarkers of drug resistance as promising targets for glioma precision medicine and predictors of patient survival. Cancer Drug Resist 2022;5:[Accept]. http://dx.doi.org/10.20517/cdr.2021.145

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