Project description:Neuroblastoma is a pediatric tumor originating from the sympathetic nervous system responsible for 10-15% of all childhood cancer deaths. Half of all neuroblastoma patients present with high-risk disease at diagnosis despite intensive multi-modal therapies of which nearly 50% relapse and die of their disease. In contrast to the overall paucity of mutations, high-risk neuroblastoma nearly invariably present with recurrent somatic segmental chromosome copy number variants. For several focal aberrations (e.g. MYCN and LIN28B amplification), the direct role in tumor formation has been established. However, for recurrent aberrations, such as chromosome 2p and 17q gains, the identification of genes contributing to tumor initiation or progression has been challenging due to the scarcity of small segmental gains or amplifications. In this study, we identified and functionally evaluated the ‘ribonucleotide reductase regulatory subunit 2’ (RRM2) as a top-ranked 2p putative co-driver and therapeutic target in high-risk neuroblastoma enforcing replicative stress resistance. RRM2 dependency was shown in vitro through knockdown in neuroblastoma cells, with transcriptome analysis revealing downregulation of pediatric tumor markers. Transgenic zebrafish lines expressing RRM2 in immature sympathoblasts were crossed with a dh-MYCN transgenic zebrafish line, with RRM2 overexpression dramatically increasing tumor penetrance from 20% to nearly 100%, accelerating neuroblastoma formation with tumors formed as early as 5 weeks of age in the double positive fish. Through CasID, we aimed to identify RRM2 promotor bound proteins. and enriched amongst others HEXIM1 and the NurRD complex. Importantly, adaptive responses regulating RRM2 expression are also controlled by ATR/CHK1 and WEE1 cell cycle checkpoint kinases, as shown by pharmacological inhibition with small molecule inhibitors. We provide in vitro and in vivo data showing that RRM2 inhibition with triapine strongly sensitizes neuroblastoma cells to CHK1 inhibition and that combined treatment could robustly induce upregulated PD-L1 surface expression. Altogether, we propose RRM2 as a bona fide target to explore novel synergistic drug combinations through phase I clinical trials.

Project description:Background: Validated markers to predict outcome in rectal cancer patients treated with multimodal therapy remain elusive. Identifying molecular profiles for disease prognosis would be required for the design of clinical trials aimed at optimizing risk-adapted therapies. We have therefore used whole genome expression profiling of tumors of a large cohort of patients enrolled in the multicenter trials of the German Rectal Cancer Study Group (GRCSG) to identify molecular profiles for individualized therapy. Methods: We prospectively collected pretherapeutic biopsies from patients (n=300) treated according to the GRCSG trial guidelines from seven different German surgical departments using rigid quality controls. These samples were profiled by global gene expression analysis and a classifier developed to predict postoperative lymph node status and Disease Free Survival (DFS). The performance of the classifier was validated with an independent, prospectively collected set of samples. Findings: The final training and test set included 198 patients. Analyzes for postoperative nodal status and DFS revealed 69 and 674 differentially regulated genes, respectively. Depending on the classifier the accuracy to predict lymph node status ranged from 64% to 69% with negative predictive values between 72% and 74%. Stratification according to DFS resulted in a good (n=99), bad (n=96) and a small very bad prognosis group (n=3). Based on linear discriminant analysis the classifier for positive lymph node status was validated in 47 independent patients and revealed an accuracy of 72% and a positive predictive value of 100%. Thereby, prediction based on molecular profiling is superior to prediction based on conventional clinical markers. Interpretation: Whole genome expression analysis of pretherapeutical biopsies resulted in a molecular classifier of disease prognosis. This classifier was successfully validated. The high positive predictive value for post therapeutic lymph node status allows identification of patients requiring alternative or intensified treatment protocols. These data are currently validated for their clinical applicability and should be taken as basis for future molecular driven clinical trials to develop risk-adapted treatments.

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Developing a Prognostic Stratification Model Based on Glutathione Metabolism in Thyroid Cancer and Validating RRM2’s Tumor‑Promoting Role

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