Tumor border sharpness correlates with HLA-G expression in low-grade gliomas

HLA-G 5'URR regulatory polymorphisms are associated with the risk of developing gliomas

BACKGROUND

Human leukocyte antigen G (HLA-G) belongs to non-classical MHC class I molecules that is involved in the suppression of immune response. As HLA-G plays important role in the maintenance of fetal tolerance, its overexpression has been associated with tumor progression. For the regulation of HLA-G levels, genetic variants within the 5' upstream regulatory region (5'URR) are of crucial importance. Our study aimed to analyze the association between 16 HLA-G 5'URR variants, sHLA-G level and clinical variables in glioma patients.

METHODS

We investigated 59 patients with gliomas (mean age 54.70 ± 15.10 years) and 131 healthy controls (mean age 41.45 ± 9.75 years). Patient's blood was obtained on the day of surgical treatment. The HLA-G 5'URR polymorphisms were typed by direct sequencing and the plasma level of sHLA-G assessed by ELISA.

RESULTS

Haploblock within HLA-G 5'URR consisting of -762T, -716G, -689G, -666T, -633A, followed by -486C and -201A alleles were significantly more frequent in patients with gliomas than in the controls (p < 0.05). No correlation of HLA-G 5'URR variants with sHLA-G plasma level was found. Analysis of HLA-G 5'URR variants with main clinical variables in patients with grade IV gliomas revealed that haploblock carriers of -762CT, -716TG, -689AG, -666GT, -633GA, -486AC, -477GC, -201GA followed by -369AC carriers tend to have lower age at onset as compared to other genotype carriers (p = 0.04).

CONCLUSION

Our results suggest genetic association of HLA-G 5'URR variants with risk of developing gliomas and possible contribution of HLA-G to disease pathology.

Multicenter clinical radiomics-integrated model based on [18F]FDG PET and multi-modal MRI predict ATRX mutation status in IDH-mutant lower-grade gliomas

OBJECTIVES

To develop a clinical radiomics-integrated model based on ¹⁸ F-fluorodeoxyglucose positron emission tomography ([¹⁸F]FDG PET) and multi-modal MRI for predicting alpha thalassemia/mental retardation X-linked (ATRX) mutation status of IDH-mutant lower-grade gliomas (LGGs).

METHODS

One hundred and two patients (47 ATRX mutant-type, 55 ATRX wild-type) diagnosed with IDH-mutant LGGs (CNS WHO grades 1 and 2) were retrospectively enrolled. A total of 5540 radiomics features were extracted from structural MR (sMR) images (contrast-enhanced T1-weighted imaging, CE-T1WI; T2-weighted imaging, and T2WI), functional MR (fMR) images (apparent diffusion coefficient, ADC; cerebral blood volume, CBV), and metabolic PET images ([¹⁸F]FDG PET). The random forest algorithm was used to establish a clinical radiomics-integrated model, integrating the optimal multi-modal radiomics model with three clinical parameters. The predictive effectiveness of the models was evaluated by receiver operating characteristic (ROC) and decision curve analysis (DCA).

RESULTS

The optimal multi-modal model incorporated sMR (CE-T1WI), fMR (ADC), and metabolic ([¹⁸F]FDG) images ([¹⁸F]FDG PET+ADC+ CE-T1WI) with the area under curves (AUCs) in the training and test groups of 0.971 and 0.962, respectively. The clinical radiomics-integrated model, incorporating [¹⁸F]FDG PET+ADC+CE-T1WI, three clinical parameters (KPS, SFSD, and ATGR), showed the best predictive effectiveness in the training and test groups (0.987 and 0.975, respectively).

CONCLUSIONS

The clinical radiomics-integrated model with metabolic, structural, and functional information based on [¹⁸F]FDG PET and multi-modal MRI achieved promising performance for predicting the ATRX mutation status of IDH-mutant LGGs.

KEY POINTS

• The clinical radiomics-integrated model based on [¹⁸F]FDG PET and multi-modal MRI achieved promising performance for predicting ATRX mutation status in LGGs. • The study investigated the value of multicenter clinical radiomics-integrated model based on [¹⁸F]FDG PET and multi-modal MRI in LGGs regarding ATRX mutation status prediction. • The integrated model provided structural, functional, and metabolic information simultaneously and demonstrated with satisfactory calibration and discrimination in the training and test groups (0.987 and 0.975, respectively).

Development of prognostic indicator based on NAD+ metabolism related genes in glioma

Background

Studies have shown that Nicotinamide adenine dinucleotide (NAD+) metabolism can promote the occurrence and development of glioma. However, the specific effects and mechanisms of NAD+ metabolism in glioma are unclear and there were no systematic researches about NAD+ metabolism related genes to predict the survival of patients with glioma.

Methods

The research was performed based on expression data of glioma cases in the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. Firstly, TCGA-glioma cases were classified into different subtypes based on 49 NAD+ metabolism-related genes (NMRGs) by consensus clustering. NAD+ metabolism-related differentially expressed genes (NMR-DEGs) were gotten by intersecting the 49 NMRGs and differentially expressed genes (DEGs) between normal and glioma samples. Then a risk model was built by Cox analysis and the least shrinkage and selection operator (LASSO) regression analysis. The validity of the model was verified by survival curves and receiver operating characteristic (ROC) curves. In addition, independent prognostic analysis of the risk model was performed by Cox analysis. Then, we also identified different immune cells, HLA family genes and immune checkpoints between high and low risk groups. Finally, the functions of model genes at single-cell level were also explored.

Results

Consensus clustering classified glioma patients into two subtypes, and the overall survival (OS) of the two subtypes differed. A total of 11 NAD+ metabolism-related differentially expressed genes (NMR-DEGs) were screened by overlapping 5,995 differentially expressed genes (DEGs) and 49 NAD+ metabolism-related genes (NMRGs). Next, four model genes, PARP9, BST1, NMNAT2, and CD38, were obtained by Cox regression and least absolute shrinkage and selection operator (Lasso) regression analyses and to construct a risk model. The OS of high-risk group was lower. And the area under curves (AUCs) of Receiver operating characteristic (ROC) curves were >0.7 at 1, 3, and 5 years. Cox analysis showed that age, grade G3, grade G4, IDH status, ATRX status, BCR status, and risk Scores were reliable independent prognostic factors. In addition, three different immune cells, Mast cells activated, NK cells activated and B cells naive, 24 different HLA family genes, such as HLA-DPA1 and HLA-H, and 8 different immune checkpoints, such as ICOS, LAG3, and CD274, were found between the high and low risk groups. The model genes were significantly relevant with proliferation, cell differentiation, and apoptosis.

Conclusion

The four genes, PARP9, BST1, NMNAT2, and CD38, might be important molecular biomarkers and therapeutic targets for glioma patients.

Deregulation of HLA-I in cancer and its central importance for immunotherapy

It is now well accepted that many tumors undergo a process of clonal selection which means that tumor antigens arising at various stages of tumor progression are likely to be represented in just a subset of tumor cells. This process is thought to be driven by constant immunosurveillance which applies selective pressure by eliminating tumor cells expressing antigens that are recognized by T cells. It is becoming increasingly clear that the same selective pressure may also select for tumor cells that evade immune detection by acquiring deficiencies in their human leucocyte antigen (HLA) presentation pathways, allowing important tumor antigens to persist within cells undetected by the immune system. Deficiencies in antigen presentation pathway can arise by a variety of mechanisms, including genetic and epigenetic changes, and functional antigen presentation is a hard phenomenon to assess using our standard analytical techniques. Nevertheless, it is likely to have profound clinical significance and could well define whether an individual patient will respond to a particular type of therapy or not. In this review we consider the mechanisms by which HLA function may be lost in clinical disease, we assess the implications for current immunotherapy approaches using checkpoint inhibitors and examine the prognostic impact of HLA loss demonstrated in clinical trials so far. Finally, we propose strategies that might be explored for possible patient stratification.

Radiomics Features Predict Telomerase Reverse Transcriptase Promoter Mutations in World Health Organization Grade II Gliomas via a Machine-Learning Approach

The detection of mutations in telomerase reverse transcriptase promoter (pTERT) is important since preoperative diagnosis of pTERT status helps with evaluating prognosis and determining the surgical strategy. Here, we aimed to establish a radiomics-based machine-learning algorithm and evaluated its performance with regard to the prediction of mutations in pTERT in patients with World Health Organization (WHO) grade II gliomas. In total, 164 patients with WHO grade II gliomas were enrolled in this retrospective study. We extracted a total of 1,293 radiomics features from multi-parametric magnetic resonance imaging scans. Elastic net (used for feature selection) and support vector machine with linear kernel were applied in nested 10-fold cross-validation loops. The predictive model was evaluated by receiver operating characteristic and precision-recall analyses. We performed an unpaired t-test to compare the posterior predictive probabilities among patients with differing pTERT statuses. We selected 12 valuable radiomics features using nested 10-fold cross-validation loops. The area under the curve (AUC) was 0.8446 (95% confidence interval [CI], 0.7735–0.9065) with an optimal summed value of sensitivity of 0.9355 (95% CI, 0.8802–0.9788) and specificity of 0.6197 (95% CI, 0.5071–0.7371). The overall accuracy was 0.7988 (95% CI, 0.7378–0.8598). The F1-score was 0.8406 (95% CI, 0.7684–0.902) with an optimal precision of 0.7632 (95% CI, 0.6818–0.8364) and recall of 0.9355 (95% CI, 0.8802–0.9788). Posterior probabilities of pTERT mutations were significantly different between patients with wild-type and mutant TERT promoters. Our findings suggest that a radiomics analysis with a machine-learning algorithm can be useful for predicting pTERT status in patients with WHO grade II glioma and may aid in glioma management.

The Role of HLA-G in Tumor Escape: Manipulating the Phenotype and Function of Immune Cells

Human leukocyte antigen-G (HLA-G) is a non-classical major histocompatibility complex class I (MHC I) molecule, and under physiological conditions, its expression is strictly restricted to the maternal–fetal interface and immune-privileged organs where HLA-G is expected to contribute to establishment and maintenance of immune tolerance. However, the expression of HLA-G has been found in various types of tumors, and the level of its expression frequently correlates with high-grade histology and poor prognosis, raising the possibility that it may play a negative role in tumor immunity. ILT2 and ILT4, present on a broad of immune cells, have been identified as the main receptors engaging HLA-G, and their interactions have been found to allow the conversion of effectors like NK cells and T cells to anergic or unresponsive state, activated DCs to tolerogenic state, and to drive the differentiation of T cells toward suppressive phenotype. Therefore, tumors can employ HLA-G to modulate the phenotype and function of immune cells, allowing them to escape immune attack. In this review, we discuss the mechanism underlying HLA-G expression and function, its role played in each step of the tumor-immunity cycle, as well as the potential to target it for therapeutic benefit.

Meningiomas: Preoperative predictive histopathological grading based on radiomics of MRI

PURPOSE

We aimed to develop a radiomics model to predict the histopathological grading of meningiomas by magnetic resonance imaging (MRI) before surgery.

METHODS

We recruited 131 patients with pathological diagnosis of meningiomas. All the patients had undergone MRI before surgery on a 3.0 T MRI scanner to obtain T1 fluid- attenuated inversion recovery (T1 FLAIR) images, T2-weighted images (T2WI) and T1 FLAIR with contrast enhancement (CE-T1 FLAIR) images covering the whole brain. The removing features with low variance, univariate feature selection, and least absolute shrinkage and selection operator (LASSO) were used to select radiomics features. Six classifiers were used to train the models (logistic regression (LR), k-nearest neighbor (KNN), decision tree (DT), support vector machine (SVM), random forests (RF), and XGBoost), and then 24 models were established using a random verification method to differentiate low-grade from high-grade meningiomas. The performance was assessed by receiver-operating characteristic (ROC) analysis, the f1-score, sensitivity, and specificity.

RESULTS

The radiomics features were significantly associated with the histopathological grading. Quantitative imaging features (n = 1409) were extracted, and nine features were selected to predict the grades of meningiomas. The best performance of the radiomics model for the degree of differentiation was obtained by SVM (area under the curve (AUC), 0.956; 95% confidence interval (CI), 0.83-1.00; sensitivity, 0.87; specificity, 0.92; f1-score, 0.90).

CONCLUSION

The radiomics models are of great value in predicting the histopathological grades of meningiomas, and have broad prospects in radiology and clinics.

HLA-G Neo-Expression on Tumors

HLA-G is known to modulate the immune system activity in tissues where physiological immune-tolerance is necessary (i.e., maternal-fetal interface, thymus, and cornea). However, the frequent neo-expression of HLA-G in many cancer types has been previously and extensively described and is correlated with a bad prognosis. Despite being an MHC class I molecule, HLA-G is highly present in tumor context and shows unique characteristics of tissue restriction of a Tumor Associated Antigen (TAA), and potent immunosuppressive activity of an Immune CheckPoint (ICP). Consequently, HLA-G appears to be an excellent molecular target for immunotherapy. Although the relevance of HLA-G in cancer incidence and development has been proven in numerous tumors, its neo-expression pattern is still difficult to determine. Indeed, the estimation of HLA-G's actual expression in tumor tissue is limited, particularly concerning the presence and percentage of the new non-canonical isoforms, for which detection antibodies are scarce or inexistent. Here, we summarize the current knowledge about HLA-G neo-expression and implication in various tumor types, pointing out the need for the development of new tools to analyze in-depth the HLA-G neo-expression patterns, opening the way for the generation of new monoclonal antibodies and cell-based immunotherapies.

HLA-G 14 bp In/Del and +3142 C/G genotypes are differentially expressed between patients with grade IV gliomas and controls

Aim: Human Leukocyte Antigen-G (HLA-G) is a non-classical class I molecule that is involved in maternal-fetal immunotolerance. In cancer, this molecule contributes to the tumor escape. The aim of this study was to evaluate the 14 bp In/Del and +3142 C > G polymorphisms of the HLA-G 3' UTR and its relation with plasma and tissue HLA-G expression in patients with grade IV (high-grade) and grade I/II (low-grade) gliomas and controls.Patients and methods: Peripheral blood and tumor biopsies were collected from 85 patients with gliomas and blood samples from 94 controls. Polymorphisms were analyzed from blood DNA. Soluble HLA-G (sHLA-G) was measured by ELISA in plasma of the subjects and the tissue expression by immunohistochemistry on patient's tissue.Results: Higher levels of sHLA-G were observed in grade IV gliomas patients than in controls (p < 0.0001). In grade IV patients, the heterozygous 14pb In/Del, +3142 C/G genotypes and Del/C*In/G haplotype were associated with higher sHLA-G levels (p < 0.0001) when compared with controls. GBM patients were stratified into high and low sHLA-G expression and an association was found between +3142 C allele and high sHLA-G plasmatic levels (p = 0.0095). Tissue HLA-G immunolabel was higher in high-grade than low-grade gliomas (p = 0.0033).Conclusion: This was the first study evaluating HLA-G 3' UTR polymorphisms and expression in patients with gliomas. The 14 bp In/Del and +3142 C/G genotypes and haplotypes showed high influence over sHLA-G expression, suggesting a heterozygous advantage in the tumor context and may contribute to a worse prognosis in glioma patients.

HLA-E expression in diffuse glioma: relationship with clinicopathological features and patient survival

BACKGROUND

Human leukocyte antigen-E (HLA-E) has been extensively investigated in various human cancers including glioma. However, the clinical significance of HLA-E expression in glioma patients has not been elucidated. The current study aimed to investigate the association of HLA-E expression with clinicopathological features and survival in patients with diffuse glioma.

METHODS

A total of 261 glioma patients were enrolled, subsequently, mRNA microarray analysis was conducted to identify the relationship of HLA-E with clinicopathological features and patient survival.

RESULTS

HLA-E was significantly overexpressed in high-grade gliomas compared to low-grade gliomas (LGGs). Moreover, HLA-E expression was significantly higher in diffuse astrocytomas than oligodendrogliomas (p = 0.032, t-test). Kaplan-Meier analysis showed that progression-free survival (PFS) and overall survival (OS) were significantly better in LGG patients with low HLA-E expression (p = 0.018 for PFS and p = 0.020 for OS, Log-rank test). Furthermore, HLA-E expression was identified to be an independent prognostic factor by Cox analysis (p = 0.020 for PFS and p = 0.024 for OS).

CONCLUSIONS

This is the first study which identified the clinical significance of HLA-E in diffuse glioma. HLA-E expression was correlated with more aggressive tumor grade and histological type and was identified as an independent prognostic biomarker in LGG patients.

Unconventional T Cell Targets for Cancer Immunotherapy

Most studies on the immunotherapeutic potential of T cells have focused on CD8 and CD4 T cells that recognize peptide antigens (Ag) presented by polymorphic major histocompatibility complex (MHC) class I and MHC class II molecules, respectively. However, unconventional T cells, which interact with MHC class Ib and MHC-I like molecules, are also implicated in tumor immunity, although their role therein is unclear. These include unconventional T cells targeting MHC class Ib molecules such as HLA-E and its murine ortholog Qa-1b, natural killer T (NKT) cells, mucosal associated invariant T (MAIT) cells, and γδ T cells. Here, we review the current understanding of the roles of these unconventional T cells in tumor immunity and discuss why further studies into the immunotherapeutic potential of these cells is warranted.

A radiomic signature as a non-invasive predictor of progression-free survival in patients with lower-grade gliomas

Objective

The aim of this study was to develop a radiomics signature for prediction of progression-free survival (PFS) in lower-grade gliomas and to investigate the genetic background behind the radiomics signature.

Methods

In this retrospective study, training (n = 216) and validation (n = 84) cohorts were collected from the Chinese Glioma Genome Atlas and the Cancer Genome Atlas, respectively. For each patient, a total of 431 radiomics features were extracted from preoperative T2-weighted magnetic resonance images. A radiomics signature was generated in the training cohort, and its prognostic value was evaluated in both the training and validation cohorts. The genetic characteristics of the group with high-risk scores were identified by radiogenomic analysis, and a nomogram was established for prediction of PFS.

Results

There was a significant association between the radiomics signature (including 9 screened radiomics features) and PFS, which was independent of other clinicopathologic factors in both the training (P < 0.001, multivariable Cox regression) and validation (P = 0.045, multivariable Cox regression) cohorts. Radiogenomic analysis revealed that the radiomics signature was associated with the immune response, programmed cell death, cell proliferation, and vasculature development. A nomogram established using the radiomics signature and clinicopathologic risk factors demonstrated high accuracy and good calibration for prediction of PFS in both the training (C-index, 0.684) and validation (C-index, 0.823) cohorts.

Conclusions

PFS can be predicted non-invasively in patients with LGGs by a group of radiomics features that could reflect the biological processes of these tumors.

•

We developed a non-invasive model for the prediction of PFS in patients with lower-grade gliomas.

•

We further revealed the biological processes underlying the radiomic signature by using comprehensive radiogenomic analysis.

•

PFS of lower-grade gliomas could be predicted effectively based on the radiomics model.

Radiomics Nomogram Building From Multiparametric MRI to Predict Grade in Patients With Glioma: A Cohort Study

BACKGROUND

Accurate classification of gliomas is crucial for prescribing therapy and assessing the prognosis of patients.

PURPOSE

To develop a radiomics nomogram using multiparametric MRI for predicting glioma grading.

STUDY TYPE

Retrospective.

POPULATION

This study involved 85 patients (training cohort: n = 56; validation cohort: n = 29) with pathologically confirmed gliomas.

FIELD STRENGTH/SEQUENCE

1.5T MR, containing contrast-enhanced T₁ -weighted (CET₁ WI), axial T₂ -weighted (T₂ WI), and apparent diffusion coefficient (ADC) sequences.

ASSESSMENT

A region of interest of the tumor was delineated. A total of 652 radiomics features were extracted and were reduced using least absolute shrinkage and selection operator regression.

STATISTICAL TESTING

Radiomic signature, participant's age, and gender were analyzed as potential predictors to perform logistic regression analysis and develop a prediction model of glioma grading, and a radiomics nomogram was used to represent this model. The performance of the nomogram was assessed in terms of discrimination, calibration, and clinical value in glioma grading.

RESULTS

The radiomic signature was significantly associated with glioma grade (P < 0.001) in both the training and validation cohorts. The performance of the radiomics nomogram derived from three MRI sequences (with C-index of 0.971 and 0.961 in the training and validation cohorts, respectively) was improved compared to those based on either CET₁ WI, T₂ WI, or ADC alone in glioma grading (with C-index of 0.914, 0.714, 0.842 in the training cohort, and 0.941, 0.500, 0.730 in the validation cohort). The nomogram derived from three sequences showed good calibration: the calibration curve showed good agreement between the estimated and the actual probability. The decision curve demonstrated that combining three sequences had more favorable clinical predictive value than single sequence imaging.

DATA CONCLUSION

We created and assessed a multiparametric MRI-based radiomics nomogram that may help clinicians classify gliomas more accurately.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:825-833.

MRI features predict p53 status in lower-grade gliomas via a machine-learning approach

Background

P53 mutation status is a pivotal biomarker for gliomas. Here, we developed a machine-learning model to predict p53 status in lower-grade gliomas based on radiomic features extracted from conventional magnetic resonance (MR) images.

Methods

Preoperative MR images were retrospectively obtained from 272 patients with primary grade II/III gliomas. The patients were randomly allocated in a 2:1 ratio to a training (n = 180) or validation (n = 92) set. A total of 431 radiomic features were extracted from each patient. The lest absolute shrinkage and selection operator (LASSO) method was used for feature selection and radiomic signature construction. Subsequently, a machine-learning model to predict p53 status was established using the selected features and a Support Vector Machine classifier. The predictive performance of all individual features and the model was calculated using receiver operating characteristic curves in both the training and validation sets.

Results

The p53-related radiomic signature was built using the LASSO algorithm; this procedure consisted of four first-order statistics or related wavelet features (including Maximum, Median, Minimum, and Uniformity), a shape and size-based feature (Spherical Disproportion), and ten textural features or related wavelet features (including Correlation, Run Percentage, and Sum Entropy). The prediction accuracies based on the area under the curve were 89.6% in the training set and 76.3% in the validation set, which were better than individual features.

Conclusions

These results demonstrate that MR image texture features are predictive of p53 mutation status in lower-grade gliomas. Thus, our procedure can be conveniently used to facilitate presurgical molecular pathological diagnosis.

•

We established a p53-related radiomic signature in lower-grade gliomas based on LASSO algorithm.

•

We developed a machine-learning model using the radiomic signature and a support vector machine.

•

P53 mutation status of lower-grade gliomas was predicted effectively based on our machine-learning model.

MRI features can predict EGFR expression in lower grade gliomas: A voxel-based radiomic analysis

OBJECTIVE

To identify the magnetic resonance imaging (MRI) features associated with epidermal growth factor (EGFR) expression level in lower grade gliomas using radiomic analysis.

METHODS

270 lower grade glioma patients with known EGFR expression status were randomly assigned into training (n=200) and validation (n=70) sets, and were subjected to feature extraction. Using a logistic regression model, a signature of MRI features was identified to be predictive of the EGFR expression level in lower grade gliomas in the training set, and the accuracy of prediction was assessed in the validation set.

RESULTS

A signature of 41 MRI features achieved accuracies of 82.5% (area under the curve [AUC] = 0.90) in the training set and 90.0% (AUC = 0.95) in the validation set. This radiomic signature consisted of 25 first-order statistics or related wavelet features (including range, standard deviation, uniformity, variance), one shape and size-based feature (spherical disproportion), and 15 textural features or related wavelet features (including sum variance, sum entropy, run percentage).

CONCLUSIONS

A radiomic signature allowing for the prediction of the EGFR expression level in patients with lower grade glioma was identified, suggesting that using tumour-derived radiological features for predicting genomic information is feasible.

KEY POINTS

• EGFR expression status is an important biomarker for gliomas. • EGFR in lower grade gliomas could be predicted using radiogenomic analysis. • A logistic regression model is an efficient approach for analysing radiomic features.

Evaluation of sHLA-G levels in serum of patients with prostate cancer identify as a potential of tumor marker

Prostate cancer is the most common cancer type in men and is the second cause of death, due to cancer, in patients over 50, after lung cancer. Prostate specific antigen (PSA) is a widely used tumor marker for prostate cancer. Recently, PSA is discovered in non-prostatic cancer tissues in men and women raising doubts about its specificity for prostatic tissues. PSA exists in low serum level in healthy men and in higher levels in many prostate disorders, including prostatitis and prostate cancer. Thus, a supplementary tumor marker is needed to accurately diagnose the cancer and to observe the patient after treatment. Recently, soluble human leukocyte antigen-G (sHLA-G) has been introduced as a new tumor marker for different cancer types, including colorectal, breast, lung, and ovary. The present descriptive-experimental study was carried out including patients with malignant prostate tumor, patients with benign prostate tumor, and a group of health men as the control group, as judged by an oncologist as well as a pathologist. After sterile blood sampling, sHLA-G was measured by enzyme-linked immunosorbent assay in each group. The data was then analyzed using one-way ANOVA. P≤0.05 was considered as statistically significant. The results showed that the mean of sHLA-G level was high in patients. Also, it was found that there was a significant difference in sHLA serum level between the three groups. The data revealed that sHLA-G can be a novel supplementary tumor marker in addition to PSA to diagnose prostate cancer.

Recent Advances in Our Understanding of HLA-G Biology: Lessons from a Wide Spectrum of Human Diseases

HLA-G is a HLA-class Ib molecule with potent immunomodulatory activities, which is expressed in physiological conditions, where modulation of the immune response is required to avoid allograft recognition (i.e., maternal-fetal interface or transplanted patients). However, HLA-G can be expressed de novo at high levels in several pathological conditions, including solid and hematological tumors and during microbial or viral infections, leading to the impairment of the immune response against tumor cells or pathogens, respectively. On the other hand, the loss of HLA-G mediated control of the immune responses may lead to the onset of autoimmune/inflammatory diseases, caused by an uncontrolled activation of the immune effector cells. Here, we have reviewed novel findings on HLA-G functions in different physiological and pathological settings, which have been published in the last two years. These studies further confirmed the important role of this molecule in the modulation of the immune system.

CD8⁺ T Cell-Independent Immune-Mediated Mechanisms of Anti-Tumor Activity

Despite the growing number of preclinical and clinical trials focused on immunotherapy for the treatment of malignant gliomas, the prognosis for this disease remains grim. Cancer immunotherapy seeks to recruit an effective immune response to eliminate tumor cells. To date, cancer vaccines have shown only limited effectiveness because of our incomplete understanding of the necessary effector cells and mechanisms that yield efficient tumor clearance. CD8+ T cell cytotoxic activity has long been proposed as the primary effector function necessary for tumor regression. However, there is increasing evidence that indicates that components of the immune system other than CD8+ T cells play important roles in tumor eradication and control. The following review should provide an understanding of the mechanisms involved in an effective antitumor response to guide future therapeutic designs. The information provided suggests an alternate means of effective tumor clearance in malignant glioma to the canonical CD8+ cytotoxic T cell mechanism.