Differences in immunogenicity between cancer mutation signatures shed light on immunoediting
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2024-04-15Author
Khehrah, Noor
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Abstract
Immunoediting is a process through which the immune system plays a role in shaping
the mutational landscape of cancer and, consequently, in cancer progression. One
critical aspect of immunoediting is the phenomenon known as neoantigen depletion.
Neoantigens are mutated peptides that may arise from somatic mutations in cancer
cells presented on the cell surface by the MHC molecules. Theoretically, these
neoantigens can mark the cancer cells to be identified and consequently eliminated
by immune cells, such as cytotoxic T-cells. Accurate neoantigen predictions allow
researchers to identify which mutations generate immunogenic peptides to initiate
an effective immune response against cancer cells. This has significant implications
for the development of personalized immunotherapies and cancer vaccines.
Cancer immunoediting not only occurs during tumor progression but also in pa tients receiving anticancer immunotherapies. Neoantigen depletion during cancer
progression contributes to innate resistance to immunotherapies, resulting in incon sistent results across patients and cancer types. Patients also acquire resistance to
immunotherapy during treatment, leading to treatment ineffectiveness. Therefore,
to effectively harness the power of the immune system against cancer and to fully
understand cancer progression, a thorough understanding of cancer immunoediting
is crucial. This thesis aims to gain a deeper understanding of some of the sources of
variation in immunogenicity, as well as potential mechanisms to escape from immune
responses. Ultimately, we are to use these findings to enhance our understanding of
the impact of immunoediting on the mutational landscape of human cancers.
Two recent high profile studies have reported that recurrent driver mutations
occur in the gaps in the capacity of MHC molecules to present neoantigens. This
implies that the immune system selects against driver mutations that can potentially
give rise to neoantigens. These findings have important implications in studying can cer progression and the role of immune system in determining how cancers develop.
Interestingly, although depletion of driver mutations predicted to be immunogenic
has been reported the same was not observed for passenger mutations. Therefore, in
Chapter 2 we tested if the passenger mutations that are predicted to be immunogenic
occur preferentially on lowly expressed or non-expressed genes which may help to explain this observation. When we controlled for gene length and sequence context,
we found no evidence to support this hypothesis. Consequently, we re-evaluated
the results reported by and found that these results are based on unjustified sta tistical assumptions. Our analysis found no link between MHC genotype and the
occurrence of driver mutations. Consistent with this, we also found no relation ship between cancer risk in individuals from the UK Biobank and the coverage of
common driver mutations predicted from their MHC genotypes.
In Chapter 3, we performed an analysis to predict immunogenicity of somatic
mutations that arise from different cancer mutation signatures. The study found
that mutated peptides resulting from specific mutation signatures were more likely
to be presented by certain HLA alleles compared to peptides originating from other
mutation signatures. Notably, the median activity of the mutation signatures in a
given cancer could be used to predict the average number of mutations inferred to be
immunogenic with high accuracy (R2 = 0.87). Our results revealed that variations
in the immunogenicity of mutations in tumors can be attributed to the differences in
immunogenicity of mutation signatures and their activities. The limited variability
in mutation signature immunogenicity and activity across different types of cancer
resulted in small variation in the expected immunogenicity of various cancer types.
Our findings also highlighted that the MHC-I genotype is the major determinant
of the predicted immunogenicity of tumors. It was also discovered that mutation
signature 20 yielded the highest proportion of immunogenic mutations, based on
the HLA allele frequencies in the TCGA cohort. When comparing different types
of cancer in the TCGA cohort, CESC had the highest expected number of immuno genic mutations, while PRAD had the highest observed proportion of immunogenic
mutations.
Recent studies have reported that patient MHC-I genotype plays a role in deter mining immunotherapy responses. However, the extent of this influence appears to
be inconsistent, and the underlying reasons for this inconsistency remain unclear.
For instance, in the case of melanoma, the B44 HLA supertype has been linked to
a better response. Interestingly, non-small cell lung cancer (NSCLC) has a simi lar somatic mutation burden and immunotherapy response as melanoma, but the
B44 supertype has not been found to have an impact on the immunotherapy response in NSCLC. This divergence has been attributed to underlying differences in
mutational processes between melanoma and NSCLC. We performed mutation sig nature analysis for two ICB treated melanoma cohorts. The findings of this analysis
revealed a significant enrichment of C > T mutations, which is consistent with pre vious studies. Furthermore, we used a combination of mutation signature activity
and patient-specific HLA genotype to estimate the expected proportion of immuno genic mutations for these cohorts. A higher expected proportion of immunogenic
mutations was associated with a tendency towards improved overall patient survival.
To gain insights into the role of immune selection in shaping the somatic mu tation landscape and consequently the progression of cancer, we must consider the
types of mutations occurring in a cancer, and the underlying mutational processes
driving them. In Chapter 4, we developed a method that considers the mutational
and evolutionary processes involved in tumor growth to identify and quantify the
immunoediting signal. The MHC-I restricted immunoediting signal was weak and
inconsistent across cancer types in the TCGA cohort. Moreover, the weak immu noediting signal persists even when we use the randomized HLA alleles. Finally,
we estimated that fewer than 1% of mutations inferred to be immunogenic, were
removed through immunosurveillance.
In summary, firstly, we investigated the relationship between the occurrence of
driver mutations in a tumor and the MHC genotype of the patient. Then, we assessed
the predicted immunogenicity of mutations arising from different somatic mutation
signatures. We also examined the variation in tumor immunogenicity based on the
activity of mutation signatures. We used the predicted immunogenicity of samples in
the TCGA cohort to evaluate the contribution of immunoediting to the mutational
landscape in cancer. We also used this method to estimate an upperbound on
immunoediting signal.