Implications of Tumor-Immune Coevolution on Cancer Evasion and Optimized Immunotherapy

Dynamic analysis of a drug resistance evolution model with nonlinear immune response

Recent studies have utilized evolutionary mechanisms to impede the emergence of drug-resistant populations. In this paper, we develop a mathematical model that integrates hormonal treatment, immunotherapy, and the interactions among three cell types: drug-sensitive cancer cells, drug-resistant cancer cells and immune effector cells. Dynamical analysis is performed, examining the existence and stability of equilibria, thereby confirming the model's interpretability. Model parameters are calibrated using available prostate cancer data and literature. Through bifurcation analysis for drug sensitivity under different immune effector cells recruitment responses, we find that resistant cancer cells grow rapidly under weak recruitment response, maintain at a low level under strong recruitment response, and both may occur under moderate recruitment response. To quantify the competitiveness of sensitive and resistant cells, we introduce the comprehensive measures R1 and R2, respectively, which determine the outcome of competition. Additionally, we introduce the quantitative indicators CIE1 and CIE2 as comprehensive measures of the immune effects on sensitive and resistant cancer cells, respectively. These two indicators determine whether the corresponding cancer cells can maintain at a low level. Our work shows that the immune system is an important factor affecting the evolution of drug resistance and provides insights into how to enhance immune response to control resistance.

Improved antitumor effects elicited by an oncolytic HSV-1 expressing a novel B7H3nb/CD3 BsAb

Oncolytic viruses have emerged as a promising modality for cancer treatment due to their unique abilities to directly destroy tumor cells and modulate the tumor microenvironment. Bispecific T-cell engagers (BsAbs) have been developed to activate and redirect cytotoxic T lymphocytes, enhancing the antitumor response. To take advantage of the specific infection capacity and carrying ability of exogenous genes, we generated a recombinant herpes simplex virus type 1 (HSV-1), HSV-1dko-B7H3nb/CD3 or HSV-1dko-B7H3nb/mCD3, carrying a B7H3nb/CD3 or B7H3nb/mCD3 BsAb that replicates and expresses BsAb in tumor cells in vitro and in vivo. The new generation of oncolytic viruses has been genetically modified using CRISPR/Cas9 technology and the cre-loxp system to increase the efficiency of HSV genome editing. Additionally, we used two fully immunocompetent models (GL261 and MC38) to assess the antitumor effect of HSV-1dko-B7H3nb/mCD3. Compared with the HSV-1dko control virus, HSV-1dko-B7H3nb/mCD3 induced enhanced anti-tumor immune responses and T-cell infiltration in both GL261 and MC38 models, resulting in improved treatment efficacy in the latter. Furthermore, flow cytometry analysis of the tumor microenvironment confirmed an increase in NK cells and effector CD8+ T cells, and a decrease in immunosuppressive cells, including FOXP3+ regulatory T cells (Tregs), myeloid-derived suppressor cells (MDSCs), and CD206+ macrophages (M2). Overall, our study identified a novel camel B7H3 nanobody and described the genetic modification of the HSV-1 genome using CRISPR/Cas9 technology and the cre-loxp system. Our findings indicate that expressing B7H3nb/CD3 BsAb could improve the antitumor effects of HSV-1 based oncolytic virus.

Optimal cancer evasion in a dynamic immune microenvironment generates diverse post-escape tumor antigenicity profiles

The failure of cancer treatments, including immunotherapy, continues to be a major obstacle in preventing durable remission. This failure often results from tumor evolution, both genotypic and phenotypic, away from sensitive cell states. Here, we propose a mathematical framework for studying the dynamics of adaptive immune evasion that tracks the number of tumor-associated antigens available for immune targeting. We solve for the unique optimal cancer evasion strategy using stochastic dynamic programming and demonstrate that this policy results in increased cancer evasion rates compared to a passive, fixed strategy. Our foundational model relates the likelihood and temporal dynamics of cancer evasion to features of the immune microenvironment, where tumor immunogenicity reflects a balance between cancer adaptation and host recognition. In contrast with a passive strategy, optimally adaptive evaders navigating varying selective environments result in substantially heterogeneous post-escape tumor antigenicity, giving rise to immunogenically hot and cold tumors.

Tumor-associated macrophage-derived GDNF promotes gastric cancer liver metastasis via a GFRA1-modulated autophagy flux

PURPOSE

Liver metastasis, a lethal malignancy of gastric cancer (GC) patients, execrably impairs their prognosis. As yet, however, few studies have been designed to identify the driving molecules during its formation, except screening evidence pausing before their functions or mechanisms. Here, we aimed to survey a key driving event within the invasive margin of liver metastases.

METHODS

A metastatic GC tissue microarray was used for exploring malignant events during liver-metastasis formation, followed by assessing the expression patterns of glial cell-derived neurotrophic factor (GDNF) and GDNF family receptor alpha 1 (GFRA1). Their oncogenic functions were determined by both loss- and gain-of-function studies in vitro and in vivo, and validated by rescue experiments. Multiple cell biological studies were performed to identify the underlying mechanisms.

RESULTS

In the invasive margin, GFRA1 was identified as a pivotal molecule involved in cellular survival during liver metastasis formation, and we found that its oncogenic role depends on tumor associated macrophage (TAM)-derived GDNF. In addition, we found that the GDNF-GFRA1 axis protects tumor cells from apoptosis under metabolic stress via regulating lysosomal functions and autophagy flux, and participates in the regulation of cytosolic calcium ion signalling in a RET-independent and non-canonical way.

CONCLUSION

From our data we conclude that TAMs, homing around metastatic nests, induce the autophagy flux of GC cells and promote the development of liver metastasis via GDNF-GFRA1 signalling. This is expected to improve the comprehension of metastatic pathogenesis and to provide a novel direction of research and translational strategies for the treatment of metastatic GC patients.

Interpretable artificial intelligence model for accurate identification of medical conditions using immune repertoire

Underlying medical conditions, such as cancer, kidney disease and heart failure, are associated with a higher risk for severe COVID-19. Accurate classification of COVID-19 patients with underlying medical conditions is critical for personalized treatment decision and prognosis estimation. In this study, we propose an interpretable artificial intelligence model termed VDJMiner to mine the underlying medical conditions and predict the prognosis of COVID-19 patients according to their immune repertoires. In a cohort of more than 1400 COVID-19 patients, VDJMiner accurately identifies multiple underlying medical conditions, including cancers, chronic kidney disease, autoimmune disease, diabetes, congestive heart failure, coronary artery disease, asthma and chronic obstructive pulmonary disease, with an average area under the receiver operating characteristic curve (AUC) of 0.961. Meanwhile, in this same cohort, VDJMiner achieves an AUC of 0.922 in predicting severe COVID-19. Moreover, VDJMiner achieves an accuracy of 0.857 in predicting the response of COVID-19 patients to tocilizumab treatment on the leave-one-out test. Additionally, VDJMiner interpretively mines and scores V(D)J gene segments of the T-cell receptors that are associated with the disease. The identified associations between single-cell V(D)J gene segments and COVID-19 are highly consistent with previous studies. The source code of VDJMiner is publicly accessible at https://github.com/TencentAILabHealthcare/VDJMiner. The web server of VDJMiner is available at https://gene.ai.tencent.com/VDJMiner/.

Targeted inducible delivery of immunoactivating cytokines reprograms glioblastoma microenvironment and inhibits growth in mouse models

Glioblastoma multiforme (GBM) is the most common and lethal brain tumor characterized by a strongly immunosuppressive tumor microenvironment (TME) that represents a barrier also for the development of effective immunotherapies. The possibility to revert this hostile TME by immunoactivating cytokines is hampered by the severe toxicity associated with their systemic administration. Here, we exploited a lentiviral vector–based platform to engineer hematopoietic stem cells ex vivo with the aim of releasing, via their tumor-infiltrating monocyte/macrophage progeny, interferon-α (IFN-α) or interleukin-12 (IL-12) at the tumor site with spatial and temporal selectivity. Taking advantage of a syngeneic GBM mouse model, we showed that inducible release of IFN-α within the TME achieved robust tumor inhibition up to eradication and outperformed systemic treatment with the recombinant protein in terms of efficacy, tolerability, and specificity. Single-cell RNA sequencing of the tumor immune infiltrate revealed reprogramming of the immune microenvironment toward a proinflammatory and antitumoral state associated with loss of a macrophage subpopulation shown to be associated with poor prognosis in human GBM. The spatial and temporal control of IL-12 release was critical to overcome an otherwise lethal hematopoietic toxicity while allowing to fully exploit its antitumor activity. Overall, our findings demonstrate a potential therapeutic approach for GBM and set the bases for a recently launched first-in-human clinical trial in patients with GBM.

Exploration of a Novel Prognostic Nomogram and Diagnostic Biomarkers Based on the Activity Variations of Hallmark Gene Sets in Hepatocellular Carcinoma

Background

The initiation and progression of tumors were due to variations of gene sets rather than individual genes. This study aimed to identify novel biomarkers based on gene set variation analysis (GSVA) in hepatocellular carcinoma.

Methods

The activities of 50 hallmark pathways were scored in three microarray datasets with paired samples with GSVA, and differential analysis was performed with the limma R package. Unsupervised clustering was conducted to determine subtypes with the ConsensusClusterPlus R package in the TCGA-LIHC (n = 329) and LIRI-JP (n = 232) cohorts. Differentially expressed genes among subtypes were identified as initial variables. Then, we used TCGA-LIHC as the training set and LIRI-JP as the validation set. A six-gene model calculating the risk scores of patients was integrated with the least absolute shrinkage and selection operator (LASSO) and stepwise regression analyses. Kaplan–Meier (KM) and receiver operating characteristic (ROC) curves were performed to assess predictive performances. Multivariate Cox regression analyses were implemented to select independent prognostic factors, and a prognostic nomogram was integrated. Moreover, the diagnostic values of six genes were explored with the ROC curves and immunohistochemistry.

Results

Patients could be separated into two subtypes with different prognoses in both cohorts based on the identified differential hallmark pathways. Six prognostic genes (ASF1A, CENPA, LDHA, PSMB2, SRPRB, UCK2) were included in the risk score signature, which was demonstrated to be an independent prognostic factor. A nomogram including 540 patients was further integrated and well-calibrated. ROC analyses in the five cohorts and immunohistochemistry experiments in solid tissues indicated that CENPA and UCK2 exhibited high and robust diagnostic values.

Conclusions

Our study explored a promising prognostic nomogram and diagnostic biomarkers in hepatocellular carcinoma.

SSTP1, a Host Defense Peptide, Exploits the Immunomodulatory IL6 Pathway to Induce Apoptosis in Cancer Cells

While the immunomodulatory pathways initiated in immune cells contribute to therapeutic response, their activation in cancer cells play a role in cancer progression. Also, many of the aberrantly expressed immunomodulators on cancer cells are considered as therapeutic targets. Here, we introduce host defense peptide (HDP), a known immuomodulator, as a therapeutic agent to target them. The cationic host defense peptides (HDPs), an integral part of the innate immune system, possess membranolytic activity, which imparts antimicrobial and antitumor efficacy to it. They act as immunomodulators by activating the immune cells. Though their antimicrobial function has been recently reassigned to immunoregulation, their antitumor activity is still attributed to its membranolytic activity. This membrane pore formation ability, which is proportional to the concentration of the peptide, also leads to side effects like hemolysis, limiting their therapeutic application. So, despite the identification of a variety of anticancer HDPs, their clinical utility is limited. Though HDPs are shown to exert the immunomodulatory activity through specific membrane targets on immune cells, their targets on cancer cells are unknown. We show that SSTP1, a novel HDP identified by shotgun cloning, binds to the active IL6/IL6Rα/gp130 complex on cancer cells, rearranging the active site residues. In contrast to the IL6 blockers inhibiting JAK/STAT activity, SSTP1 shifts the proliferative IL6/JAK/STAT signaling to the apoptotic IL6/JNK/AP1 pathway. In IL6Rα-overexpressing cancer cells, SSTP1 induces apoptosis at low concentration through JNK pathway, without causing significant membrane disruption. We highlight the importance of immunomodulatory pathways in cancer apoptosis, apart from its established role in immune cell regulation and cancer cell proliferation. Our study suggests that identification of the membrane targets for the promising anticancer HDPs might lead to the identification of new drugs for targeted therapy.

Epstein-Barr Virus-Associated Malignancies and Immune Escape: The Role of the Tumor Microenvironment and Tumor Cell Evasion Strategies

Simple Summary

The Epstein–Barr virus, also termed human herpes virus 4, is a human pathogenic double-stranded DNA virus. It is highly prevalent and has been linked to the development of 1–2% of cancers worldwide. EBV-associated malignancies encompass various structural and epigenetic alterations. In addition, EBV-encoded gene products and microRNAs interfere with innate and adaptive immunity and modulate the tumor microenvironment. This review provides an overview of the characteristic features of EBV with a focus on the intrinsic and extrinsic immune evasion strategies, which contribute to EBV-associated malignancies.

Abstract

The detailed mechanisms of Epstein–Barr virus (EBV) infection in the initiation and progression of EBV-associated malignancies are not yet completely understood. During the last years, new insights into the mechanisms of malignant transformation of EBV-infected cells including somatic mutations and epigenetic modifications, their impact on the microenvironment and resulting unique immune signatures related to immune system functional status and immune escape strategies have been reported. In this context, there exists increasing evidence that EBV-infected tumor cells can influence the tumor microenvironment to their own benefit by establishing an immune-suppressive surrounding. The identified mechanisms include EBV gene integration and latent expression of EBV-infection-triggered cytokines by tumor and/or bystander cells, e.g., cancer-associated fibroblasts with effects on the composition and spatial distribution of the immune cell subpopulations next to the infected cells, stroma constituents and extracellular vesicles. This review summarizes (i) the typical stages of the viral life cycle and EBV-associated transformation, (ii) strategies to detect EBV genome and activity and to differentiate various latency types, (iii) the role of the tumor microenvironment in EBV-associated malignancies, (iv) the different immune escape mechanisms and (v) their clinical relevance. This gained information will enhance the development of therapies against EBV-mediated diseases to improve patient outcome.

Immunotherapy in the Treatment of Urothelial Bladder Cancer: Insights From Single-Cell Analysis

Urothelial bladder cancer (UBC) is a global challenge of public health with limited therapeutic options. Although the emergence of cancer immunotherapy, most notably immune checkpoint inhibitors, represents a major breakthrough in the past decade, many patients still suffer from unsatisfactory clinical outcome. A thorough understanding of the fundamental cellular and molecular mechanisms responsible for antitumor immunity may lead to optimized treatment guidelines and new immunotherapeutic strategies. With technological developments and protocol refinements, single-cell approaches have become powerful tools that provide unprecedented insights into the kaleidoscopic tumor microenvironment and intricate cell-cell communications. In this review, we summarize recent applications of single-cell analysis in characterizing the UBC multicellular ecosystem, and discuss how to leverage the high-resolution information for more effective immune-based therapies.

Rapid Assessment of T-Cell Receptor Specificity of the Immune Repertoire

Accurate assessment of TCR-antigen specificity at the whole immune repertoire level lies at the heart of improved cancer immunotherapy, but predictive models capable of high-throughput assessment of TCR-peptide pairs are lacking. Recent advances in deep sequencing and crystallography have enriched the data available for studying TCR-p-MHC systems. Here, we introduce a pairwise energy model, RACER, for rapid assessment of TCR-peptide affinity at the immune repertoire level. RACER applies supervised machine learning to efficiently and accurately resolve strong TCR-peptide binding pairs from weak ones. The trained parameters further enable a physical interpretation of interacting patterns encoded in each specific TCR-p-MHC system. When applied to simulate thymic selection of an MHC-restricted T-cell repertoire, RACER accurately estimates recognition rates for tumor-associated neoantigens and foreign peptides, thus demonstrating its utility in helping address the large computational challenge of reliably identifying the properties of tumor antigen-specific T-cells at the level of an individual patient's immune repertoire.