Distinct sets of molecular characteristics define tumor-rejecting neoantigens

Challenges in identifying tumor-rejecting neoantigens limit the efficacy of neoantigen vaccines to treat cancers, including cutaneous squamous cell carcinoma (cSCC). A minority of human cSCC tumors shared neoantigens, supporting the need for personalized vaccines. Using a UV-induced mouse cSCC model which recapitulated the mutational signature and driver mutations found in human disease, we found that CD8 T cells constrain cSCC. Two MHC class I neoantigens were identified that constrained cSCC growth. Compared to the wild-type peptides, one tumor-rejecting neoantigen exhibited improved MHC binding and the other had increased solvent accessibility of the mutated residue. Across known neoantigens that do not impact MHC binding, structural modeling of the peptide/MHC complexes indicated that increased solvent accessibility, which will facilitate TCR recognition of the neoantigen, distinguished tumor-rejecting from non-immunogenic neoantigens. This work reveals characteristics of tumor-rejecting neoantigens that may be of considerable importance in identifying optimal vaccine candidates in cSCC and other cancers.

Abstract A001: Shared gene expression and immune pathway changes associated with progression from nevi to melanoma

There is a need to identify biomarkers of melanoma progression to assist the development of chemoprevention strategies to lower melanoma incidence. In this study, we assessed the feasibility of creating a molecular signature for melanomagenesis using three publicly available RNA sequencing and microarray expression datasets. We performed differential expression and regularized regression analyses across nevi and melanoma samples to identify consistent genes associated with melanomagenesis. The regularized regression models demonstrated that a small number of genes could successfully distinguish between nevi and melanoma, providing evidence for the feasibility of creating a molecular signature. Differential expression analysis identified consistent upregulation of C1QB, CXCL9, CXCL10, DFNA5 (GSDME), FCGR1B, and PRAME in melanoma and consistent downregulation of SCGB1D2 in melanoma compared to nevi. Additionally, each of these genes demonstrated a linear association with the progression from benign nevi to dysplastic nevi, to radial growth phase melanoma to vertical growth phase melanoma, providing additional evidence for their role in melanomagenesis. Subsequent pathway analysis demonstrated significant enrichment of immune-related pathways among the differentially expressed genes. Overall, this study 1) demonstrates the feasibility of creating a gene signature for melanomagenesis and 2) highlights genes and pathways of interest for melanoma progression. We are in the process of generating a new dataset with benign nevi, dysplastic nevi, and melanoma with which to build and validate a molecular signature of melanoma.

Citation Format: Elizabeth S. Borden, Anngela C. Adams, Kenneth H. Buetow, Melissa A. Wilson, Julie E. Bauman, Clara Curiel-Lewandrowski, H.-H. Sherry Chow, Bonnie J. LaFleur, Karen Taraszka Hastings. Shared gene expression and immune pathway changes associated with progression from nevi to melanoma [abstract]. In: Proceedings of the Second Biennial NCI Meeting: Translational Advances in Cancer Prevention Agent Development (TACPAD); 2022 Sep 7-9. Philadelphia (PA): AACR; Can Prev Res 2022;15(12 Suppl_2): Abstract nr A001.

Abstract A006: Shared gene expression and immune pathway changes associated with progression from nevi to melanoma

There is a need to identify biomarkers of melanoma progression to assist the development of chemoprevention strategies to lower melanoma incidence. In this study, we assessed the feasibility of creating a molecular signature for melanomagenesis using three publicly available RNA sequencing and microarray expression datasets. We performed differential expression and regularized regression analyses across nevi and melanoma samples to identify consistent genes associated with melanomagenesis. The regularized regression models demonstrated that a small number of genes could successfully distinguish between nevi and melanoma, providing evidence for the feasibility of creating a molecular signature. Differential expression analysis identified consistent upregulation of C1QB, CXCL9, CXCL10, DFNA5 (GSDME), FCGR1B, and PRAME in melanoma and consistent downregulation of SCGB1D2 in melanoma compared to nevi. Additionally, each of these genes demonstrated a linear association with the progression from benign nevi to dysplastic nevi, to radial growth phase melanoma to vertical growth phase melanoma, providing additional evidence for their role in melanomagenesis. Subsequent pathway analysis demonstrated significant enrichment of immune-related pathways among the differentially expressed genes. Overall, this study 1) demonstrates the feasibility of creating a gene signature for melanomagenesis and 2) highlights genes and pathways of interest for melanoma progression. We are in the process of generating a new dataset with benign nevi, dysplastic nevi, and melanoma with which to build and validate a molecular signature of melanoma.

Citation Format: Elizabeth S. Borden, Anngela C. Adams, Kenneth H. Buetow, Melissa A. Wilson, Julie E. Bauman, Clara Curiel-Lewandrowski, H.-H. Sherry Chow, Bonnie J. LaFleur, Karen Taraszka Hastings. Shared gene expression and immune pathway changes associated with progression from nevi to melanoma [abstract]. In: Proceedings of the Second Biennial NCI Meeting: Translational Advances in Cancer Prevention Agent Development (TACPAD); 2022 Sep 7-9. Philadelphia (PA): AACR; Can Prev Res 2022;15(12 Suppl_2): Abstract nr A006.

NeoScore Integrates Characteristics of the Neoantigen:MHC Class I Interaction and Expression to Accurately Prioritize Immunogenic Neoantigens

Accurate prioritization of immunogenic neoantigens is key to developing personalized cancer vaccines and distinguishing those patients likely to respond to immune checkpoint inhibition. However, there is no consensus regarding which characteristics best predict neoantigen immunogenicity, and no model to date has both high sensitivity and specificity and a significant association with survival in response to immunotherapy. We address these challenges in the prioritization of immunogenic neoantigens by (1) identifying which neoantigen characteristics best predict immunogenicity; (2) integrating these characteristics into an immunogenicity score, the NeoScore; and (3) demonstrating a significant association of the NeoScore with survival in response to immune checkpoint inhibition. One thousand random and evenly split combinations of immunogenic and nonimmunogenic neoantigens from a validated dataset were analyzed using a regularized regression model for characteristic selection. The selected characteristics, the dissociation constant and binding stability of the neoantigen:MHC class I complex and expression of the mutated gene in the tumor, were integrated into the NeoScore. A web application is provided for calculation of the NeoScore. The NeoScore results in improved, or equivalent, performance in four test datasets as measured by sensitivity, specificity, and area under the receiver operator characteristics curve compared with previous models. Among cutaneous melanoma patients treated with immune checkpoint inhibition, a high maximum NeoScore was associated with improved survival. Overall, the NeoScore has the potential to improve neoantigen prioritization for the development of personalized vaccines and contribute to the determination of which patients are likely to respond to immunotherapy.

Cancer Neoantigens: Challenges and Future Directions for Prediction, Prioritization, and Validation

Prioritization of immunogenic neoantigens is key to enhancing cancer immunotherapy through the development of personalized vaccines, adoptive T cell therapy, and the prediction of response to immune checkpoint inhibition. Neoantigens are tumor-specific proteins that allow the immune system to recognize and destroy a tumor. Cancer immunotherapies, such as personalized cancer vaccines, adoptive T cell therapy, and immune checkpoint inhibition, rely on an understanding of the patient-specific neoantigen profile in order to guide personalized therapeutic strategies. Genomic approaches to predicting and prioritizing immunogenic neoantigens are rapidly expanding, raising new opportunities to advance these tools and enhance their clinical relevance. Predicting neoantigens requires acquisition of high-quality samples and sequencing data, followed by variant calling and variant annotation. Subsequently, prioritizing which of these neoantigens may elicit a tumor-specific immune response requires application and integration of tools to predict the expression, processing, binding, and recognition potentials of the neoantigen. Finally, improvement of the computational tools is held in constant tension with the availability of datasets with validated immunogenic neoantigens. The goal of this review article is to summarize the current knowledge and limitations in neoantigen prediction, prioritization, and validation and propose future directions that will improve personalized cancer treatment.

Shared Gene Expression and Immune Pathway Changes Associated with Progression from Nevi to Melanoma

Simple Summary

Melanoma is a deadly skin cancer, and the incidence of melanoma is rising. Chemoprevention, using small molecule drugs to prevent the development of cancer, is a key strategy that could reduce the burden of melanoma on society. The long-term goal of our study is to develop a gene signature biomarker of progression from nevi to melanoma. We found that a small number of genes can distinguish nevi from melanoma and identified shared genes and immune-related pathways that are associated with progression from nevi to melanoma across independent datasets. This study demonstrates (1) a novel approach to aid melanoma chemoprevention trials by using a gene signature as a surrogate endpoint and (2) the feasibility of determining a gene signature biomarker of melanoma progression.

Abstract

There is a need to identify molecular biomarkers of melanoma progression to assist the development of chemoprevention strategies to lower melanoma incidence. Using datasets containing gene expression for dysplastic nevi and melanoma or melanoma arising in a nevus, we performed differential gene expression analysis and regularized regression models to identify genes and pathways that were associated with progression from nevi to melanoma. A small number of genes distinguished nevi from melanoma. Differential expression of seven genes was identified between nevi and melanoma in three independent datasets. C1QB, CXCL9, CXCL10, DFNA5 (GSDME), FCGR1B, and PRAME were increased in melanoma, and SCGB1D2 was decreased in melanoma, compared to dysplastic nevi or nevi that progressed to melanoma. Further supporting an association with melanomagenesis, these genes demonstrated a linear change in expression from benign nevi to dysplastic nevi to radial growth phase melanoma to vertical growth phase melanoma. The genes associated with melanoma progression showed significant enrichment of multiple pathways related to the immune system. This study demonstrates (1) a novel application of bioinformatic approaches to aid clinical trials of melanoma chemoprevention and (2) the feasibility of determining a gene signature biomarker of melanomagenesis.

Penalized regression analysis identifies features of the peptide:MHC class I interaction and mRNA expression as key to prioritizing neoantigen immunogenicity

Current approaches to prioritization of tumor-specific neoantigens have low sensitivity and specificity, directly impacting the development of personalized vaccines and the prediction of immunotherapy efficacy. Many biological features have been suggested to contribute to accurate neoantigen prioritization. We calculated predictors for a comprehensive set of biological features for presentation and recognition of neoantigens on a validated neoantigen dataset. One thousand random, evenly-split combinations of immunogenic and non-immunogenic neoantigens were analyzed using penalized regression (lasso) for variable selection. This analysis isolated the peptide:MHC class I dissociation constant, binding stability, and mRNA expression as the key predictive variables of tumor-specific neoantigens. Based on these results, we fit a logistic regression model and tested its performance on three independent datasets. We compared our results to the models by Wells et al., Łuksza et al., and Zhou et al. Our model has improved performance as measured by the area under the receiver operator curve (AUC) compared to Łuksza and Zhou et al. Our model selects the same terms as Wells et al. and performs equivalently. However, it has the advantage of providing a predicted immunogenicity score for each individual neoantigen. Individual antigen approaches are useful for prioritizing vaccine candidates and predicting response to immunotherapy. Future work will focus on the application of this predictor to patient response to immunotherapy.

Neoantigen Fitness Model Predicts Lower Immune Recognition of Cutaneous Squamous Cell Carcinomas Than Actinic Keratoses

A low percentage of actinic keratoses progress to develop into cutaneous squamous cell carcinoma. The immune mechanisms that successfully control or eliminate the majority of actinic keratoses and the mechanisms of immune escape by invasive squamous cell carcinoma are not well-understood. Here, we took a systematic approach to evaluate the neoantigens present in actinic keratosis and cutaneous squamous cell carcinoma specimens. We compared the number of mutations, the number of neoantigens predicted to bind MHC class I, and the number of neoantigens that are predicted to bind MHC class I and be recognized by a T cell receptor in actinic keratoses and cutaneous squamous cell carcinomas. We also considered the relative binding strengths to both MHC class I and the T cell receptor in a fitness cost model that allows for a comparison of the immune recognition potential of the neoantigens in actinic keratosis and cutaneous squamous cell carcinoma samples. The fitness cost was subsequently adjusted by the expression rates of the neoantigens to examine the role of neoantigen expression in tumor immune evasion. Our analyses indicate that, while the number of mutations and neoantigens are not significantly different between actinic keratoses and cutaneous squamous cell carcinomas, the predicted immune recognition of the neoantigen with the highest expression-adjusted fitness cost is lower for cutaneous squamous cell carcinomas compared with actinic keratoses. These findings suggest a role for the down-regulation of expression of highly immunogenic neoantigens in the immune escape of cutaneous squamous cell carcinomas. Furthermore, these findings highlight the importance of incorporating additional factors, such as the quality and expression of the neoantigens, rather than focusing solely on tumor mutational burden, in assessing immune recognition potential.