Tumor-immune Interactions Research Articles

Abstract 2659: Therapeutic administration of an engineered sphingosine-1-phosphate lyase improves T cell trafficking to tumors and enhances antitumor response

Abstract Introduction: Immunotherapy has revolutionized the landscape of cancer treatment; however, to date, only limited aspects of the tumor-immune interaction have been exploited successfully, e.g., relief of immune checkpoints, increase of tumor-associated antigen presentation. Although it is well-known that trafficking and entry of T cells from the circulation into tumors is often impaired, leading to an immune “cold”, checkpoint inhibitor-refractory state, there are no available therapies that address this important aspect of antitumor immunity. Sphingosine-1-phosphate (S1P) is a pleiotropic, bioactive lipid that has been shown to regulate lymphocyte egress from lymph nodes through its interaction with one of its several receptors, S1P1. Clinically, tumor expression levels of the sphingosine-1-phosphate-producing enzyme sphingosine kinase 1 (SPHK1) strongly and negatively correlates with survival of melanoma patients after immune checkpoint inhibitor (ICI) therapy, and pre-clinically correlates with reduced tumor infiltrating lymphocytes (TILs). We therefore hypothesize that aberrant S1P signaling in tumors and in their draining lymph nodes (TDLN) causes immune exclusion by impeding lymphocyte egress from the TDLN, ultimately restricting immune cell infiltration of tumors. Methods: We tested the therapeutic efficacy of an engineered S1P-degrading enzyme, S1P lyase (S1PL), on established (~100mm^3) syngeneic B16, CT26, and MC38 murine tumors. Using flow cytometry, we performed immune profiling of systemic, TDLN, and intratumoral T cell populations, assessing both activation status and expression of the S1P1 receptor. Results: Treatment of tumor-bearing animals with S1PL depleted systemic S1P, and dramatically increased both trafficking of lymphocytes out of TDLNs and T cell infiltration of tumors relative to control-treated animals. Mice treated with S1PL evinced a higher proportion of S1P1+ circulating and intratumoral T cells relative to control-treated mice, suggesting that the increase in liberation of T cells from the TDLN was effected through the S1P/S1P1 axis. This increased lymphocyte trafficking correlated with strong inhibition of tumor growth when S1PL was administered as a single agent. Conclusions: Therapeutic administration of S1PL may represent an attractive strategy for modulating lymphocyte trafficking to improve the efficacy of therapies that rely on the adaptive immune system to exert antitumor activity. This approach may be particularly potent in settings wherein tumor-mediated immune exclusion is a dominant method of immune escape. Citation Format: Alessandra Araujo, Max Rodnick-Smith, Ranya Al-Khaledy, George Georgiou, Mark Badeaux, Everett Stone. Therapeutic administration of an engineered sphingosine-1-phosphate lyase improves T cell trafficking to tumors and enhances antitumor response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2659.

Abstract 6883: Investigation of tumor-immune interactions in pediatric sarcomas using single-cell multi-omics

Abstract Background: Pediatric sarcoma represents the second most common solid tumor in children. Despite advances in multimodal therapies, one-third of sarcoma patients do not survive the disease. Over the past few decades, the development of novel therapeutic strategies for sarcoma has been impeded by an insufficient understanding of the tumor microenvironment (TME). In particular, the potential of immunotherapy remains constrained by the gap of knowledge about the molecular and functional phenotypes of the immune cells in the sarcoma TME. Methods: To address this knowledge gap, we collected treatment-naïve tumor samples from 32 patients with five pediatric sarcoma subtypes. We employed a single-nuclei RNA+ATAC-Seq multi-omics assay to comprehensively characterize the transcriptomic and epigenomic heterogeneity of tumor and immune cells within the sarcoma TME. Results: Our analysis revealed that tumor-associated macrophages (TAMs) were the most abundant immune cell type in the sarcoma TME. We identified seven distinct TAM subpopulations, each exhibiting unique gene expression and chromatin accessibility patterns. Among these, TAM subsets with higher expression of SPP1 and C1QC genes displayed the strongest immunosuppressive potential. Transcriptional regulatory network (TRN) analysis identified transcription factors that were most likely to drive the phenotypes of SPP1+ and C1QC+ TAMs, including ELF2, JUND and RUNX1. Through cell-cell communication analysis, we discovered that SPP1+ TAMs interact with T cells via SPP1-CD44 signaling, while C1QC+ TAMs interact with NK cells through HLA-E-KLRC1 signaling, suggesting the potential involvement of these signaling pathways in TAM-mediated suppression of these cytotoxic lymphocytes. We prioritized ligand-receptor gene pairs, such as SEMA3D-NRP1 and LTBP3-ITGB5, which likely mediate the interaction between malignant sarcoma cells and SPP1+/C1QC+ TAMs based on their dominance across sarcoma subtypes and association with poorer clinical outcomes. On the other hand, our analysis revealed that sarcoma subtype-specific interactions between TAMs and malignant cells are common, highlighting the heterogeneity of signaling events across sarcoma subtypes. Conclusion and Significance: Our study presents a comprehensive single-cell multi-omics characterization of pediatric sarcomas and sheds light on its tumor microenvironment. By identifying unique markers for immunosuppressive TAM subpopulations and elucidating intercellular interactions promoting tumor development, this study proposes potential therapeutic targets for sarcoma treatment based on the role of TAMs in sarcoma TME. Citation Format: Zhan Zhang, Kyung Jin Ahn, Changya Chen, Rumeysa Biyik Sit, Hesham Mohei, Brian Lockhart, Malay Haldar, Kathrin Bernt, Vinodh Pillai, Kai Tan. Investigation of tumor-immune interactions in pediatric sarcomas using single-cell multi-omics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6883.

Nuclear protein NOP2 serves as a poor-prognosis predictor of LUAD and aggravates the malignancy of lung adenocarcinoma cells.

Recent studies have shown that NOP2, a nucleolar protein, is up-regulated in various cancers, suggesting a potential link to tumor aggressiveness and unfavorable outcomes. This study examines NOP2's role in lung adenocarcinoma (LUAD), a context where its implications remain unclear. Utilizing bioinformatics, we assessed 513 LUAD and 59 normal tissue samples from The Cancer Genome Atlas (TCGA) to explore NOP2's diagnostic and prognostic significance in LUAD. Additionally, in vitro experiments compared NOP2 expression between Beas-2b and A549 cells. Advanced databases and analytical tools, including LINKEDOMICS, STRING, and TISIDB, were employed to further elucidate NOP2's association with LUAD. Our findings indicate a significantly higher expression of NOP2 mRNA and protein in A549 cells compared to Beas-2b cells (P < 0.001). In LUAD, elevated NOP2 levels were linked to decreased Overall Survival (OS) and advanced clinical stages. Univariate Cox analysis revealed that high NOP2 expression correlated with poorer OS in LUAD (P < 0.01), a finding independently supported by multivariate Cox analysis (P < 0.05). The relationship between NOP2 expression and LUAD risk was presented via a Nomogram. Additionally, Gene Set Enrichment Analysis (GSEA) identified seven NOP2-related signaling pathways. A focal point of our research was the interplay between NOP2 and tumor-immune interactions. Notably, a negative correlation was observed between NOP2 expression and the immune infiltration levels of macrophages, neutrophils, mast cells, Natural Killer (NK) cells, and CD8 + T cells in LUAD. Moreover, the expression of NOP2 was related to the sensitivity of various chemotherapeutic drugs. In vitro, we found that downregulating NOP2 can decrease the proliferation, migration and invasion of A549 cells. Furthermore, NOP2 can regulate Caspase3-mediated apoptosis. Collectively, particularly regarding prognosis, immune infiltration and vitro experiments, these findings suggest NOP2's potential of serving as a poor-prognostic biomarker for LUAD and aggravating the malignancy of lung adenocarcinoma cells.

Translational Frontiers and Clinical Opportunities of Immunologically Fitted Radiotherapy.

Ionizing radiation can have a wide range of impacts on tumor-immune interactions, which are being studied with the greatest interest and at an accelerating pace by the medical community. Despite its undeniable immunostimulatory potential, it clearly appears that radiotherapy as it is prescribed and delivered nowadays often alters the host's immunity toward a suboptimal state. This may impair the full recovery of a sustained and efficient antitumor immunosurveillance posttreatment. An emerging concept is arising from this awareness and consists of reconsidering the way of designing radiation treatment planning, notably by taking into account the individualized risks of deleterious radio-induced immune alteration that can be deciphered from the planned beam trajectory through lymphocyte-rich organs. In this review, we critically appraise key aspects to consider while planning immunologically fitted radiotherapy, including the challenges linked to the identification of new dose constraints to immune-rich structures. We also discuss how pharmacologic immunomodulation could be advantageously used in combination with radiotherapy to compensate for the radio-induced loss, for example, with (i) agonists of interleukin (IL)2, IL4, IL7, IL9, IL15, or IL21, similarly to G-CSF being used for the prophylaxis of severe chemo-induced neutropenia, or with (ii) myeloid-derived suppressive cell blockers.

Supramolecular Heterodimer Peptides Assembly for Nanoparticles Functionalization.

Nanoparticle (NP) surface functionalization with proteins, including monoclonal antibodies (mAbs), mAb fragments, and various peptides, has emerged as a promising strategy to enhance tumor targeting specificity and immune cell interaction. However, these methods often rely on complex chemistry and suffer from batch-dependent outcomes, primarily due to limited control over the protein orientation and quantity on NP surfaces. To address these challenges, a novel approach based on the supramolecular assembly of two peptides is presented to create a heterotetramer displaying VHHs on NP surfaces. This approach effectively targets both tumor-associated antigens (TAAs) and immune cell-associated antigens. In vitro experiments showcase its versatility, as various NP types are biofunctionalized, including liposomes, PLGA NPs, and ultrasmall silica-based NPs, and the VHHs targeting of known TAAs (HER2 for breast cancer, CD38 for multiple myeloma), and an immune cell antigen (NKG2D for natural killer (NK) cells) is evaluated. In in vivo studies using a HER2+ breast cancer mouse model, the approach demonstrates enhanced tumor uptake, retention, and penetration compared to the behavior of nontargeted analogs, affirming its potential for diverse applications.

Metabolic reprogramming and signalling cross-talks in tumour-immune interaction: a system-level exploration.

Tumour-immune microenvironment (TIME) is pivotal in tumour progression and immunoediting. Within TIME, immune cells undergo metabolic adjustments impacting nutrient supply and the anti-tumour immune response. Metabolic reprogramming emerges as a promising approach to revert the immune response towards a pro-inflammatory state and conquer tumour dominance. This study proposes immunomodulatory mechanisms based on metabolic reprogramming and employs the regulatory flux balance analysis modelling approach, which integrates signalling, metabolism and regulatory processes. For the first time, a comprehensive system-level model is constructed to capture signalling and metabolic cross-talks during tumour-immune interaction and regulatory constraints are incorporated by considering the time lag between them. The model analysis identifies novel features to enhance the immune response while suppressing tumour activity. Particularly, altering the exchange of succinate and oxaloacetate between glioma and macrophage enhances the pro-inflammatory response of immune cells. Inhibition of glutamate uptake in T-cells disrupts the antioxidant mechanism of glioma and reprograms metabolism. Metabolic reprogramming through adenosine monophosphate (AMP)-activated protein kinase (AMPK), coupled with glutamate uptake inhibition, was identified as the most impactful combination to restore T-cell function. A comprehensive understanding of metabolism and gene regulation represents a favourable approach to promote immune cell recovery from tumour dominance.

CLCN3 in mediating the proliferation of human ovarian cancer cells.

Chloride channel-3 (CLCN3), a crucial component of the voltage-gated chloride channel family, is implicated in numerous physiological and pathophysiological processes. This study aimed to investigate the characteristics of CLCN3 in pancancer and its influence on the immune response through the use of a range of databases. Concurrently, we assessed the impact of CLCN3 on the proliferation of ovarian cancer (OC) cells and explored its potential mechanisms. We employed the Tumor Immune Estimation Resource (TIMER) 2.0 and Clinical Proteomic Tumor Analysis Consortium (CPTAC) databases to examine the messenger RNA (mRNA) and the protein expression of CLCN3 across various cancers. The prognostic significance of CLCN3 was evaluated using the Gene Expression Profiling Interactive Analysis 2.0 (GEPIA 2.0) database. The University of Alabama at Birmingham Cancer Data Analysis Portal (UALCAN) facilitated the analysis of CLCN3 promoter methylation levels. The association between CLCN3 expression and tumor-infiltrating immune cells was investigated using various algorithms. The cBioportal database facilitated the analysis of CLCN3 mutations and mutation sites across various cancers. The Tumor-Immune System Interactions Database (TISIDB) database was employed to explore the correlation between CLCN3 expression and immune or molecular subtypes across a variety of cancer types. We collected ovarian tissue samples, encompassing both normal ovarian and OC tissues. The human OC cell lines, SKOV3 cells and OVCAR433 cells, were cultured. CLCN3 expression was determined via reverse-transcription quantitative polymerase chain reaction (RT-qPCR), while phosphatidylinositol 3-kinase/Akt kinase (PI3K/AKT) expression was detected using Western blot. We utilized small interfering RNA (siRNA) technology to suppress CLCN3 expression. The proliferative capacity of SKOV3 and OVCAR433 cells was assessed using the Cell Counting Kit 8 (CCK-8) assay. CLCN3 demonstrated an aberrant expression in a number of cancer types and was markedly reduced in OC tissues. Poor prognosis in cervical squamous cell cancer and myeloid leukemia was linked to excessive expression of CLCN3. The examination of immune cell infiltration, which included CD8+ T cells, B cells, T regulatory cells, and cancer-associated fibroblast cells, showed a strong association with aberrant CLCN3 expression. Following the use of siRNA technology, the ability of the ovarian carcinoma cell line SKOV3 and OVCAR433 to proliferate as well as the expression of PI3K/AKT both increased. CLCN3 is a possible biomarker for immune-related processes and the prognosis of cancer, and the PI3K/AKT signaling pathway may affect OC cells' ability to proliferate.

Collagen Mineralization Decreases NK Cell-Mediated Cytotoxicity of Breast Cancer Cells via Increased Glycocalyx Thickness.

Skeletal metastasis is common in patients with advanced breast cancer and often caused by immune evasion of disseminated tumor cells (DTCs). In the skeleton, tumor cells not only disseminate to the bone marrow but also to osteogenic niches in which they interact with newly mineralizing bone extracellular matrix (ECM). However, it remains unclear how mineralization of collagen type I, the primary component of bone ECM, regulates tumor-immune cell interactions. Here, a combination of synthetic bone matrix models with controlled mineral content, nanoscale optical imaging, and flow cytometry are utilized to evaluate how collagen type I mineralization affects the biochemical and biophysical properties of the tumor cell glycocalyx, a dense layer of glycosylated proteins and lipids decorating their cell surface. These results suggest that collagen mineralization upregulates mucin-type O-glycosylation and sialylation by tumor cells, which increases their glycocalyx thickness while enhancing resistance to attack by natural killer (NK) cells. These changes are functionally linked as treatment with a sialylation inhibitor decreased mineralization-dependent glycocalyx thickness and made tumor cells more susceptible to NK cell attack. Together, these results suggest that interference with glycocalyx sialylation may represent a therapeutic strategy to enhance cancer immunotherapies targeting bone-metastatic breast cancer.

Location matters: spatial dynamics of tumor-infiltrating T cell subsets is prognostic in colon cancer.

Colon cancer is a heterogeneous disease and consists of various molecular subtypes. Despite advances in high-throughput expression profiling, limitations remain in predicting clinical outcome and assigning specific treatment to individual cases. Tumor-immune interactions play a critical role, with tumors that activate the immune system having better outcome for the patient. The localization of T cells within tumor epithelium, to enable direct contact, is essential for antitumor function, but bulk DNA/RNA sequencing data lacks spatial distribution information. In this study, we provide spatial T cell tumor distribution and connect these data with previously determined genomic data in the AC-ICAM colon cancer patient cohort. Colon cancer patients (n=90) with transcriptome data available were selected. We used a custom multiplex immunofluorescence assay on colon tumor tissue sections for quantifying T cell subsets spatial distribution in the tumor microenvironment, in terms of cell number, location, mutual distance, and distance to tumor cells. Statistical analyses included the previously determined Immunologic Constant of Rejection (ICR) transcriptome correlation and patient survival, revealing potential prognostic value in T cell spatial distribution. T cell phenotypes were characterized and CD3+CD8-FoxP3- T cells were found to be the predominant tumor-infiltrating subtype while CD3+FoxP3+ T cells and CD3+CD8+ T cells showed similar densities. Spatial distribution analysis elucidated that proliferative T cells, characterized by Ki67 expression, and Granzyme B-expressing T cells were predominantly located within the tumor epithelium. We demonstrated an increase in immune cell density and a decrease in the distance of CD3+CD8+ T cells to the nearest tumor cell, in the immune active, ICR High, immune subtypes. Higher densities of stromal CD3+FoxP3+ T cells showed enhanced survival outcomes, and patients exhibited superior clinical benefits when greater spatial distances were observed between CD3+CD8-FoxP3- or CD3+CD8+ T cells and CD3+FoxP3+ T cells. Our study's in-depth analysis of the spatial distribution and densities of major T cell subtypes within the tumor microenvironment has provided valuable information that paves the way for further research into the intricate relationships between immune cells and colon cancer development.

A comprehensive analysis of the prognostic and immunological role of ANK3 in pan-cancer.

Cancer is a common cause of death around the world. Immunotherapy plays a significant role in cancer treatment but still has limitations. The ankyrin-3 (ANK3) gene has been shown to have a variety of biological roles and has also been shown to be closely linked to individual cancers. We systematically investigated the role of ANK3 in pan-cancer, particularly in relation to immunity. We collected data from a number of databases, including the The University of ALabama at Birmingham CANcer data analysis Portal (UALCAN), tumor-immune system interactions (TISIDB), cBioPortal, Tumor Immune Estimation Resource (TIMER), Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), BioGRID, and SangerBox databases. R (version 3.6.3) was used for the statistical analysis and data visualization. The expression of ANK3 in tumors and its effects on patient prognosis, immune infiltration, neoantigens, the microenvironment, immune checkpoints (ICs), the tumor mutation burden, microsatellite instability (MSI), methylation, mismatch repair (MMR) genes, and cancer-associated fibroblasts were investigated. A gene set enrichment analysis (GSEA) was also conducted. The ANK3 gene was differentially expressed at the messenger RNA (mRNA) and protein levels in various human tumors. The prognosis of patients with different types of malignancies was correlated with the level of ANK3 expression. The immunological microenvironment was also linked to ANK3 expression, especially in colon adenocarcinoma (COAD), kidney renal clear cell carcinoma (KIRC), and liver hepatocellular carcinoma (LIHC). ANK3 was also associated with ICs, immune neoantigens, MSI, the tumor mutation load, MMR genes, and DNA methylation. Finally, we found the key pathway related to the ANK3 gene through the enrichment analysis. ANK3 could serve as a new biomarker specific to prognosis and immunotherapy in various cancers. Our findings could contribute to the development of novel strategies for treating malignancies.

Abstract A021: Evolutionary immunotherapy in NSCLC: An integrated approach

Abstract Purpose: Our study examines tumor growth dynamics and immunosuppression under the presence of immune attack to identify optimal immune pulsing treatment strategies. Motivation: Checkpoint inhibitors disrupting the PD-1/PD-L1 axis are used in multiple cancer subtypes. However, not all patients respond, and predictive biomarkers are needed to better stratify patients. Therapy for PD-L1, the most studied biomarker, has given mixed results in non-small cell lung cancer (NSCLC). The complexity of the immune response and plastic nature of PD-L1/PD-1 expression on tumor and immune cells contributes to the heterogeneity of success. IFN-γ, a cytokine released by activated immune cells, induces PD-L1 expression in some tumor cells. Chronic IFN-γ results in long-term PD-L1 expression. However, relaxation dynamics following removal of IFN-γ remains unexplored. In this study, we investigate these complex PD-L1 dynamics through a unique combination of in vitro studies and mathematical modeling. METHODS: In vitro experimental data: We have collected in vitro and temporal confocal microscopy images of PD-L1 expression on NSCLC cells, which were either untreated or treated for 48 hours with high dose IFN-γ followed by chronic low dose IFN-γ for 21 days.Agent-based model: To reinforce our in vitro experiments and findings, we developed a hybrid agent-based model (ABM) to study PD-L1 dynamics. The agents in the ABM are tumor cells having variable PD-L1 expression, and immune cells that secrete IFN-γ. Tumor cells can be randomly seeded or seeded as a cluster of cells. IFN-γ is modeled as either a binary well-mixed pulse (to simulate the in vitro experimental setup), or as a diffusible via a reaction-diffusion process (to simulate the tumor-immune cell interactions in an in-vivo spatial manner). This allows us to investigate the effects of various pulsing strategies of immunotherapy on tumor evolution where each pulse can be a variable dose of immune cells, given at regular or irregular intervals. RESULTS: Our in vitro experimental data and microscopy images confirm that PD-L1 expression continues to increase within the cells after a 48-hour IFN-γ exposure followed by low levels of IFN-γ. Our ABM has been calibrated to capture in vitro data dynamics for different conditions. Our initial results indicate that pulsing immunotherapy may lead to better overall immune efficacy than continuous immunotherapy, by preventing sustained suppression that increases immune cell exhaustion, despite the potential for tumor regrowth between pulses. We also hypothesize that pulsing immunotherapy will have different immunosuppression effects when tumors are clustered or randomly distributed. CONCLUSION: We have quantified the temporal dynamics of PD-L1 expression in NSCLC cells under different conditions of IFN-γ, and built an ABM to study tumor-immune evolution under various pulsing strategies. The results of this work could lead to improved therapy schedules in patients responsive to checkpoint inhibitors that exploit the complex dynamics of immune response and suppression. Citation Format: Sandhya Prabhakaran, Taylor M. Bursell, Kimberly Luddy, Julian Pineiro, Rafael Bravo, Jeffrey West, Megan Johnson, Mark Robertson-Tessi, Amer A. Beg, Jhanelle E. Gray, Scott Antonia, Robert A. Gatenby, Alexander R. A. Anderson. Evolutionary immunotherapy in NSCLC: An integrated approach [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr A021.

Integrative HLA typing of tumor and adjacent normal tissue can reveal insights into the tumor immune response

BackgroundThe HLA complex is the most polymorphic region of the human genome, and its improved characterization can help us understand the genetics of human disease as well as the interplay between cancer and the immune system. The main function of HLA genes is to recognize “non-self” antigens and to present them on the cell surface to T cells, which instigate an immune response toward infected or transformed cells. While sequence variation in the antigen-binding groove of HLA may modulate the repertoire of immunogenic antigens presented to T cells, alterations in HLA expression can significantly influence the immune response to pathogens and cancer.MethodsRNA sequencing was used here to accurately genotype the HLA region and quantify and compare the level of allele-specific HLA expression in tumors and patient-matched adjacent normal tissue. The computational approach utilized in the study types classical and non-classical Class I and Class II HLA alleles from RNA-seq while simultaneously quantifying allele-specific or personalized HLA expression. The strategy also uses RNA-seq data to infer immune cell infiltration into tumors and the corresponding immune cell composition of matched normal tissue, to reveal potential insights related to T cell and NK cell interactions with tumor HLA alleles.ResultsThe genotyping method outperforms existing RNA-seq-based HLA typing tools for Class II HLA genotyping. Further, we demonstrate its potential for studying tumor-immune interactions by applying the method to tumor samples from two different subtypes of breast cancer and their matched normal breast tissue controls.ConclusionsThe integrative RNA-seq-based HLA typing approach described in the study, coupled with HLA expression analysis, neoantigen prediction and immune cell infiltration, may help increase our understanding of the interplay between a patient’s tumor and immune system; and provide further insights into the immune mechanisms that determine a positive or negative outcome following treatment with immunotherapy such as checkpoint blockade.

Optimal dosage protocols for mathematical models of synergy of chemo- and immunotherapy.

The release of tumor antigens during traditional cancer treatments such as radio- or chemotherapy leads to a stimulation of the immune response which provides synergistic effects these treatments have when combined with immunotherapies. A low-dimensional mathematical model is formulated which, depending on the values of its parameters, encompasses the 3 E's (elimination, equilibrium, escape) of tumor immune system interactions. For the escape situation, optimal control problems are formulated which aim to revert the process to the equilibrium scenario. Some numerical results are included.

CX3CR1 is a potential biomarker of immune microenvironment and prognosis in epithelial ovarian cancer.

Immunotherapy is less efficient for epithelial ovarian cancer and lacks ideal biomarkers to select the best beneficiaries for immunotherapy. CX3CR1 as chemokine receptor mainly expressed on immune cell membranes, and combined with its unique ligand CX3CL1, mediates tissue chemotaxis and adhesion of immune cells. However, the immune functional and prognostic value of CX3CR1 in epithelial ovarian cancer has not been clarified. A comprehensive retrospective analysis was performed by using the online database to identify the underlying immunological mechanisms and prognostic value of CX3CR1. The Human Protein Atlas, gene expression profiling interactive analysis, and TISIDB (an integrated repository portal for tumor-immune system interactions) database showed that CX3CR1 expressed higher in epithelial ovarian cancer than that in normal ovarian tissue. Four hundred twenty-two cases from Gene Expression Profiling Interactive Analysis and 1656 cases from Kaplan-Meier plotter database showed higher expression of CX3CR1 (above median) was associated with unfavorable overall survival. TIMER, UALCAN, and TISIDB database were applied to validate CX3CR1 negative impact on overall survival. In addition, correlation analysis showed that the expression level of CX3CR1 was positive association with infiltrating levels of B cells (R = 0.31, P = 3.10e-12), CD8+ T cells (R = 0.26, P = 7.93e-09), CD4+ T cells (R = 0.11, P = 1.41e-02), macrophages (R = 0.32, P = 4.29e-13), dendritic cells (R = 0.27, P = 2.98e-09), and neutrophil (R = 0.25, P = 3.25e-08) in epithelial ovarian cancer. Therefore, CX3CR1 involved in reshaping the immune microenvironment for epithelial ovarian cancer and maybe a potential immunotherapy target and prognostic marker for ovarian cancer.

The impact of radio-chemotherapy on tumour cells interaction with optimal control and sensitivity analysis

Oncologists and applied mathematicians are interested in understanding the dynamics of cancer-immune interactions, mainly due to the unpredictable nature of tumour cell proliferation. In this regard, mathematical modelling offers a promising approach to comprehend this potentially harmful aspect of cancer biology. This paper presents a novel dynamical model that incorporates the interactions between tumour cells, healthy tissue cells, and immune-stimulated cells when subjected to simultaneous chemotherapy and radiotherapy for treatment. We analysed the equilibria and investigated their local stability behaviour. We also study transcritical, saddle–node, and Hopf bifurcations analytically and numerically. We derive the stability and direction conditions for periodic solutions. We identify conditions that lead to chaotic dynamics and rigorously demonstrate the existence of chaos. Furthermore, we formulated an optimal control problem that describes the dynamics of tumour-immune interactions, considering treatments such as radiotherapy and chemotherapy as control parameters. Our goal is to utilize optimal control theory to reduce the cost of radiotherapy and chemotherapy, minimize the harmful effects of medications on the body, and mitigate the burden of cancer cells by maintaining a sufficient population of healthy cells. Cost-effectiveness analysis is employed to identify the most economical strategy for reducing the disease burden. Additionally, we conduct a Latin hypercube sampling-based uncertainty analysis to observe the impact of parameter uncertainties on tumour growth, followed by a sensitivity analysis. Numerical simulations are presented to elucidate how dynamic behaviour of model is influenced by changes in system parameters. The numerical results validate the analytical findings and illustrate that a multi-therapeutic treatment plan can effectively reduce tumour burden within a given time frame of therapeutic intervention.

Dynamics aspects and bifurcations of a tumor-immune system interaction under stationary immunotherapy

We consider a three-dimensional mathematical model that describes the interaction between the effector cells, tumor cells, and the cytokine (IL-2) of a patient. This is called the Kirschner–Panetta model. Our objective is to explain the tumor oscillations in tumor sizes as well as long-term tumor relapse. We then explore the effects of adoptive cellular immunotherapy on the model and describe under what circumstances the tumor can be eliminated or can remain over time but in a controlled manner. Nonlinear dynamics of immunogenic tumors are given, for example: we prove that the trajectories of the associated system are bounded and defined for all positive time; there are some invariant subsets; there are open subsets of parameters, such that the system in the first octant has at most five equilibrium solutions, one of them is tumor-free and the others are of co-existence. We are able to prove the existence of transcritical and pitchfork bifurcations from the tumor-free equilibrium point. Fixing an equilibrium and introducing a small perturbation, we are able to show the existence of a Hopf periodic orbit, showing a cyclic behavior among the population, with a strong dominance of the parental anomalous growth cell population. The previous information reveals the effects of the parameters. In our study, we observe that our mathematical model exhibits a very rich dynamic behavior and the parameter μ̃ (death rate of the effector cells) and p̃1 (production rate of the effector cell stimulated by the cytokine IL-2) plays an important role. More precisely, in our approach the inequality μ̃2>p̃1 is very important, that is, the death rate of the effector cells is greater than the production rate of the effector cell stimulated by the cytokine IL-2. Finally, medical implications and a set of numerical simulations supporting the mathematical results are also presented.

An AI-assisted integrated, scalable, single-cell phenomic-transcriptomic platform to elucidate intratumor heterogeneity against immune response.

We present a novel framework combining single-cell phenotypic data with single-cell transcriptomic analysis to identify factors underpinning heterogeneity in antitumor immune response. We developed a pairwise, tumor-immune discretized interaction assay between natural killer (NK-92MI) cells and patient-derived head and neck squamous cell carcinoma (HNSCC) cell lines on a microfluidic cell-trapping platform. Furthermore we generated a deep-learning computer vision algorithm that is capable of automating the acquisition and analysis of a large, live-cell imaging data set (>1 million) of paired tumor-immune interactions spanning a time course of 24 h across multiple HNSCC lines (n = 10). Finally, we combined the response data measured by Kaplan-Meier survival analysis against NK-mediated killing with downstream single-cell transcriptomic analysis to interrogate molecular signatures associated with NK-effector response. As proof-of-concept for the proposed framework, we efficiently identified MHC class I-driven cytotoxic resistance as a key mechanism for immune evasion in nonresponders, while enhanced expression of cell adhesion molecules was found to be correlated with sensitivity against NK-mediated cytotoxicity. We conclude that this integrated, data-driven phenotypic approach holds tremendous promise in advancing the rapid identification of new mechanisms and therapeutic targets related to immune evasion and response.

INVESTIGATION OF FRACTIONAL-ORDERED TUMOR-IMMUNE INTERACTION MODEL VIA FRACTIONAL-ORDER DERIVATIVE

Dynamical analysis of system of ordinary differential equations (ODEs) is an interesting topic among researchers and several tools have been discovered since so far to deal with its several other features. There are many built-in functions designed in MATLAB that researchers can use as a tool for the system of ODEs with integer order but in the similar systems have fractional-order derivative is a difficult task. Therefore, the purpose of this work is to use the Caputo fractional-order derivative to qualitatively analyze and then numerically simulate the tumor-immune system interaction model. Moreover, the Schauder fixed point theorem is used to establish the condition for the existence of at least one solution, while the Banach fixed point theorem is utilized to guarantee the existence of a unique solution. Moreover, the stability in the trajectories of considered system achieved with the aid of Ulam–Hyers (UH) stability. In addition, the numerical computations are performed using Variational Iteration Method (VIM) and the visualized results are shown using MATLAB.

Potential Perturbations of Critical Cancer-regulatory Genes in Triple-Negative Breast Cancer Cells Within the Humanized Microenvironment of Patient-derived Xenograft Models.

In this study, we aimed to establish humanized patient-derived xenograft (PDX) models for triple-negative breast cancer (TNBC) using cord blood (CB) hematopoietic stem cells (HSCs). Additionally, we attempted to characterize the immune microenvironment of the humanized PDX model to understand the potential implications of altered tumor-immune interactions in the humanized PDX model on the behavior of TNBC cells. To establish a humanized mouse model, high-purity CD34+ HSCs from CB were transplanted into immunodeficient NOD scid γ mice. Peripheral and intratumoral immune cell compositions of humanized and non-humanized mice were compared. Additionally, RNA sequencing of the tumor tissues was performed to characterize the gene expression features associated with humanization. After transplanting the CD34+ HSCs, CD45+ human immune cells appeared within five weeks. A humanized mouse model showed viable human immune cells in the peripheral blood, lymphoid organs, and in the tumor microenvironment. Humanized TNBC PDX models showed varying rates of tumor growth compared to that of non-humanized mice. RNA sequencing of the tumor tissue showed significant alterations in tumor tissues from the humanized models. tumor necrosis factor receptor superfamily member 11B (TNFRSF11B) is a shared downregulated gene in tumor tissues from humanized models. Silencing of TNFRSF11B in TNBC cell lines significantly reduced cell proliferation, migration, and invasion in vitro. Additionally, TNFRSF11B silenced cells showed decreased tumorigenicity and metastatic capacity in vivo. Humanized PDX models successfully recreated tumor-immune interactions in TNBC. TNFRSF11B, a commonly downregulated gene in humanized PDX models, may play a key role in tumor growth and metastasis. Differential tumor growth rates and gene expression patterns highlighted the complexities of the immune response in the tumor microenvironment of humanized PDX models.

HNF4A-Bridging the Gap Between Intestinal Metaplasia and Gastric Cancer.

Intestinal metaplasia (IM) of gastric epithelium has traditionally been regarded as an irreversible stage in the process of the Correa cascade. Exploring the potential molecular mechanism of IM is significant for effective gastric cancer prevention. The GSE78523 dataset, obtained from the Gene Expression Omnibus (GEO) database, was analyzed using RStudio software to identify the differently expressed genes (DEGs) between IM tissues and normal gastric epithelial tissues. Subsequently, gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, Gene Set Enrichment Analysis (GESA), and protein-protein interaction (PPI) analysis were used to find potential genes. Additionally, the screened genes were analyzed for clinical, immunological, and genetic correlation aspects using single gene clinical correlation analysis (UALCAN), Tumor-Immune System Interactions Database (TISIDB), and validated through western blot experiments. Enrichment analysis showed that the lipid metabolic pathway was significantly associated with IM tissues and the apolipoprotein B (APOB) gene was identified in the subsequent analysis. Experiment results and correlation analysis showed that the expression of APOB was higher in IM tissues than in normal tissues. This elevated expression of APOB was also found to be associated with the expression levels of hepatocyte nuclear factor 4A (HNF4A) gene. HNF4A was also found to be associated with immune cell infiltration to gastric cancer and was linked to the prognosis of gastric cancer patients. Moreover, HNF4A was also highly expressed in both IM tissues and gastric cancer cells. Our findings indicate that HNF4A regulates the microenvironment of lipid metabolism in IM tissues by targeting APOB. Higher expression of HNF4A tends to lead to a worse prognosis in gastric cancer patients implying it may serve as a predictive indicator for the progression from IM to gastric cancer.