Oh, Young Taek

Abstract Current transcriptomic classification of Glioblastoma Multiforme (GBM) has been ineffective to predict survival and therapeutic vulnerabilities. Recently, we proposed a four-group functional classification of GBM that included proliferative/progenitor, neuronal, mitochondrial and glycolytic/plurimetabolic subtypes with prognostic and therapeutic implications as the mitochondrial subtype carries the best survival and exhibits distinct sensitivity to mitochondrial OXPHOS inhibitors. To uncover novel therapeutic targets for each functional GBM subtype, we focused on protein kinases for their attractive features as both drivers and drug targets, with current availability of 62 FDA-approved inhibitors available for cancer precision therapeutics. We designed an unbiased integrative, machine learning-based proteomics/phosphoproteomics network for the identification of Master Kinases (MKs) responsible for effecting key phenotypic hallmarks of each of the four GBM subtypes. Here we report the identification and validation of protein kinase C delta (PRKCd) and DNA-PKcs as MKs that sustain the glycolytic/plurimetabolic and proliferative/progenitor GBM subtypes, respectively. Genetic and pharmacologic inactivation of PKCd in GBM patient-derived organoids of the glycolytic/plurimetabolic subtype blocked glucose uptake and lipid accumulation, resulting in marked anti-tumor effects. We also validated the role of PKCd in oncometabolic processes at the intersection between insulin, IGF, and lipid metabolism. DNA-PKcs was qualified as MK for the proliferative/progenitor GBM subtype, which is characterized by high basal level of replication stress. Biochemical experiments showed activation of DNA-PK in GBM patient-derived organoids of the proliferative/progenitor subgroup. Targeting DNA-PK in proliferative/progenitor GBM organoids with the clinically tested DNA-PKcs inhibitor nedisertib in association with gamma irradiation, the essential component of the standard of care for patients with GBM, led to an unsustainable level of DNA damage and massive GBM cell death selectively in this GBM subtype. As DNA-PKcs inhibitors have been introduced into clinical trials, our findings suggest that pre-selection of patient with PPR tumors is likely to enhance therapeutic success.

Abstract Tumor heterogeneity and broad therapeutic resistance are major challenges when treating patients with glioblastoma (GBM). Moreover, different from other cancer types, the absence of clinically useful classifiers for glioblastoma has hampered the translation of genomic and proteomic information to improve diagnosis and design precision therapeutics for patients. We analyzed a large dataset of human GBM to determine the multi-omics features characterizing the four GBM subtypes that we have recently identified through single-cell RNA sequencing and validated in bulk tumor analysis. Each subtype exhibits activation of unique functional traits that confer distinct therapeutic vulnerabilities. The examination of proteomics, phosphoproteomics, metabolomics, lipidomics and acetylomics data revealed that each GBM subtype has a coherent molecular structure driving the dominant function traceable in each analytical platform. Functional classes are not a specific attribute of GBM as we identified the same subtypes in breast and lung cancer. To test the translational impact of proteomic data in GBM, we developed an unbiased protein kinase signaling network approach for the selection of master kinases (MKs) aberrantly activated in each GBM subtype. We identified therapeutically actionable MKs and novel phosphorylation substrates that we experimentally validated. To provide rapid translation of the functional classifier for precision medicine in GBM, we developed a probabilistic classification tool which determines the probability that a patient’s GBM belongs to one of the four subtypes, exhibiting optimal performance when using RNA extracted from either frozen and paraffin-embedded tissues. The algorithm is publicly accessible and can be used to evaluate the association of therapeutic response with GBM subtypes and as tool for selection criteria in prospective clinical trials.

Abstract The proteogenomic characterization of human tumors has begun to uncover molecular attributes enriched in different tumor subtypes and proposed a panoply of subtype-specific targets. As these studies lacked experimental testing of potential targets in high-quality cell models accurately reflecting individual tumor subtypes, it remains unclear whether any of the proposed targets retained value for precision medicine. The absence of clinically useful classifiers further hampered translation of proteogenomic information to better diagnose and treat cancer patients. In the case of the brain tumor glioblastoma (GBM), such challenges are exacerbated by tumor heterogeneity and broad therapeutic resistance. Here, we analyzed a large dataset of human GBM to determine the multi-omics features that characterize four GBM subtypes we recently identified through single cell RNAseq analysis. Each subtype exhibits activation of unique hallmark functional traits including distinct sensitivity to inhibitors of mitochondrial respiration for the mitochondrial subtype. The inspection of proteomics, phosphoproteomics, metabolomics, lipidomics and acetylomics data revealed that each GBM subtype has a coherent molecular structure that drives the dominant function and can be extracted by each analytical platform. Stratification of tumors in four functional classes is not a specific attribute of GBM as we were able to unbiasedly identify the same subtypes in breast and lung cancer. To identify actionable targets in GBM we developed an unbiased protein kinase signaling network approach for the selection of master kinases aberrantly activated in each GBM subtype. The experimental follow-up using a library of annotated GBM patient derived organoids (PDOs) established that inhibition of DNA-PK with the clinically tested compound nedisertib radiosensitizes proliferative/progenitor PDOs by exacerbating replication stress-induced DNA damage in this GBM subtype. Furthermore, genetic and pharmacological tools qualified PKCδ as vulnerable target for the broadly resistant glycolytic/plurimetabolic GBM subtype. Thus, together with the targeting of mitochondrial GBM with OXPHOS inhibitors, the functional classification delivers experimentally validated actionable targets for three GBM subtypes. To provide rapid translation of the classifier for precision medicine in GBM, we developed a probabilistic classification tool which determines the probability that a patient’s GBM belongs to one of the four subtypes based on transcriptomic features and exhibits optimal performance when using RNA extracted from either frozen and paraffin embedded tissues. The algorithm is publicly accessible. It can be used in retrospective studies to evaluate the association of therapeutic response with GBM subtypes and as tool for selection criteria in prospective clinical trials. Citation Format: Simona Migliozzi, Young Taek Oh, Luciano Garofano, Hasanain Mohammad, Fulvio D'Angelo, Ryan D. Najac, Franck Bielle, Karima Mokhtari, Jann N. Sarkaria, Michele Ceccarelli, Marc Sanson, Anna Lasorella, Antonio Iavarone. Pathway based analysis of glioblastoma by multi-omics with applications in targeted therapy, prognosis and probabilistic classification [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4014.