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Functional precision medicine pipeline combines comparative transcriptomics and tumor organoid modeling to identify be-spoke treatment strategies for glioblastoma

ABSTRACT: Li-Fraumeni syndrome (LFS) is a hereditary cancer predisposition syndrome caused by germline mutations in TP53. TP53 is the most common mutated gene in human cancer occurring in 30-50% of glioblastomas (GBM). Here, we highlight a precision medicine platform to identify potential targets for a GBM patient with LFS. We used novel comparative transcriptomics analysis to identify genes that are uniquely overexpressed in a LFS GBM patient relative to a cancer compen-dium of 12,747 tumor RNA sequencing datasets including 200 GBMs. We then used ex vivo pa-tient derived organoid (PDO) viability assays with 4 patient derived cell lines to test efficacy of our identified target. Our comparative transcriptomics bioinformatics pipeline identified that STAT1 and STAT2 were significantly overexpressed in our patient indicating ruxolitinib, a Janus kinase 1 and 2 inhibitor, as a potential therapy. In our institutional high-grade glioma cohort of 45 patients, the LFS patient had the highest level of STAT1 and STAT2 expression. STAT1 and STAT2 expression levels in 4 cell lines derived from patients (including the LFS patient) corre-lated with levels identified in the respective parent tumors. Using 2D and 3D assays from pa-tient derived cells, our LFS patient of interest was among the most sensitive to ruxolitinib in comparison to patients with lower STAT1 and STAT2 expression levels. Additionally a sphe-roid-based drug screening assay (3D-PREDICT) was performed and used to identify further therapeutic targets. This manuscript supports the use of comparative transcriptomics to identify personalized therapeutic targets in a functional precision medicine platform for malignant brain tumors.

ORGANISM(S): Homo sapiens

PROVIDER: GSE188739 | GEO | 2021/12/02

REPOSITORIES: GEO

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Project description:Type I Interferons (IFN-I) mediate cellular responses to virus infection. IFN-I induce IFN stimulated gene (ISG) expression by phosphorylating STAT1 and STAT2, and together with interferon regulatory factor (IRF)9, form the transcription complex ISGF3 that binds to the interferon-stimulated response element (ISRE) in ISG promoters. As a component of ISGF3 it is clear that STAT2 plays an essential role in the transcriptional responses to IFN-I with a strong dependence on STAT1. Previously, we showed that STAT2 also forms homodimers that interact with IRF9 (STAT2-IRF9) to activate transcription of ISRE containing ISGs in response to IFN-I. Indeed, evidence is accumulating for the existence of a STAT1-independent IFN-I signaling pathway, where STAT2-IRF9 can substitute the role of ISGF3. Here, we provide further insight in the transcriptional regulation and the biological implications of STAT2-IRF9 dependent IFN-I signaling. In human STAT1 KO cells overexpressing human STAT2 (U3C-STAT2) we observed that in response to IFN-I STAT2 homodimers interact with IRF9 to regulate ISG transcription. The IFN-I-induced phosphorylation profile of STAT2 in U3C-STAT2 was prolonged as compared to WT cells (2fTGH), which corresponded with the expression pattern of OAS2 that also depended on IRF9. Subsequent microarray analysis of IFN-I treated 2fTGH and U3C-STAT2 extended our initial observations and identified more than 60 known antiviral ISGs commonly up-regulated in both cell types. The expression profile of these ISGs was delayed and prolonged in U3C-STAT2 as opposed to the early and transient response in 2fTGH. Moreover, U3C-STAT2 were able to restore an antiviral response upon EMCV and VSV infection that was comparable to the response in 2fTGH. Together, our results strongly suggest that an alternative IFN-I-mediated, STAT2-IRF9 dependent signaling pathway exists that can generate an antiviral response without STAT1 and could be beneficial for example against viruses that directly block STAT1 and impair the formation of ISGF3.

Project description:Type I Interferons (IFN-I) mediate cellular responses to virus infection. IFN-I induces IFN-stimulated gene (ISG) expression by phosphorylating STAT1 and STAT2, and together with interferon regulatory factor (IRF9), form the transcription complex ISGF3 that binds to the interferon-stimulated response element (ISRE) in ISG promoters. As a component of ISGF3, it is clear that STAT2 plays an essential role in the transcriptional responses to IFN-I with a strong dependence on STAT1. Previously, we showed that STAT2 also forms homodimers that interact with IRF9 (STAT2-IRF9) to activate transcription of ISRE-containing ISGs in response to IFN-I. Indeed, evidence is accumulating for the existence of a STAT1-independent IFN-I signaling pathway, where STAT2-IRF9 can substitute the role of ISGF3. Here, we provide further insight in the transcriptional regulation and the biological implications of STAT2-IRF9 dependent IFN-I signaling. In human STAT1 KO cells overexpressing human STAT2 (U3C-STAT2), we observed that in response to IFN-I, STAT2 homodimers interact with IRF9 to regulate ISG transcription. The IFN-I-induced phosphorylation profile of STAT2 in U3C-STAT2 was prolonged as compared to WT cells (2fTGH), which corresponded with the expression pattern of OAS2 that also depended on IRF9. Subsequent microarray analysis of IFN-I treated 2fTGH and U3C-STAT2 extended our initial observations and identified more than 60 known antiviral ISGs commonly up-regulated in both cell types. The expression profile of these ISGs was delayed and prolonged in U3C-STAT2 as opposed to the early and transient response in 2fTGH. Moreover, U3C-STAT2 were able to restore an antiviral response upon EMCV and VSV infection that was comparable to the response in 2fTGH. Together, our results strongly suggest that an alternative IFN-I-mediated, STAT2-IRF9 dependent signaling pathway exists that can generate an antiviral response without STAT1 and could be beneficial for example against viruses that directly block STAT1 and impair the formation of ISGF3.

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Functional precision medicine pipeline combines comparative transcriptomics and tumor organoid modeling to identify be-spoke treatment strategies for glioblastoma

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