A New Pipeline to Predict and Confirm Tumor Neoantigens Predict Better Response to Immune Checkpoint Blockade

Dual mRNA nanoparticles strategy for enhanced pancreatic cancer treatment and β-elemene combination therapy

Pancreatic ductal adenocarcinoma (PDAC) is notoriously immune-resistant, limiting the clinical efficacy of single-agent immune modulators and thereby necessitating the exploration of multimodal immunotherapy combinations. Traditional approaches combining conventional immune checkpoint inhibitors with neoantigen vaccines have shown some promise in treating PDAC but are often compromised by intratumoral T lymphocyte exhaustion and systemic toxicity. Hence, novel approaches are needed to address these challenges. Herein, we demonstrate that mRNA polymeric nanoparticles encoding anti-PD-1 antibodies in situ at the tumor site enhance the therapeutic efficacy of neoantigen-based mRNA vaccine for PDAC. This mRNA-based, in situ anti-PD-1 antibody production strategy also protects tumor-infiltrating T cells from PD-1 inhibition, potentially reducing the toxicities induced by systemic checkpoint inhibition. Our study may provide an innovative dual mRNA nanoparticle strategy for effective tumor neoantigen immunotherapy, as well as an mRNA cancer combination therapy strategy with other clinically approved drugs (e.g., β-elemene).

Shifting cold to hot tumors by nanoparticle-loaded drugs and products

Cold tumors lack antitumor immunity and are resistant to therapy, representing a major challenge in cancer medicine. Because of the immunosuppressive spirit of the tumor microenvironment (TME), this form of tumor has a low response to immunotherapy, radiotherapy, and also chemotherapy. Cold tumors have low infiltration of immune cells and a high expression of co-inhibitory molecules, such as immune checkpoints and immunosuppressive molecules. Therefore, targeting TME and remodeling immunity in cold tumors can improve the chance of tumor repression after therapy. However, tumor stroma prevents the infiltration of inflammatory cells and hinders the penetration of diverse molecules and drugs. Nanoparticles are an intriguing tool for the delivery of immune modulatory agents and shifting cold to hot tumors. In this review article, we discuss the mechanisms underlying the ability of nanoparticles loaded with different drugs and products to modulate TME and enhance immune cell infiltration. We also focus on newest progresses in the design and development of nanoparticle-based strategies for changing cold to hot tumors. These include the use of nanoparticles for targeted delivery of immunomodulatory agents, such as cytokines, small molecules, and checkpoint inhibitors, and for co-delivery of chemotherapy drugs and immunomodulatory agents. Furthermore, we discuss the potential of nanoparticles for enhancing the efficacy of cancer vaccines and cell therapy for overcoming resistance to treatment.

Predicting patient outcomes after treatment with immune checkpoint blockade: A review of biomarkers derived from diverse data modalities

Immune checkpoint blockade (ICB) therapy targeting cytotoxic T-lymphocyte-associated protein 4, programmed death 1, and programmed death ligand 1 has shown durable remission and clinical success across different cancer types. However, patient outcomes vary among disease indications. Studies have identified prognostic biomarkers associated with immunotherapy response and patient outcomes derived from diverse data types, including next-generation bulk and single-cell DNA, RNA, T cell and B cell receptor sequencing data, liquid biopsies, and clinical imaging. Owing to inter- and intra-tumor heterogeneity and the immune system's complexity, these biomarkers have diverse efficacy in clinical trials of ICB. Here, we review the genetic and genomic signatures and image features of ICB studies for pan-cancer applications and specific indications. We discuss the advantages and disadvantages of computational approaches for predicting immunotherapy effectiveness and patient outcomes. We also elucidate the challenges of immunotherapy prognostication and the discovery of novel immunotherapy targets.

Refining mutanome-based individualised immunotherapy of melanoma using artificial intelligence

Using the particular nature of melanoma mutanomes to develop medicines that activate the immune system against specific mutations is a game changer in immunotherapy individualisation. It offers a viable solution to the recent rise in resistance to accessible immunotherapy alternatives, with some patients demonstrating innate resistance to these drugs despite past sensitisation to these agents. However, various obstacles stand in the way of this method, most notably the practicality of sequencing each patient's mutanome, selecting immunotherapy targets, and manufacturing specific medications on a large scale. With the robustness and advancement in research techniques, artificial intelligence (AI) is a potential tool that can help refine the mutanome-based immunotherapy for melanoma. Mutanome-based techniques are being employed in the development of immune-stimulating vaccines, improving current options such as adoptive cell treatment, and simplifying immunotherapy responses. Although the use of AI in these approaches is limited by data paucity, cost implications, flaws in AI inference capabilities, and the incapacity of AI to apply data to a broad population, its potential for improving immunotherapy is limitless. Thus, in-depth research on how AI might help the individualisation of immunotherapy utilising knowledge of mutanomes is critical, and this should be at the forefront of melanoma management.

A synthetic DNA template for fast manufacturing of versatile single epitope mRNA

A flexible, affordable, and rapid vaccine platform is necessary to unlock the potential of personalized cancer vaccines in order to achieve full clinical efficiency. mRNA cancer vaccine manufacture relies on the rigid sequence design of multiepitope constructs produced by laborious bacterial cloning and time-consuming plasmid preparation. Here, we introduce a synthetic DNA template (SDT) assembly process, which allows cost- and time-efficient manufacturing of single (neo)epitope mRNA. We benchmarked SDT-derived mRNA against mRNA derived from a plasmid DNA template (PDT), showing that monocyte-derived dendritic cells (moDCs) electroporated with SDT-mRNA or PDT-mRNA, encoding HLA-I- or HLA-II-restricted (neo)epitopes, equally activated T cells that were modified to express the cognate T cell receptors. Furthermore, we validated the SDT-mRNA platform for neoepitope immunogenicity screening using the characterized HLA-A2-restricted neoepitope DHX40B and four new candidate HLA-A2-restricted melanoma neoepitopes. Finally, we compared SDT-mRNA with PDT-mRNA for vaccine development purposes. moDCs electroporated with mRNA encoding the HLA-A2-restricted, mutated Melan-A/Mart-1 epitope together with TriMix mRNA-generated high levels of functional Melan-A/Mart-1-specific CD8⁺ T cells. In conclusion, SDT single epitope mRNA can be manufactured in a more flexible, cost-efficient, and time-efficient way compared with PDT-mRNA, allowing prompt neoepitope immunogenicity screening, and might be exploited for the development of personalized cancer vaccines.

Current and Future Biomarkers for Immune Checkpoint Inhibitors in Head and Neck Squamous Cell Carcinoma

With the introduction of immunotherapy, significant improvement has been made in the treatment of head and neck squamous cell carcinoma (HNSCC). However, only a small subset of patients with HNSCC benefit from immunotherapy. The current biomarker, a programmed cell death protein ligand 1 (PD-L1) expression that is widely used in treatment decision making for advanced HNSCC, has only a moderate predictive value. Additionally, PD-L1-based assay has critical inherent limitations due to its highly dynamic nature and lack of standardization. With the advance in molecular techniques and our understanding of biology, more reliable, reproducible, and practical novel biomarkers are being developed. These include but are not limited to neoantigen/mutation characteristics, immune transcriptomes, tumor-infiltrating immune cell composition, cancer epigenomic, proteomics and metabolic characteristics, and plasma-based and organoid assays.

The predictive efficacy of tumor mutation burden in immunotherapy across multiple cancer types: A meta-analysis and bioinformatics analysis

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The FDA has approved pembrolizumab in all cancers with TMB > 10Mut/Mb based on the findings from the phase 2 KEYNOTE-158 study. However, predictive efficacy of TMB is greeted with many skepticisms.

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Cancer patients with high TMB have a better prognosis. Notably, there is no evidence shown whether the good prognosis is caused by the tumor itself or by immunotherapy.

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We compared the meta subgroup analysis by tumor types with non-ICIs from TCGA database and found that immunotherapy can increase, not reduce, cancer patients’ OS with high TMB.

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TMB is a promising therapeutic and prognostic biomarker for immunotherapy, which indicates a better ORR, DCB, OS and PFS.

Purpose

To explore the predictive efficacy of tumor mutation burden (TMB) as a potential biomarker for cancer patients treated with Immune checkpoint inhibitors (ICIs).

Methods

We systematically searched PubMed, Cochrane Library, Embase and Web of Science for clinical studies (published between Jan 1, 2014 and Aug 30, 2021) comparing immunotherapy patients with high TMB to patients with low TMB. Our main endpoints were objective response rate (ORR), durable clinical benefit (DCB), overall survival (OS) and progress-free Survival (PFS). Moreover, we downloaded simple nucleotide variation (SNV) data of 33 major cancer types from the TCGA database as non-ICIs group, and compared the high TMB patients’ OS between the non-ICIs group and meta-analysis results.

Results

Of 10,450 identified studies, 41 were eligible and were included in our analysis (7713 participants). Compared with low TMB patients receiving ICIs, high TMB yielded a better ORR (RR = 2.73; 95% CI: 2.31–3.22; P = 0.043) and DCB (RR = 1.93; 95% CI: 1.64–2.28; P = 0.356), and a significantly increased OS (HR =0.24; 95% CI: 0.21–0.28; P < 0.001) and PFS (HR = 0.38; 95% CI: 0.34–0.42; P < 0.001). Furthermore, compared with non-ICIs group from the TCGA database, immunotherapy can improve OS in some cancer types with high TMB and better prognosis, including colorectal cancer, gastric cancer, lung cancer, melanoma and pan-cancer.

Conclusion

TMB is a promising therapeutic and prognostic biomarker for immunotherapy, which indicates a better ORR, DCB, OS and PFS. If there is a standard for TMB assessment and cut-off, it could improve the management of different cancers.

Prognostic Value of Neoantigen Load in Immune Checkpoint Inhibitor Therapy for Cancer

Immune checkpoint inhibitors (ICIs) have made great progress in the field of tumors and have become a promising direction of tumor treatment. With advancements in genomics and bioinformatics technology, it is possible to individually analyze the neoantigens produced by somatic mutations of each patient. Neoantigen load (NAL), a promising biomarker for predicting the efficacy of ICIs, has been extensively studied. This article reviews the research progress on NAL as a biomarker for predicting the anti-tumor effects of ICI. First, we provide a definition of NAL, and summarize the detection methods, and their relationship with tumor mutation burden. In addition, we describe the common genomic sources of NAL. Finally, we review the predictive value of NAL as a tumor prediction marker based on various clinical studies. This review focuses on the predictive ability of NAL’s ICI efficacy against tumors. In melanoma, lung cancer, and gynecological tumors, NAL can be considered a predictor of treatment efficacy. In contrast, the use of NAL for urinary system and liver tumors requires further research. When NAL alone is insufficient to predict efficacy, its combination with other indicators can improve prediction efficiency. Evaluating the response of predictive biomarkers before the treatment initiation is essential for guiding the clinical treatment of cancer. The predictive power of NAL has great potential; however, it needs to be based on more accurate sequencing platforms and technologies.

A Bayesian network meta-analysis of comparison of cancer therapeutic vaccines for melanoma

Several approaches to active immunotherapy for melanoma, including peptide-based vaccines (PVs), autologous tumour cell vaccines (TCVs), allogeneic TCVs and autologous dendritic cell vaccines (DCVs), have been investigated in clinical trials. However, comprehensive evidence comparing these interventions remains unavailable. The objective of this study was to expand previous work to compare and rank the immunotherapeutic strategies for melanoma in terms of overall survival and toxic effects with a Bayesian network meta-analysis. Methodologically, we performed a network meta-analysis of head-to-head randomized controlled trials comparing and ranking cancer vaccine approaches for patients with melanoma. PubMed, MEDLINE, Embase, the Cochrane Central Register of Controlled Trials, the WHO International Clinical Trials Registry Platform and ClinicalTrials.gov were searched up to 31 July 2020. We estimated summary hazard ratios for death and risk ratios for toxicity. The effects of the underlying prognostic variable on survival benefits were examined by meta-regression. We performed subgroup analysis for the outcomes based on metastatic categories. Overall, we identified 4776 citations, of which 15 head-to-head randomized controlled trials (3162 participants) were included in the analysis. In terms of efficacy, allogeneic tumour cell vaccines plus immunotherapy adjuvants, peptide-based vaccines plus immunotherapy adjuvants and standard therapy were more effective than peptide vaccines. The proportion of women was inversely associated with mortality risk. For safety, all treatments were inferior to allogeneic tumour cell vaccines except for allogeneic tumour cell vaccines plus chemotherapy. Peptide vaccines plus immunotherapy adjuvants led to an increased risk of adverse events compared to allogeneic tumour cell vaccines plus immunotherapy adjuvants. These results suggest that allogeneic TCV and autologous DCV are better than standard therapy. PV plus immune modulators are the most effective strategy among all comparable strategies but is associated with increased toxicity. Any combination regimens for cancer therapeutic vaccines need to be balanced between risk and benefit profiles.

Identification of a prognostic signature based on immune-related genes in bladder cancer

Bladder cancer (BLCA) has a high incidence and recurrence rate, and the effect of immunotherapy varies from person to person. Immune-related genes (IRGs) have been shown to be associated with immunotherapy and prognosis in many other cancers, but their role in immunogenic BLCA is less well defined. In this study, we constructed an eight-IRG risk model, which demonstrated strong prognostic and immunotherapeutic predictive power. The signature was significantly related to tumor clinicopathological characteristics, tumor class, immune cell infiltration and mutation status. Additionally, a nomogram containing the risk score and other potential risk factors could effectively predict the long-term overall survival probability of BLCA patients. The enriched mechanisms identified by gene set enrichment analysis suggested that the reason why this signature can accurately distinguish high- and low-risk populations may be closely related to the different degrees of innate immune response and T cell activation in different patients.