Link to publication on Research at Birmingham portal General rights Unless a licence is specified above, all rights (including copyright and moral rights) in this document are retained by the authors and/or the copyright holders. The express permission of the copyright holder must be obtained for any use of this material other than for purposes permitted by law. • Users may freely distribute the URL that is used to identify this publication. • Users may download and/or print one copy of the publication from the University of Birmingham research portal for the purpose of private study or non-commercial research. • User may use extracts from the document in line with the concept of 'fair dealing' under the Copyright, Designs and Patents Act 1988 (?) • Users may not further distribute the material nor use it for the purposes of commercial gain. Where a licence is displayed above, please note the terms and conditions of the licence govern your use of this document. When citing, please reference the published version. Take down policy While the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.

Patient-based cancer models are essential tools for studying tumor biology and for the assessment of drug responses in a translational context. We report the establishment a large cohort of unique organoids and patient-derived orthotopic xenografts (PDOX) of various glioma subtypes, including gliomas with mutations in IDH1, and paired longitudinal PDOX from primary and recurrent tumors of the same patient. We show that glioma PDOXs enable long-term propagation of patient tumors and represent clinically relevant patient avatars that retain histopathological, genetic, epigenetic, and transcriptomic features of parental tumors. We find no evidence of mouse-specific clonal evolution in glioma PDOXs. Our cohort captures individual molecular genotypes for precision medicine including mutations in IDH1, ATRX, TP53, MDM2/4, amplification of EGFR, PDGFRA, MET, CDK4/6, MDM2/4, and deletion of CDKN2A/B, PTCH, and PTEN. Matched longitudinal PDOX recapitulate the limited genetic evolution of gliomas observed in patients following treatment. At the histological level, we observe increased vascularization in the rat host as compared to mice. PDOX-derived standardized glioma organoids are amenable to high-throughput drug screens that can be validated in mice. We show clinically relevant responses to temozolomide (TMZ) and to targeted treatments, such as EGFR and CDK4/6 inhibitors in (epi)genetically defined subgroups, according to MGMT promoter and EGFR/CDK status, respectively. Dianhydrogalactitol (VAL-083), a promising bifunctional alkylating agent in the current clinical trial, displayed high therapeutic efficacy, and was able to overcome TMZ resistance in glioblastoma. Our work underscores the clinical relevance of glioma organoids and PDOX models for translational research and personalized treatment studies and represents a unique publicly available resource for precision oncology.

Treatment options for adult patients with glioma has remained largely unchanged over the past three decades. Targeted inhibitors and immunotherapies have improved outcomes for many cancer types but their relevance in glioma is unclear. The inevitability of glioma disease recurrence demands an understanding of mechanisms driving therapy resistance. The Glioma Longitudinal Analysis (GLASS) Consortium was initiated to establish a definitive portrait of the recurrence process and to discover vulnerabilities that render the tumor sensitive to therapeutic intervention. GLASS is a community-driven effort that seeks to overcome the logistical challenges in constructing adequately powered longitudinal genomic glioma datasets by pooling data from patients treated at institutions worldwide. Currently, the GLASS Data Resource comprises DNA sequencing data (exome and/or whole-genome) from 288 patients of whom high-quality data in at least two time points are present from 222 patients (n = 134 IDHwt, n = 63 IDHmutant-noncodel, n = 25 IDHmutant-codel). We inferred longitudinal mutation, copy number, clonal frequency, and neoantigen profiles and demonstrated that driver genes found at initial disease persisted into recurrence. Treatment with alkylating-agents resulted in a hypermutator phenotype at different rates across glioma subtypes, most frequently among IDHmutant-noncodels, and hypermutation was not associated with differences in overall survival. Acquired aneuploidy was frequently detected in recurrent IDHmutant-noncodel gliomas and further converged with acquired cell cycle pathway alterations and poor outcomes. We showed that the clonal architecture of each tumor remains largely intact over time and that genetic drift was associated with increased survival. Finally, we found that neoantigens were exposed to stable selective pressures throughout a tumor’s progression. Our results collectively suggest that the strongest selective pressures occur early during glioma development and that current therapies shape this evolution in a largely stochastic manner. The GLASS Data Resource provides a genomic reference to study the patterns of glioma evolution.

Intratumour heterogeneity provides tumours with the ability to adapt and acquire treatment resistance. The development of more effective and personalised treatments for cancers, therefore, requires accurate characterisation of the clonal architecture of tumours, enabling evolutionary dynamics to be tracked. Many methods exist for achieving this from bulk tumour sequencing data, involving identifying mutations and performing subclonal deconvolution, but there is a lack of systematic benchmarking to inform researchers on which are most accurate, and how dataset characteristics impact performance. To address this, we use the most comprehensive tumour genome simulation tool available for such purposes to create 80 bulk tumour whole exome sequencing datasets of differing depths, tumour complexities, and purities, and use these to benchmark subclonal deconvolution pipelines. We conclude that i) tumour complexity does not impact accuracy, ii) increasing either purity or purity-corrected sequencing depth improves accuracy, and iii) the optimal pipeline consists of Mutect2, FACETS and PyClone-VI. We have made our benchmarking datasets publicly available for future use.