Oligodendroglial tumours: subventricular zone involvement and seizure history are associated with CIC mutation status

MRI-based radiomics approach for the prediction of recurrence-free survival in triple-negative breast cancer after breast-conserving surgery or mastectomy

To explore the value of a radiomics signature and develop a nomogram combined with a radiomics signature and clinical factors for predicting recurrence-free survival in triple-negative breast cancer patients. We enrolled 151 patients from the cancer imaging archive who underwent preoperative contrast-enhanced magnetic resonance imaging. They were assigned to training, validation and external validation cohorts. Image features with coefficients not equal to zero in the 10-fold cross-validation were selected to generate a radiomics signature. Based on the optimal cutoff value of the radiomics signature determined by maximally selected log-rank statistics, patients were stratified into high- and low-risk groups in the training and validation cohorts. Kaplan-Meier survival analysis was performed for both groups. Kaplan-Meier survival distributions in these groups were compared using log-rank tests. Univariate and multivariate Cox regression analyses were used to construct clinical and combined models. Concordance index was used to assess the predictive performance of the 3 models. Calibration of the combined model was assessed using calibration curves. Four image features were selected to generate the radiomics signature. The Kaplan-Meier survival distributions of patients in the 2 groups were significantly different in the training (P < .001) and validation cohorts (P = .001). The C-indices of the radiomics model, clinical model, and combined model in the training and validation cohorts were 0.772, 0.700, 0.878, and 0.744, 0.574, 0.777, respectively. The C-indices of the radiomics model, clinical model, and combined model in the external validation cohort were 0.778, 0.733, 0.822, respectively. The calibration curves of the combined model showed good calibration. The radiomics signature can predict recurrence-free survival of patients with triple-negative breast cancer and improve the predictive performance of the clinical model.

Development and validation of an MRI-based nomogram for the preoperative prediction of tumor mutational burden in lower-grade gliomas

BACKGROUND

High tumor mutational burden (TMB) is an emerging biomarker of sensitivity to immune checkpoint inhibitors. In this study, we aimed to determine the value of magnetic resonance (MR)-based preoperative nomogram in predicting TMB status in lower-grade glioma (LGG) patients.

METHODS

Overall survival (OS) data were derived from The Cancer Genome Atlas (TCGA) and then analyzed by using the Kaplan-Meier method and time-dependent receiver operating characteristic (tdROC) analysis. The magnetic resonance imaging (MRI) data of 168 subjects obtained from The Cancer Imaging Archive (TCIA) were retrospectively analyzed. The correlation was explored by univariate and multivariate regression analyses. Finally, we performed tenfold cross validation. TMB values were retrieved from the supplementary information of a previously published article.

RESULTS

The high TMB subtype was associated with the shortest median OS (high vs. low: 50.9 vs. 95.6 months, P<0.05). The tdROC for the high-TMB tumors was 74% (95% CI: 61-86%) for survival at 12 months, and 71% (95% CI: 60-82%) for survival at 24 months. Multivariate logistic regression analysis confirmed that three risk factors [extranodular growth: odds ratio (OR): 8.367, 95% CI: 3.153-22.199, P<0.01; length-width ratio ≥ median: OR: 1.947, 95% CI: 1.025-3.697, P<0.05; frontal lobe: OR: 0.455, 95% CI: 0.229-0.903, P<0.05] were significant independent predictors of high-TMB tumors. The nomogram showed good calibration and discrimination. This model had an area under the curve (AUC) of 0.736 (95% CI: 0.655-0.817). Decision curve analysis (DCA) demonstrated that the nomogram was clinically useful. The average accuracy of the tenfold cross validation was 71.6% for high-TMB tumors.

CONCLUSIONS

Our results indicated that a distinct OS disadvantage was associated with the high TMB group. In addition, extranodular growth, nonfrontal lobe tumors and length-width ratio ≥ median can be conveniently used to facilitate the prediction of high-TMB tumors.

Molecular profiling of pediatric and adolescent ependymomas: identification of genetic variants using a next-generation sequencing panel

PURPOSE

Ependymoma (EPN) accounts for approximately 10% of all primary central nervous system (CNS) tumors in children and in most cases, chemotherapy is ineffective and treatment remains challenging. We investigated molecular alterations, with a potential prognostic marker and therapeutic target in EPNs of childhood and adolescence, using a next-generation sequencing (NGS) panel specific for pediatric neoplasms.

METHODS

We selected 61 samples with initial diagnosis of EPN from patients treated at Pediatric Oncology Institute-GRAACC/UNIFESP. All samples were divided according to the anatomical compartment of the CNS - 42 posterior fossa (PF), 14 supratentorial (ST), and five spinal (SP). NGS was performed to identify somatic genetic variants in tumor samples using the Oncomine Childhood Cancer Research Assay® (OCCRA®) panel, from Thermo Fisher Scientific®.

RESULTS

Genetic variants were identified in 24 of 61 (39.3%) tumors and over 90% of all variants were pathogenic or likely pathogenic. The most commonly variants detected were in CIC, ASXL1, and JAK2 genes and have not been reported in EPN yet. MN1-BEND2 fusion, alteration recently described in a new CNS tumor type, was identified in one ST sample that was reclassified as astroblastoma. Additionally, YAP1-MAMLD1 fusion, a rare event associated with good outcome in ST-EPN, was observed in two patients diagnosed under 2 years old.

CONCLUSIONS

Molecular profiling by the OCCRA® panel showed novel alterations in pediatric and adolescent EPNs, which highlights the clinical importance in identifying genetic variants for patients' prognosis and therapeutic orientation.

A Genome-Wide Profiling of Glioma Patients with an IDH1 Mutation Using the Catalogue of Somatic Mutations in Cancer Database

Simple Summary

Glioma patients that present a somatic mutation in the isocitrate dehydrogenase 1 (IDH1) gene have a significantly better prognosis and overall survival than patients with the wild-type genotype. An IDH1 mutation is hypothesized to occur early during cellular transformation and leads to further genetic instability. A genome-wide profiling of glioma patients in the Catalogue of Somatic Mutations in Cancer (COSMIC) database was performed to classify the genetic differences in IDH1-mutant versus IDH1-wildtype patients. This classification will aid in a better understanding of how this specific mutation influences the genetic make-up of glioma and the resulting prognosis. Key differences in co-mutation and gene expression levels were identified that correlate with an improved prognosis.

Abstract

Gliomas are differentiated into two major disease subtypes, astrocytoma or oligodendroglioma, which are then characterized as either IDH (isocitrate dehydrogenase)-wild type or IDH-mutant due to the dramatic differences in prognosis and overall survival. Here, we investigated the genetic background of IDH1-mutant gliomas using the Catalogue of Somatic Mutations in Cancer (COSMIC) database. In astrocytoma patients, we found that IDH1 is often co-mutated with TP53, ATRX, AMBRA1, PREX1, and NOTCH1, but not CHEK2, EGFR, PTEN, or the zinc finger transcription factor ZNF429. The majority of the mutations observed in these genes were further confirmed to be either drivers or pathogenic by the Cancer-Related Analysis of Variants Toolkit (CRAVAT). Gene expression analysis showed down-regulation of DRG2 and MSN expression, both of which promote cell proliferation and invasion. There was also significant over-expression of genes such as NDRG3 and KCNB1 in IDH1-mutant astrocytoma patients. We conclude that IDH1-mutant glioma is characterized by significant genetic changes that could contribute to a better prognosis in glioma patients.

Multi-Omics Data Integration Analysis of an Immune-Related Gene Signature in LGG Patients With Epilepsy

Background

The tumor immune microenvironment significantly affects tumor occurrence, progression, and prognosis, but its impact on the prognosis of low-grade glioma (LGG) patients with epilepsy has not been reported. Hence, the purpose of this study is to explore its effect on LGG patients with epilepsy.

Methods

The data of LGG patients derived from the TCGA database. The level of immune cell infiltration and the proportion of 22 immune cells were evaluated by ESTIMATE and CIBERSORT algorithms, respectively. The Cox and LASSO regression analysis was adopted to determine the DEGs, and further established the clustering and risk score models. The association between genomic alterations and risk score was investigated using CNV and somatic mutation data. GSVA was adopted to identify the immunological pathways, immune infiltration and inflammatory profiles related to the signature genes. The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm and GDSC database were used to predict the patient’s response to immunotherapy and chemotherapy, respectively.

Results

The prognosis of LGG patients with epilepsy was associated with the immune score. Three prognostic DEGs (ABCC3, PDPN, and INA) were screened out. The expression of signature genes was regulated by DNA methylation. The clustering and risk score models could stratify glioma patients into distinct prognosis groups. The risk score was an independent predictor in prognosis, with a high risk-score indicating a poor prognosis, more malignant clinicopathological and genomic aberration features. The nomogram had the better predictive ability. Patients at high risk had a higher level of macrophage infiltration and increased inflammatory activities associated with T cells and macrophages. While the higher percentage of NK CD56bright cell and more active inflammatory activity associated with B cell were present in the low-risk patients. The signature genes participated in the regulation of immune-related pathways, such as IL6-JAK-STAT3 signaling, IFN-α response, IFN-γ response, and TNFA-signaling-via-NFKB pathways. The high-risk patients were more likely to benefit from anti-PD1 and temozolomide (TMZ) treatment.

Conclusion

An immune-related gene signature was established based on ABCC3, PDPN, and INA, which can be used to predict the prognosis, immune infiltration status, immunotherapy and chemotherapy response of LGG patients with epilepsy.

Lower-Grade Gliomas: Predicting DNA Methylation Subtyping and its Consequences on Survival with MR Features

RATIONALE AND OBJECTIVES

To explore associations between MR imaging features, DNA methylation subtyping, and survival in lower-grade gliomas (LGG).

MATERIALS AND METHODS

The MR data from 170 patients generated with the Cancer Imaging Archive were reviewed. The correlation was evaluated by Fisher's Exact Test, Pearson Chi-Square and binary regression analysis. Survival analysis was conducted by using time-dependent ROC analysis and the Kaplan-Meier method (the worst prognosis subgroup).

RESULTS

Identified were 9 (5.3%) M1-subtype, 18 (10.6%) M2-subtype, 48 (28.2%) M3-subtype, 31 (18.2%) M4-subtype and 64 (37.6%) M5-subtype. Patients with M4-subtype had the shortest median OS (49.3 vs. 28.4) months(p < 0.05). The time-dependent ROC for the M4-subtype was 0.83 (95% confidence interval 0.72-0.95) for survival at 12 months, 0.82 (95% confidence interval 0.70-0.94) for survival at 24 months, and 0.74 (95% confidence interval 0.62-0.86) for survival at 36 months. After uni- and multivariate analysis, a nomogram was built based on proportion contrast-enhanced (CE) tumor, extranodular growth, volume_{_cutoff_median}, and location. For the prediction of M4-subtype, the nomogram showed good discrimination, with an area under the curve (AUC) of 0.886 (95% CI: 0.820-952) and was well calibrated. On multivariate logistic regression analysis, volume ≥60cm³ (OR: 0.200; p < 0.001; 95%CI: 0.048-0.834) was associated with M1-subtype (AUC: 0.690). Hemorrhage (OR: 5.443; p = 0.002; 95%CI: 1.844-16.069) and volume > median (OR: 3.256; p = 0.05; 95%CI: 0.992-10.686) were associated with M2-subtype (AUC: 0.733). Proportion CE tumor<=5% (OR: 3.968; P=0.002; 95%CI: 1.634-9.635) was associated with M3-subtype (AUC: 0.632). Poorly-defined (OR: 2.258; p = 0.05; 95%CI: 1.000-5.101) and volume > median (OR: 2.447; p = 0.01; 95%CI: 1.244-4.813) were associated with M5-subtype (AUC: 0.645). Decision curve analysis indicated predictions for all models were clinically useful.

CONCLUSION

This preliminary radiogenomics analysis of lower-grade gliomas demonstrated associations between MR features and DNA methylation subtyping. The shortest survival was observed in patients with M4-subtype. And we have constructed nomogram that enables more accurate predictions of M4-subtype.

Magnetic Resonance Features of Lower-grade Gliomas in Prediction of the Reverse Phase Protein A

OBJECTIVES

The Cancer Genome Atlas Research Network identified 4 novel protein expression-defined subgroups in patients with lower-grade gliomas (LGGs). The RPPA3 subtype had high levels of Epidermal Growth Factor Receptor and Human epidermal growth factor receptor-2, further increasing the chances for targeted therapy. In this study, we aimed to explore the relationships between magnetic resonance features and reverse phase protein array (RPPA) subtypes (R1-R4).

METHODS

Survival estimates for the Cancer Genome Atlas cohort were generated using the Kaplan-Meier method and time-dependent receiver operating characteristic curves. A total of 153 patients with LGG with brain magnetic resonance imaging from The Cancer Imaging Archive were retrospectively analyzed. Least absolute shrinkage and selection operator algorithm was used to reduce the feature dimensions of the RPPA3 subtype.

RESULTS

A total of 51 (33.3%) RPPA1 subtype, 42 (27.4) RPPA2 subtype, 19 (12.4%) RPPA3 subtype, and 38 (24.8%) RPPA4 subtype were identified. On multivariate logistic regression analysis, subventricular zone involvement [odds ratio (OR), 0.370; P = 0.006; 95% confidence interval (CI), 0.181-0.757) was associated with RPPA1 subtype [area under the curve (AUC), 0.598]. Volume of 60 cm3 or greater (OR, 5.174; P < 0.001; 95% CI, 2.182-12.267) was associated with RPPA2 subtype (AUC, 0.684). Proportion contrast-enhanced tumor greater than 5% (OR, 4.722; P = 0.010; 95% CI, 1.456-15.317), extranodular growth (OR, 5.524; P = 0.010; 95% CI, 1.509-20.215), and L/CS ratio equal to or greater than median (OR, 0.132; P = 0.003; 95% CI, 0.035-0.500) were associated with RPPA3 subtype (AUC, 0.825). Proportion contrast-enhanced tumor greater than 5% (OR, 0.206; P = 0.005; 95% CI, 0.068-0.625) was associated with RPPA4 subtype (AUC, 0.638). For the prediction of RPPA3 subtype, the nomogram showed good discrimination, with an AUC of 0.825 (95% CI, 0.711-0.939) and was well calibrated. The RPPA3 subtype was associated with shortest mean overall survival (RPPA3 subtype vs other: 613 vs 873 days; P < 0.05). The time-dependent receiver operating characteristic curves for the RPPA3 subtype was 0.72 (95% CI, 0.60-0.84) for survival at 1 year. Decision curve analysis indicated that prediction for the RPPA3 model was clinically useful.

CONCLUSIONS

The RPPA3 subtype is an unfavorable prognostic biomarker for overall survival in patients with LGG. Radiogenomics analysis of magnetic resonance features can predict the RPPA subtype preoperatively and may be of clinical value in tailoring the management strategies in patients with LGG.

Development of MR-based preoperative nomograms predicting DNA copy number subtype in lower grade gliomas with prognostic implication

OBJECTIVES

We aimed to determine the value of MR-based preoperative nomograms in predicting DNA copy number (CN) subtype in lower grade glioma (LGG) patients.

METHODS

The overall survival (OS) data were analyzed. MRI data of 170 subjects were retrospectively analyzed. The correlation was explored by univariate and multivariate regression analysis.

RESULTS

CN2 subtype was associated with shortest median OS (CN2 subtype vs. others: 46.8 vs. 221.7 months, p < 0.05). The time-dependent receiver operating characteristic for the CN2 subtype was 0.80 (95% CI: 0.74-0.85) for survival at 1 year, 0.80 (95% CI: 0.75-0.85) for survival at 2 years, and 0.77 (95% CI: 0.73-0.83) for survival at 3 years. On multivariate analysis, hemorrhage (OR: 0.118; p < 0.001; 95% CI: 0.037-0.376), poorly defined margin (OR: 4.592; p < 0.001; 95% CI: 1.965-10.730), extranodular growth (OR: 0.247; p = 0.006; 95% CI: 0.091-0.671), and volume ≥ 60 cm³ (OR: 4.734.256; p < 0.001; 95% CI: 2.051-10.924) were associated with CN1 subtype (AUC: 0.781). Proportion CE tumor (OR: 5.905; p = 0.007; 95% CI: 1.622-21.493), extranodular growth (OR: 9.047; p = 0.001; 95% CI: 2.349-34.846), width ≥ median (OR: 0.231; p = 0.049; 95% CI: 0.054-0.998), and depth ≥ median (OR: 0.192; p = 0.023; 95% CI: 0.046-0.799) were associated with CN2 subtype (AUC: 0.854). Necrosis/cystic (OR: 6.128; p = 0.007; 95% CI: 1.635-22.968), hemorrhage (OR: 5.752; p = 0.002; 95% CI: 1.953-16.942), poorly defined margin (OR: 0.164; p < 0.001; 95% CI: 0.063-0.427), and volume ≥ median (OR: 4.422; p < 0.001; 95% CI: 1.925-10.160) were associated with CN3 subtype (AUC: 0.808). All three nomograms showed good discrimination and calibration. Decision curve analysis supported that all nomograms were clinically useful. The average accuracy of the tenfold cross-validation was 0.680 (CN1), 0.794 (CN2), and 0.894 (CN3), respectively.

CONCLUSIONS

The shortest OS was observed in patients with CN2 subtype. This preliminary radiogenomics analysis revealed that the MR-based preoperative nomograms provide individualized prediction of DNA copy number subtype in LGG patients.

KEY POINTS

• This preliminary radiogenomics analysis of LGG revealed that the MR-based preoperative nomograms provide individualized prediction of DNA copy number subtype in LGG patients. • The AUC for the ROC curve was 0.781 for CN1 subtype, 0.854 for CN2 subtype, and 0.808 for CN3 subtype. Decision curve analysis supported that all nomograms were clinically useful. • The sensitivity was 0.779 (CN1), 0.731 (CN2), and 0.851 (CN3), respectively. The specificity was 0.664 (CN1), 0.872 (CN2), and 0.625 (CN3), respectively. And the accuracy was 0.717 (CN1), 0.849 (CN2), and 0.692 (CN3), respectively.

The Prognostic Value of MRI Subventricular Zone Involvement and Tumor Genetics in Lower Grade Gliomas

BACKGROUND AND PURPOSE

Glioblastomas (GBMs) that involve the subventricular zone (SVZ) have a poor prognosis, possibly due to recruitment of neural stem cells. The purpose of this study was to evaluate whether SVZ involvement by lower grade gliomas (LGG), WHO grade II and III, similarly predicts poorer outcomes. We further assessed whether tumor genetics and cellularity are associated with SVZ involvement and outcomes.

METHODS

Forty-five consecutive LGG patients with preoperative imaging and next generation sequencing were included in this study. Regional SVZ involvement and whole tumor apparent diffusion coefficient (ADC) values, as a measure of cellularity, were assessed on magnetic resonance imaging. Progression was determined by RANO criteria. Kaplan-Meier curves and Cox regression analyses were used to determine the hazard ratios (HR) for progression and survival.

RESULTS

Frontal, parietal, temporal, and overall SVZ involvement and ADC values were not associated with progression or survival (P ≥ .05). However, occipital SVZ involvement, seen in two patients, was associated with a higher risk of tumor progression (HR = 6.6, P = .016) and death (HR = 31.5, P = .015), CDKN2A/B mutations (P = .03), and lower ADC histogram values at the 5th (P = .026) and 10th percentiles (P = .046). Isocitrate dehydrogenase, phosphatase and tensin homolog, epidermal growth factor receptor, and cyclin-dependent kinase 4 mutations were also prognostic (P ≤ .05).

CONCLUSIONS

Unlike in GBM, overall SVZ involvement was not found to strongly predict poor prognosis in LGGs. However, occipital SVZ involvement, though uncommon, was prognostic and found to be associated with CDKN2A/B mutations and tumor hypercellularity. Further investigation into these molecular mechanisms underlying occipital SVZ involvement in larger cohorts is warranted.