An immune-related gene signature for determining Ewing sarcoma prognosis based on machine learning

Decoding the immune landscape in Ewing sarcoma pathogenesis: The role of tumor infiltrating immune cells and immune milieu

Ewing sarcoma (EwS) is the second most prevalent pediatric bone malignancy, characterized by its aggressive behavior and unfavorable prognosis. The tumor microenvironment (TME) of EwS is shaped by immunosuppressive components, including myeloid-derived suppressor cells, tumor-associated macrophages, and immune checkpoint molecules such as PD-1/PD-L1 and HLA-G. These elements impair anti-tumor immune responses by modulating the function of tumor-infiltrating immune cells, such as regulatory T cells (Tregs), CD8+ T cells, and natural killer cells. Chemokines, including CXCL9 and CXCL12, and cytokines, such as transforming growth factor-beta and interleukin-10, further contribute to immune suppression and promote metastatic dissemination. Recent advances in immunotherapy have highlighted the therapeutic potential of modulating immune cells and signaling pathways to enhance anti-tumor immunity. This review provides a comprehensive analysis of the complex immune landscape within the EwS TME, focusing on the mechanistic roles of key immune components and their potential as therapeutic targets. Understanding these interactions could pave the way for innovative treatment strategies to improve clinical outcomes in patients with EwS.

The Prognostic Impact of the Tumor Immune Microenvironment in Synovial Sarcoma: An Immunohistochemical Analysis Using Digital Pathology and Conventional Interpretation

BACKGROUND AND OBJECTIVES

Innate and adaptive immune responses serve a crucial role in neoplasms. The interaction of immune cells with the neoplastic tissue influences tumor behavior, resulting in either pro-tumorigenic or anti-tumorigenic effects. However, the prognostic significance of the tumor immune microenvironment (TIME) in synovial sarcoma (SS) remains poorly studied. This study aimed to analyze the TIME of SS to determine its impact on the prognosis by examining the intratumoral lymphocytic and macrophagic infiltrate and its potential correlation with survival and recurrence.

METHODS

We conducted a retrospective observational study of 49 fusion-confirmed SS cases collected from two different institutions. We obtained clinical and follow-up data, and SSs were histologically classified according to WHO criteria. Immunohistochemical analysis, including of CD163, CD68, CD3, CD8, and CD20, was conducted in tissue microarrays using an analog scale. We examined the whole-slide tissue for the 23 cases with sufficient material available and then assessed the positive area by scanning the slides and analyzing the images using QuPath (0.4.4, Belfast, Northern Ireland) to calculate the positive area in an immune hotspot. We correlated the expression of these markers with clinical outcomes. A log-rank test and Kaplan-Meyer curves were used as appropriate (significance: p ≤ 0.05).

RESULTS

The most frequent morphological subtype was monophasic (59.6%), followed by biphasic (26.9%) and undifferentiated (7%). The mean disease specific survival (DSS) was 55.3 months, with a median of 33 months. The median overall survival (OS) was 50 months (range: 2-336 months). Both evaluation methods showed a good correlation for all antibodies, with Chi-square values of p < 0.05. All cases showed variable amounts of CD163-positive macrophages. The cases that showed a higher density of CD163-positive macrophages in whole-slide images subjected to digital analysis demonstrated an improved OS and DSS on Kaplan-Meier curves. Cases with lower CD8 and CD3 positivity showed a tendency toward faster progression and a slightly worse prognosis.

CONCLUSIONS

The tumor immune microenvironment in sarcomas is a complex system that requires further investigation to fully understand its impact on tumorigenesis and patient clinical outcomes. Our results demonstrate that a higher amount of intratumoral CD163-positive macrophage infiltrate is associated with an increased OS and DSS. Our findings show that digital pathology is more precise than subjective quantitative analysis.

Identification of novel diagnostic and prognostic microRNAs in sarcoma on TCGA dataset: bioinformatics and machine learning approach

The discovery of unique microRNA (miR) patterns and their corresponding genes in sarcoma patients indicates their involvement in cancer development and suggests their potential use in medical management. MiRs were identified from The Cancer Genome Atlas (TCGA) dataset, with a Deep Neural Network (DNN) employed for novel miR identification. MiRDB facilitated target predictions. Functional enrichment analysis, identify critical pathways, protein-protein interaction network, and diseases/clinical data correlations were explored. COX regression, Kaplan-Meier analyses, and CombioROC was also utilized. The population consisted of 119 females and 142 males, and 1046 miRs were uncovered. Ten miRs was selected for further analysis using DNN. Upon analyzing for gene ontology, it was found that these genes showed enrichment in various activities. We identified a significant association between the overall survival rate of sarcoma patients and miRs levels. The combination of miR.3688 and miR.3936 achieved the greatest diagnostic standing. MiRs have the capability to screen sarcoma patients to identify undetected tumors, predict prognosis, and pinpoint prospective targets for treatment. Further large clinical trials are required to validate our findings.

Tumor-associated stroma shapes the spatial tumor immune microenvironment of primary Ewing sarcomas

To date, few studies have detailed the tumor microenvironment (TME) of Ewing sarcoma (EwS). The TME has a vital role in cancer survival and progression with implications in drug resistance and immune escape. By performing spatially resolved transcriptomic analysis of primary treatment-naïve EwS samples, we discovered greater stromal enrichment in localized EwS tumors compared to metastatic EwS tumors. Through spatial ligand-receptor analysis, we show that the stromal enriched regions harbor unique extracellular matrix related cytokines, immune recruitment and proinflammatory microenvironmental signals, implying EwS stroma may play an anti-tumorigenic role by acting as an immune recruitment center. All EwS tumors expressed pro-tumorigenic MIF-CD74 immune signaling, suggesting a potential immune-evasive mechanism and immunotherapy target. Our findings provide insight into tumor cell/stromal cell interactions in EwS and serve as a valuable resource for further investigations in the tumor immune microenvironment of EwS.

Higher TIGIT+ γδ TCM cells may predict poor prognosis in younger adult patients with non-acute promyelocytic AML

Introduction

γδ T cells recognize and exert cytotoxicity against tumor cells. They are also considered potential immune cells for immunotherapy. Our previous study revealed that the altered expression of immune checkpoint T-cell immunoreceptor with immunoglobulin and ITIM domain (TIGIT) on γδ T cells may result in immunosuppression and is possibly associated with a poor overall survival in acute myeloid leukemia (AML). However, whether γδ T-cell memory subsets are predominantly involved and whether they have a relationship with clinical outcomes in patients with AML under the age of 65 remain unclear.

Methods

In this study, we developed a multicolor flow cytometry-based assay to monitor the frequency and distribution of γδ T-cell subsets, including central memory γδ T cells (TCM γδ), effector memory γδ T cells (TEM γδ), and TEM expressing CD45RA (TEMRA γδ), in peripheral blood from 30 young (≤65 years old) patients with newly diagnosed non-acute promyelocytic leukemia (also known as M3) AML (AMLy-DN), 14 young patients with AML in complete remission (AMLy-CR), and 30 healthy individuals (HIs).

Results

Compared with HIs, patients with AMLy-DN exhibited a significantly higher differentiation of γδ T cells, which was characterized by decreased TCM γδ cells and increased TEMRA γδ cells. A generally higher TIGIT expression was observed in γδ T cells and relative subsets in patients with AMLy-DN, which was partially recovered in patients with AMLy-CR. Furthermore, 17 paired bone marrow from patients with AMLy-DN contained higher percentages of γδ and TIGIT+ γδ T cells and a lower percentage of TCM γδ T cells. Multivariate logistic regression analyses revealed the association of high percentage of TIGIT+ TCM γδ T cells with an increased risk of poor induction chemotherapy response.

Conclusions

In this study, we investigated the distribution of γδ T cells and their memory subsets in patients with non-M3 AML and suggested TIGIT+ TCM γδ T cells as potential predictive markers of induction chemotherapy response.

Prognostic value of PRR11 and immune cell infiltration in Ewing sarcoma

Ewing's sarcoma (ES) is the second most common bone and soft tissue malignancy in children and adolescents with a poor prognosis. The identification of genes with prognostic value may contribute to the prediction and treatment of this disease. The GSE17679, GSE68776, GSE63155, and GSE63156 datasets were downloaded from the Gene Expression Omnibus database and qualified. Prognostic value of differentially expressed genes (DEGs) between the normal and tumor groups and immune cell infiltration were explored by several algorithms. A prognostic model was established and validated. Finally, functional analyses of the DEGs were performed. Proline rich 11 (PRR11) and mast cell infiltration were noted as the key indicators for the prognosis of ES. Kaplan-Meier and scatter plots for the training and two validation sets showed that patients in the low-PRR11 expression group were associated with better outcomes than those in the high-PRR11 expression group. The concordance indices and calibration analyses of the prognostic model indicated good predictive accuracy in the training and validation sets. The area under the curve values obtained through the receiver operating characteristic analysis for 1-, 3-, 5-year prediction were ≥ 0.75 in the three cohorts, suggesting satisfactory sensitivity and specificity of the model. Decision curve analyses suggested that patients could benefit more from the model than the other strategies. Functional analyses suggested that DEGs were mainly clustered in the cell cycle pathway. PRR11 and mast cell infiltration are potential prognostic indicators in ES. PRR11 possibly affects the prognosis of patients with ES through the cell cycle pathway.

Prognostic characteristics of a six-gene signature based on ssGSEA in sarcoma

BACKGROUND

Sarcoma is a rare malignant tumor originating of the interstitial or connective tissue with a poor prognosis. Next-generation sequencing technology offers new opportunities for accurate diagnosis and treatment of sarcomas. There is an urgent need for new gene signature to predict prognosis and evaluate treatment outcomes.

METHODS

We used transcriptome data from the Cancer Genome Atlas (TCGA) database and single sample gene set enrichment analysis (ssGSEA) to explore the cancer hallmarks most associated with prognosis in sarcoma patients. Then, weighted gene coexpression network analysis, univariate COX regression analysis and random forest algorithm were used to construct prognostic gene characteristics. Finally, the prognostic value of gene markers was validated in the TCGA and Integrated Gene Expression (GEO) (GSE17118) datasets, respectively.

RESULTS

MYC targets V1 and V2 are the main cancer hallmarks affecting the overall survival (OS) of sarcoma patients. A six-gene signature including VEGFA, HMGB3, FASN, RCC1, NETO2 and BIRC5 were constructed. Kaplan-Meier analysis suggested that higher risk scores based on the six-gene signature associated with poorer OS (P < 0.001). The receiver Operating characteristic curve showed that the risk score based on the six-gene signature was a good predictor of sarcoma, with an area under the curve (AUC) greater than 0.73. In addition, the prognostic value of the six-gene signature was validated in GSE17118 with an AUC greater than 0.72.

CONCLUSION

This six-gene signature is an independent prognostic factor in patients with sarcoma and is expected to be a potential therapeutic target for sarcoma.

Intratumoral NKp46+ natural killer cells are spatially distanced from T and MHC-I+ cells with prognostic implications in soft tissue sarcoma

Introduction

Soft tissue sarcomas (STS) are rare, heterogenous malignancies with an unmet need for novel immunotherapies. Tumor infiltrating lymphocytes (TILs) have been linked with favorable outcomes in STS patients, though the contribution of natural killer (NK) cells and spatial relationships of TILs with MHC-I expressing cells lacks detailed characterization.

Experimental design

Using archived and prospectively collected specimens, we evaluated intratumoral NK cells by immunohistochemistry (IHC), flow cytometry, and immunofluorescence (IF). We assessed spatial localization of NK and T cells by multiplex IF, analyzing the effects of MHC-I expression status on NK and T cell clustering.

Results

Both intratumoral NKp46 and CD56dim expression were associated with significantly improved overall survival (P=0.05), while higher infiltrates of CD56bright NK cells predicted a worse prognosis (P=0.05). The presence of intratumoral NK cells was inversely proportional to CD3+ T cells. Spatial analyses showed NK cells preferentially clustering close to other NK cells with sparse CD3+ T and CD8+ T cells in range (P<0.0001). Additionally, CD3+ T and CD8+ T cells showed significantly greater co-localization with MHC-I+ cells, compared to NK cells (P<0.0001). After neoadjuvant radiotherapy, there was greater CD8 clustering, while after neoadjuvant chemotherapy, there was overall lower TIL clustering.

Conclusion

Intratumoral NK cells are prognostic in STS and localize closer to MHC-I- cells than T cells. Although both NK and T cells are associated with improved survival in STS, their differential distribution in the TME based on MHC-I expression status may serve as a biomarker for improved immunotherapy treatment selection.

Molecular and biologic biomarkers of Ewing sarcoma: A systematic review

With an annual incidence of less than 1%, Ewing sarcoma mainly occurs in children and young adults. It is not a frequent tumor but is the second most common bone malignancy in children. It has a 5-year survival rate of 65-75%; however, it has a poor prognosis when it relapses in patients. A genomic profile of this tumor can potentially help identify poor prognosis patients earlier and guide their treatment. A systematic review of the articles concerning genetic biomarkers in Ewing sarcoma was conducted using the Google Scholar, Cochrane, and PubMed database. There were 71 articles discovered. Numerous diagnostic, prognostic, and predictive biomarkers were found. However, more research is necessary to confirm the role of some of the mentioned biomarkers. .

Overall survival comparison between pediatric and adult Ewing sarcoma of bone and adult nomogram construction: a large population-based analysis

Background

Ewing sarcoma (ES) is a common primary bone tumor in children. Our study aimed to compare overall survival (OS) between pediatric and adult bone ES patients, identify independent prognostic factors and develop a nomogram for predicting OS in adult patients with ES of bone.

Methods

We retrospectively analyzed data for the 2004-2015 period from the Surveillance, Epidemiology, and End Results (SEER) database. To guarantee well-balanced characteristics between the comparison groups, propensity score matching (PSM) was used. Kaplan-Meier (KM) curves were used to compare OS between pediatric and adult patients with ES of bone. Univariate and multivariate Cox regression analyses were used to screen independent prognostic factors for ES of bone, and a prognostic nomogram was constructed by using the factors identified. The prediction accuracy and clinical benefit were evaluated using receiver operating characteristic (ROC) curves, areas under the curves (AUCs), calibration curves, and decision curve analysis (DCA).

Results

Our results showed that adult ES patients had lower OS than younger ES patients. Age, surgery, chemotherapy, and TNM stage were independent risk factors for bone ES in adults and were used to develop a nomogram. AUCs for 3-, 5-, and 10-year OS were 76.4 (67.5, 85.3), 77.3 (68.6, 85.9) and 76.6 (68.6, 84.5), respectively. Calibration curves and DCA results indicated excellent performance for our nomogram.

Conclusion

We found that ES pediatric patients have better OS than adult ES patients, and we constructed a practical nomogram to predict the 3-, 5- and 10-year OS of adult patients with ES of bone based on independent prognostic factors (age, surgery, chemotherapy, T stage, N stage and M stage).

Prospects and Advances in Adoptive Natural Killer Cell Therapy for Unmet Therapeutic Needs in Pediatric Bone Sarcomas

Malignant bone tumors are aggressive tumors, with a high tendency to metastasize, that are observed most frequently in adolescents during rapid growth spurts. Pediatric patients with malignant bone sarcomas, Ewing sarcoma and osteosarcoma, who present with progressive disease have dire survival rates despite aggressive therapy. These therapies can have long-term effects on bone growth, such as decreased bone mineral density and reduced longitudinal growth. New therapeutic approaches are therefore urgently needed for targeting pediatric malignant bone tumors. Harnessing the power of the immune system against cancer has improved the survival rates dramatically in certain cancer types. Natural killer (NK) cells are a heterogeneous group of innate effector cells that possess numerous antitumor effects, such as cytolysis and cytokine production. Pediatric sarcoma cells have been shown to be especially susceptible to NK-cell-mediated killing. NK-cell adoptive therapy confers numerous advantages over T-cell adoptive therapy, including a good safety profile and a lack of major histocompatibility complex restriction. NK-cell immunotherapy has the potential to be a new therapy for pediatric malignant bone tumors. In this manuscript, we review the general characteristics of osteosarcoma and Ewing sarcoma, discuss the long-term effects of sarcoma treatment on bones, and the barriers to effective immunotherapy in bone sarcomas. We then present the laboratory and clinical studies on NK-cell immunotherapy for pediatric malignant bone tumors. We discuss the various donor sources and NK-cell types, the engineering of NK cells and combinatorial treatment approaches that are being studied to overcome the current challenges in adoptive NK-cell therapy, while suggesting approaches for future studies on NK-cell immunotherapy in pediatric bone tumors.

Small round cell sarcomas

Undifferentiated small round cell sarcomas (SRCSs) of bone and soft tissue comprise a heterogeneous group of highly aggressive tumours associated with a poor prognosis, especially in metastatic disease. SRCS entities mainly occur in the third decade of life and can exhibit striking disparities regarding preferentially affected sex and tumour localization. SRCSs comprise new entities defined by specific genetic abnormalities, namely EWSR1-non-ETS fusions, CIC-rearrangements or BCOR genetic alterations, as well as EWSR1-ETS fusions in the prototypic SRCS Ewing sarcoma. These gene fusions mainly encode aberrant oncogenic transcription factors that massively rewire the transcriptome and epigenome of the as yet unknown cell or cells of origin. Additional mutations or copy number variants are rare at diagnosis and, depending on the tumour entity, may involve TP53, CDKN2A and others. Histologically, these lesions consist of small round cells expressing variable levels of CD99 and specific marker proteins, including cyclin B3, ETV4, WT1, NKX3-1 and aggrecan, depending on the entity. Besides locoregional treatment that should follow standard protocols for sarcoma management, (neo)adjuvant treatment is as yet ill-defined but generally follows that of Ewing sarcoma and is associated with adverse effects that might compromise quality of life. Emerging studies on the molecular mechanisms of SRCSs and the development of genetically engineered animal models hold promise for improvements in early detection, disease monitoring, treatment-related toxicity, overall survival and quality of life.

Ferroptosis-related gene signature predicts prognosis in kidney renal papillary cell carcinoma

Ferroptosis, an iron-dependent form of selective cell death, is involved in the development of many cancers. However, the role of ferroptosis-related genes (FRGs) in kidney renal papillary cell carcinoma (KIRP) is unclear. In this study, we examined the mRNA expression profiles and clinical data of patients with KIRP from the TCGA cohort. Consequently, 41 differentially-expressed FRGs were screened using the limma package, and 17 prognostic-related FRGs were identified by survival analysis and univariate Cox regression analyses. Thereafter, a ferroptosis-related gene prognostic index (FRGPI) was constructed based on five FRGs (AKR1C3, SAT1, FANCD2, HSBP1 and SQLE), using lasso Cox and multivariate Cox regression analyses. KIRP patients with high FRGPI scores displayed worse outcomes. Furthermore, the FRGPI was shown to be a reliable independent prognostic factor in both the training and testing cohorts. Comprehensive analysis also showed that the FRGPI can distinguish gene mutation, functional enrichment of immune cells and molecular function-related pathways. Interestingly, low FRGPI score could be more benefit from immune checkpoint inhibitors (ICIs) therapy. Then, the two hub prognostic genes (AKR1C3 and FANCD2) as a risk gene for KIRP were identified based on the FRGPI module, and the expression profiles of these two genes were validated using human KIRP cells, besides, we furthermore discovered that Fancd2 is significantly up-regulated in most cancers and is associated with prognosis. In conclusion, these findings showed that FRGPI can accurately predict the prognosis of patients with KIRP, suggesting that this risk model is a promising prognostic biomarker for these patients. Moreover, targeting ferroptosis (FANCD2) could be a potential therapeutic alternative for various cancers.

Prognostic Characteristics of Immune-Related Genes and the Related Regulatory Axis in Patients With Stage N+M0 Breast Cancer

Breast cancer (BRCA) has the highest incidence rate among female tumours. The function of the immune system affects treatment efficacy and prognosis in patients with BRCA. However, the exact role of immune-related genes (IRGs) in stage N+M0 BRCA is unknown. We constructed a predictive risk scoring model with five IRGs (CDH1, FGFR3, INHBA, S100B, and SCG2) based on the clinical, mutation, and RNA sequencing data of individuals with stage N+M0 BRCA sourced from The Cancer Genome Atlas. Results from the Shandong Cancer Hospital and Institute validation cohort suggested that regardless of clinical stage, tumour size, or the number of lymph node metastases, this model was able to reliably discriminate low-risk patients from high-risk ones and assess the prognosis of patients with stage N+M0 BRCA, and low-risk patients could benefit more from immunotherapy than high-risk patients. In addition, significant inter-group variations in immunocyte infiltration and the tumour microenvironment were observed. Moreover, risk score and age were found to be independent factors in multivariate COX regression analysis, which influenced the outcome of patients with stage N+M0 BRCA. Based on the above findings, we plotted a prognostic nomogram. Finally, we constructed a lncRNA KCNQ1OT1-LINC00665-TUG1/miR-9-5p/CDH1 regulatory axis of the ceRNA network to explore the mechanism of BRCA progression. In summary, we conducted a systemic and extensive bioinformatics investigation and established an IRG-based prognostic scoring model. Finally, we constructed a ceRNA regulatory axis that might play a significant role in BRCA development. More research is required to confirm this result. Scoring system-based patient grouping can help predict the outcome of patients with stage N+M0 BRCA more effectively and determine their sensitivity to immunotherapies, which will aid the development of personalised therapeutic strategies and inspire the research and development of novel medications.

Pediatric Sarcomas: The Next Generation of Molecular Studies

Pediatric sarcomas constitute one of the largest groups of childhood cancers, following hematopoietic, neural, and renal lesions. Partly because of their diversity, they continue to offer challenges in diagnosis and treatment. In spite of the diagnostic, nosologic, and therapeutic gains made with genetic technology, newer means for investigation are needed. This article reviews emerging technology being used to study human neoplasia and how these methods might be applicable to pediatric sarcomas. Methods reviewed include single cell RNA sequencing (scRNAseq), spatial multi-omics, high-throughput functional genomics, and clustered regularly interspersed short palindromic sequence-Cas9 (CRISPR-Cas9) technology. In spite of these advances, the field continues to be challenged by a dearth of properly annotated materials, particularly from recurrences and metastases and pre- and post-treatment samples.

Tumor Microenvironment-Mediated Immune Profiles Characterized by Distinct Survival Outcome and Immunotherapeutic Efficacy in Breast Cancer

Background: Numerous reports have highlighted that the tumor microenvironment (TME) is closely linked to survival outcome and therapeutic efficacy. However, a comprehensive investigation of the TME feature in breast cancer (BC) has not been performed.

Methods: Here, we performed consensus clustering analysis based on TME cell expression profiles to construct TME pattern clusters and TME-related gene signature in BC. GSVA combined with CIBERSORT and ssGSEA algorithms were applied to evaluate the differences in biological pathway and immune cell infiltration level, respectively. The PCA method was employed to construct TME-score to quantify the TME-mediated pattern level in individual BC patients.

Results: We determined two distinct TME gene clusters among 3,738 BC samples, which exhibited distinct survival outcome and enriched biological processes. The TME features demonstrated that these two clusters corresponded to the established immune profiles: hot and cold tumor phenotypes, respectively. Based on TME-related signature genes, we constructed the TME-score and stratified BC patients into low and high TME-score groups. Patients with high TME-score exhibited favorable outcome and increased infiltration of immune cells. Further investigation revealed that high TME-score was also related with high expression of immunosuppressive molecules, decreased tumor mutation burden (TMB), and high rate of mutation in significantly mutated genes (SMGs) (e.g., PIK3CA and CDH1).

Conclusion: Assessing the TME-mediated pattern level of individual BC patients will assist us in better understanding the responses of BC patients to immunotherapies and directing more effective immunotherapeutic approaches.

A novel immune-related gene signature predicting survival in sarcoma patients

Sarcomas are a heterogeneous group of rare mesenchymal tumors. The migration of immune cells into these tumors and the prognostic impact of tumor-specific factors determining their interaction with these tumors remain poorly understood. The current risk stratification system is insufficient to provide a precise survival prediction and treatment response. Thus, valid prognostic models are needed to guide treatment. This study analyzed the gene expression and outcome of 980 sarcoma patients from seven public datasets. The abundance of immune cells and the response to immunotherapy was calculated. Immune-related genes (IRGs) were screened through a weighted gene co-expression network analysis (WGCNA). A least absolute shrinkage and selection operator (LASSO) Cox regression was used to establish a powerful IRG signature predicting prognosis. The identified IRG signature incorporated 14 genes and identified high-risk patients in sarcoma cohorts. The 14-IRG signature was identified as an independent risk factor for overall and disease-free survival. Moreover, the IRG signature acted as a potential indicator for immunotherapy. The nomogram based on the risk score was built to provide a more accurate survival prediction. The decision tree with IRG risk score discriminated risk subgroups powerfully. This proposed IRG signature is a robust biomarker to predict outcomes and treatment responses in sarcoma patients.

Artificial intelligence applied to musculoskeletal oncology: a systematic review

Developments in artificial intelligence have the potential to improve the care of patients with musculoskeletal tumors. We performed a systematic review of the published scientific literature to identify the current state of the art of artificial intelligence applied to musculoskeletal oncology, including both primary and metastatic tumors, and across the radiology, nuclear medicine, pathology, clinical research, and molecular biology literature. Through this search, we identified 252 primary research articles, of which 58 used deep learning and 194 used other machine learning techniques. Articles involving deep learning have mostly involved bone scintigraphy, histopathology, and radiologic imaging. Articles involving other machine learning techniques have mostly involved transcriptomic analyses, radiomics, and clinical outcome prediction models using medical records. These articles predominantly present proof-of-concept work, other than the automated bone scan index for bone metastasis quantification, which has translated to clinical workflows in some regions. We systematically review and discuss this literature, highlight opportunities for multidisciplinary collaboration, and identify potentially clinically useful topics with a relative paucity of research attention. Musculoskeletal oncology is an inherently multidisciplinary field, and future research will need to integrate and synthesize noisy siloed data from across clinical, imaging, and molecular datasets. Building the data infrastructure for collaboration will help to accelerate progress towards making artificial intelligence truly useful in musculoskeletal oncology.

Evaluation of deep learning techniques for identification of sarcoma-causing carcinogenic mutations

The abnormal growth of human healthy cells is called cancer. One of the major types of cancer is sarcoma, mostly found in human bones and soft tissue cells. It commonly occurs in children. According to a survey of the United States of America, there are more than 17,000 sarcoma patients registered each year which is 15% of all cancer cases. Recognition of cancer at its early stage saves many lives. The proposed study developed a framework for the early detection of human sarcoma cancer using deep learning Recurrent Neural Network (RNN) algorithms. The DNA of a human cell is made up of 25,000 to 30,000 genes. Each gene is represented by sequences of nucleotides. The nucleotides in a sequence of a driver gene can change which is termed as mutations. Some mutations can cause cancer. There are seven types of a gene whose mutation causes sarcoma cancer. The study uses the dataset which has been taken from more than 134 samples and includes 141 mutations in 8 driver genes. On these gene sequences RNN algorithms Long and Short-Term Memory (LSTM), Gated Recurrent Units and Bi-directional LSTM (Bi-LSTM) are used for training. Rigorous testing techniques such as Self-consistency testing, independent set testing, 10-fold cross-validation test are applied for the validation of results. These validation techniques yield several metrics such as Area Under the Curve (AUC), sensitivity, specificity, Mathew's correlation coefficient, loss, and accuracy. The proposed algorithm exhibits an accuracy of 99.6% with an AUC value of 1.00.

Pyroptosis-Mediated Molecular Subtypes are Characterized by Distinct Tumor Microenvironment Infiltration Characteristics in Breast Cancer

Background

Numerous reports have highlighted that pyroptosis is closely linked to tumorigenesis and drug resistance of tumors. However, the potential role of pyroptosis in regulating immune cell infiltration in tumor microenvironment (TME) remains unclear.

Methods

Here, we performed consensus clustering analysis based on the expression of 10 typical pyroptosis-related regulators (PRRs) to construct pyroptosis-mediated tumor pattern clusters and pyroptosis-related gene signature in breast cancer (BC). GSVA combined with ssGSEA methods were applied to evaluate the differences in biological pathway and immune cell infiltration level, respectively. The PCA method was employed to construct the pyro-score to quantify the pyroptosis pattern level of individual BC patient.

Results

We determined three distinct pyro-clusters among 1852 BC samples, which exhibited different survival outcomes and enriched biological processes. The TME features demonstrated that these three clusters corresponded to three established immune profiles: immune-desert, immune-excluded and immune-inflamed phenotype, respectively. Based on pyroptosis-related signature genes, we constructed the pyro-score and stratified BC patients into high and low pyro-score group. Patients with high pyro-score exhibited favorable outcome and increased infiltration of immune cells. Further investigation revealed that high pyro-score was also related to high expression of immunosuppressive molecules, decreased tumor mutation burden (TMB) and high rate of mutation in significantly mutated genes (SMGs) (eg, PIK3CA and CDH1).

Conclusion

This research emphasizes the indispensable role of pyroptosis in TME complexity and diversity. Assessing the pyroptosis pattern level of individual BC patient will assist us in better understanding TME features and directing more effective immunotherapeutic approaches.

Prognosis Implication of a Novel Metabolism-Related Gene Signature in Ewing Sarcoma

Ewing sarcoma (ES) is one of the most common bone cancers in adolescents and children. Growing evidence supports the view that metabolism pathways play critical roles in numerous cancers (He et al. (2020)). However, the correlation between metabolism-associated genes (MTGs) and Ewing sarcoma has not been investigated systematically. Here, based on the univariate Cox regression analysis, we get survival genes from differentially expressed genes (DEGs) from Gene Expression Omnibus (GEO) cohort. Multivariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were employed to establish the MTG signature. Comprehensive survival analyses including receiver operating characteristic (ROC) curves and Kaplan-Meier analysis were applied to estimate the independent prognostic value of the signature. The ICGC cohort served as the validation cohort. A nomogram was constructed based on the risk score of the MTG signature and other independent clinical variables. The CIBERSORT algorithm was applied to estimate immune infiltration. In addition, we explored the correlation between MTG signature and immune checkpoints. Collectively, this work presents a novel MTG signature for prognostic prediction of Ewing sarcoma. It also suggests six genes that are potential prognostic indicators and therapeutic targets for ES.

Pyroptosis-Related Gene Signature Is a Novel Prognostic Biomarker for Sarcoma Patients

Sarcoma is a rare and an extremely aggressive form of cancer that originates from mesenchymal cells. Pyroptosis exerts a dual effect on tumours by inhibiting tumour cell proliferation while creating a microenvironment suitable for tumour cell development and proliferation. However, the significance of pyroptosis-related gene (PRG) expression in sarcoma has not yet been evaluated. Here, we conduct a retrospective analysis to examine PRG expression in 256 sarcoma samples from The Cancer Genome Atlas database. We identified the PRGs that had a significant correlation with overall patient survival in sarcoma by performing a univariate Cox regression analysis. Subsequently, we conducted a LASSO regression analysis and created a risk model for a six-PRG signature. As indicated from the Kaplan–Meier analysis, this signature revealed a significant difference between high- and low-risk sarcoma patients. A receiver operating characteristic curve analysis confirmed that this signature could predict overall patient survival in sarcoma patients with high sensitivity and specificity. Gene ontology annotation and Kyoto Encyclopaedia of Genes and Genomes pathway enrichment analyses revealed that five independent PRGs were closely associated with increased immune activity. Moreover, we also deciphered that increased number of immune cells infiltrated the tumour microenvironment in sarcoma. In brief, the PRG signature can effectively act as novel prognostic biomarker for sarcoma patients and is associated with the tumour immune microenvironment.

A Study of the Recent Trends of Immunology: Key Challenges, Domains, Applications, Datasets, and Future Directions

The human immune system is very complex. Understanding it traditionally required specialized knowledge and expertise along with years of study. However, in recent times, the introduction of technologies such as AIoMT (Artificial Intelligence of Medical Things), genetic intelligence algorithms, smart immunological methodologies, etc., has made this process easier. These technologies can observe relations and patterns that humans do and recognize patterns that are unobservable by humans. Furthermore, these technologies have also enabled us to understand better the different types of cells in the immune system, their structures, their importance, and their impact on our immunity, particularly in the case of debilitating diseases such as cancer. The undertaken study explores the AI methodologies currently in the field of immunology. The initial part of this study explains the integration of AI in healthcare and how it has changed the face of the medical industry. It also details the current applications of AI in the different healthcare domains and the key challenges faced when trying to integrate AI with healthcare, along with the recent developments and contributions in this field by other researchers. The core part of this study is focused on exploring the most common classifications of health diseases, immunology, and its key subdomains. The later part of the study presents a statistical analysis of the contributions in AI in the different domains of immunology and an in-depth review of the machine learning and deep learning methodologies and algorithms that can and have been applied in the field of immunology. We have also analyzed a list of machine learning and deep learning datasets about the different subdomains of immunology. Finally, in the end, the presented study discusses the future research directions in the field of AI in immunology and provides some possible solutions for the same.

Immune-associated molecular occurrence and prognosis predictor of hepatocellular carcinoma: an integrated analysis of GEO datasets

Hepatocellular carcinoma (HCC) is the fifth most common cancer and the second most common cause of cancer-related deaths worldwide. As immune response failure is the main factor in the occurrence and poor prognosis of HCC, our study aimed to develop an immune-associated molecular occurrence and prognosis predictor (IMOPP) of HCC. To that end, we discovered a 4-gene immune-associated gene signature: C-C motif chemokine ligand 14 (CCL14), kallikrein B1 (KLKB1), vasoactive intestinal peptide receptor 1 (VIPR1), and cluster of differentiation 4 (CD4). When tested on three cohorts as an immune-associated molecular occurrence predictor (IMOP), it had high sensitivity, specificity, and area under the receiver operating characteristics curve. When tested as an immune-associated molecular prognosis predictor (IMPP), it stratified the HCC prognosis for overall survival (Kaplan-Meier analysis, log rank P = 0.0016; Cox regression, HR = 1.832, 95% CI = 1.173-2.859, P = 0.008) and disease-free survival (Kaplan-Meier analysis, log rank P = 0.0227). IMPP also significantly correlated with the clinicopathological characteristics of HCC; integrating it with clinicopathological characteristics improved the accuracy of a nomogram for overall survival prediction (C-index: 0.7097 vs. 0.6631). In HCC tumor microenviroments, the proportion of CD8⁺ T cells significantly differed between IMOP-stratified groups. We conclude that IMOPP can potentially predict the occurrence of HCC in high-risk populations and improve prognostic accuracy by providing new biomarkers for risk stratification. In addition, we believe that the IMOP mechanism may be related to its effect on the proportion of CD8⁺ T cells in tumor-infiltrating lymphocytes.

Prognostic Immune-Related Genes of Patients With Ewing's Sarcoma

Ewing's sarcoma (ES) is an extremely aggressive malignant bone tumor with a high incidence among children and adolescents. The immune microenvironment plays an important role in ES development. The aim of the current study was to investigate the immune microenvironment in ES patients to identify immune-related gene signatures. Single-sample gene set enrichment analysis (ssGSEA) was used to cluster the RNA sequences of 117 ES patients, and their immune cell infiltration data were downloaded and evaluated based on the Gene Expression Omnibus (GEO) database. High, medium, and low immune cell infiltration clusters were identified. Based on the comparison of clusters with high and low immune cell infiltration, normal skeletal muscle cells, and ES, we identified 198 common differentially expressed genes. GO and KEGG enrichment analyses indicated the underlying immune mechanism in ES. Cox and LASSO regression analyses were conducted to select immune-related prognostic genes. An external dataset from the International Cancer Genome Consortium (ICGC) was used to validate our results. Ten immune-related, independent prognostic genes (FMO2, GLCE, GPR64, IGFBP4, LOXHD1, PBK, SNAI2, SPP1, TAPT1-AS1, and ZIC2) were selected for analysis. These 10 immune-related genes signature were determined to exhibit independent prognostic significance for ES. The results of this study provide an approach for predicting the prognosis and survival of ES patients, and the elucidated genes may be a promising target for immunotherapy.

Ewing Sarcoma-Diagnosis, Treatment, Clinical Challenges and Future Perspectives

Ewing sarcoma, a highly aggressive bone and soft-tissue cancer, is considered a prime example of the paradigms of a translocation-positive sarcoma: a genetically rather simple disease with a specific and neomorphic-potential therapeutic target, whose oncogenic role was irrefutably defined decades ago. This is a disease that by definition has micrometastatic disease at diagnosis and a dismal prognosis for patients with macrometastatic or recurrent disease. International collaborations have defined the current standard of care in prospective studies, delivering multiple cycles of systemic therapy combined with local treatment; both are associated with significant morbidity that may result in strong psychological and physical burden for survivors. Nevertheless, the combination of non-directed chemotherapeutics and ever-evolving local modalities nowadays achieve a realistic chance of cure for the majority of patients with Ewing sarcoma. In this review, we focus on the current standard of diagnosis and treatment while attempting to answer some of the most pressing questions in clinical practice. In addition, this review provides scientific answers to clinical phenomena and occasionally defines the resulting translational studies needed to overcome the hurdle of treatment-associated morbidities and, most importantly, non-survival.