POTN: A Human Leukocyte Antigen-A2 Immunogenic Peptides Screening Model and Its Applications in Tumor Antigens Prediction

Integration of bioinformatics and machine learning strategies identifies APM-related gene signatures to predict clinical outcomes and therapeutic responses for breast cancer patients

BACKGROUND

Tumor antigenicity and efficiency of antigen presentation jointly influence tumor immunogenicity, which largely determines the effectiveness of immune checkpoint blockade (ICB). However, the role of altered antigen processing and presentation machinery (APM) in breast cancer (BRCA) has not been fully elucidated.

METHODS

A series of bioinformatic analyses and machine learning strategies were performed to construct APM-related gene signatures to guide personalized treatment for BRCA patients. A single-sample gene set enrichment analysis (ssGSEA) algorithm and weighted gene co-expression network analysis (WGCNA) were combined to screen for BRCA-specific APM-related genes. The non-negative matrix factorization (NMF) algorithm was used to divide the cohort into different clusters and the fgsea algorithm was applied to investigate the altered signaling pathways. Random survival forest (RSF) and the least absolute shrinkage and selection operator (Lasso) Cox regression analysis were combined to construct an APM-related risk score (APMrs) signature to predict overall survival. Furthermore, a nomogram and decision tree were generated to improve predictive accuracy and risk stratification for individual patients. Based on Tumor Immune Dysfunction and Exclusion (TIDE) method, random forest (RF) and Lasso logistic regression model were combined to establish an APM-related immunotherapeutic response score (APMis). Finally, immune infiltration, immunomodulators, mutational patterns, and potentially applicable drugs were comprehensively analyzed in different APM-related risk groups. IHC staining was used to assess the expression of APM-related genes in clinical samples.

RESULTS

In this study, APMrs and APMis showed favorable performances in risk stratification and therapeutic prediction for BRCA patients. APMrs exhibited more powerful prognostic capacity and accurate survival prediction compared to conventional clinicopathological features. APMrs was closely associated with distinct mutational patterns, immune cell infiltration and immunomodulators expression. Furthermore, the two APM-related gene signatures were independently validated in external cohorts with prognosis or immunotherapeutic responses. Potential applicable drugs and targets were mined in the APMrs-high group. APM-related genes were further validated in our in-house samples.

CONCLUSION

The APM-related gene signatures established in our study could improve the personalized assessment of survival risk and guide ICB decision-making for BRCA patients.

Screening and identification of HLA-A2-restricted neoepitopes for immunotherapy of non-microsatellite instability-high colorectal cancer

Colorectal cancer has one of the highest mortality rates among malignant tumors, and most patients with non-microsatellite instability-high (MSI-H) colorectal cancer do not benefit from targeted therapy or immune checkpoint inhibitors. Identification of immunogenic neoantigens is a promising strategy for inducing specific antitumor T cells for cancer immunotherapy. Here, we screened potential high-frequency neoepitopes from non-MSI-H colorectal cancer and tested their abilities to induce tumor-specific cytotoxic T cell responses. Three HLA-A2-restricted neoepitopes (P31, P50, and P52) were immunogenic and could induce cytotoxic T lymphocytes in peripheral blood mononuclear cells from healthy donors and colorectal cancer patients. Cytotoxic T lymphocytes induced in HLA-A2.1/K^b transgenic mice could recognize and lyse mutant neoepitope-transfected HLA-A2⁺ cancer cells. Adoptive transfer of cytotoxic T lymphocytes induced by the peptide pool of these three neoepitopes effectively inhibited tumor growth and increased the therapeutic effects of anti-PD-1 antibody. These results revealed the potential of high-frequency mutation-specific peptide-based immunotherapy as a personalized treatment approach for patients with non-MSI-H colorectal cancer. The combination of adoptive T cell therapy based on these neoepitopes with immune checkpoint inhibitors, such as anti-PD-1, could provide a promising treatment strategy for non-MSI-H colorectal cancer.