HCV- and HBV-mediated liver cancer converge on similar transcriptomic landscapes and immune profiles.

Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related deaths worldwide, and a large proportion of HCC is attributable to viral causes including hepatitis B (HBV) and C virus (HCV). The pathogenesis of viral-mediated HCC can differ between HBV and HCV, but it is unclear how much these differences influence the tumors' final molecular and immune profiles. Additionally, there are known sex differences in the molecular etiology of HCC, but sex differences have not been explored in the context of viral-mediated HCC. To determine the extent to which the viral status and sex impact the molecular and immune profiles of HCC, we performed differential expression and immune cell deconvolution analyses. We identified a large number of differentially expressed genes unique to the HBV or HCV tumor:tumor-adjacent comparison. Pathway enrichment analyses demonstrated that the changes unique to the HCV tumor:tumor-adjacent tissue were predominated by changes in the immune pathways. Immune cell deconvolution demonstrated that HCV tumor-adjacent tissue had the largest immune cell infiltrate, with no difference in the immune profiles within HBV and HCV tumor samples. We subsequently segregated the differential expression analyses by sex, but demonstrated that the low number of female samples led to an overestimate of differentially expressed genes unique to male tumors. This limitation highlights the importance of additional sampling of female HCC tumors to allow for a more complete analysis of the sex differences in HCC. Overall, this work demonstrates the convergence of HBV- and HCV-mediated HCC on a similar transcriptomic landscape and immune profile despite differences in the surrounding tissue.

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.

High GILT Expression Is Associated with Improved Survival in Metastatic Melanoma Patients Treated with Immune Checkpoint Inhibition.

Gamma-interferon-inducible lysosomal thiol reductase (GILT) is critical for MHC class II restricted presentation of multiple melanoma antigens. There is variable GILT protein expression in malignant melanocytes in melanoma specimens. High GILT mRNA expression in melanoma specimens is associated with improved overall survival, before the advent of immune checkpoint inhibitors (ICI). However, the association of GILT in metastatic melanoma with survival in patients treated with ICI and the cell type expressing GILT associated with survival have not been determined. Using RNA sequencing datasets, high GILT mRNA expression in metastatic melanoma specimens was associated with improved progression-free and overall survival in patients treated with ICI. A clinical dataset of metastatic melanoma specimens was generated and annotated with clinical information. Positive GILT immunohistochemical staining in antigen presenting cells and melanoma cells was observed in 100% and 65% of metastatic melanoma specimens, respectively. In the subset of patients treated with ICI in the clinical dataset, high GILT protein expression within melanoma cells was associated with improved overall survival. The association of GILT mRNA and protein expression with survival was independent of cancer stage. These studies support that high GILT mRNA expression in bulk tumor samples and high GILT protein expression in melanoma cells is associated with improved survival in ICI-treated patients. These findings support further investigation of GILT as a biomarker to predict the response to ICI.

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.

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.

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

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.

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.