CTNNB1 , the gene encoding β-catenin, is a frequent target for oncogenic mutations activating the canonical Wnt signaling pathway, typically through missense mutations within a degron hotspot motif in exon 3. Here, we combine saturation genome editing with a fluorescent reporter assay to quantify signaling phenotypes for all 342 possible missense mutations in the mutation hotspot. Our data define the genetic requirements for β-catenin degron function, refine the consensus motif for substrate recognition by β-TRCP and reveal diverse levels of signal activation among known driver mutations. Tumorigenesis in different human tissues involves selection for CTNNB1 mutations spanning distinct ranges of predicted activity. In hepatocellular carcinoma, mutation effect scores distinguish two tumor subclasses with different levels of β-catenin signaling, and weaker mutations predict greater immune cell infiltration in the tumor microenvironment. Our work provides a resource to understand mutational diversity within a pan-cancer mutation hotspot, with potential implications for targeted therapy.

Deciphering the structural variation across tumour genomes is crucial to determine the events driving tumour progression and better understand tumour adaptation and evolution. High grade serous ovarian cancer (HGSOC) is an exemplar tumour type showing extreme, but poorly characterised structural diversity. Here, we comprehensively describe the mutational landscape driving HGSOC, exploiting a large (N = 324), deeply whole genome sequenced dataset. We reveal two divergent evolutionary trajectories, affecting patient survival and involving differing genomic environments. One involves homologous recombination repair deficiency (HRD) while the other is dominated by whole genome duplication (WGD) with frequent chromothripsis, breakage-fusion-bridges and extra-chromosomal DNA. These trajectories contribute to structural variation hotspots, containing candidate driver genes with significantly altered expression. While structural variation predominantly drives tumorigenesis, we find high mtDNA mutation loads associated with shorter patient survival. We show that a combination of mutations in the mitochondrial and nuclear genomes impact prognosis, suggesting strategies for patient stratification.

Mitochondrial DNA (mtDNA) mutations are prevalent across cancer genomes, and growing evidence implicates their multifaceted role in energy metabolism with tumorigenesis. Ovarian cancer, in particular, demonstrates high mtDNA copy numbers and increased incidences of truncating and missense mtDNA mutations, with heteroplasmy levels predictive of prognosis. This review provides a comprehensive description of published mtDNA sequencing data in ovarian cancer, the majority being high-grade serous samples, encompassing both coding and non-coding regions. MtDNA mutations within non-coding regions, such as the D-loop control region, can affect mtDNA replication and transcription, hence affecting overall mtDNA copy numbers, while mtDNA mutations within coding regions can directly impact respiratory complex function and downstream metabolic pathways. MtDNA mutations may serve as clinically valuable diagnostic biomarkers for ovarian cancer and predictors for chemoresistance. We also explore ongoing efforts to deepen our understanding of mitochondrial oncogenetics through the creation of novel cancer models enabled by mitochondrial gene editing techniques. Developing robust human ovarian cancer cell models will be critical to elucidate mechanistic and phenotypic consequences of mtDNA mutations, assess drug response and resistance and identify new therapeutic targets to advance precision oncology in this emerging field.