scholarly journals CD11c-CD8 Spatial Cross Presentation: A Novel Approach to Link Immune Surveillance and Patient Survival in Soft Tissue Sarcoma

Cancers ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 1175
Author(s):  
Yanhong Su ◽  
Panagiotis Tsagkozis ◽  
Andri Papakonstantinou ◽  
Nicholas P. Tobin ◽  
Okan Gultekin ◽  
...  
Keyword(s):  
Soft Tissue ◽  
Tumor Microenvironment ◽  
Soft Tissue Sarcoma ◽  
Patient Survival ◽  
Immune Surveillance ◽  
Multivariable Analysis ◽  
Predictive Biomarkers ◽  
University Hospital ◽  
Immune Reactivity ◽  
Cross Presentation

Checkpoint inhibitors are slowly being introduced in the care of specific sarcoma subtypes such as undifferentiated pleomorphic sarcoma, alveolar soft part sarcoma, and angiosarcoma even though formal indication is lacking. Proper biomarkers to unravel potential immune reactivity in the tumor microenvironment are therefore expected to be highly warranted. In this study, intratumoral spatial cross presentation was investigated as a novel concept where immune cell composition in the tumor microenvironment was suggested to act as a proxy for immune surveillance. Double immunohistochemistry revealed a prognostic role of direct spatial interactions between CD11c+ antigen-presenting cells (APCs) and CD8+ cells in contrast to each marker alone in a soft tissue sarcoma (STS) cohort of 177 patients from the Karolinska University Hospital (MFS p = 0.048, OS p = 0.025). The survival benefit was verified in multivariable analysis (MFS p = 0.012, OS p = 0.004). Transcriptomics performed in the TCGA sarcoma cohort confirmed the prognostic value of combining CD11c with CD8 (259 patients, p = 0.005), irrespective of FOXP3 levels and in a CD274 (PD-LI)-rich tumor microenvironment. Altogether, this study presents a histopathological approach to link immune surveillance and patient survival in STS. Notably, spatial cross presentation as a prognostic marker is distinct from therapy response-predictive biomarkers such as immune checkpoint molecules of the PD-L1/PD1 pathway.

scholarly journals PLCG2 as a potential indicator of tumor microenvironment remodeling in soft tissue sarcoma

Medicine ◽  
2021 ◽  
Vol 100 (11) ◽  
pp. e25008
Author(s):  
Zhengtian Li ◽  
Rong Zhao ◽  
Wenkang Yang ◽  
Chan Li ◽  
Jun Huang ◽  
...  
Keyword(s):  
Soft Tissue ◽  
Tumor Microenvironment ◽  
Soft Tissue Sarcoma ◽  
Potential Indicator

scholarly journals The PTEN Tumor Suppressor Gene in Soft Tissue Sarcoma

Cancers ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1169 ◽  
Author(s):  
Sioletic Stefano ◽  
Scambia Giovanni
Keyword(s):  
Soft Tissue ◽  
Tumor Suppressor ◽  
Soft Tissue Sarcoma ◽  
Biological Significance ◽  
Treatment Strategies ◽  
Predictive Biomarkers ◽  
Suppressor Gene ◽  
Oncogenic Signaling ◽  
Tensin Homolog ◽  
Pathologic Features

Soft tissue sarcoma (STS) is a rare malignancy of mesenchymal origin classified into more than 50 different subtypes with distinct clinical and pathologic features. Despite the poor prognosis in the majority of patients, only modest improvements in treatment strategies have been achieved, largely due to the rarity and heterogeneity of these tumors. Therefore, the discovery of new prognostic and predictive biomarkers, together with new therapeutic targets, is of enormous interest. Phosphatase and tensin homolog (PTEN) is a well-known tumor suppressor that commonly loses its function via mutation, deletion, transcriptional silencing, or protein instability, and is frequently downregulated in distinct sarcoma subtypes. The loss of PTEN function has consequent alterations in important pathways implicated in cell proliferation, survival, migration, and genomic stability. PTEN can also interact with other tumor suppressors and oncogenic signaling pathways that have important implications for the pathogenesis in certain STSs. The aim of the present review is to summarize the biological significance of PTEN in STS and its potential role in the development of new therapeutic strategies.

Predictive biomarkers of trabectedin (TR) and olaparib (OL) synergism in preclinical models of bone and soft tissue sarcoma (BSTS).

2014 ◽  
Vol 32 (15_suppl) ◽  
pp. 10569-10569
Author(s):  
Ymera Pignochino ◽  
Federica Capozzi ◽  
Carmine Dell' Aglio ◽  
Marco Basiricò ◽  
Annalisa Lorenzato ◽  
...  
Keyword(s):  
Soft Tissue ◽  
Soft Tissue Sarcoma ◽  
Predictive Biomarkers ◽  
Preclinical Models

Veristrat based stratification of responses to immune checkpoint blockade (ICB).

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e20512-e20512
Author(s):  
Paul R. Walker ◽  
Nitika Sharma ◽  
Chipman Robert Geoffrey Stroud ◽  
Mahvish Muzaffar ◽  
Cynthia R. Cherry ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Median Survival ◽  
Immune Checkpoint ◽  
Immune Surveillance ◽  
Predictive Biomarkers ◽  
Immune Checkpoint Blockade ◽  
Checkpoint Blockade ◽  
Clinicopathologic Characteristics ◽  
Metastatic Nsclc ◽  
Platinum Based Chemotherapy

e20512 Background: Veristrat (Biodesix, Boulder, CO) is a blood based proteomic assay that is dominated by inflammatory proteins and is prognostic and predictive in metastatic NSCLC after treatment with platinum based chemotherapy (Gregorc et al, Lancet 2014). Smoldering inflammation in the tumor microenvironment regulates and escalates cancer invasion, angiogenesis and immune surveillance escape (Balkwill and Mantovani, Lancet 2001). There is preclinical evidence to suggest that the tumor microenvironment can be altered with immunomodulatory interventions (Martino et al, 2016). While immune checkpoint blockade has shown durable benefit in metastatic NSCLC, the response rates still hover around 20%. As a result, identification of predictive biomarkers are of critical importance. The predictive value of the Veristrat assay with respect to ICB is poorly defined. Methods: At our institution, 83 pts with metastatic lung cancer pts were treated with nivolumab between 6/2015 to 12/2016. The following clinicopathologic characteristics were abstracted from medical records: tumor histology, Veristrat status, no. of doses of nivolumab, irAEs and overall survival. Results: Of the 83 pts, 65 pts were found to have NSCLC. Veristrat status was available for 17/65 of these pts. Nine pts were identified to have “Veristrat good” and seven pts were “Veristrat poor”. Median number of nivolumab doses was 4. Median survival for all patients was 30 weeks. Median survival was 20 weeks for “Veristrat poor” and 26 weeks for “Veristrat good”(p = 0.68). Grade 3-4 irAEs were noted in 5/9 patients with “Veristrat good” and 5/7 patients with “Veristrat poor”. Conclusions: “Veristrat poor” pts treated with ICB have a lower median survival as compared to “Veristrat good” pts. Our study did not meet statistically significant end point due to limited sample size. Further studies are needed to identify poorly immunogenic tumors and develop novel treatment approaches to optimize outcomes. [Table: see text]

scholarly journals Identification of Prognostic lncRNA Related to the Immune Microenvironment of Soft Tissue Sarcoma

2021 ◽  
Author(s):  
Wang-Ying Dai ◽  
Bin Wang ◽  
Jian-Yi Li ◽  
Jun-Cheng Zhu ◽  
Zong-ping Luo
Keyword(s):  
Cluster Analysis ◽  
High Risk ◽  
Soft Tissue ◽  
Tumor Microenvironment ◽  
Soft Tissue Sarcoma ◽  
Expression Analysis ◽  
Risk Group ◽  
Immune Microenvironment ◽  
Kaplan Meier ◽  
Unsupervised Cluster Analysis

Abstract Background: Soft tissue sarcoma is a malignant tumor with high degree of malignancy and poor prognosis, originating from mesenchymal tissue. Long non-coding RNAs (lncRNAs) are involved in various biological and pathological processes in the body. They target mRNA through transcription or post-transcription, resulting in the occurrence, invasion, and metastasis of tumors. Therefore, they are highly relevant with regard to early diagnoses and as prognostic indicators.Objective: The objective of the present study was to identify immune-related lncRNAs associated with the tumor microenvironment that can be used to predict soft tissue sarcomas.Methods: Clinical data and follow-up data were obtained from the cBioPortal database, and RNA sequencing data used for the model structure can be accessed from. The Cancer Genome Atlas (TCGA) database. LncRNAs were screened by differential expression analysis and co-expression analysis. The Cox regression model and Kaplan–Meier analysis were used to study the association between lncRNAs and soft tissue sarcoma prognosis in the immune microenvironment. Unsupervised cluster analysis was then completed to discover the impact of screening lncRNAs on disease. Lastly, we constructed an mRNA-lncRNA network by Cytoscape software.Results: Unsupervised cluster analysis revealed that the 210 lncRNAs screened showed strong correlation with the tumor immune microenvironment. Two signatures containing seven and five lncRNAs related to the tumor microenvironment were constructed and used to predict overall survival (OS) and disease-free survival (DFS). The Kaplan–Meier（K-M） survival curve showed that the prognoses of patients in the high-risk and low-risk groups differed significantly, and the prognosis associated with the low-risk group was better than that associated with the high-risk group. Two nomograms with predictive capabilities were established.Conclusion: The results indicate that seven OS- and five DFS-related lncRNAs are correlated with the prognosis of soft tissue sarcoma.

scholarly journals PET/MR fusion texture analysis for the clinical outcome prediction in soft-tissue sarcoma

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Wenzhe Zhao ◽  
Xin Huang ◽  
Geliang Wang ◽  
Jianxin Guo
Keyword(s):  
Soft Tissue ◽  
Soft Tissue Sarcoma ◽  
Univariate Analysis ◽  
Multivariable Analysis ◽  
Classification Performance ◽  
Fusion Method ◽  
Mr Images ◽  
Feature Level Fusion ◽  
Optimal Classification ◽  
Level Fusion

Abstract Background Various fusion strategies (feature-level fusion, matrix-level fusion, and image-level fusion) were used to fuse PET and MR images, which might lead to different feature values and classification performance. The purpose of this study was to measure the classification capability of features extracted using various PET/MR fusion methods in a dataset of soft-tissue sarcoma (STS). Methods The retrospective dataset included 51 patients with histologically proven STS. All patients had pre-treatment PET and MR images. The image-level fusion was conducted using discrete wavelet transformation (DWT). During the DWT process, the MR weight was set as 0.1, 0.2, 0.3, 0.4, …, 0.9. And the corresponding PET weight was set as 1- (MR weight). The fused PET/MR images was generated using the inverse DWT. The matrix-level fusion was conducted by fusing the feature calculation matrix during the feature extracting process. The feature-level fusion was conducted by concatenating and averaging the features. We measured the predictive performance of features using univariate analysis and multivariable analysis. The univariate analysis included the Mann-Whitney U test and receiver operating characteristic (ROC) analysis. The multivariable analysis was used to develop the signatures by jointing the maximum relevance minimum redundancy method and multivariable logistic regression. The area under the ROC curve (AUC) value was calculated to evaluate the classification performance. Results By using the univariate analysis, the features extracted using image-level fusion method showed the optimal classification performance. For the multivariable analysis, the signatures developed using the image-level fusion-based features showed the best performance. For the T1/PET image-level fusion, the signature developed using the MR weight of 0.1 showed the optimal performance (0.9524(95% confidence interval (CI), 0.8413–0.9999)). For the T2/PET image-level fusion, the signature developed using the MR weight of 0.3 showed the optimal performance (0.9048(95%CI, 0.7356–0.9999)). Conclusions For the fusion of PET/MR images in patients with STS, the signatures developed using the image-level fusion-based features showed the optimal classification performance than the signatures developed using the feature-level fusion and matrix-level fusion-based features, as well as the single modality features. The image-level fusion method was more recommended to fuse PET/MR images in future radiomics studies.

scholarly journals Addressing the Adult Soft Tissue Sarcoma Microenvironment with Intratumoral Immunotherapy

Sarcoma ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Shailaja Raj ◽  
Lance D. Miller ◽  
Pierre L. Triozzi
Keyword(s):  
Soft Tissue ◽  
Tumor Microenvironment ◽  
Soft Tissue Sarcoma ◽  
Immune Cells ◽  
Crucial Role ◽  
Dynamic Equilibrium ◽  
Heterogeneous Group ◽  
Complex Interactions

Sarcoma is comprised of a heterogeneous group of tumors originating from the mesenchyme. Sarcoma is also the first tumor that responded to immunotherapeutic agents often termed as “Coley’s toxins.” However, immunotherapy is yet to establish its presence in sarcomas. Complex interactions between tumor and immune cells in the tumor microenvironment play a crucial role in response to immunotherapy. There is a dynamic equilibrium created by the immune cells infiltrating the tumor, and this forms the basis of tumor evasion. Manipulating the intratumoral microenvironment will help overcome tumor evasion.

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