Lymphocyte homing and recirculation with tumor tertiary lymphoid structure formation: predictions for successful cancer immunotherapy.

The capacity of lymphocytes continuously home to lymphoid structures is remarkable for cancer immunosurveillance and immunotherapy. Lymphocyte homing and recirculation within the tumor microenvironment (TME) are now understood to be adaptive processes that are regulated by specialized cytokines and adhesion molecule signaling cascades. Restricted lymphocyte infiltration and recirculation have emerged as key mechanisms contributing to poor responses in cancer immunotherapies like chimeric antigen receptor (CAR)-T cell therapy and immune checkpoint blockades (ICBs). Uncovering the kinetics of lymphocytes in tumor infiltration and circulation is crucial for improving immunotherapies. In this review, we discuss the current insights into the adhesive and migrative molecules involved in lymphocyte homing and transmigration. The potential mechanisms within the TME that restrain lymphocyte infiltration are also summarized. Advanced on these, we outline the determinates for tertiary lymphoid structures (TLSs) formation within tumors, placing high expectations on the prognostic values of TLSs as therapeutic targets in malignancies.

A Pathologically Friendly Strategy for Determining the Organ-specific Spatial Tumor Microenvironment Topology in Lung Adenocarcinoma Through the Integration of snRandom-seq and Imaging Mass Cytometry.

Heterogeneous organ-specific responses to immunotherapy exist in lung cancer. Dissecting tumor microenvironment (TME) can provide new insights into the mechanisms of divergent responses, the process of which remains poor, partly due to the challenges associated with single-cell profiling using formalin-fixed paraffin-embedded (FFPE) materials. In this study, single-cell nuclei RNA sequencing and imaging mass cytometry (IMC) are used to dissect organ-specific cellular and spatial TME based on FFPE samples from paired primary lung adenocarcinoma (LUAD) and metastases. Single-cell analyses of 84 294 cells from sequencing and 250 600 cells from IMC reveal divergent organ-specific immune niches. For sites of LUAD responding well to immunotherapy, including primary LUAD and adrenal gland metastases, a significant enrichment of B, plasma, and T cells is detected. Spatially resolved maps reveal cellular neighborhoods recapitulating functional units of the tumor ecosystem and the spatial proximity of B and CD4+ T cells at immunogenic sites. Various organ-specific densities of tertiary lymphoid structures are observed. Immunosuppressive sites, including brain and liver metastases, are deposited with collagen I, and T cells at these sites highly express TIM-3. This study originally deciphers the single-cell landscape of the organ-specific TME at both cellular and spatial levels for LUAD, indicating the necessity for organ-specific treatment approaches.

Pan-Cancer Single-Cell and Spatial-Resolved Profiling Reveals the Immunosuppressive Role of APOE+ Macrophages in Immune Checkpoint Inhibitor Therapy.

The heterogeneity of macrophages influences the response to immune checkpoint inhibitor (ICI) therapy. However, few studies explore the impact of APOE+ macrophages on ICI therapy using single-cell RNA sequencing (scRNA-seq) and machine learning methods. The scRNA-seq and bulk RNA-seq data are Integrated to construct an M.Sig model for predicting ICI response based on the distinct molecular signatures of macrophage and machine learning algorithms. Comprehensive single-cell analysis as well as in vivo and in vitro experiments are applied to explore the potential mechanisms of the APOE+ macrophage in affecting ICI response. The M.Sig model shows clear advantages in predicting the efficacy and prognosis of ICI therapy in pan-cancer patients. The proportion of APOE+ macrophages is higher in ICI non-responders of triple-negative breast cancer compared with responders, and the interaction and longer distance between APOE+ macrophages and CD8+ exhausted T (Tex) cells affecting ICI response is confirmed by multiplex immunohistochemistry. In a 4T1 tumor-bearing mice model, the APOE inhibitor combined with ICI treatment shows the best efficacy. The M.Sig model using real-world immunotherapy data accurately predicts the ICI response of pan-cancer, which may be associated with the interaction between APOE+ macrophages and CD8+ Tex cells.

A multiomics analysis-assisted deep learning model identifies a macrophage-oriented module as a potential therapeutic target in colorectal cancer.

Colorectal cancer (CRC) is a common malignancy involving multiple cellular components. The CRC tumor microenvironment (TME) has been characterized well at single-cell resolution. However, a spatial interaction map of the CRC TME is still elusive. Here, we integrate multiomics analyses and establish a spatial interaction map to improve the prognosis, prediction, and therapeutic development for CRC. We construct a CRC immune module (CCIM) that comprises FOLR2+ macrophages, exhausted CD8+ T cells, tolerant CD8+ T cells, exhausted CD4+ T cells, and regulatory T cells. Multiplex immunohistochemistry is performed to depict the CCIM. Based on this, we utilize advanced deep learning technology to establish a spatial interaction map and predict chemotherapy response. CCIM-Net is constructed, which demonstrates good predictive performance for chemotherapy response in both the training and testing cohorts. Lastly, targeting FOLR2+ macrophage therapeutics is used to disrupt the immunosuppressive CCIM and enhance the chemotherapy response in vivo.

A novel molecular subtyping based on multi-omics analysis for prognosis predicting in colorectal melanoma: A 16-year prospective multicentric study.

Colorectal melanoma (CRM) is a rare malignant tumor with severe complications, and there is currently a lack of systematic research. We conducted a study that combined proteomics and mutation data of CRM from a cohort of three centers over a 16-years period (2005-2021). The patients were divided into a training set consisting of two centers and a testing set comprising the other center. Unsupervised clustering was conducted on the training set to form two molecular subtypes for clinical characterization and functional analysis. The testing set was used to validate the survival differences between the two subtypes. The comprehensive analysis identified two subtypes of CRM: immune exhausted C1 cluster and DNA repair C2 cluster. The former subtype exhibited characteristics of metabolic disturbance, immune suppression, and poor prognosis, along with APC mutations. A machine learning algorithm named Support Vector Machine (SVM) was applied to predict the classification of CRM patients based on protein expression in the external testing cohort. Two subtypes of primary CRM with clinical and proteomic characteristics provides a reference for subsequent diagnosis and treatments.

Hierarchical transcriptional network governing heterogeneous T cell exhaustion and its implications for immune checkpoint blockade.

The fundamental principle of immune checkpoint blockade (ICB) is to protect tumor-infiltrating T cells from being exhausted. Despite the remarkable success achieved by ICB treatment, only a small group of patients benefit from it. Characterized by a hypofunctional state with the expression of multiple inhibitory receptors, exhausted T (Tex) cells are a major obstacle in improving ICB. T cell exhaustion is a progressive process which adapts to persistent antigen stimulation in chronic infections and cancers. In this review, we elucidate the heterogeneity of Tex cells and offer new insights into the hierarchical transcriptional regulation of T cell exhaustion. Factors and signaling pathways that induce and promote exhaustion are also summarized. Moreover, we review the epigenetic and metabolic alterations of Tex cells and discuss how PD-1 signaling affects the balance between T cell activation and exhaustion, aiming to provide more therapeutic targets for applications of combinational immunotherapies.

An immunometabolism subtyping system identifies S100A9+ macrophage as an immune therapeutic target in colorectal cancer based on multiomics analysis.

Immunometabolism in the tumor microenvironment (TME) and its influence on the immunotherapy response remain uncertain in colorectal cancer (CRC). We perform immunometabolism subtyping (IMS) on CRC patients in the training and validation cohorts. Three IMS subtypes of CRC, namely, C1, C2, and C3, are identified with distinct immune phenotypes and metabolic properties. The C3 subtype exhibits the poorest prognosis in both the training cohort and the in-house validation cohort. The single-cell transcriptome reveals that a S100A9+ macrophage population contributes to the immunosuppressive TME in C3. The dysfunctional immunotherapy response in the C3 subtype can be reversed by combination treatment with PD-1 blockade and an S100A9 inhibitor tasquinimod. Taken together, we develop an IMS system and identify an immune tolerant C3 subtype that exhibits the poorest prognosis. A multiomics-guided combination strategy by PD-1 blockade and tasquinimod improves responses to immunotherapy by depleting S100A9+ macrophages in vivo.

CHD4 orchestrates the symphony of T and B lymphocytes development and a good mediator in preventing from autoimmune disease.

Chromodomain helicase DNA binding protein 4 (CHD4) is an ATPase subunit of the nucleosome remodeling and deacetylation complex. It has been implicated in gene transcription, DNA damage repair, maintenance of genome stability, and chromatin assembly. Meanwhile, it is highly related to cell cycle progression and the proceeding of malignancy. Most of the previous studies were focused on the function of CHD4 with tumor cells, cancer stem cells, and cancer cells multidrug resistance. Recently, some researchers have explored the CHD4 functions on the development and differentiation of adaptive immune cells, such as T and B lymphocytes. In this review, we will discuss details of CHD4 in lymphocyte differentiation and development, as well as the critical role of CHD4 in the pathogenesis of the autoimmune disease.

Diagnosis of Heart Failure Complicated with Sleep Apnea Syndrome by Thoracic Computerized Tomography under Artificial Intelligence Algorithm.

The aim of this study was to explore the application effect of thoracic computerized tomography (CT) under single threshold segmentation algorithm in the diagnosis of heart failure (HF) complicated with sleep apnea syndrome. 30 patients diagnosed with HF complicated with sleep apnea syndrome were chosen for the research. Another 30 patients without sleep apnea syndrome were selected as the control group, whose age, height, and weight were similar to those of the experimental group. Then, a model for thoracic CT image segmentation was proposed under the single threshold segmentation algorithm, and the faster region convolutional neural network (Faster RCNN) was applied to label the thoracic respiratory lesions. All the patients underwent thoracic CT examination, and the obtained images were processed using the algorithm model above. After that, the morphology of the patient's respiratory tract after treatment was observed. The results suggested that the improved single threshold segmentation algorithm was effective for the image segmentation of patient lesions, and the Faster RCNN could effectively finish the labeling of the lesion area in the CT image. The classification accuracy of the Faster RCNN was about 0.966, and the loss value was about 0.092. With CT scanning under the algorithm, it was found that the airway collapse of the posterior palatal area, retrolingual area, and laryngopharyngeal area of the sleep apnea syndrome patients was significantly greater than that of the control group (P < 0.05). But there was no significant difference of the collapse of the nasopharyngeal area between the two groups (P > 0.05). The single threshold segmentation algorithm had a better segmentation accuracy for thoracic CT images in patients with HF and sleep apnea syndrome, so it had a highly promising application prospect in the diagnosis of the disease.

Tolerance to dichloroacetonitrile-induced neurotoxicity in streptozotocin-induced diabetic rats.

Diabetes mellitus has potential to alter the toxicity of hazardous chemicals. Dichloroacetonitrile (DCAN) is one of high-risk nitrogenous disinfection by-products. This study evaluated the neurotoxicity of DCAN (11, 44 and 88mg/kg) in normoglycaemic and streptozotocin (STZ)-induced diabetic rats via orally for 28days. STZ diabetes prolonged the median survival time and total lethal time after DCAN (88mg/kg) exposure when compared with that observed in normoglycaemic rats. DCAN altered motor activity and induced anxiety behaviour in normoglycaemic rats; but it did not exaggerate behavioural changes in STZ diabetic rats. DCAN -induced brain oxidative damage by compensatory increase glutathione content and decrease malonaldehyde levels; but it did not induce oxidative damage in diabetic rats. STZ diabetes slowed down the pathological pace of DCAN-induced brain mitochondrial dysfunction by decreasing reactive oxygen species and increasing cytochrome C oxidase activity. In conclusion, the present study indicated that STZ diabetic rats are resistant to DCAN-induced neurotoxicity at the dosage and with the dosage schedule in 28-day subacute toxicity test.

Comparison of the effect of p65 siRNA and curcumin in promoting apoptosis in esophageal squamous cell carcinoma cells and in nude mice.

The activation of the NF-κB signaling pathway plays a critical role in carcinogenesis. The role of the NF-κB pathway in esophageal squamous cell carcinoma (ESCC) remains ill-defined. The objective was to detect whether p65siRNA and curcumin could promote ESCC cell apoptosis and increase the sensitivity of ESCC cells to chemotherapeutic drugs by inhibiting the NF-κB signaling pathway, and to compared these two treatments. In the present study, the status of the NF-κB pathway, in the two ESCC cell lines Eca109 and EC9706, was analyzed and the ability of p65 siRNA and curcumin alone or in combination with 5-FU to modulate this pathway in vitro and in vivo was investigated. The results showed that the NF-κB signaling pathway in the ESCC cell lines was constitutively activated. Both p65 siRNA and curcumin mediated suppression of activation of the NF-κB signaling pathway via inhibition of the expression of p65 or IκBα phosphorylation in ESCC cell lines. The cells treated with combination of p65 siRNA or curcumin and 5-FU revealed a lower cell viability and higher apoptosis compared to those treated with 5-FU alone. In a human ESCC xenograft model, p65 siRNA or curcumin and 5-FU alone reduced the tumor volume, respectively, but their combination had the strongest anticancer effects. Curcumin was more effective than p65 siRNA in vitro and in vivo. Overall, our results indicate that the constitutively activated NF-κB signaling pathway plays a crucial role in these two ESCC cell lines and both p65siRNA and curcumin can promote ESCC cell apoptosis and enhance the sensitivity to 5-FU through suppression of the NF-κB signaling pathway. It is still a long time before RNA interference will be used in the clinic. Therefore, curcumin is proved to be useful in the treatment of ESCC as it is a pharmacologically safe compound without side effects.