Expression and clinical implications of leucine-rich repeat containing 15 (LRRC15) in osteosarcoma

Evolving Tumor Characteristics and Smart Nanodrugs for Tumor Immunotherapy

Typical physiological characteristics of tumors, such as weak acidity, low oxygen content, and upregulation of certain enzymes in the tumor microenvironment (TME), provide survival advantages when exposed to targeted attacks by drugs and responsive nanomedicines. Consequently, cancer treatment has significantly progressed in recent years. However, the evolution and adaptation of tumor characteristics still pose many challenges for current treatment methods. Therefore, efficient and precise cancer treatments require an understanding of the heterogeneity degree of various factors in cancer cells during tumor evolution to exploit the typical TME characteristics and manage the mutation process. The highly heterogeneous tumor and infiltrating stromal cells, immune cells, and extracellular components collectively form a unique TME, which plays a crucial role in tumor malignancy, including proliferation, invasion, metastasis, and immune escape. Therefore, the development of new treatment methods that can adapt to the evolutionary characteristics of tumors has become an intense focus in current cancer treatment research. This paper explores the latest understanding of cancer evolution, focusing on how tumors use new antigens to shape their "new faces"; how immune system cells, such as cytotoxic T cells, regulatory T cells, macrophages, and natural killer cells, help tumors become "invisible", that is, immune escape; whether the diverse cancer-associated fibroblasts provide support and coordination for tumors; and whether it is possible to attack tumors in reverse. This paper discusses the limitations of targeted therapy driven by tumor evolution factors and explores future strategies and the potential of intelligent nanomedicines, including the systematic coordination of tumor evolution factors and adaptive methods, to meet this therapeutic challenge.

Unlocking Overexpressed Membrane Proteins to Guide Breast Cancer Precision Medicine

Breast cancer (BC) is a prevalent form of cancer affecting women worldwide. However, the effectiveness of current BC drugs is limited by issues such as systemic toxicity, drug resistance, and severe side effects. Consequently, there is an urgent need for new therapeutic targets and improved tumor tracking methods. This study aims to address these challenges by proposing a strategy for identifying membrane proteins in tumors that can be targeted for specific BC therapy and diagnosis. The strategy involves the analyses of gene expressions in breast tumor and non-tumor tissues and other healthy tissues by using comprehensive bioinformatics analysis from The Cancer Genome Atlas (TCGA), UALCAN, TNM Plot, and LinkedOmics. By employing this strategy, we identified four transcripts (LRRC15, EFNA3, TSPAN13, and CA12) that encoded membrane proteins with an increased expression in BC tissue compared to healthy tissue. These four transcripts also demonstrated high accuracy, specificity, and accuracy in identifying tumor samples, as confirmed by the ROC curve. Additionally, tissue microarray (TMA) analysis revealed increased expressions of the four proteins in tumor tissues across all molecular subtypes compared to the adjacent breast tissue. Moreover, the analysis of human interactome data demonstrated the important roles of these proteins in various cancer-related pathways. Taken together, these findings suggest that LRRC15, EFNA3, TSPAN13, and CA12 can serve as potential biomarkers for improving cancer diagnosis screening and as suitable targets for therapy with reduced side effects and enhanced efficacy.

RUNX1 Ameliorates Rheumatoid Arthritis Progression through Epigenetic Inhibition of LRRC15

Leucine-rich repeat containing 15 (LRRC15) has been identified as a contributing factor for cartilage damage in osteoarthritis; however, its involvement in rheumatoid arthritis (RA) and the underlying mechanisms have not been well characterized. The purpose of this study was to explore the function of LRRC15 in RA-associated fibroblast-like synoviocytes (RA-FLS) and in mice with collagen-induced arthritis (CIA) and to dissect the epigenetic mechanisms involved. LRRC15 was overexpressed in the synovial tissues of patients with RA, and LRRC15 overexpression was associated with increased proliferative, migratory, invasive, and angiogenic capacities of RA-FLS and accelerated release of pro-inflammatory cytokines. LRRC15 knockdown significantly inhibited synovial proliferation and reduced bone invasion and destruction in CIA mice. Runt-related transcription factor 1 (RUNX1) transcriptionally represses LRRC15 by binding to core-binding factor subunit beta (CBF-β). Overexpression of RUNX1 significantly inhibited the invasive phenotype of RA-FLS and suppressed the expression of proinflammatory cytokines. Conversely, the effects of RUNX1 were significantly reversed after overexpression of LRRC15 or inhibition of RUNX1-CBF-β interactions. Therefore, we demonstrated that RUNX1-mediated transcriptional repression of LRRC15 inhibited the development of RA, which may have therapeutic effects for RA patients.

Estimation of cell lineages in tumors from spatial transcriptomics data

Spatial transcriptomics (ST) technology through in situ capturing has enabled topographical gene expression profiling of tumor tissues. However, each capturing spot may contain diverse immune and malignant cells, with different cell densities across tissue regions. Cell type deconvolution in tumor ST data remains challenging for existing methods designed to decompose general ST or bulk tumor data. We develop the Spatial Cellular Estimator for Tumors (SpaCET) to infer cell identities from tumor ST data. SpaCET first estimates cancer cell abundance by integrating a gene pattern dictionary of copy number alterations and expression changes in common malignancies. A constrained regression model then calibrates local cell densities and determines immune and stromal cell lineage fractions. SpaCET provides higher accuracy than existing methods based on simulation and real ST data with matched double-blind histopathology annotations as ground truth. Further, coupling cell fractions with ligand-receptor coexpression analysis, SpaCET reveals how intercellular interactions at the tumor-immune interface promote cancer progression.

LRRC superfamily expression in stromal cells predicts the clinical prognosis and platinum resistance of ovarian cancer

BACKGROUND

Leucine-rich repeat sequence domains are known to mediate protein‒protein interactions. Recently, some studies showed that members of the leucine rich repeat containing (LRRC) protein superfamily may become new targets for the diagnosis and treatment of tumours. However, it is not known whether any of the LRRC superfamily genes is expressed in the stroma of ovarian cancer (OC) and is associated with prognosis.

METHODS

The clinical data and transcriptional profiles of OC patients from the public databases TCGA (n = 427), GTEx (n = 88) and GEO (GSE40266 and GSE40595) were analysed by R software. A nomogram model was also generated through R. An online public database was used for auxiliary analysis of prognosis, immune infiltration and protein‒protein interaction (PPI) networks. Immunohistochemistry and qPCR were performed to determine the protein and mRNA levels of genes in high-grade serous ovarian cancer (HGSC) tissues of participants and the MRC-5 cell line induced by TGF-β.

RESULTS

LRRC15 and LRRC32 were identified as differentially expressed genes from the LRRC superfamily by GEO transcriptome analysis. PPI network analysis suggested that they were most enriched in TGF-β signalling. The TCGA-GTEx analysis results showed that only LRRC15 was highly expressed in both cancer-associated fibroblasts (CAFs) and the tumour stroma of OC and was related to clinical prognosis. Based on this, we developed a nomogram model to predict the incidence of adverse outcomes in OC. Moreover, LRRC15 was positively correlated with CAF infiltration and negatively correlated with CD8 + T-cell infiltration. As a single indicator, LRRC15 had the highest accuracy (AUC = 0.920) in predicting the outcome of primary platinum resistance.

CONCLUSIONS

The LRRC superfamily is related to the TGF-β pathway in the microenvironment of OC. LRRC15, as a stromal biomarker, can predict the clinical prognosis of HGSC and promote the immunosuppressive microenvironment. LRRC15 may be a potential therapeutic target for reversing primary resistance in OC.

Current Status and Prospects of Targeted Therapy for Osteosarcoma

Osteosarcoma (OS) is a highly malignant tumor occurring in bone tissue with a high propensity to metastasize, and its underlying mechanisms remain largely elusive. The OS prognosis is poor, and improving the survival of OS patients remains a challenge. Current treatment methods such as surgical approaches, chemotherapeutic drugs, and immunotherapeutic drugs remain ineffective. As research progresses, targeted therapy is gradually becoming irreplaceable. In this review, several treatment modalities for osteosarcoma, such as surgery, chemotherapy, and immunotherapy, are briefly described, followed by a discussion of targeted therapy, the important targets, and new technologies for osteosarcoma treatment.

Targeting CAFs to overcome anticancer therapeutic resistance

The view of cancer as a tumor cell-centric disease is now replaced by our understanding of the interconnection and dependency of tumor stroma. Cancer-associated fibroblasts (CAFs), the most abundant stromal cells in the tumor microenvironment (TME), are involved in anticancer therapeutic resistance. As we unearth more solid evidence on the link between CAFs and tumor progression, we gain insight into the role of CAFs in establishing resistance to cancer therapies. Herein, we review the origin, heterogeneity, and function of CAFs, with a focus on how CAF subsets can be used as biomarkers and can contribute to therapeutic resistance in cancer. We also depict current breakthroughs in targeting CAFs to overcome anticancer therapeutic resistance and discuss emerging CAF-targeting modalities.

Exploiting LRRC15 as a novel therapeutic target in cancer

Abundant fibrotic stroma is a typical feature of most solid tumors, and stromal activation promotes oncogenesis, therapy resistance, and metastatic dissemination of cancer cells. Therefore, targeting the tumor stroma in combination with standard-of-care therapies has become a promising therapeutic strategy in recent years. The leucine-rich repeat-containing protein (LRRC15) is involved in cell-cell and cell-matrix interactions and came into focus as a promising anti-cancer target owing to its overexpression in mesenchymal-derived tumors such as sarcoma, glioblastoma, and melanoma and in cancer-associated fibroblasts in the microenvironment of breast, head and neck, lung, and pancreatic tumors. Effective targeting of LRRC15 using specific antibody-drug conjugates (ADC) has the potential to improve the outcome of patients with LRRC15-positive cancers of mesenchymal origin or stromal desmoplasia. Moreover, LRRC15 expression may serve as a predictive biomarker that could be utilized in the preclinical assessment of cancer patients to support personalized clinical outcomes. This review focuses on the role of LRRC15 in cancer, including clinical trials involving LRRC15-targeted therapies, such as the ABBV-085 ADC for patients with LRRC15-positive tumors. This review spans perceived knowledge gaps and highlights the clinical avenues that need to be explored to provide better therapeutic outcomes in patients.

Targeted Delivery of Chemotherapeutic Agents for Osteosarcoma Treatment

Since osteosarcoma (OS) is an aggressive bone cancer with unknown molecular pathways of etiology and pathophysiology, improving patient survival has long been a challenge. The conventional therapy is a complex multidisciplinary management that include radiotherapy, chemotherapy which followed by surgery and then post-operative adjuvant chemotherapy. However, they have severe side effects because the majority of the medicines used have just a minor selectivity for malignant tissue. As a result, treating tumor cells specifically without damaging healthy tissue is currently a primary goal in OS therapy. The coupling of chemotherapeutic drugs with targeting ligands is a unique therapy method for OS that, by active targeting, can overcome the aforementioned hurdles. This review focuses on advances in ligands and chemotherapeutic agents employed in targeted delivery to improve the capacity of active targeting and provide some insight into future therapeutic research for OS.

Antibody Conjugates for Sarcoma Therapy: How Far along Are We?

Sarcomas are one of the most difficult type of cancer to manage and treat because of their extremely heterogeneous molecular and morphological features. Despite the progress made over the years in the establishment of standard protocols for high and low grading/staging sarcoma patients, mostly with chemotherapy and/or radiotherapy, 50% of treated patients experience relapse episodes. Because of this, in the last 20 years, new therapeutic approaches for sarcoma treatment have been evaluated in preclinical and clinical studies. Among them, antibody-based therapies have been the most studied. Immunoconjugates consist of a carrier portion, frequently represented by an antibody, linked to a toxic moiety, i.e., a drug, toxin, or radionuclide. While the efficacy of immunoconjugates is well demonstrated in the therapy of hematological tumors and more recently also of epithelial ones, their potential as therapeutic agents against sarcomas is still not completely explored. In this paper, we summarize the results obtained with immunoconjugates targeting sarcoma surface antigens, considering both preclinical and clinical studies. To date, the encouraging results obtained in preclinical studies allowed nine immunoconjugates to enter clinical trials, demonstrating the validity of immunotherapy as a promising pharmacological tool also for sarcoma therapy.

Advancing therapy for osteosarcoma

Improving the survival of patients with osteosarcoma has long proved challenging, although the treatment of this disease is on the precipice of advancement. The increasing feasibility of molecular profiling together with the creation of both robust model systems and large, well-annotated tissue banks has led to an increased understanding of osteosarcoma biology. The historical invariability of survival outcomes and the limited number of agents known to be active in the treatment of this disease facilitate clinical trials designed to identify efficacious novel therapies using small cohorts of patients. In addition, trial designs will increasingly consider the genetic background of the tumour through biomarker-based patient selection, thereby enriching for clinical activity. Indeed, osteosarcoma cells are known to express a number of surface proteins that might be of therapeutic relevance, including B7-H3, GD2 and HER2, which can be targeted using antibody-drug conjugates and/or adoptive cell therapies. In addition, immune-checkpoint inhibition might augment the latter approach by helping to overcome the immunosuppressive tumour microenvironment. In this Review, we provide a brief overview of current osteosarcoma therapy before focusing on the biological insights from the molecular profiling and preclinical modelling studies that have opened new therapeutic opportunities in this disease.

The relationship between the expression of Ki-67 and the prognosis of osteosarcoma

Background

A number of studies have linked positive Ki-67 expression with the prognosis of osteosarcoma (OS) patients. However, the results have been conflicting. To address this controversy, we conducted an analysis using a meta-analysis and a TCGA dataset to estimate the value of Ki-67 expression in the prognosis of OS.

Methods

A comprehensive search for relevant papers was conducted using NCBI PubMed, Embase, Springer, ISI Web of Knowledge, the Cochrane Library, and CNKI regardless of the publication year. The associations between Ki-67 expression and the clinical features and main prognostic outcomes of OS were measured. The TCGA dataset was also analyzed. The pooled odds ratio (OR) and its 95% confidential intervals (CIs) were utilized for statistical analysis.

Results

Overall, a total of 12 studies with 500 cases were included, and the results indicated that the expression of Ki-67 was significantly associated with Enneking stage (OR = 6.88, 95% CI: 2.92–16.22, p < 0.05), distant metastasis (OR = 3.04, 95% CI: 1.51–6.12, p < 0.05) and overall survival (OR = 8.82, 95% CI: 4.68–16.65, p < 0.05) in OS patients. Additionally, we observed no significant heterogeneity among all retrieved studies. Associations between Ki-67 expression and overall survival and disease-free survival of sarcoma were confirmed using the TCGA and Kaplan-Meier plotter datasets.

Conclusion

The present study strongly suggests that positive Ki-67 expression was associated with Enneking stage, distant metastasis, and overall survival of OS, and it may be used as a potential biomarker to predict prognosis and guide clinical therapy for OS.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-07880-y.