Interplay between Solid Tumors and Tumor Microenvironment

What's new about the tumor microenvironment of urothelial carcinoma?

Urothelial carcinoma is a significant global health concern that accounts for a substantial part of cancer diagnoses and deaths worldwide. The tumor microenvironment is a complex ecosystem composed of stromal cells, soluble factors, and altered extracellular matrix, that mutually interact in a highly immunomodulated environment, with a prominent role in tumor development, progression, and treatment resistance. This article reviews the current state of knowledge of the different cell populations that compose the tumor microenvironment of urothelial carcinoma, its main functions, and distinct interactions with other cellular and non-cellular components, molecular alterations and aberrant signaling pathways already identified. It also focuses on the clinical implications of these findings, and its potential to translate into improved quality of life and overall survival. Determining new targets or defining prognostic signatures for urothelial carcinoma is an ongoing challenge that could be accelerated through a deeper understanding of the tumor microenvironment.

ALKBH5 promotes non-small cell lung cancer progression and susceptibility to anti-PD-L1 therapy by modulating interactions between tumor and macrophages

BACKGROUND

Understanding the mechanisms that mediate the interaction between tumor and immune cells may provide therapeutic benefit to patients with cancer. The N6-methyladenosine (m6A) demethylase, ALKBH5 (alkB homolog 5), is overexpressed in non-small cell lung cancer. However, its role in the tumor microenvironment is unknown.

METHODS

Datasets and tissue samples were used to determine the relationship between ALKBH5 expression and immunotherapy efficacy. Bioinformatic analysis, colorimetric assay to determine m6A RNA methylation, dual luciferase reporter assay, RNA/m6A-modified RNA immunoprecipitation, RNA stability assay, and RNA sequencing were used to investigate the regulatory mechanism of ALKBH5 in non-small cell lung cancer. In vitro and in vivo assays were performed to determine the contribution of ALKBH5 to the development of non-small cell lung cancer.

RESULTS

ALKBH5 was upregulated in primary non-small cell lung cancer tissues. ALKBH5 was positively correlated with programmed death-ligand 1 expression and macrophage infiltration and was associated with immunotherapy response. JAK2 was identified as a target of ALKBH5-mediated m6A modification, which activates the JAK2/p-STAT3 pathway to promote non-small cell lung cancer progression. ALKBH5 was found to recruit programmed death-ligand 1-positive tumor-associated macrophages and promote M2 macrophage polarization by inducing the secretion of CCL2 and CXCL10. ALKBH5 and tumor-associated macrophage-secreted IL-6 showed a synergistic effect to activate the JAK2/p-STAT3 pathway in cancer cells.

CONCLUSIONS

ALKBH5 promotes non-small cell lung cancer progression by regulating cancer and tumor-associated macrophage behavior through the JAK2/p-STAT3 pathway and the expression of CCL2 and CXCL10, respectively. These findings suggest that targeting ALKBH5 is a promising strategy of enhancing the anti-tumor immune response in patients with NSCLC and that identifying ALKBH5 status could facilitate prediction of clinical response to anti-PD-L1 immunotherapy.

Efficient 3D imaging and pathological analysis of the human lymphoma tumor microenvironment using light-sheet immunofluorescence microscopy

Rationale: The composition and spatial structure of the lymphoma tumor microenvironment (TME) provide key pathological insights for tumor survival and growth, invasion and metastasis, and resistance to immunotherapy. However, the 3D lymphoma TME has not been well studied owing to the limitations of current imaging techniques. In this work, we take full advantage of a series of new techniques to enable the first 3D TME study in intact lymphoma tissue. Methods: Diverse cell subtypes in lymphoma tissues were tagged using a multiplex immunofluorescence labeling technique. To optically clarify the entire tissue, immunolabeling-enabled three-dimensional imaging of solvent-cleared organs (iDISCO+), clear, unobstructed brain imaging cocktails and computational analysis (CUBIC) and stabilization to harsh conditions via intramolecular epoxide linkages to prevent degradation (SHIELD) were comprehensively compared with the ultimate dimensional imaging of solvent-cleared organs (uDISCO) approach selected for clearing lymphoma tissues. A Bessel-beam light-sheet fluorescence microscope (B-LSFM) was developed to three-dimensionally image the clarified tissues at high speed and high resolution. A customized MATLAB program was used to quantify the number and colocalization of the cell subtypes based on the acquired multichannel 3D images. By combining these cutting-edge methods, we successfully carried out high-efficiency 3D visualization and high-content cellular analyses of the lymphoma TME. Results: Several antibodies, including CD3, CD8, CD20, CD68, CD163, CD14, CD15, FOXP3 and Ki67, were screened for labeling the TME in lymphoma tumors. The 3D imaging results of the TME from three types of lymphoma, reactive lymphocytic hyperplasia (RLN), diffuse large B-cell lymphoma (DLBCL), and angioimmunoblastic T-cell lymphoma (AITL), were quantitatively analyzed, and their cell number, localization, and spatial correlation were comprehensively revealed. Conclusion: We present an advanced imaging-based method for efficient 3D visualization and high-content cellular analysis of the lymphoma TME, rendering it a valuable tool for tumor pathological diagnosis and other clinical research.

Pan-cancer analysis and experimental validation identify ndc1 as a potential immunological, prognostic and therapeutic biomarker in pancreatic cancer

NDC1 is a transmembrane nucleoporin that participates in cell mitosis. In the field of oncology, NDC1 has shown its potential as a prognostic marker for multiple tumors. However, pan-cancer analysis of NDC1 to fully explore its role in tumors has not been performed and little is reported on its role in pancreatic cancers. In the present study, a pan-cancer analysis of NDC1 was performed using a bioinformatic approach. Survival analysis was performed by univariate Cox regression analysis and Kaplan-Meier survival analysis. Subsequently, the relationship between NDC1 and immune cell infiltration, TMB/MSI and drug sensitivity was analyzed. Moreover, the mechanism of NDC1 in pancreatic cancer were further analyzed by GSEA, GSVA. Finally, we conducted in vitro experiments including MTT, scratch, EdU, and apoptosis assays to explore the function of NDC1 in pancreatic cancer cells. High expression of NDC1 was demonstrated in 28 cancer types. Univariate Cox regression analysis revealed that NDC1 expression was closely associated with the survival outcome of 15 cancer types, and further Kaplan-Meier survival analysis showed negative associations with the progression-free survival in 14 cancers. In addition, a significant association between the NDC1 expression and immune cell infiltration in tumor microenvironment, immune-related genes, common tumor-regulatory and drug sensitivity was observed. Furthermore, NDC1 is abnormally expressed in pancreatic cancer, and is closely related to the prognosis of pancreatic cancer patients and chemosensitivity. The study reveals that NDC1 could be used as a potential immunological, prognostic and therapeutic target for pancreatic cancer.

Immunohistochemical Evidence Linking Interleukin-22 Tissue Expression Levels to FOXP3+ Cells and Neutrophil Densities in the Mycosis Fungoides Microenvironment

BACKGROUND

Emerging data indicate that the cellular microenvironment and interleukins (IL) play a crucial role in mycosis fungoides (MF). We aimed to explore the potential association between the composition of the cellular microenvironment and the expression of IL-22 and IL-17A in MF skin lesions.

METHODS

The study encompassed 16 cases of MF of different stages, for which sufficient skin tissue for immunohistochemistry and frozen tissue for reverse transcription-polymerase chain reaction, both taken from the same lesion, were available. Histological evaluation of eosinophils, neutrophils, CD20+, CD4+, CD8+, FOXP3+, CD56+, and CD1a+ cells was conducted. Additionally, mRNA expression levels of IL-22 and IL-17 mRNA were quantified using reverse transcription-quantitative polymerase chain reaction. SPSS version 28 (IBM Corp., Armonk, NY) was utilized for statistical analysis.

RESULTS

Among the cases examined, three were in the patch stage, eight in the plaque stage, and five in the transformation to high-grade large cell lymphoma (t-LCL). B-lymphocytes, neutrophils, and eosinophils were primarily observed in t-LCL cases. IL-22 levels displayed a significant association with IL-17A levels (Pearson's r = 0.961, p < 0.001), FOXP3+ cells (Pearson's r = 0.851, p < 0.001), and neutrophil density (Pearson's r = 0.586, p = 0.014). No correlation was detected between IL-17A levels and the evaluated subtypes of microenvironmental cells.

CONCLUSION

The microenvironment of MF lesions with t-LCL is noticeably different from early MF in terms of cellular composition. Histopathological identification of the cellular microenvironment may serve as an indicator of IL-22 tissue levels. These results implicate certain types of cells in IL-22 expression in the MF microenvironment and may contribute to advancing our knowledge on the pathogenesis and progression of the disease.

Three Strategies in Engineering Nanomedicines for Tumor Microenvironment-Enabled Phototherapy

Canonical phototherapeutics have several limitations, including a lack of tumor selectivity, nondiscriminatory phototoxicity, and tumor hypoxia aggravation. The tumor microenvironment (TME) is characterized by hypoxia, acidic pH, and high levels of H2 O2 , GSH, and proteases. To overcome the shortcomings of canonical phototherapy and achieve optimal theranostic effects with minimal side effects, unique TME characteristics are employed in the development of phototherapeutic nanomedicines. In this review, the effectiveness of three strategies for developing advanced phototherapeutics based on various TME characteristics is examined. The first strategy involves targeted delivery of phototherapeutics to tumors with the assistance of TME-induced nanoparticle disassembly or surface modification. The second strategy involves near-infrared absorption increase-induced phototherapy activation triggered by TME factors. The third strategy involves enhancing therapeutic efficacy by ameliorating TME. The functionalities, working principles, and significance of the three strategies for various applications are highlighted. Finally, possible challenges and future perspectives for further development are discussed.

Identifies KCTD5 as a novel cancer biomarker associated with programmed cell death and chemotherapy drug sensitivity

BACKGROUND

More and more studies have demonstrated that potassium channel tetramerization domain-containing 5 (KCTD5) plays an important role in the development of cancer, but there is a lack of comprehensive research on the biological function of this protein in pan-cancer. This study systematically analyzed the expression landscape of KCTD5 in terms of its correlations with tumor prognosis, the immune microenvironment, programmed cell death, and drug sensitivity.

METHODS

We investigated a number of databases, including TCGA, GEPIA2, HPA, TISIDB, PrognoScan, GSCA, CellMiner, and TIMER2.0. The study evaluated the expression of KCTD5 in human tumors, as well as its prognostic value and its association with genomic alterations, the immune microenvironment, tumor-associated fibroblasts, functional enrichment analysis, and anticancer drug sensitivity. Real-time quantitative PCR and flow cytometry analysis were performed to determine the biological functions of KCTD5 in lung adenocarcinoma cells.

RESULTS

The results indicated that KCTD5 is highly expressed in most cancers and that its expression is significantly correlated with tumor prognosis. Moreover, KCTD5 expression was related to the immune microenvironment, infiltration by cancer-associated fibroblasts, and the expression of immune-related genes. Functional enrichment analysis revealed that KCTD5 is associated with apoptosis, necroptosis, and other types of programmed cell death. In vitro experiments showed that knockdown of KCTD5 promoted apoptosis of A549 cells. Correlation analysis confirmed that KCTD5 was positively correlated with the expression of the anti-apoptotic genes Bcl-xL and Mcl-1. Additionally, KCTD5 was significantly associated with sensitivity to multiple antitumor drugs.

CONCLUSION

Our results suggest that KCTD5 is a potential molecular biomarker that can be used to predict patient prognosis, immunoreactions and drug sensitivity in pan-cancer. KCTD5 plays an important role in regulating programmed cell death, especially apoptosis.

Construction of a pancreatic cancer prediction model for oxidative stress-related lncRNA

Long non-coding RNAs (lncRNAs) may play a role in oxidative stress by altering the tumor microenvironment, thereby affecting pancreatic cancer progression. There is currently limited information on oxidative stress-related lncRNAs as novel prognostic markers of pancreatic cancer. Gene expression and clinical data of patients with pancreatic cancer were downloaded from The Cancer Genome Atlas (TCGA-PAAD) and the International Cancer Genome Consortium (ICGC-PACA) database. A weighted gene co-expression network analysis (WGCNA) was constructed to identify genes that were differentially expressed between normal and tumor samples. Based on the TCGA-PAAD cohort, a prediction model was established using lasso regression and Cox regression. The TCGA-PAAD and ICGC-PACA cohorts were used for internal and external validation, respectively. Furthermore, a nomogram based on clinical characteristics was used to predict mortality of patients. Differences in mutational status and tumor-infiltrating immune cells between risk subgroups were also explored and model-based lncRNAs were analyzed for potential immune-related therapeutic drugs. A prediction model for 6-lncRNA was established using lasso regression and Cox regression. Kaplan-Meier survival curves and receiver operating characteristic (ROC) curves indicated that patients with lower risk scores had a better prognosis. Combined with Cox regression analysis of clinical features, risk score was an independent factor predicting overall survival of patients with pancreatic cancer in both the TCGA-PAAD and ICGC-PACA cohorts. Mutation status and immune-related analysis indicated that the high-risk group had a significantly higher gene mutation rate and a higher possibility of immune escape, respectively. Furthermore, the model genes showed a strong correlation with immune-related therapeutic drugs. A pancreatic cancer prediction model based on oxidative stress-related lncRNA was established, which may be used as a biomarker related to the prognosis of pancreatic cancer to evaluate the prognosis of pancreatic cancer patients.

The Insulin Receptor Substrate 2 Mediates the Action of Insulin on HeLa Cell Migration via the PI3K/Akt Signaling Pathway

Insulin signaling plays an important role in the development and progression of cancer since it is involved in proliferation and migration processes. It has been shown that the A isoform of the insulin receptor (IR-A) is often overexpressed, and its stimulation induces changes in the expression of the insulin receptor substrates (IRS-1 and IRS-2), which are expressed differently in the different types of cancer. We study the participation of the insulin substrates IRS-1 and IRS-2 in the insulin signaling pathway in response to insulin and their involvement in the proliferation and migration of the cervical cancer cell line. Our results showed that under basal conditions, the IR-A isoform was predominantly expressed. Stimulation of HeLa cells with 50 nM insulin led to the phosphorylation of IR-A, showing a statistically significant increase at 30 min (p ≤ 0.05). Stimulation of HeLa cells with insulin induces PI3K and AKT phosphorylation through the activation of IRS2, but not IRS1. While PI3K reached the highest level at 30 min after treatment (p ≤ 0.05), AKT had the highest levels from 15 min (p ≤ 0.05) and remained constant for 6 h. ERK1 and ERK2 expression was also observed, but only ERK2 was phosphorylated in a time-dependent manner, reaching a maximum peak 5 min after insulin stimulation. Although no effect on cell proliferation was observed, insulin stimulation of HeLa cells markedly promoted cell migration.

Immunotherapy approaches for rare pediatric solid tumors: advances and future directions

PURPOSE OF REVIEW

Immunotherapy for pediatric tumors is rapidly evolving. From major successes in pediatric hematologic malignancies, immunotherapy utility increased in the pediatric solid tumor landscape. Numerous pediatric solid tumors are defined as rare with limitations in diagnosis and treatment. This review will describe four major immunotherapies used in pediatrics and discuss results seen in rare pediatric tumors. We will also briefly review the challenges of immunotherapy in solid tumors and opportunities to drive this therapy forward.

RECENT FINDINGS

Despite rare success employing immunotherapy for pediatric solid tumors, recently there have been several successes in pediatric rare solid tumors. After describing the evolving landscape of rare pediatric tumors, we will demonstrate the successes or disappointments of immunotherapy. We will describe the mechanism of four immunotherapies used in the pediatrics, followed by the published results. Finally, we will discuss the challenges and opportunities for immunotherapies in pediatric rare tumors.

SUMMARY

Pediatric rare tumors are lacking in treatment options. Despite numerous disappointments utilizing immunotherapies in the more common pediatric solid tumors, there have been several successes within the pediatric rare tumor landscape. Much work is still needed to enhance our understanding and knowledge on utilizing these immunotherapies for pediatric rare solid tumors.

The Exposure to Human Breast Cancer Cells Altered 14 Post-Translational Modifications of Human Serum Albumin

Purpose

Serum albumin is in contact with practically all cells in the human body, including tumor cells in cancer patients. The purpose of this study was to explore whether cancer cells affect post-translational modifications (PTMs) of albumin.

Material and methods

Mass spectrometry was used to identify the PTMs. Purified human serum albumin was incubated with human breast cancer cells MDA-MB-231, MDA-MB-468, MCF7, or kept in water or in cell culture media. PTMs which were affected upon exposure of the albumin to cancer cells were identified. Three-dimensional analysis was performed to locate PTMs in albumin.

Results

We report here that an exposure to human breast cancer cells affected post-translational modifications (PTMs) of 14 peptides of human serum albumin (HSA). PTMs at 8 peptides were observed upon exposure of HSA to metastatic MDA-MB-231 and MDA-MB-468 breast cancer cells. PTMs at another 6 peptides were lost in MDA-MB-231 and MDA-MB-468 cells, while these 6 PTMs were observed in HSA exposed to conditionally tumorigenic MCF7 cells, or in HSA kept in water or a cell culture medium. Cancer cell altered phosphorylation, deamidation followed by methylation, acetylation, myristylation, palmitoylation, methylation, cysteine persulfide, and S-6-FMN cysteine modifications were detected in HSA. These PTMs locate predominantly in IB and IIA domains of HSA. Three-dimensional analysis showed that this region corresponds to the lipid-binding site and Sudlow's site 1.

Conclusion

Data reported here show that 14 PTMs of human serum albumin can be modified upon its exposure to human breast cancer cells.

N6-methylandenosine-related immune genes correlate with prognosis and immune landscapes in gastric cancer

OBJECTIVES

This study aimed to probe into the significance of N6-methyladenosine (m6A)-related immune genes (m6AIGs) in predicting prognoses and immune landscapes of patients with gastric cancer (GC).

METHODS

The clinical data and transcriptomic matrix of GC patients were acquired from The Cancer Genome Atlas database. The clinically meaningful m6AIGs were acquired by univariate Cox regression analysis. GC patients were stratified into different clusters via consensus clustering analysis and different risk subgroups via m6AIGs prognostic signature. The clinicopathological features and tumor microenvironment (TME) in the different clusters and different risk subgroups were explored. The predictive performance was evaluated using the KM method, ROC curves, and univariate and multivariate regression analyses. Moreover, we fabricated a nomogram based on risk scores and clinical risk characteristics. Biological functional analysis was performed based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways. The connectivity map was used to screen out potential small molecule drugs for GC patients.

RESULTS

A total of 14 prognostic m6AIGs and two clusters based on 14 prognostic m6AIGs were identified. A prognostic signature based on 4 m6AIGs and a nomogram based on independent prognostic factors was constructed and validated. Different clusters and different risk subgroups were significantly correlated with TME scores, the distribution of immune cells, and the expression of immune checkpoint genes. Some malignant and immune biological processes and pathways were correlated with the patients with poor prognosis. Ten small molecular drugs with potential therapeutic effect were screened out.

CONCLUSIONS

This study revealed the prognostic role and significant values of m6AIGs in GC, which enhanced the understanding of m6AIGs and paved the way for developing predictive biomarkers and therapeutic targets for GC.