The roles of tumour metabolic phenotypes in genomic instability

High grade serous ovarian cancer (HGSOC) is an archetypal tumour type showing extreme genomic instability. We have discovered that HGSOC patients' tumours also often acquire mitochondrial (mt) mutations predicted to alter mt metabolism, based upon a large (N=324) tumour cohort with deep whole genome sequencing and RNAseq. These patients have significantly poorer survival but the mechanisms linking mt mutations to clinical outcomes are unknown (Ewing et al, 2024). It has long been known that disrupted mt function can cause the accumulation of the metabolite lacate, aiding tumour proliferation (Li et al, 2022), as well as efficient DNA repair (Chen et al, 2024). Thus it is possible that in HGSOC mt dysfunction may lead to metabolic phenotypes that underpin tumour aggressiveness and prime tumours for resistance to genotoxic agents, such as cisplatin and radiotherapy, resulting in poorer survival.

Analysis of pan-cancer metabolomic and expression data has identified gene–metabolite interactions (GMIs) linking gene expression patterns to the levels of particular metabolites in tumour samples (Benedetti et al, 2023). Recently it has been shown that GMIs can be exploited to impute otherwise unmeasured metabolite levels in tumours using a novel Bayesian appraoch to process bulk RNAseq data (Xie et al, 2024). In this project we will use the same approach to infer the metabolic phenotypes present in our HGSOC cohort and determine whether they are associated with differences in patient survival and genomic insability, as reflected in tumour mutational burdens. We will address the following aims.

1. Identify the range of metabolic phenotypes in HGSOC tumours based upon imputation from RNAseq data, and identify those associated with the presence of mt mutations.

2. Determine the association of metabolic phenotypes with tumour mutational burdens, complex structural variation and DNA repair phenotypes.

3. Determine the association of these metabolic phenotypes with patient outcomes.

The project would best suit a bioinformatics student with an interest in cancer genomics or an enthusiastic biologist keen to develop bioinformatics skills.

References

1. Ewing et al. 2024. Divergent trajectories to structural diversity impact patient survival in high grade serous ovarian cancer. bioRxiv doi: https://doi.org/10.1101/2024.01.12.575376
2. Benedetti et al. 2023. A multimodal atlas of tumour metabolism reveals the architecture of gene-metabolite covariation. Nat Metab 5: 1029-1044.
3. Chen et al. 2024. NBS1 lactylation is required for efficient DNA repair and chemotherapy resistance. Nature 631: 663-669.
4. Li et al. 2022. Lactate metabolism in human health and disease. Signal Transduct Target Ther 7: 305.
5. Xie et al. 2024. UnitedMet harnesses RNA-metabolite covariation to impute metabolite levels in clinical samples. biorxiv doi: https://doi.org/10.1101/2024.05.24.24307903