A Radiosensitivity Gene Signature and XPO1 Predict Clinical Outcomes for Glioma Patients

Predictive value of magnetic resonance imaging radiomics-based machine learning for disease progression in patients with high-grade glioma

Background

Accurately predicting the prognosis of patients with high-grade glioma (HGG) is potentially important for treatment. However, the predictive value of images of various magnetic resonance imaging (MRI) sequences for prognosis at different time points is unknown. We established predictive machine learning models of HGG disease progression and recurrence using MRI radiomics and explored the factors influencing prediction accuracy.

Methods

Radiomics features were extracted from T1-weighted (T1WI), contrast-enhanced T1-weighted (CE-T1WI), T2-weighted (T2WI), and fluid-attenuated inversion recovery (FLAIR) images (postoperative radiotherapy planning MRI images) obtained from 162 patients with HGG. The Mann-Whitney U test and least absolute shrinkage and selection operator (LASSO) algorithm were used for feature selection. Machine learning models were used to build prediction models to estimate disease progression or recurrence. The influence of different MRI sequences, regions of interest (ROIs), and prediction time points was also explored. The receiver operating characteristic (ROC) curve was used to evaluate the discriminative performance of each model, and the DeLong test was employed to compare the ROC curves.

Results

Radiomics features from T2WI and FLAIR demonstrated greater predictive value for disease progression compared with T1WI or CE-TIWI. The best predictive models, with areas under the ROC curves (AUCs) of 0.70, 0.68, 0.78, 0.78, and 0.78 for predicting disease progression at the 6th, 9th, 12th, 15th, and 18th month after radiotherapy, respectively, were obtained by combining clinical features with gross tumor volume (GTV) and clinical target volume (CTV) features extracted from T2WI and FLAIR.

Conclusions

Structural MRI obtained before radiotherapy can be used to predict the disease progression or posttreatment recurrence of HGG. When using MRI radiomics to predict long-term outcomes as opposed to short-term outcomes, better predictive results may be obtained.

DNA Methylation-Driven Genes for Developing Survival Nomogram for Low-Grade Glioma

Low-grade gliomas (LGG) are heterogeneous, and the current predictive models for LGG are either unsatisfactory or not user-friendly. The objective of this study was to establish a nomogram based on methylation-driven genes, combined with clinicopathological parameters for predicting prognosis in LGG. Differential expression, methylation correlation, and survival analysis were performed in 516 LGG patients using RNA and methylation sequencing data, with accompanying clinicopathological parameters from The Cancer Genome Atlas. LASSO regression was further applied to select optimal prognosis-related genes. The final prognostic nomogram was implemented together with prognostic clinicopathological parameters. The predictive efficiency of the nomogram was internally validated in training and testing groups, and externally validated in the Chinese Glioma Genome Atlas database. Three DNA methylation-driven genes, ARL9, CMYA5, and STEAP3, were identified as independent prognostic factors. Together with IDH1 mutation status, age, and sex, the final prognostic nomogram achieved the highest AUC value of 0.930, and demonstrated stable consistency in both internal and external validations. The prognostic nomogram could predict personal survival probabilities for patients with LGG, and serve as a user-friendly tool for prognostic evaluation, optimizing therapeutic regimes, and managing LGG patients.

A Phase II Study of the Efficacy and Safety of Oral Selinexor in Recurrent Glioblastoma

PURPOSE

Selinexor is an oral selective inhibitor of exportin-1 (XPO1) with efficacy in various solid and hematologic tumors. We assessed intratumoral penetration, safety, and efficacy of selinexor monotherapy for recurrent glioblastoma.

PATIENTS AND METHODS

Seventy-six adults with Karnofsky Performance Status ≥ 60 were enrolled. Patients undergoing cytoreductive surgery received up to three selinexor doses (twice weekly) preoperatively (Arm A; n = 8 patients). Patients not undergoing surgery received 50 mg/m2 (Arm B, n = 24), or 60 mg (Arm C, n = 14) twice weekly, or 80 mg once weekly (Arm D; n = 30). Primary endpoint was 6-month progression-free survival rate (PFS6).

RESULTS

Median selinexor concentrations in resected tumors from patients receiving presurgical selinexor was 105.4 nmol/L (range 39.7-291 nmol/L). In Arms B, C, and D, respectively, the PFS6 was 10% [95% confidence interval (CI), 2.79-35.9], 7.7% (95% CI, 1.17-50.6), and 17% (95% CI, 7.78-38.3). Measurable reduction in tumor size was observed in 19 (28%) and RANO-response rate overall was 8.8% [Arm B, 8.3% (95% CI, 1.0-27.0); C: 7.7% (95% CI, 0.2-36.0); D: 10% (95% CI, 2.1-26.5)], with one complete and two durable partial responses in Arm D. Serious adverse events (AEs) occurred in 26 (34%) patients; 1 (1.3%) was fatal. The most common treatment-related AEs were fatigue (61%), nausea (59%), decreased appetite (43%), and thrombocytopenia (43%), and were manageable by supportive care and dose modification. Molecular studies identified a signature predictive of response (AUC = 0.88).

CONCLUSIONS

At 80 mg weekly, single-agent selinexor induced responses and clinically relevant PFS6 with manageable side effects requiring dose reductions. Ongoing trials are evaluating safety and efficacy of selinexor in combination with other therapies for newly diagnosed or recurrent glioblastoma.

Association Between Oral Microbiota and Human Brain Glioma Grade: A Case-Control Study

Gliomas are the most prevalent form of primary malignant brain tumor, which currently have no effective treatments. Evidence from human studies has indicated that oral microbiota is closely related to cancers; however, whether oral microbiota plays a role in glioma malignancy remains unclear. The present study aimed to investigate the association between oral microbiota and grade of glioma and examine the relationship between malignancy-related oral microbial features and the isocitrate dehydrogenase 1 (IDH1) mutation in glioma. High-grade glioma (HGG; n=23) patients, low-grade glioma (LGG; n=12) patients, and healthy control (HCs; n=24) participants were recruited for this case-control study. Saliva samples were collected and analyzed for 16S ribosomal RNA (rRNA) sequencing. We found that the shift in oral microbiota β-diversity was associated with high-grade glioma (p=0.01). The phylum Patescibacteria was inversely associated with glioma grade (LGG and HC: p=0.035; HGG and HC: p<0.01). The genera Capnocytophaga (LGG and HC: p=0.043; HGG and HC: p<0.01) and Leptotrichia (LGG and HC: p=0.044; HGG and HC: p<0.01) were inversely associated with glioma grades. The genera Bergeyella and Capnocytophaga were significantly more positively correlated with the IDH1 mutation in gliomas when compared with the IDH1-wild-type group. We further identified five oral microbial features (Capnocytophaga Porphyromonas, Haemophilus, Leptotrichia, and TM7x) that accurately discriminated HGG from LGG (area under the curve [AUC]: 0.63, 95% confidence interval [CI]: 0.44-0.83) and HCs (AUC: 0.79, 95% CI: 0.68-0.92). The functional prediction analysis of oral bacterial communities showed that genes involved in cell adhesion molecules (p<0.001), extracellular matrix molecule-receptor interaction (p<0.001), focal adhesion (p<0.001), and regulation of actin cytoskeleton (p<0.001) were associated with glioma grades, and some microbial gene functions involving lipid metabolism and the adenosine 5'-monophosphate-activated protein kinase signaling pathway were significantly more enriched in IDH1 mutant gliomas than compared with the IDH1-wild-type gliomas. In conclusion, our work revealed oral microbiota features and gene functions that were associated with glioma malignancy and the IDH1 mutation in glioma.

Prognostic and predictive value of FCER1G in glioma outcomes and response to immunotherapy

PURPOSE

Glioma is the most prevalent malignant form of brain tumors, with a dismal prognosis. Currently, cancer immunotherapy has emerged as a revolutionary treatment for patients with advanced highly aggressive therapy-resistant tumors. However, there is no effective biomarker to reflect the response to immunotherapy in glioma patient so far. So we aim to assess the clinical predictive value of FCER1G in patients with glioma.

METHODS

The expression level and correlation between clinical prognosis and FER1G levels were analyzed with the data from CGGA, TCGA, and GEO database. Univariate and multivariate cox regression model was built to predict the prognosis of glioma patients with multiple factors. Then the correlation between FCER1G with immune cell infiltration and activation was analyzed. At last, we predict the immunotherapeutic response in both high and low FCER1G expression subgroups.

RESULTS

FCER1G was significantly higher in glioma with greater malignancy and predicted poor prognosis. In multivariate analysis, the hazard ratio of FCER1G expression (Low versus High) was 0.66 and 95 % CI is 0.54 to 0.79 (P < 0.001), whereas age (HR = 1.26, 95 % CI  1.04-1.52), grade (HR = 2.75, 95 % CI 2.06-3.68), tumor recurrence (HR = 2.17, 95 % CI  1.81-2.62), IDH mutant (HR = 2.46, 95 % CI 1.97-3.01) and chemotherapeutic status (HR = 1.4, 95 % CI  1.20-1.80) are also included. Furthermore, we illustrated that gene FCER1G stratified glioma cases into high and low FCER1G expression subgroups that demonstrated with distinct clinical outcomes and T cell activation. At last, we demonstrated that high FCER1G levels presented great immunotherapeutic response in glioma patients.

CONCLUSIONS

This study demonstrated FCER1G as a novel predictor for clinical diagnosis, prognosis, and response to immunotherapy in glioma patient. Assess expression of FCER1G is a promising method to discover patients that may benefit from immunotherapy.