De Noon, Solange; (2025) Deciphering osteosarcoma development and evolution using whole genome and single cell technologies. Doctoral thesis (Ph.D), UCL (University College London).

Text Denoon PhD Thesis- Edited version.pdf - Accepted Version Access restricted to UCL open access staff until 1 June 2026. Download (46MB)

Abstract

Background: Osteosarcoma is an aggressive tumour of bone characterised by structural genomic complexity and lack of recurrent molecular features. Progress in the management of osteosarcoma has stalled over the past four decades. Evidence-based trials of targeted therapies and immunotherapy have thus far yielded unsatisfactory results. There remains no means of predicting patient response to an intensive chemotherapy regimen. The lack of an informative molecular-guided classification of this disease is an additional challenge to research. Previous studies have demonstrated extensive genetic heterogeneity in osteosarcoma, and without identifying relevant groups for more tailored therapeutic approaches or accurate prognostication it remains difficult to make significant progress. Aim: The aim of this work is to investigate the complexity of osteosarcoma using modern technologies and approaches on a large dataset for the purpose of developing a more informative classification. To achieve this, study objectives were as follows: (1) characterization of somatic mutational and driver landscapes and investigation of associations with pathological features (2) reconstructing timelines of key genomic events through temporal and spatial dissection of driver and structural alterations (3) identification of transcriptomic profiles which correspond to genomic and or clinically distinct patient groups. Datasets: Whole genome sequencing (WGS) data from four sequencing projects (n=200) including the Genomics England 100,000 Genome Project were uniformly realigned and processed alongside newly generated WGS data from uncommon osteosarcoma variants to create one of the largest datasets of osteosarcoma whole genomes, incorporating multiple histological subtypes and a cohort of 56 multisample cases. In parallel, single nuclei RNA (snRNA) sequencing data of 55 fresh frozen samples from 20 osteosarcomas was generated using 10X Genomics droplet based single-cell RNA sequencing platform. Results: Analysis of the somatic mutational landscape of osteosarcoma reveals these tumours are dominated by structural driver events arising as part of complex genomic rearrangements (CGRs). The data have also shown that the burden of loss of heterozygosity (LOH) in high grade conventional osteosarcoma (HGOS) with complex genomes identifies a subset of patients with superior clinical outcomes. By adapting a method of individual structural breakpoint timing relative to whole genome duplication (WGD), relative timing of these CGRs is achieved. These results reveal that WGD precedes most CGRs and enables the construction of a timeline of osteosarcoma development in which early tumour suppressor drivers such as TP53 help trigger WGD, followed by a rapid period of accumulation of CGRs which often generate late oncogenes. Characterisation of osteosarcoma at a single cell RNA level illustrates that tumour cell transcriptomes demonstrate distinct expression profiles which are associated with clinical behaviour. Tumour cells are directly compared to foetal osteoblasts to quantify osteosarcoma differentiation, revealing downregulation of osteoblast maturation signalling in favour of cell proliferation and motility in high grade osteosarcoma relative to low grade subtypes. This work highlights the scope of genetic and transcriptomic complexity which characterises osteosarcoma. Comparative analysis between subtypes with known clinical behaviour are capitalised upon to identify specific features which may predict patient outcomes and therefore allow more tailored clinical management.

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