Abstract
Over the past few decades, the survival rate for non-small cell lung cancer (NSCLC) patients, most lung cancer patients, has significantly improved due to advancements in treatment options, resulting in prolonging lifespan and increasing survival rates. Cancer immunotherapy harnesses the patient's own immune system to recognize and eliminate cancer cells, offering a promising alternative treatment approach for cancer. One of the most significant breakthroughs in immunotherapy for NSCLC is the identification of neoantigens. Neoantigens, which are unique peptides presented by tumor cells and recognized by the immune system, offer a highly specific target for cancer treatment due to their absence in normal tissues. Neoantigen-targeted therapies, including personalized cancer vaccines and adoptive T-cell therapies (ACT), have shown promise in treating advanced solid tumors. Identifying immunogenic neoantigens remains a challenging task due to the high complexity of polymorphic MHC molecules and the vast array of peptides they present. Using next-generation sequencing, prediction algorithms can identify tumor-specific somatic mutations at both the genome and transcriptome levels and predict which neoantigens may be presented on the cell surface based on the corresponding MHC class. However, current prediction algorithms for identifying neoantigens often lack the direct evidence of antigen presentation.
A current limitation of MS-based immunopeptidomics is its reliance on databases containing cancer-specific variant sequences, such as those arising from somatic mutations that often lead to neoantigens. To overcome this challenge, proteogenomics can be employed to incorporate variant protein sequences, allowing the identification of variant antigens through tandem mass spectrometry.
In a previous PhD study, preliminary results were generated on both squamous and adenocarcinoma NSCLC fresh frozen tissues (n=12), as well as healthy surrounding tissues. Also, RNAseq was performed to identify variants, to construct patient-specific databases and to predict the HLA I and HLA II subtype of the patients. The integration of patient-specific variants into the reference proteome for a mass spectrometry-based immunopeptidomics approach has led to the identification of some of these variants among HLA class I and II presented peptides. These potential neoantigens are unique to the tumor tissue and could serve as promising targets for targeted immunotherapies in NSCLC. To confirm these findings, it is necessary to further validate the tumor specificity and immunogenicity of these neoantigens. This can be achieved by generating transcriptomic data from the patient's healthy tissue to verify whether the identified variant antigens are derived from tumor-specific genomic alterations. While the current data compares the immunopeptidome between healthy and tumor tissues, it lacks transcriptomic data from healthy tissue, which would help confirm the presence of these neoantigens specifically in tumor tissue. The immunogenicity of the identified neoantigens can be tested using ELISPOT assays, thereby narrowing down the list to only actionable neoantigens, which is a critical criterion for potential therapeutic targets.
In addition, we identified a group of antigens known as tumor-associated antigens, which are derived from proteins present in both healthy and tumor tissue but appear to be exclusively presented in the immunopeptidome of tumor tissue. This finding is potentially significant because these antigens are shared across multiple patients, unlike the patient-specific variants, which would only benefit those with the exact mutation. A targeted approach against these tumor-associated antigens could benefit a larger group of patients. However, this finding needs to be validated in a larger cohort, and the immunogenicity of these antigens must also be tested.
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