VENUS, a Novel Selection Approach to Improve the Accuracy of Neoantigens' Prediction

Phase I Trial of Viral Vector-Based Personalized Vaccination Elicits Robust Neoantigen-Specific Antitumor T-Cell Responses

PURPOSE

Personalized vaccines targeting multiple neoantigens (nAgs) are a promising strategy for eliciting a diversified antitumor T-cell response to overcome tumor heterogeneity. NOUS-PEV is a vector-based personalized vaccine, expressing 60 nAgs and consists of priming with a nonhuman Great Ape Adenoviral vector (GAd20) followed by boosts with Modified Vaccinia Ankara. Here, we report data of a phase Ib trial of NOUS-PEV in combination with pembrolizumab in treatment-naïve patients with metastatic melanoma (NCT04990479).

PATIENTS AND METHODS

The feasibility of this approach was demonstrated by producing, releasing, and administering to 6 patients 11 of 12 vaccines within 8 weeks from biopsy collection to GAd20 administration.

RESULTS

The regimen was safe, with no treatment-related serious adverse events observed and mild vaccine-related reactions. Vaccine immunogenicity was demonstrated in all evaluable patients receiving the prime/boost regimen, with detection of robust neoantigen-specific immune responses to multiple neoantigens comprising both CD4 and CD8 T cells. Expansion and diversification of vaccine-induced T-cell receptor (TCR) clonotypes was observed in the posttreatment biopsies of patients with clinical response, providing evidence of tumor infiltration by vaccine-induced neoantigen-specific T cells.

CONCLUSIONS

These findings indicate the ability of NOUS-PEV to amplify and broaden the repertoire of tumor-reactive T cells to empower a diverse, potent, and durable antitumor immune response. Finally, a gene signature indicative of the reduced presence of activated T cells together with very poor expression of the antigen-processing machinery genes has been identified in pretreatment biopsies as a potential biomarker of resistance to the treatment.

Prime and pull of T cell responses against cancer-exogenous antigens is effective against CPI-resistant tumors

Neoantigen (neoAg)-based cancer vaccines expand preexisting antitumor immunity and elicit novel cancer-specific T cells. However, at odds with prophylactic vaccines, therapeutic antitumor immunity must be induced when the tumor is present and has already established an immunosuppressive environment capable of rapidly impairing the function of anticancer neoAg T cells, thereby leading to lack of efficacy. To overcome tumor-induced immunosuppression, we first vaccinated mice bearing immune checkpoint inhibitor (CPI)-resistant tumors with an adenovirus vector encoding a set of potent cancer-exogenous CD8 and CD4 T cell epitopes (Ad-CAP1), and then "taught" cancer cells to express the same epitopes by using a tumor-retargeted herpesvirus vector (THV-CAP1). Potent CD8 effector T lymphocytes were elicited by Ad-CAP1, and subsequent THV-CAP1 delivery led to a significant delay in tumor growth and even cure.

Personalized Cancer Vaccines Go Viral: Viral Vectors in the Era of Personalized Immunotherapy of Cancer

The aim of personalized cancer vaccines is to elicit potent and tumor-specific immune responses against neoantigens specific to each patient and to establish durable immunity, while minimizing the adverse events. Over recent years, there has been a renewed interest in personalized cancer vaccines, primarily due to the advancement of innovative technologies for the identification of neoantigens and novel vaccine delivery platforms. Here, we review the emerging field of personalized cancer vaccination, with a focus on the use of viral vectors as a vaccine platform. The recent advancements in viral vector technology have led to the development of efficient production processes, positioning personalized viral vaccines as one of the preferred technologies. Many clinical trials have shown the feasibility, safety, immunogenicity and, more recently, preliminary evidence of the anti-tumor activity of personalized vaccination, fostering active research in the field, including further clinical trials for different tumor types and in different clinical settings.

Systems Biology Approaches for the Improvement of Oncolytic Virus-Based Immunotherapies

Oncolytic virus (OV)-based immunotherapy is mainly dependent on establishing an efficient cell-mediated antitumor immunity. OV-mediated antitumor immunity elicits a renewed antitumor reactivity, stimulating a T-cell response against tumor-associated antigens (TAAs) and recruiting natural killer cells within the tumor microenvironment (TME). Despite the fact that OVs are unspecific cancer vaccine platforms, to further enhance antitumor immunity, it is crucial to identify the potentially immunogenic T-cell restricted TAAs, the main key orchestrators in evoking a specific and durable cytotoxic T-cell response. Today, innovative approaches derived from systems biology are exploited to improve target discovery in several types of cancer and to identify the MHC-I and II restricted peptide repertoire recognized by T-cells. Using specific computation pipelines, it is possible to select the best tumor peptide candidates that can be efficiently vectorized and delivered by numerous OV-based platforms, in order to reinforce anticancer immune responses. Beyond the identification of TAAs, system biology can also support the engineering of OVs with improved oncotropism to reduce toxicity and maintain a sufficient portion of the wild-type virus virulence. Finally, these technologies can also pave the way towards a more rational design of armed OVs where a transgene of interest can be delivered to TME to develop an intratumoral gene therapy to enhance specific immune stimuli.

Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy

The field of cancer neoantigen investigation has developed swiftly in the past decade. Predicting novel and true neoantigens derived from large multi-omics data became difficult but critical challenges. The rise of Artificial Intelligence (AI) or Machine Learning (ML) in biomedicine application has brought benefits to strengthen the current computational pipeline for neoantigen prediction. ML algorithms offer powerful tools to recognize the multidimensional nature of the omics data and therefore extract the key neoantigen features enabling a successful discovery of new neoantigens. The present review aims to outline the significant technology progress of machine learning approaches, especially the newly deep learning tools and pipelines, that were recently applied in neoantigen prediction. In this review article, we summarize the current state-of-the-art tools developed to predict neoantigens. The standard workflow includes calling genetic variants in paired tumor and blood samples, and rating the binding affinity between mutated peptide, MHC (I and II) and T cell receptor (TCR), followed by characterizing the immunogenicity of tumor epitopes. More specifically, we highlight the outstanding feature extraction tools and multi-layer neural network architectures in typical ML models. It is noted that more integrated neoantigen-predicting pipelines are constructed with hybrid or combined ML algorithms instead of conventional machine learning models. In addition, the trends and challenges in further optimizing and integrating the existing pipelines are discussed.

Characteristics of clinical trials for non-small cell lung cancer therapeutic vaccines registered on ClinicalTrials.gov

Background

Even after complete surgical treatment or chemotherapy, Non-Small Cell Lung Cancer (NSCLC) patients are also at substantial risk for recurrence and spread trend. Therapeutic cancer vaccination could increase the anti-tumor immune response and prevent tumor relapse. This study aimed to assess the characteristics of NSCLC therapeutic vaccines registered on ClinicalTrials.gov.

Methods

We conducted a cross-sectional, descriptive study of clinical trials for Non-Small Cell Lung Cancer Therapeutic Vaccines Registered on ClinicalTrials.gov (https://clinicaltrials.gov/) through March 17, 2022.

Results

This study encompassed 117 registered trials included for data analysis. The number of trials was significantly correlated with a beginning year (r = 0.504, P < 0.010). Of these trials, 45.30% were completed, 12.82% were terminated, and 8.55% were withdrawn. More than half of trials (52.99%) were funded by industry, and more than half of trials (52.14%) were located in economically developed North America. Regarding study designs of these trials, 27.35% were randomized, 52.14% were single group assignment, 83.76% were without masking, 35.90% were phase 1, and more than half of the trials (56.41%) recruited less than 50 participants. The highest proportion of vaccine types was protein/peptide vaccines (41.88%). Regarding TNM staging, the highest proportion of the trials is stage III-IV (26.50%).

Conclusion

The number of clinical trials about the cancer therapeutic vaccines was sustained an increase in recent years. The main characteristic of clinical trials for NSCLC therapeutic vaccines is lack of randomized control, lack of mask, and recruiting less than 50 participants. In recent years, the protein/peptide vaccines for NSCLC active immunotherapy have been well studied.

Seq2Neo: A Comprehensive Pipeline for Cancer Neoantigen Immunogenicity Prediction

Neoantigens derived from somatic DNA alterations are ideal cancer-specific targets. In recent years, the combination therapy of PD-1/PD-L1 blockers and neoantigen vaccines has shown clinical efficacy in original PD-1/PD-L1 blocker non-responders. However, not all somatic DNA mutations result in immunogenicity among cancer cells and efficient tools to predict the immunogenicity of neoepitopes are still urgently needed. Here, we present the Seq2Neo pipeline, which provides a one-stop solution for neoepitope feature prediction using raw sequencing data. Neoantigens derived from different types of genome DNA alterations, including point mutations, insertion deletions and gene fusions, are all supported. Importantly, a convolutional neural network (CNN)-based model was trained to predict the immunogenicity of neoepitopes and this model showed an improved performance compared to the currently available tools in immunogenicity prediction using independent datasets. We anticipate that the Seq2Neo pipeline could become a useful tool in the prediction of neoantigen immunogenicity and cancer immunotherapy. Seq2Neo is open-source software under an academic free license (AFL) v3.0 and is freely available at Github.