Immune classification of osteosarcoma

A detoxified TLR4 agonist inhibits tumour growth and lung metastasis of osteosarcoma by promoting CD8+ cytotoxic lymphocyte infiltration

BACKGROUND

Osteosarcoma is the most common malignant bone tumour with limited treatment options and poor outcomes in advanced metastatic cases. Current immunotherapies show limited efficacy, highlighting the need for novel therapeutic approaches. Systemic immune activation by Toll-like receptor 4 (TLR4) immunostimulants has shown great promise; however, current TLR4 agonists' toxicity hinders this systemic approach in patients with osteosarcoma.

METHODS

We compared the antitumour effect of lipopolysaccharides (LPS) with that of an innovative chemically detoxified TLR4 agonist (Lipo-MP-LPS) in a syngeneic metastatic osteosarcoma mouse model. Lipo-MP-LPS exhibited an optimal safety and solubility profile for systemic administration at an effective dose. We evaluated tumour growth, lung metastases, and immune cell infiltration in wild-type and TLR4-mutant mice and performed selective immunodepletion.

RESULTS

Lipo-MP-LPS exhibited antitumour effects against localised osteosarcoma tumours and lung metastases, like those of natural LPS. Lipo-MP-LPS promoted CD8+ T cells and M1 macrophages infiltration in primary tumours and CD8+ T cells in metastases, with an M1-phenotype macrophage shift. The Lipo-MP-LPS antitumour effects were found to depend on TLR4 and CD8+ T cells, but not on macrophages.

CONCLUSION

Lipo-MP-LPS inhibited tumour growth and lung metastasis of osteosarcoma by promoting CD8 + T cell infiltration, indicating its therapeutic potential for advanced osteosarcoma.

Exploration of metastasis-related signatures in osteosarcoma based on tumor microenvironment by integrated bioinformatic analysis

Background

The present study aims to explore the metastasis-related signatures in connection with tumor microenvironment (TME), revealing new molecular targets promising in improving osteosarcoma (OS) patients' outcomes.

Methods

The high-throughput sequencing data was downloaded from the TARGET database and performed the ESTIMATE algorithm. Metastasis-related information was obtained from the GSE21257 dataset. Differentially expressed genes (DEGs) associated with the stromal and immune cell infiltration patterns were identified. DEGs with similar biological functions were grouped into the same module by Gene Ontology (GO) analysis and MCODE analysis. Prognostic DEGs were selected in two datasets through survival analysis. Weighted gene co-expression network analysis (WGCNA) was performed to find metastasis-related modules and genes. RT-PCR was utilized to evaluate the expression of the key prognostic DEGs associated with metastasis in OS patients.

Results

The median scores of the stromal and immune groups of OS samples were 58 and -416, and a total of 200 overlapping DEGs were identified. These DEGs basically played fundamental roles in immune response relevant GO terms and were clustered into 9 different modules. Among them, 24 metastasis-related DEGs were selected from the GSE21257 dataset which contains the stromal and immune cell infiltration patterns. Finally, IRF8, HLA-DMA, and HLA-DMB were proved to exhibit significant higher expression levels in cancerous tissues than in para-cancerous tissues for OS patients.

Conclusion

We identified three principal genes as promising signatures for predicting the survival the prognosis of OS patients. Exploration of metastasis-related signatures in TME may be valuable for enhancing treatment strategies for OS.

Advances on immunotherapy for osteosarcoma

Osteosarcoma is the most common primary bone cancer in children and young adults. Limited progress has been made in improving the survival outcomes in patients with osteosarcoma over the past four decades. Especially in metastatic or recurrent osteosarcoma, the survival rate is extremely unsatisfactory. The treatment of osteosarcoma urgently needs breakthroughs. In recent years, immunotherapy has achieved good therapeutic effects in various solid tumors. Due to the low immunogenicity and immunosuppressive microenvironment of osteosarcoma, immunotherapy has not yet been approved in osteosarcoma patients. However, immune-based therapies, including immune checkpoint inhibitors, chimeric antigen receptor T cells, and bispecfic antibodies are in active clinical development. In addition, other immunotherapy strategies including modified-NK cells/macrophages, DC vaccines, and cytokines are still in the early stages of research, but they will be hot topics for future study. In this review, we showed the functions of cell components including tumor-promoting and tumor-suppressing cells in the tumor microenvironment of osteosarcoma, and summarized the preclinical and clinical research results of various immunotherapy strategies in osteosarcoma, hoping to provide new ideas for future research in this field.

C-C Motif Chemokine Ligand 5 (CCL5): A Potential Biomarker and Immunotherapy Target for Osteosarcoma

BACKGROUND

Osteosarcoma (OS) is the most common primary malignant tumor of bone tissue, which has an insidious onset and is difficult to detect early, and few early diagnostic markers with high specificity and sensitivity. Therefore, this study aims to identify potential biomarkers that can help diagnose OS in its early stages and improve the prognosis of patients.

METHODS

The data sets of GSE12789, GSE28424, GSE33382 and GSE36001 were combined and normalized to identify Differentially Expressed Genes (DEGs). The data were analyzed by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genome (KEGG) and Disease Ontology (DO). The hub gene was selected based on the common DEG that was obtained by applying two regression methods: the Least Absolute Shrinkage and Selection Operator (LASSO) and Support vVector Machine (SVM). Then the diagnostic value of the hub gene was evaluated in the GSE42572 data set. Finally, the correlation between immunocyte infiltration and key genes was analyzed by CIBERSORT.

RESULTS

The regression analysis results of LASSO and SVM are the following three DEGs: FK501 binding protein 51 (FKBP5), C-C motif chemokine ligand 5 (CCL5), complement component 1 Q subcomponent B chain (C1QB). We evaluated the diagnostic performance of three biomarkers (FKBP5, CCL5 and C1QB) for osteosarcoma using receiver operating characteristic (ROC) analysis. In the training group, the area under the curve (AUC) of FKBP5, CCL5 and C1QB was 0.907, 0.874 and 0.676, respectively. In the validation group, the AUC of FKBP5, CCL5 and C1QB was 0.618, 0.932 and 0.895, respectively. It is noteworthy that these genes were more expressed in tumor tissues than in normal tissues by various immune cell types, such as plasma cells, CD8+ T cells, T regulatory cells (Tregs), activated NK cells, activated dendritic cells and activated mast cells. These immune cell types are also associated with the expression levels of the three diagnostic genes that we identified.

CONCLUSION

We found that CCL5 can be considered an early diagnostic gene of osteosarcoma, and CCL5 interacts with immune cells to influence tumor occurrence and development. These findings have important implications for the early detection of osteosarcoma and the identification of novel therapeutic targets.

Development and validation of an immune-related gene prognostic index for lung adenocarcinoma

BACKGROUND

Lung cancer is the most common malignant tumor in the world, and its prognosis is still not optimistic. The aim of this study was to establish an immune-related gene (IRG) prognostic index (IRGPI) for lung adenocarcinoma (LUAD) based on IRGs, and to explore the prognosis, molecular and immune features, and response to immune checkpoint inhibitor (ICI) therapy in IRGPI-classified different subgroups of LUAD.

METHODS

Based on the LUAD transcriptome RNA-sequencing data in TCGA database, the differentially expressed genes (DEGs) were selected. Subsequently, DEGs were intersected with IRGs to obtain differentially expressed immune-related genes (DEIRGs). Weighted gene co-expression network analysis (WGCNA) identified hub genes in DEIRGs. Finally, univariate and multivariate Cox regression analyses were used to build an IRGPI model. Subsequently, TCGA patients were divided into high- and low-risk groups, and the survival of patients in different groups was further analyzed. Besides, we validated the molecular and immune characteristics, relationship with immune checkpoints, angiogenesis-related genes, and immune subtypes distribution in different subgroups. Meanwhile, we further validated the response to ICI therapy in different subgroups.

RESULTS

The IRGPI was constructed based on 13 DEIRGs. Compared with the low-risk group, overall survival (OS) was lower in the high-risk group, and the high-risk score was independently associated with poorer OS. Besides, the high-risk score was associated with cell cycle pathway, high mutation rate of TP53 and KRAS, high infiltration of M0 macrophages, and immunosuppressive state, and these patients had poorer prognosis but the TIDE score of the high-risk group was lower than that of the other group, which means that the high-risk group could benefit more from ICI treatment. In contrast, the low-risk score was related to low mutation rate of TP53 and KRAS, high infiltration of plasma cells, and immunoactive state, and these patients had better prognosis but the low-risk group less benefit from ICI treatment based on the results of TIDE score.

CONCLUSIONS

IRGPI is a prospective biomarker based on IRGs that can distinguish high- and low-risk groups to predict patient prognosis, help characterize the tumor immune microenvironment, and evaluate the benefit of ICI therapy in LUAD.

The mitochondrial energy metabolism pathway-related signature predicts prognosis and indicates immune microenvironment infiltration in osteosarcoma

BACKGROUND

Abnormalities in the mitochondrial energy metabolism pathways are closely related to the occurrence and development of many cancers. Furthermore, abnormal genes in mitochondrial energy metabolism pathways may be novel targets and biomarkers for the diagnosis and treatment of osteosarcoma. In this study, we aimed to establish a mitochondrial energy metabolism-related gene signature for osteosarcoma prognosis.

METHODS

We first obtained differentially expressed genes based on the metastatic status of 84 patients with osteosarcoma from the TARGET database. After Venn analysis of differentially expressed genes and mitochondrial energy metabolism pathway-related genes (MMRGs), 2 key genes were obtained using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analysis. Next, we used these 2 genes to establish a prognostic signature. Subsequent analyses elucidated the correlation between these 2 key genes with clinical features and 28 types of immune cells. Pathway changes in osteosarcoma pathogenesis under different metastatic states were clarified using gene set enrichment analysis (GSEA) of differentially expressed genes.

RESULTS

A gene signature composed of 2 key prognosis-related genes (KCNJ5 and PFKFB2) was identified. A risk score was calculated based on the gene signature, which divided osteosarcoma patients into low- or high-risk groups that showed good and poor prognosis, respectively. High expression of these 2 key genes is associated with low-risk group in patients with osteosarcoma. We constructed an accurate nomogram to help clinicians assess the survival time of patients with osteosarcoma. The results of immune cell infiltration level showed that the high-risk group had lower levels of immune cell infiltration. GSEA revealed changes in immune regulation and hypoxia stress pathways in osteosarcoma under different metastatic states.

CONCLUSION

Our study identified an excellent gene signature that could be helpful in improving the prognosis of patients with osteosarcoma.

Mathematical and Machine Learning Models of Renal Cell Carcinoma: A Review

This review explores the multifaceted landscape of renal cell carcinoma (RCC) by delving into both mechanistic and machine learning models. While machine learning models leverage patients' gene expression and clinical data through a variety of techniques to predict patients' outcomes, mechanistic models focus on investigating cells' and molecules' interactions within RCC tumors. These interactions are notably centered around immune cells, cytokines, tumor cells, and the development of lung metastases. The insights gained from both machine learning and mechanistic models encompass critical aspects such as signature gene identification, sensitive interactions in the tumors' microenvironments, metastasis development in other organs, and the assessment of survival probabilities. By reviewing the models of RCC, this study aims to shed light on opportunities for the integration of machine learning and mechanistic modeling approaches for treatment optimization and the identification of specific targets, all of which are essential for enhancing patient outcomes.

Metastasis-Related Signature for Clinically Predicting Prognosis and Tumor Immune Microenvironment of Osteosarcoma Patients

Osteosarcoma is the most prevalent clinical malignant bone tumor in adolescents. The prognosis of metastatic osteosarcoma is still very poor. The aim of our study was to investigate the clinical diagnosis and prognostic significance of metastasis related genes (MRGs) in patients with osteosarcoma. Clinical information and RNA sequencing data with osteosarcoma patients were obtained and set as the training set from UCSC databases. GSE21257 were downloaded and chosen as the verification cohort. An eight gene metastasis related risk signature including MYC, TAC4, ABCA4, GADD45GIP1, TNFRSF21, HERC5, MAGEA11, and PDE1B was built to predict the overall survival of osteosarcoma patients. Based on risk assessments, patients were classified into high- and low-risk groups. The high-risk patients had higher risk score and shorter survival time. ROC curves revealed that this risk signature can accurately predict survival times of osteosarcoma patients at the 1-, 2-, 3-, 4- and 5- year. GSEA revealed that MYC targets, E2F targets, mTORC1 signaling, Wnt /β-catenin signaling and cell cycle were upregulated, and cell adhesion molecules, and primary immunodeficiency were decreased in high-risk group. MRGs were highly linked with the tumor immune microenvironment and ICB response. These results identified that MRGs as a novel prognostic and diagnostic biomarker in osteosarcoma.

New gene signature from the dominant infiltration immune cell type in osteosarcoma predicts overall survival

The immune microenvironment of osteosarcoma (OS) has been reported to play an important role in disease progression and prognosis. However, owing to tumor heterogeneity, it is not ideal to predict OS prognosis by examining only infiltrating immune cells. This work aimed to build a prognostic gene signature based on similarities in the immune microenvironments of OS patients. Public datasets were used to examine the correlated genes, and the most consistent dominant infiltrating immune cell type was identified. The LASSO Cox regression model was used to establish a multiple-gene risk prediction signature. A nine-gene prognostic signature was generated from the correlated genes for M0 macrophages and then proven to be effective and reliable in validation cohorts. Signature comparison indicated the priority of the signature. Multivariate Cox regression models indicated that the signature risk score is an independent prognostic factor for OS patients regardless of the Huvos grade in all datasets. In addition, the results of the association between the signature risk score and chemotherapy sensitivity also showed that there was no significant difference in the sensitivity of any drugs between the low- and high-risk groups. A GSEA of GO and KEGG pathways found that antigen processing- and presentation-related biological functions and olfactory transduction receptor signaling pathways have important roles in signature functioning. Our findings showed that M0 macrophages were the dominant infiltrating immune cell type in OS and that the new gene signature is a promising prognostic model for OS patients.

Decoding the Impact of Tumor Microenvironment in Osteosarcoma Progression and Metastasis

Osteosarcoma (OS) is a heterogeneous, highly metastatic bone malignancy in children and adolescents. Despite advancements in multimodal treatment strategies, the prognosis for patients with metastatic or recurrent disease has not improved significantly in the last four decades. OS is a highly heterogeneous tumor; its genetic background and the mechanism of oncogenesis are not well defined. Unfortunately, no effective molecular targeted therapy is currently available for this disease. Understanding osteosarcoma's tumor microenvironment (TME) has recently gained much interest among scientists hoping to provide valuable insights into tumor heterogeneity, progression, metastasis, and the identification of novel therapeutic avenues. Here, we review the current understanding of the TME of OS, including different cellular and noncellular components, their crosstalk with OS tumor cells, and their involvement in tumor progression and metastasis. We also highlight past/current clinical trials targeting the TME of OS for effective therapies and potential future therapeutic strategies with negligible adverse effects.

Managing the immune microenvironment of osteosarcoma: the outlook for osteosarcoma treatment

Osteosarcoma, with poor survival after metastasis, is considered the most common primary bone cancer in adolescents. Notwithstanding the efforts of researchers, its five-year survival rate has only shown limited improvement, suggesting that existing therapeutic strategies are insufficient to meet clinical needs. Notably, immunotherapy has shown certain advantages over traditional tumor treatments in inhibiting metastasis. Therefore, managing the immune microenvironment in osteosarcoma can provide novel and valuable insight into the multifaceted mechanisms underlying the heterogeneity and progression of the disease. Additionally, given the advances in nanomedicine, there exist many advanced nanoplatforms for enhanced osteosarcoma immunotherapy with satisfactory physiochemical characteristics. Here, we review the classification, characteristics, and functions of the key components of the immune microenvironment in osteosarcoma. This review also emphasizes the application, progress, and prospects of osteosarcoma immunotherapy and discusses several nanomedicine-based options to enhance the efficiency of osteosarcoma treatment. Furthermore, we examine the disadvantages of standard treatments and present future perspectives for osteosarcoma immunotherapy.

Bio-Mechanical Model of Osteosarcoma Tumor Microenvironment: A Porous Media Approach

Osteosarcoma is the most common malignant bone tumor in children and adolescents with a poor prognosis. To describe the progression of osteosarcoma, we expanded a system of data-driven ODE from a previous study into a system of Reaction-Diffusion-Advection (RDA) equations and coupled it with Biot equations of poroelasticity to form a bio-mechanical model. The RDA system includes the spatio-temporal information of the key components of the tumor microenvironment. The Biot equations are comprised of an equation for the solid phase, which governs the movement of the solid tumor, and an equation for the fluid phase, which relates to the motion of cells. The model predicts the total number of cells and cytokines of the tumor microenvironment and simulates the tumor's size growth. We simulated different scenarios using this model to investigate the impact of several biomedical settings on tumors' growth. The results indicate the importance of macrophages in tumors' growth. Particularly, we have observed a high co-localization of macrophages and cancer cells, and the concentration of tumor cells increases as the number of macrophages increases.

Heparin and Heparin-Based Drug Delivery Systems: Pleiotropic Molecular Effects at Multiple Drug Resistance of Osteosarcoma and Immune Cells

One of the main problems of modern health care is the growing number of oncological diseases both in the elderly and young population. Inadequately effective chemotherapy, which remains the main method of cancer control, is largely associated with the emergence of multidrug resistance in tumor cells. The search for new solutions to overcome the resistance of malignant cells to pharmacological agents is being actively pursued. Another serious problem is immunosuppression caused both by the tumor cells themselves and by antitumor drugs. Of great interest in this context is heparin, a biomolecule belonging to the class of glycosaminoglycans and possessing a broad spectrum of biological activity, including immunomodulatory and antitumor properties. In the context of the rapid development of the new field of “osteoimmunology,” which focuses on the collaboration of bone and immune cells, heparin and delivery systems based on it may be of intriguing importance for the oncotherapy of malignant bone tumors. Osteosarcoma is a rare but highly aggressive, chemoresistant malignant tumor that affects young adults and is characterized by constant recurrence and metastasis. This review describes the direct and immune-mediated regulatory effects of heparin and drug delivery systems based on it on the molecular mechanisms of (multiple) drug resistance in (onco) pathological conditions of bone tissue, especially osteosarcoma.

Immune checkpoints in osteosarcoma: Recent advances and therapeutic potential

Osteosarcoma is the most common primary malignant bone tumor and is associated with a high risk of recurrence and distant metastasis. Effective treatment for osteosarcoma, especially advanced osteosarcoma, has stagnated over the past four decades. The advent of immune checkpoint inhibitor (ICI) has transformed the treatment paradigm for multiple malignant tumor types and indicated a potential therapeutic strategy for osteosarcoma. In this review, we discuss recent advances in immune checkpoints, including programmed cell death protein-1 (PD-1), programmed cell death protein ligand-1 (PD-L1), and cytotoxic T lymphocyte-associated antigen-4 (CTLA-4), and their related ICIs for osteosarcoma treatment. We present the main existing mechanisms of resistance to ICIs therapy in osteosarcoma. Moreover, we summarize the current strategies for improving the efficacy of ICIs in osteosarcoma and address the potential predictive biomarkers of ICIs treatment in osteosarcoma.

Auxiliary Segmentation Method of Osteosarcoma in MRI Images Based on Denoising and Local Enhancement

Osteosarcoma is a bone tumor which is malignant. There are many difficulties when doctors manually identify patients’ MRI images to complete the diagnosis. The osteosarcoma in MRI images is very complex, making its recognition and segmentation resource-consuming. Automatic osteosarcoma area segmentation can solve these problems to a certain extent. However, existing studies usually fail to balance segmentation accuracy and efficiency. They are either sensitive to noise with low accuracy or time-consuming. So we propose an auxiliary segmentation method based on denoising and local enhancement. The method first optimizes the osteosarcoma images, including removing noise using the Edge Enhancement based Transformer for Medical Image Denoising (Eformer) and using a non-parameter method to localize and enhance the tumor region in MRI images. Osteosarcoma was then segmented by Deep Feature Aggregation for Real-Time Semantic Segmentation (DFANet). Our method achieves impressive segmentation accuracy. Moreover, it is efficient in both time and space. It can provide information about the location and extent of the osteosarcoma as a basis for further diagnosis.

Immune Microenvironment in Osteosarcoma: Components, Therapeutic Strategies and Clinical Applications

Osteosarcoma is a primary malignant tumor that tends to threaten children and adolescents, and the 5-year event-free survival rate has not improved significantly in the past three decades, bringing grief and economic burden to patients and society. To date, the genetic background and oncogenesis mechanisms of osteosarcoma remain unclear, impeding further research. The tumor immune microenvironment has become a recent research hot spot, providing novel but valuable insight into tumor heterogeneity and multifaceted mechanisms of tumor progression and metastasis. However, the immune microenvironment in osteosarcoma has been vigorously discussed, and the landscape of immune and non-immune component infiltration has been intensively investigated. Here, we summarize the current knowledge of the classification, features, and functions of the main infiltrating cells, complement system, and exosomes in the osteosarcoma immune microenvironment. In each section, we also highlight the complex crosstalk network among them and the corresponding potential therapeutic strategies and clinical applications to deepen our understanding of osteosarcoma and provide a reference for imminent effective therapies with reduced adverse effects.

Five hypoxia and immunity related genes as potential biomarkers for the prognosis of osteosarcoma

Osteosarcoma accounts for a frequently occurring cancer of the primary skeletal system. In osteosarcoma cells, a hypoxic microenvironment is commonly observed that drives tumor growth, progression, and heterogeneity. Hypoxia and tumor-infiltrating immune cells might be closely related to the prognosis of osteosarcoma. In this study, we aimed to determine the biomarkers and therapeutic targets related to hypoxia and immunity through bioinformatics methods to improve the clinical prognosis of patients. We downloaded the gene expression data of osteosarcoma samples and normal samples in the UCSC Xena database and GTEx database, respectively, and downloaded the validation dataset (GSE21257) in the GEO database. Subsequently, we performed GO enrichment analysis and KEGG pathway enrichment analysis on the data of the extracted osteosarcoma hypoxia-related genes. Through univariate COX regression analysis, lasso regression analysis, multivariate COX regression analysis, etc., we established a predictive model for the prognosis of osteosarcoma. Five genes, including ST3GAL4, TRIM8, STC2, TRPS1, and FAM207A, were found by screening. In particular, we analyzed the immune cell composition of each gene based on the five genes through the CIBERSORT algorithm and verified each gene at the cell and tissue level. Our findings are valuable for the clinical diagnosis and treatment of this disease.

Identification of Immune-Related Genes for Establishment of Prognostic Index in Hepatocellular Carcinoma

Background: Immune checkpoint inhibitor (ICI) therapy has been proved to be a promising therapy to many types of solid tumors. However, effective biomarker for estimating the response to ICI therapy and prognosis of hepatocellular carcinoma (HCC) patients remains underexplored. The aim of this study is to build a novel immune-related prognostic index based on transcriptomic profiles. Methods: Weighted gene co-expression network analysis (WGCNA) was conducted to identify immune-related hub genes that are differentially expressed in HCC cohorts. Next, univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) analysis were used to detect hub genes associated to overall survival (OS). To validate the immune-related prognostic index, univariate and multivariate Cox regression analysis were performed. CIBERSORT and ESTIMATE were used to explore the tumor microenvironment and immune infiltration level. Results: The differential expression analysis detected a total of 148 immune-related genes, among which 25 genes were identified to be markedly related to overall survival in HCC patients. LASSO analysis yielded 10 genes used to construct the immune-related gene prognostic index (IRGPI), by which a risk score is computed to estimate low vs. high risk indicating the response to ICI therapy and prognosis. Further analysis confirmed that this immune-related prognostic index is an effective indicator to immune infiltration level, response to ICI treatment and OS. The IRGPI low-risk patients had better overall survival (OS) than IRGPI high-risk patients on two independent cohorts. Moreover, we found that IRGPI high-risk group was correlated with high TP53 mutation rate, immune-suppressing tumor microenvironment, and these patients acquired less benefit from ICI therapy. In contrast, IRGPI-low risk group was associated with low TP53 and PIK3CA mutation rate, high infiltration of naive B cells and T cells, and these patients gained relatively more benefit from ICI therapy.

A Mathematical Model of Breast Tumor Progression Based on Immune Infiltration

Breast cancer is the most prominent type of cancer among women. Understanding the microenvironment of breast cancer and the interactions between cells and cytokines will lead to better treatment approaches for patients. In this study, we developed a data-driven mathematical model to investigate the dynamics of key cells and cytokines involved in breast cancer development. We used gene expression profiles of tumors to estimate the relative abundance of each immune cell and group patients based on their immune patterns. Dynamical results show the complex interplay between cells and molecules, and sensitivity analysis emphasizes the direct effects of macrophages and adipocytes on cancer cell growth. In addition, we observed the dual effect of IFN-γ on cancer proliferation, either through direct inhibition of cancer cells or by increasing the cytotoxicity of CD8+ T-cells.

Immune pathways and TP53 missense mutations are associated with longer survival in canine osteosarcoma

Osteosarcoma affects about 2.8% of dogs with cancer, with a one-year survival rate of approximately 45%. The purpose of this study was to characterize mutation and expression profiles of osteosarcoma and its association with outcome in dogs. The number of somatic variants identified across 26 samples ranged from 145 to 2,697 with top recurrent mutations observed in TP53 and SETD2. Additionally, 47 cancer genes were identified with copy number variations. Missense TP53 mutation status and low pre-treatment blood monocyte counts were associated with a longer disease-free interval (DFI). Patients with longer DFI also showed increased transcript levels of anti-tumor immune response genes. Although, T-cell and myeloid cell quantifications were not significantly associated with outcome; immune related genes, PDL-1 and CD160, were correlated with T-cell abundance. Overall, the association of gene expression and mutation profiles to outcome provides insights into pathogenesis and therapeutic interventions in osteosarcoma patients.

Investigating Optimal Chemotherapy Options for Osteosarcoma Patients through a Mathematical Model

Simple Summary

Osteosarcoma is a rare type of cancer with poor prognoses. However, to the best of our knowledge, there are no mathematical models that study the impact of chemotherapy treatments on the osteosarcoma microenvironment. In this study, we developed a data driven mathematical model to analyze the dynamics of the important players in three groups of osteosarcoma tumors with distinct immune patterns in the presence of the most common chemotherapy drugs. The results indicate that the treatments’ start times and optimal dosages depend on the unique growth rate of the tumor, which implies the necessity of personalized medicine. Furthermore, the developed model can be extended by others to build models that can recommend individual-specific optimal dosages.

Abstract

Since all tumors are unique, they may respond differently to the same treatments. Therefore, it is necessary to study their characteristics individually to find their best treatment options. We built a mathematical model for the interactions between the most common chemotherapy drugs and the osteosarcoma microenvironments of three clusters of tumors with unique immune profiles. We then investigated the effects of chemotherapy with different treatment regimens and various treatment start times on the behaviors of immune and cancer cells in each cluster. Saliently, we suggest the optimal drug dosages for the tumors in each cluster. The results show that abundances of dendritic cells and HMGB1 increase when drugs are given and decrease when drugs are absent. Populations of helper T cells, cytotoxic cells, and IFN-γ grow, and populations of cancer cells and other immune cells shrink during treatment. According to the model, the MAP regimen does a good job at killing cancer, and is more effective than doxorubicin and cisplatin combined or methotrexate alone. The results also indicate that it is important to consider the tumor’s unique growth rate when deciding the treatment details, as fast growing tumors need early treatment start times and high dosages.

Identification of two immune subtypes in osteosarcoma based on immune gene sets

Osteosarcoma (OS) is a highly aggressive cancer with poor prognosis, which mainly occurs in teenagers. Recent studies have shown that tumor-infiltrating immune cells play an important role in the progression of OS. In the present study, we identified two immune subtypes of OS (referred to as high and low immune cell infiltration subtypes, respectively) based on immune-related gene sets using TARGET and GEO cohort datasets. Elevated immune scores, increased stromal scores, decreased tumor purities, and higher infiltration of CD8 + T cells and M1 macrophages were observed for the high immune cell infiltration subtype. Moreover, the high immune cell infiltration subtype was characterized by high expression of immune checkpoint molecules. Gene set enrichment analysis showed that "B cell receptor signaling pathway" and "T cell receptor signaling pathway" gene sets were enriched in the high immune cell infiltration subtype. In addition, patients in the high immune cell infiltration subtype had better prognosis than patients in the low immune cell infiltration subtype. Furthermore, differentially expressed genes were screened according to the two OS subtypes and a risk model was generated by multivariate Cox regression analysis to predict the prognosis of OS patients. These results in this study showed that OS patients could be divided into two immune subtypes and offered a novel two-gene risk signature to predict the prognosis of patients with OS.

Data Driven Mathematical Model of FOLFIRI Treatment for Colon Cancer

Many colon cancer patients show resistance to their treatments. Therefore, it is important to consider unique characteristic of each tumor to find the best treatment options for each patient. In this study, we develop a data driven mathematical model for interaction between the tumor microenvironment and FOLFIRI drug agents in colon cancer. Patients are divided into five distinct clusters based on their estimated immune cell fractions obtained from their primary tumors' gene expression data. We then analyze the effects of drugs on cancer cells and immune cells in each group, and we observe different responses to the FOLFIRI drugs between patients in different immune groups. For instance, patients in cluster 3 with the highest T-reg/T-helper ratio respond better to the FOLFIRI treatment, while patients in cluster 2 with the lowest T-reg/T-helper ratio resist the treatment. Moreover, we use ROC curve to validate the model using the tumor status of the patients at their follow up, and the model predicts well for the earlier follow up days.

Data-Driven Mathematical Model of Osteosarcoma

As the immune system has a significant role in tumor progression, in this paper, we develop a data-driven mathematical model to study the interactions between immune cells and the osteosarcoma microenvironment. Osteosarcoma tumors are divided into three clusters based on their relative abundance of immune cells as estimated from their gene expression profiles. We then analyze the tumor progression and effects of the immune system on cancer growth in each cluster. Cluster 3, which had approximately the same number of naive and M2 macrophages, had the slowest tumor growth, and cluster 2, with the highest population of naive macrophages, had the highest cancer population at the steady states. We also found that the fastest growth of cancer occurred when the anti-tumor immune cells and cytokines, including dendritic cells, helper T cells, cytotoxic cells, and IFN-γ, switched from increasing to decreasing, while the dynamics of regulatory T cells switched from decreasing to increasing. Importantly, the most impactful immune parameters on the number of cancer and total cells were the activation and decay rates of the macrophages and regulatory T cells for all clusters. This work presents the first osteosarcoma progression model, which can be later extended to investigate the effectiveness of various osteosarcoma treatments.