An analysis pipeline for understanding 6-thioguanine effects on a mouse tumour genome.

Mouse tumour models are extensively used as a pre-clinical research tool in the field of oncology, playing an important role in anticancer drugs discovery. Accordingly, in cancer genomics research, the demand for next-generation sequencing (NGS) is increasing, and consequently, the need for data analysis pipelines is likewise growing. Most NGS data analysis solutions to date do not support mouse data or require highly specific configuration for their use. Here, we present a genome analysis pipeline for mouse tumour NGS data including the whole-genome sequence (WGS) data analysis flow for somatic variant discovery, and the RNA-seq data flow for differential expression, functional analysis and neoantigen prediction. The pipeline is based on standards and best practices and integrates mouse genome references and annotations. In a recent study, the pipeline was applied to demonstrate the efficacy of low dose 6-thioguanine (6TG) treatment on low-mutation melanoma in a pre-clinical mouse model. Here, we further this study and describe in detail the pipeline and the results obtained in terms of tumour mutational burden (TMB) and number of predicted neoantigens, and correlate these with 6TG effects on tumour volume. Our pipeline was expanded to include a neoantigen analysis, resulting in neopeptide prediction and MHC class I antigen presentation evaluation. We observed that the number of predicted neoepitopes were more accurate indicators of tumour immune control than TMB. In conclusion, this study demonstrates the usability of the proposed pipeline, and suggests it could be an essential robust genome analysis platform for future mouse genomic analysis.

Unraveling tumor specific neoantigen immunogenicity prediction: a comprehensive analysis.

Introduction: Identification of tumor specific neoantigen (TSN) immunogenicity is crucial to develop peptide/mRNA based anti-tumoral vaccines and/or adoptive T-cell immunotherapies; thus, accurate in-silico classification/prioritization proves critical for cost-effective clinical applications. Several methods were proposed as TSNs immunogenicity predictors; however, comprehensive performance comparison is still lacking due to the absence of well documented and adequate TSN databases. Methods: Here, by developing a new curated database having 199 TSNs with experimentally-validated MHC-I presentation and positive/negative immune response (ITSNdb), sixteen metrics were evaluated as immunogenicity predictors. In addition, by using a dataset emulating patient derived TSNs and immunotherapy cohorts containing predicted TSNs for tumor neoantigen burden (TNB) with outcome association, the metrics were evaluated as TSNs prioritizers and as immunotherapy response biomarkers. Results: Our results show high performance variability among methods, highlighting the need for substantial improvement. Deep learning predictors were top ranked on ITSNdb but show discrepancy on validation databases. In overall, current predicted TNB did not outperform existing biomarkers. Conclusion: Recommendations for their clinical application and the ITSNdb are presented to promote development and comparison of computational TSNs immunogenicity predictors.

Neoantigen Cancer Vaccines: Real Opportunity or Another Illusion?

In this communication, we will analyze some important factors and immunological phenomena related to neoantigen cancer vaccines, with particular emphasis on recently published Phase I clinical trials. Several obstacles and issues are addressed that challenge the current paradigm and inquire if neoantigens, which are essentially single-use vaccine candidates, are legitimate targets to induce protective immune responses with regard to the evolving mutational landscape. We also share insights into the striking similarities between cancer and antigenically variable pathogens and suggest that any successful vaccine against either should demonstrate a similar property: efficient induction of a diverse pool of immune cells equipped to prevent immune escape. Hence, to confront antigenic variability directly, we have employed our innovative vaccine concept, Variable Epitope Libraries, composed of large combinatorial libraries of heavily mutated epitopes, as a "universal" vaccine platform. Collectively, we offer critical analyses on key issues, which ultimately reflect on the prospective clinical relevance of personalized neoantigen vaccines which is still undefined.

Biological bases of cancer immunotherapy.

Immunotherapy has changed the landscape of cancer treatment and has significantly improved the outcome of several cancer types including breast, lung, colorectal and prostate. Neoantigen recognition and immune checkpoint inhibitors are nowadays the milestones of different immunotherapeutic regimes; however, high cost, primary and acquired resistance and the high variability of responses make their extensive use difficult. The development of better predictive biomarkers that represent tumour diversity shows promise because there is a significant body of clinical data showing a spectrum of immunotherapeutic responses that might be related back to their specific characteristics. This article makes a conceptual and historical review to summarise the main advances in our understanding of the role of the immune system in cancer, while describing the methodological details that have been successfully implemented on cancer treatments and that may hold the key to improved therapeutic approaches.

Human Endogenous Retrovirus K in Cancer: A Potential Biomarker and Immunotherapeutic Target.

In diseases where epigenetic mechanisms are changed, such as cancer, many genes show altered gene expression and inhibited genes become activated. Human endogenous retrovirus type K (HERV-K) expression is usually inhibited in normal cells from healthy adults. In tumor cells, however, HERV-K mRNA expression has been frequently documented to increase. Importantly, HERV-K-derived proteins can act as tumor-specific antigens, a class of neoantigens, and induce immune responses in different types of cancer. In this review, we describe the function of the HERV-K HML-2 subtype in carcinogenesis as biomarkers, and their potential as targets for cancer immunotherapy.

Generation of cancer vaccine immunogens derived from major histocompatibility complex (MHC) class I molecules using variable epitope libraries.

Although various immune checkpoint inhibitors (ICIs), used for the treatment of advanced cancer, showed remarkably durable tumor regression in a subset of patients, there are important limitations in a large group of non-responders, and the generation of novel immunogens capable of inducing protective cellular immune responses is a priority in cancer immunotherapy field. During the last decades, several types of vaccine immunogens have been used in numerous preclinical studies and clinical trials. However, although immunity to tumor Ags can be elicited by most vaccines tested, their clinical efficacy remains modest. Recently, we have developed an innovative vaccine concept, called Variable Epitope Libraries (VELs), with the purpose to exploit the high antigenic variability of many important pathogens and tumor cells as starting points for the construction of a new class of vaccine immunogens capable of inducing the largest possible repertoire of both B and T cells. In the present study, we decided to generate VEL immunogens derived from both classical and non-classical major histocompatibility complex (MHC) class I molecules. The MHC molecules, responsible for antigen presentation and subsequent activation of T lymphocytes, undergo multiple modifications that directly affect their proper function, resulting in immune escape of tumor cells. Two large VELs derived from multi-epitope region of H2-Kd and Qa-2 sequences (46 and 34 amino acids long, respectively), along with their wild type counterparts have been generated as synthetic peptides and tested in an aggressive 4T1 mouse model of breast cancer. Significant inhibition of tumor growth and the reduction of metastatic lesions in the lungs of immunized mice were observed. This study demonstrated for the first time the successful application of VELs carrying combinatorial libraries of epitope variants derived from MHC class I molecules as novel vaccine immunogens.