Systematic in vitro analysis of therapy resistance in glioblastoma cell lines by integration of clonogenic survival data with multi-level molecular data
Abstract Despite intensive basic scientific, translational, and clinical efforts in the last decades, glioblastoma remains a devastating disease with a highly dismal prognosis. Apart from the implementation of temozolomide into the clinical routine, novel treatment approaches have largely failed, em...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2023-03-01
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| Series: | Radiation Oncology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13014-023-02241-4 |
| _version_ | 1797863975683096576 |
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| author | Leon Emanuel Schnöller Daniel Piehlmaier Peter Weber Nikko Brix Daniel Felix Fleischmann Alexander Edward Nieto Martin Selmansberger Theresa Heider Julia Hess Maximilian Niyazi Claus Belka Kirsten Lauber Kristian Unger Michael Orth |
| author_facet | Leon Emanuel Schnöller Daniel Piehlmaier Peter Weber Nikko Brix Daniel Felix Fleischmann Alexander Edward Nieto Martin Selmansberger Theresa Heider Julia Hess Maximilian Niyazi Claus Belka Kirsten Lauber Kristian Unger Michael Orth |
| author_sort | Leon Emanuel Schnöller |
| collection | DOAJ |
| description | Abstract Despite intensive basic scientific, translational, and clinical efforts in the last decades, glioblastoma remains a devastating disease with a highly dismal prognosis. Apart from the implementation of temozolomide into the clinical routine, novel treatment approaches have largely failed, emphasizing the need for systematic examination of glioblastoma therapy resistance in order to identify major drivers and thus, potential vulnerabilities for therapeutic intervention. Recently, we provided proof-of-concept for the systematic identification of combined modality radiochemotherapy treatment vulnerabilities via integration of clonogenic survival data upon radio(chemo)therapy with low-density transcriptomic profiling data in a panel of established human glioblastoma cell lines. Here, we expand this approach to multiple molecular levels, including genomic copy number, spectral karyotyping, DNA methylation, and transcriptome data. Correlation of transcriptome data with inherent therapy resistance on the single gene level yielded several candidates that were so far underappreciated in this context and for which clinically approved drugs are readily available, such as the androgen receptor (AR). Gene set enrichment analyses confirmed these results, and identified additional gene sets, including reactive oxygen species detoxification, mammalian target of rapamycin complex 1 (MTORC1) signaling, and ferroptosis/autophagy-related regulatory circuits to be associated with inherent therapy resistance in glioblastoma cells. To identify pharmacologically accessible genes within those gene sets, leading edge analyses were performed yielding candidates with functions in thioredoxin/peroxiredoxin metabolism, glutathione synthesis, chaperoning of proteins, prolyl hydroxylation, proteasome function, and DNA synthesis/repair. Our study thus confirms previously nominated targets for mechanism-based multi-modal glioblastoma therapy, provides proof-of-concept for this workflow of multi-level data integration, and identifies novel candidates for which pharmacological inhibitors are readily available and whose targeting in combination with radio(chemo)therapy deserves further examination. In addition, our study also reveals that the presented workflow requires mRNA expression data, rather than genomic copy number or DNA methylation data, since no stringent correlation between these data levels could be observed. Finally, the data sets generated in the present study, including functional and multi-level molecular data of commonly used glioblastoma cell lines, represent a valuable toolbox for other researchers in the field of glioblastoma therapy resistance. |
| first_indexed | 2024-04-09T22:44:09Z |
| format | Article |
| id | doaj.art-c26cb10a07c6433199767a70dbfa306c |
| institution | Directory Open Access Journal |
| issn | 1748-717X |
| language | English |
| last_indexed | 2024-04-09T22:44:09Z |
| publishDate | 2023-03-01 |
| publisher | BMC |
| record_format | Article |
| series | Radiation Oncology |
| spelling | doaj.art-c26cb10a07c6433199767a70dbfa306c2023-03-22T11:58:14ZengBMCRadiation Oncology1748-717X2023-03-0118111710.1186/s13014-023-02241-4Systematic in vitro analysis of therapy resistance in glioblastoma cell lines by integration of clonogenic survival data with multi-level molecular dataLeon Emanuel Schnöller0Daniel Piehlmaier1Peter Weber2Nikko Brix3Daniel Felix Fleischmann4Alexander Edward Nieto5Martin Selmansberger6Theresa Heider7Julia Hess8Maximilian Niyazi9Claus Belka10Kirsten Lauber11Kristian Unger12Michael Orth13Department of Radiation Oncology, University Hospital, LMU MünchenResearch Unit Radiation Cytogenetics (ZYTO), Helmholtz Center Munich, German Research Center for Environmental Health GmbHResearch Unit Radiation Cytogenetics (ZYTO), Helmholtz Center Munich, German Research Center for Environmental Health GmbHDepartment of Radiation Oncology, University Hospital, LMU MünchenDepartment of Radiation Oncology, University Hospital, LMU MünchenDepartment of Radiation Oncology, University Hospital, LMU MünchenResearch Unit Radiation Cytogenetics (ZYTO), Helmholtz Center Munich, German Research Center for Environmental Health GmbHResearch Unit Radiation Cytogenetics (ZYTO), Helmholtz Center Munich, German Research Center for Environmental Health GmbHResearch Unit Radiation Cytogenetics (ZYTO), Helmholtz Center Munich, German Research Center for Environmental Health GmbHDepartment of Radiation Oncology, University Hospital, LMU MünchenDepartment of Radiation Oncology, University Hospital, LMU MünchenDepartment of Radiation Oncology, University Hospital, LMU MünchenResearch Unit Radiation Cytogenetics (ZYTO), Helmholtz Center Munich, German Research Center for Environmental Health GmbHDepartment of Radiation Oncology, University Hospital, LMU MünchenAbstract Despite intensive basic scientific, translational, and clinical efforts in the last decades, glioblastoma remains a devastating disease with a highly dismal prognosis. Apart from the implementation of temozolomide into the clinical routine, novel treatment approaches have largely failed, emphasizing the need for systematic examination of glioblastoma therapy resistance in order to identify major drivers and thus, potential vulnerabilities for therapeutic intervention. Recently, we provided proof-of-concept for the systematic identification of combined modality radiochemotherapy treatment vulnerabilities via integration of clonogenic survival data upon radio(chemo)therapy with low-density transcriptomic profiling data in a panel of established human glioblastoma cell lines. Here, we expand this approach to multiple molecular levels, including genomic copy number, spectral karyotyping, DNA methylation, and transcriptome data. Correlation of transcriptome data with inherent therapy resistance on the single gene level yielded several candidates that were so far underappreciated in this context and for which clinically approved drugs are readily available, such as the androgen receptor (AR). Gene set enrichment analyses confirmed these results, and identified additional gene sets, including reactive oxygen species detoxification, mammalian target of rapamycin complex 1 (MTORC1) signaling, and ferroptosis/autophagy-related regulatory circuits to be associated with inherent therapy resistance in glioblastoma cells. To identify pharmacologically accessible genes within those gene sets, leading edge analyses were performed yielding candidates with functions in thioredoxin/peroxiredoxin metabolism, glutathione synthesis, chaperoning of proteins, prolyl hydroxylation, proteasome function, and DNA synthesis/repair. Our study thus confirms previously nominated targets for mechanism-based multi-modal glioblastoma therapy, provides proof-of-concept for this workflow of multi-level data integration, and identifies novel candidates for which pharmacological inhibitors are readily available and whose targeting in combination with radio(chemo)therapy deserves further examination. In addition, our study also reveals that the presented workflow requires mRNA expression data, rather than genomic copy number or DNA methylation data, since no stringent correlation between these data levels could be observed. Finally, the data sets generated in the present study, including functional and multi-level molecular data of commonly used glioblastoma cell lines, represent a valuable toolbox for other researchers in the field of glioblastoma therapy resistance.https://doi.org/10.1186/s13014-023-02241-4GlioblastomaTherapy resistanceMulti-level molecular dataCorrelation analysis |
| spellingShingle | Leon Emanuel Schnöller Daniel Piehlmaier Peter Weber Nikko Brix Daniel Felix Fleischmann Alexander Edward Nieto Martin Selmansberger Theresa Heider Julia Hess Maximilian Niyazi Claus Belka Kirsten Lauber Kristian Unger Michael Orth Systematic in vitro analysis of therapy resistance in glioblastoma cell lines by integration of clonogenic survival data with multi-level molecular data Radiation Oncology Glioblastoma Therapy resistance Multi-level molecular data Correlation analysis |
| title | Systematic in vitro analysis of therapy resistance in glioblastoma cell lines by integration of clonogenic survival data with multi-level molecular data |
| title_full | Systematic in vitro analysis of therapy resistance in glioblastoma cell lines by integration of clonogenic survival data with multi-level molecular data |
| title_fullStr | Systematic in vitro analysis of therapy resistance in glioblastoma cell lines by integration of clonogenic survival data with multi-level molecular data |
| title_full_unstemmed | Systematic in vitro analysis of therapy resistance in glioblastoma cell lines by integration of clonogenic survival data with multi-level molecular data |
| title_short | Systematic in vitro analysis of therapy resistance in glioblastoma cell lines by integration of clonogenic survival data with multi-level molecular data |
| title_sort | systematic in vitro analysis of therapy resistance in glioblastoma cell lines by integration of clonogenic survival data with multi level molecular data |
| topic | Glioblastoma Therapy resistance Multi-level molecular data Correlation analysis |
| url | https://doi.org/10.1186/s13014-023-02241-4 |
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