Despite increasing reports indicating the prognostic significance of immune cell abundance in high grade serous ovarian cancer (HGSOC), mutational processes by which tumours gain immunogenicity or develop immune evasion are yet to be identified. Mutational processes that play a key role in HGSOC tumourigenesis leave imprints of copy-number aberrations (CNA) in the patient’s genome. These CNA can be summarised into seven copy number (CN) signatures, each associated with a specific mutational process. To identify mutational process associated with determining the immune response to HGSOC, I investigated the correlation between CN signatures and the density of key prognostic immune cells in a cohort of 172 HGSOC samples. The exposure to signatures in each sample was previously quantified. I first developed two multiplex IHC protocols against CD3, CD8, FOXP3 and CD20, and quantified the total, tumour and stroma densities of CD3+, CD8+, CD8−, FOXP3+ and CD20+cells in the patient cohort using HALO image analysis software. I also quantified the abundance of other immune cells in the samples using gene expression data. I used linear mixed modelling to fit the immune densities, and assessed the ability of CN signatures to predict the model. This revealed that the presence of CN signature 6, associated with high genomic CN states, is inversely correlated with all measures of immune cell densities, while the presence of homologous recombination deficiency (HRD)-associated CN signature 7 correlates positively with immune cell abundance (p > 0.05, ANOVA). Samples with a combination of absent CN signature 6 and present CN signature 7 had the highest abundance of immune infiltrates. These results indicate that the balance between high CN states and HRD is associated with determining the immune response in HGSOC. Feature expansion of the immune markers analysed using gene expression data and imaging mass cytometry will also be discussed.