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Identification of the immune gene expression signature associated with recurrence of high-grade gliomas

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Abstract

High-grade gliomas (HGGs), the most common and aggressive primary brain tumors in adults, inevitably recur due to incomplete surgery or resistance to therapy. Intratumoral genomic and cellular heterogeneity of HGGs contributes to therapeutic resistance, recurrence, and poor clinical outcomes. Transcriptomic profiles of HGGs at recurrence have not been investigated in detail. Using targeted sequencing of cancer-related genes and transcriptomics, we identified single nucleotide variations, small insertions and deletions, copy number aberrations (CNAs), as well as gene expression changes and pathway deregulation in 16 pairs of primary and recurrent HGGs. Most of the somatic mutations identified in primary HGGs were not detected after relapse, suggesting a subclone substitution during the tumor progression. We found a novel frameshift insertion in the ZNF384 gene which may contribute to extracellular matrix remodeling. An inverse correlation of focal CNAs in EGFR and PTEN genes was detected. Transcriptomic analysis revealed downregulation of genes involved in messenger RNA splicing, cell cycle, and DNA repair, while genes related to interferon signaling and phosphatidylinositol (PI) metabolism are upregulated in secondary HGGs when compared to primary HGGs. In silico analysis of the tumor microenvironment identified M2 macrophages and immature dendritic cells as enriched in recurrent HGGs, suggesting a prominent immunosuppressive signature. Accumulation of those cells in recurrent HGGs was validated by immunostaining. Our findings point to a substantial transcriptomic deregulation and a pronounced infiltration of immature dendritic cells in recurrent HGG, which may impact the effectiveness of frontline immunotherapies in the GBM management.

Key messages

  • Most of the somatic mutations identified in primary HGGs were not detected after relapse.

  • Focal CNAs in EGFR and PTEN genes are inversely correlated in primary and recurrent HGGs.

  • Transcriptomic changes and distinct immune-related signatures characterize HGG recurrence.

  • Recurrent HGGs are characterized by a prominent infiltration of immature dendritic and M2 macrophages.

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Data availability

The data that support the findings of this study are openly available at the European Genome-phenome Archive (EGA), reference number EGAS00001004606.

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Acknowledgments

We would like to thank the physicians who performed the surgeries, the patients for their consent for the use of their biological material for this research, and Dr. Chinchu Jayaprakash for improving the language quality of this manuscript.

Code availability

Codes generated during the current study are available upon reasonable request.

Funding

Studies were supported by the Foundation for Polish Science TEAM-TECH Core Facility project “NGS platform for comprehensive diagnostics and personalized therapy in neuro-oncology.” The use of CePT infrastructure, financed by the European Union, The European Regional Development Fund within the Operational Programme “Innovative economy” for 2007–2013, is highly appreciated.

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Authors and Affiliations

  1. Nencki Institute of Experimental Biology, Warsaw, Poland

    Adria-Jaume Roura, Bartlomiej Gielniewski, Paulina Pilanc, Paulina Szadkowska, Marta Maleszewska, Sylwia K. Krol, Bartosz Wojtas & Bozena Kaminska

  2. Clinical Department of Neurosurgery, St. Raphael Hospital, Andrzej Frycz Modrzewski Krakow University, Krakow, Poland

    Ryszard Czepko

  3. Department of Neurosurgery, Regional Hospital, Medical University of Silesia, Sosnowiec, Poland

    Wojciech Kaspera

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  1. Adria-Jaume Roura
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Contributions

Design of the study, data analysis, data interpretation, and manuscript preparation were performed by Adria-Jaume Roura. RNA/DNA isolation, sequencing, and clinical table preparation were performed by Bartlomiej Gielniewski and Paulina Pilanc. Immunohistochemistry was carried out by Paulina Pilanc. Experimental design and preparation of the materials for experiments were performed by Marta Maleszweska and Sylwia K. Krol. Preparation of the clinical samples and collection of clinical information were performed by Ryszard Czepko and Wojciech Kaspera. Study design, data interpretation, and manuscript preparation were performed by Bartosz Wojtas and Bozena Kaminska. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Bartosz Wojtas.

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Conflict of interest

The authors declare that they have no conflicts of interest.

Ethical approval

This study was approved by the Bioethics Committees of St. Raphael Hospital, Andrzej Frycz Modrzewski Krakow University, Krakow, Poland (Nr. 73/KBL/OIL/2015); Medical University of Silesia, Sosnowiec, Poland; and Mazovian Brodno Hospital, Warsaw, Poland (Nr. KNW/0022/KB1/46/I/16), and have therefore been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.

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Supplementary Information

Supplementary Figure S1

High-throughput sequencing (HTS) pipeline, genetic alterations and ZNF384 survival analysis. (A) Representation of the HTS pipeline implemented in this study. (B) Oncoplot showing recurrent somatic mutations. Each column represents a sample and each row a different gene; genes are ordered by the frequency of occurrence. (C) Mutational hot-spot and its effect on the ZNF384 protein structure, where domain and aminoacidic change are indicated. (D) Kaplan-Meier survival analysis between low- and high- expressed ZNF384 in High-grade glioma (TCGA-GBM/LGG data). Dashed lines represent the survival median line for each of the groups and Log Rank Test was utilized (PDF 665 kb)

Supplementary Figure S2

Identification of cancer driver genes based on spatial clustering (A) Potential cancer driver genes in primary and (B) recurrent cohorts, respectively, based on oncodriveCLUST algorithm. Numbers enclosed in square brackets represent the number of clusters found in the gene, and dots in red correspond to statistically significant clusters (PDF 632 kb)

Supplementary Figure S3

Focal copy number changes in EGFR and PTEN genes. Somatic copy number changes from matched tumor-normal pairs showing relative changes in the tumor samples. Each dot corresponds to the adjusted copynumber median of EGFR and PTEN, which were calculated for each of the primary (A) and recurrent (B) HGGs (PDF 85 kb)

Supplementary Figure S4

Functional enrichment obtained using Reactome database. Visualization of the relationship among (A) up-regulated and down-regulated (B) enriched gene sets that are differentially expressed between recurrent and primary HGGs (PDF 305 kb)

Supplementary Figure S5

Functional enrichment obtained using KEGG database. Visualization of the relationship among (A) up-regulated and down-regulated (B) enriched gene sets among differentially expressed between recurrent and primary HGGs (PDF 158 kb)

Supplementary Figure S6

In-silico validation of xCell cell enrichment analysis obtained by xCell. Cell type enrichment analysis from normalized gene expression using (A) CIBERSORT, (B) QuantiSEQ, (C) mcp-COUNTER and (D) TIMER approaches. Scatter- and density plots are shown for each of the analysis (A-D). Top density plots show the cell enrichment score distribution of x-axis while right density plots show the distribution in the y-axis. Selected and significant signatures are presented based on P-values, which were calculated using Wilcoxon signed-rank test (PDF 133 kb)

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Roura, AJ., Gielniewski, B., Pilanc, P. et al. Identification of the immune gene expression signature associated with recurrence of high-grade gliomas. J Mol Med 99, 241–255 (2021). https://doi.org/10.1007/s00109-020-02005-7

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  • DOI: https://doi.org/10.1007/s00109-020-02005-7

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