Mokhtari, Karima

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.

Abstract BACKGROUND: Glioma accounts for ~27% of all primary brain tumors and is responsible for ~13,000 cancer-related deaths in the US each year. Glioma tumors can be broadly classified into glioblastoma (GBM) and lower-grade non-GBM. Typically gliomas have a poor prognosis irrespective of medical care, with the most common form, GBM, having a five-year survival rate of only 5%. While genome-wide association studies (GWAS) have transformed our understanding of glioma susceptibility, individual studies have had limited power to identify risk loci. METHODS: We performed the largest glioma GWAS to date, comprising a meta-analysis of six existing GWAS (6,405 cases, 14,100 controls) as well as new GWAS from the Glioma International Case Control Consortium (GICC; 4,572 cases and 3,286 controls) and University of California, San Francisco (UCSF)-Mayo (1,519 cases, 804 controls), totaling 12,496 cases (6,191 classified as GBM, 5,819 as non-GBM) and 18,190 controls. RESULTS: We identified five new risk loci for GBM at 1p31.3 (rs12752552; near JAK1, P=2.04×10-9, odds ratio (OR)=1.22), 11q14.1 (rs11233250; P=9.95×10-10, OR=1.24), 16p13.3 (rs2562152; near MPG, P=1.93x10-8, OR=1.21), 16q12.1 (rs10852606; HEATR3, P=1.29×10-11, OR=1.18), 22q13.1 (rs2235573; P=1.76×10-10, OR=1.15) and eight for non-GBM at 1q32.1 (rs4252707; MDM4, P=3.34×10-9, OR=1.19), 1q44 (rs12076373; AKT3, P=2.63×10-10, OR=1.23), 2q33.3 (rs7572263; near IDH1, P=2.18×10-10, OR=1.20), 3p14.1 (rs11706832; LRIG1, P=7.66×10-9, OR=1.15), 10q24.33 (rs11598018; OBFC1, P=3.39×10-8, OR=1.14), 11q21 (rs7107785; P=3.87×10-10, OR=1.16), 14q12 (rs10131032; P=5.07x10-11, OR=1.33) and 16p13.3 (rs3751667; P=2.61×10-9, OR=1.18). Case-only analyses confirmed the specificity of 11q14.1, 16p13.3 and 22q13.1 associations for GBM and 1q44, 2q33.3, 3p14.1, 11q21 and 14q12 for non-GBM tumors. In the combined meta-analysis, among previously published glioma risk SNPs, those for all glioma at 17p13.1 (TP53), GBM at 5p15.33 (TERT), 7p11.2 (EGFR), 9p21.3 (CDKN2B-AS1) and 20q13.33 (RTEL1) and for non-GBM at 8q24.21 (CCDC26), 11q23.2, 11q23.3 (PHLDB1) and 15q24.2 (ETFA) showed even greater evidence for association. SNPs at 10q25.2 and 12q12.1 for non-GBM tumors retained genome-wide significance (i.e. P<5.0x10-8). Associations at the previously reported loci for GBM at 3q26.2 (near TERC) and 12q23.33 (POLR3B) did not retain statistical significance. CONCLUSIONS: Our findings substantiate genetic susceptibility to GBM and non-GBM glioma being highly distinct, consistent with their distinctive molecular profiles presumably resulting from different etiological pathways. Functional analyses should lead to further insights into the biological basis of the different glioma histologies. Such information can inform gene discovery initiatives and therefore have a measurable impact on the successful development of new therapeutic agents. Citation Format: Ben Kinnersley, Beatrice S. Melin, Jill S. Barnholtz-Sloan, Margaret R. Wrensch, Christoffer Johansen, Dora Il’yasova, Quinn Ostrom, and members of GICC, Karim Labreche, Jeanette E. Eckel-Passow, Paul A. Decker, Marianne Labussière, Ahmed Idbaih, Khe Hoang-Xuan, Anna-Luisa Di Stefano, Karima Mokhtari, Jean-Yves Delattre, Pilar Galan, Konstantinos Gousias, Johannes Schramm, Minouk J. Schoemaker, Sarah J. Fleming, Stefan Herms, Stefanie Heilmann, Marcus M. Nöthen, Heinz-Erich Wichmann, Stefan Schreiber, Anthony Swerdlow, Mark Lathrop, Matthias Simon, Marc Sanson, Preetha Rajaraman, Stephen Chanock, Martha Linet, Zhaoming Wang, Meredith Yeager, Rose K. Lai, Elizabeth B. Claus, Sara H. Olson, Robert B. Jenkins, Richard S. Houlston, Melissa L. Bondy. Genome-wide association study of glioma reveals specific differences in genetic susceptibility to glioblastoma and non-glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1302. doi:10.1158/1538-7445.AM2017-1302