A genetic model for central chondrosarcoma evolution correlates with patient outcome

Background Central conventional chondrosarcoma (CS) is the most common subtype of primary malignant bone tumour in adults. Treatment options are usually limited to surgery, and prognosis is challenging. These tumours are characterised by the presence and absence of IDH1 and IDH2 mutations, and recently, TERT promoter alterations have been reported in around 20% of cases. The effect of these mutations on clinical outcome remains unclear. The purpose of this study was to determine if prognostic accuracy can be improved by the addition of genomic data, and specifically by examination of IDH1, IDH2, and TERT mutations. Methods In this study, we combined both archival samples and data sourced from the Genomics England 100,000 Genomes Project (n = 356). Mutations in IDH1, IDH2, and TERT were profiled using digital droplet PCR (n = 346), whole genome sequencing (n=68), or both (n = 64). Complex events and other genetic features were also examined, along with methylation array data (n = 84). We correlated clinical features and patient outcomes with our genetic findings. Results IDH2-mutant tumours occur in older patients and commonly present with high-grade or dedifferentiated disease. Notably, TERT mutations occur most frequently in IDH2-mutant tumours, although have no effect on survival in this group. In contrast, TERT mutations are rarer in IDH1-mutant tumours, yet they are associated with a less favourable outcome in this group. We also found that methylation profiles distinguish IDH1- from IDH2-mutant tumours. IDH wild-type tumours rarely exhibit TERT mutations and tend to be diagnosed in a younger population than those with tumours harbouring IDH1 and IDH2 mutations. A major genetic feature of this group is haploidisation and subsequent genome doubling. These tumours evolve less frequently to dedifferentiated disease and therefore constitute a lower risk group. Conclusions Tumours with IDH1 or IDH2 mutations or those that are IDHwt have significantly different genetic pathways and outcomes in relation to TERT mutation. Diagnostic testing for IDH1, IDH2, and TERT mutations could therefore help to guide clinical monitoring and prognostication

The Immunogenomic Landscape of Soft Tissue Sarcoma

Cancer is a multifaceted disease influenced by various factors, including genomic alterations and methylation changes, and cancer progression is heavily impacted by the tumour microenvironment (TME). In this study, these metrics were analysed to gain a deeper understanding of their dynamic interplay in soft tissue sarcomas (STS). By examining the selective forces exerted by the immune system, this work aimed to uncover associations with different immune responses, and establish potential biomarkers for treatment stratification. This line of research holds promise in unmasking key factors influencing immune recognition and evasion. A comprehensive approach was used, integrating genomic, transcriptomic, methylation, and immunohistochemistry data. Firstly, to gain further insights into the TME, RNA-seq data was used to quantify immune infiltration. This allowed for a TME stratification system to be established, enabling these samples to be grouped based on whether the TME phenotype was immunologically “hot” or “cold”. GSEA analyses of these groups showed a number of different pathways enriched in both categories. Next, genomic mechanisms of immune evasion were considered. This included an exploration of HLA loss-of-heterozygosity (LOH), which has previously been implicated in other cancer types. HLA LOH was frequent in the STS cohorts but was not found to be specific to a TME group. Finally, the impact of methylation changes on the immune response was evaluated. A regression model identified numerous CpG probes in the promoter region of genes of interest which were frequently methylated. This included probes in the promoter region of genes important to the immune response, such as B2M, highlighting the importance of methylation as a possible immune evasion mechanism. Overall, this thesis sheds light on the intricate relationship between tumours and the immune system, providing insights into immune recognition, evasion mechanisms, and how this may aid in the development of effective immunotherapy stratification strategies

Signatures of copy number alterations in human cancer

Gains and losses of DNA are prevalent in cancer and emerge as a consequence of inter-related processes of replication stress, mitotic errors, spindle multipolarity and breakage-fusion-bridge cycles, among others, which may lead to chromosomal instability and aneuploidy1,2. These copy number alterations contribute to cancer initiation, progression and therapeutic resistance3-5. Here we present a conceptual framework to examine the patterns of copy number alterations in human cancer that is widely applicable to diverse data types, including whole-genome sequencing, whole-exome sequencing, reduced representation bisulfite sequencing, single-cell DNA sequencing and SNP6 microarray data. Deploying this framework to 9,873 cancers representing 33 human cancer types from The Cancer Genome Atlas6 revealed a set of 21 copy number signatures that explain the copy number patterns of 97% of samples. Seventeen copy number signatures were attributed to biological phenomena of whole-genome doubling, aneuploidy, loss of heterozygosity, homologous recombination deficiency, chromothripsis and haploidization. The aetiologies of four copy number signatures remain unexplained. Some cancer types harbour amplicon signatures associated with extrachromosomal DNA, disease-specific survival and proto-oncogene gains such as MDM2. In contrast to base-scale mutational signatures, no copy number signature was associated with many known exogenous cancer risk factors. Our results synthesize the global landscape of copy number alterations in human cancer by revealing a diversity of mutational processes that give rise to these alterations

Additional file 2 of A genetic model for central chondrosarcoma evolution correlates with patient outcome

Additional file 2: Supplementary Methods