TP53 Mutation Is a Prognostic Factor in Lower Grade Glioma and May Influence Chemotherapy Efficacy

Metabolic Insight into Glioma Heterogeneity: Mapping Whole Exome Sequencing to In Vivo Imaging with Stereotactic Localization and Deep Learning

Intratumoral heterogeneity (ITH) complicates the diagnosis and treatment of glioma, partly due to the diverse metabolic profiles driven by underlying genomic alterations. While multiparametric imaging enhances the characterization of ITH by capturing both spatial and functional variations, it falls short in directly assessing the metabolic activities that underpin these phenotypic differences. This gap stems from the challenge of integrating easily accessible, colocated pathology and detailed genomic data with metabolic insights. This study presents a multifaceted approach combining stereotactic biopsy with standard clinical open-craniotomy for sample collection, voxel-wise analysis of MR images, regression-based GAM, and whole-exome sequencing. This work aims to demonstrate the potential of machine learning algorithms to predict variations in cellular and molecular tumor characteristics. This retrospective study enrolled ten treatment-naïve patients with radiologically confirmed glioma. Each patient underwent a multiparametric MR scan (T1W, T1W-CE, T2W, T2W-FLAIR, DWI) prior to surgery. During standard craniotomy, at least 1 stereotactic biopsy was collected from each patient, with screenshots of the sample locations saved for spatial registration to pre-surgical MR data. Whole-exome sequencing was performed on flash-frozen tumor samples, prioritizing the signatures of five glioma-related genes: IDH1, TP53, EGFR, PIK3CA, and NF1. Regression was implemented with a GAM using a univariate shape function for each predictor. Standard receiver operating characteristic (ROC) analyses were used to evaluate detection, with AUC (area under curve) calculated for each gene target and MR contrast combination. Mean AUC for five gene targets and 31 MR contrast combinations was 0.75 ± 0.11; individual AUCs were as high as 0.96 for both IDH1 and TP53 with T2W-FLAIR and ADC, and 0.99 for EGFR with T2W and ADC. These results suggest the possibility of predicting exome-wide mutation events from noninvasive, in vivo imaging by combining stereotactic localization of glioma samples and a semi-parametric deep learning method. The genomic alterations identified, particularly in IDH1, TP53, EGFR, PIK3CA, and NF1, are known to play pivotal roles in metabolic pathways driving glioma heterogeneity. Our methodology, therefore, indirectly sheds light on the metabolic landscape of glioma through the lens of these critical genomic markers, suggesting a complex interplay between tumor genomics and metabolism. This approach holds potential for refining targeted therapy by better addressing the genomic heterogeneity of glioma tumors.

Genetic mutation patterns among glioblastoma patients in the Taiwanese population - insights from a single institution retrospective study

This study utilized Next-Generation Sequencing (NGS) to explore genetic determinants of survival duration in Glioblastoma Multiforme (GBM) patients. We categorized 30 primary GBM patients into two groups based on their survival periods: extended survival (over two years, N = 17) and abbreviated survival (under two years, N = 13). For identifying pathogenic or likely pathogenic variants, we leveraged the ClinVar database. The cohort, aged 23 to 66 (median: 53), included 17 patients in Group A (survival >2 years, 10 males, 7 females), and 13 patients in Group B (survival <2 years, 8 males, 5 females), with a 60% to 40% male-to-female ratio. Identified mutations included CHEK2 (c.1477 G > A, p.E493K), IDH1 (c.395 G > A, p.R132H), and TP53 mutations. Non-coding regions exhibited variants in the TERT promoter (c.-146C > T, c.-124C > T) and TP53 RNA splicing site (c.376-2 A > C, c.376-2 A > G). While Group A had more mutations, statistical significance wasn't reached, likely due to sample size. Notably, TP53, and ATR displayed a trend toward significance. Surprisingly, TP53 mutations were more prevalent in Group A, contradicting Western findings on poorer GBM prognosis. In Taiwanese GBM patients, bevacizumab usage is linked to improved survival rates, affirming its safety and effectiveness. EGFR mutations are infrequent, suggesting potential distinctions in carcinogenic pathways. Further research on EGFR mutations and amplifications is essential for refining therapeutic approaches. TP53 mutations are associated with enhanced survival, but their functional implications necessitate detailed exploration. This study pioneers genetic analysis in Taiwanese GBM patients using NGS, advancing our understanding of their genetic landscape.

Deciphering Glioblastoma: Fundamental and Novel Insights into the Biology and Therapeutic Strategies of Gliomas

Gliomas constitute a diverse and complex array of tumors within the central nervous system (CNS), characterized by a wide range of prognostic outcomes and responses to therapeutic interventions. This literature review endeavors to conduct a thorough investigation of gliomas, with a particular emphasis on glioblastoma (GBM), beginning with their classification and epidemiological characteristics, evaluating their relative importance within the CNS tumor spectrum. We examine the immunological context of gliomas, unveiling the intricate immune environment and its ramifications for disease progression and therapeutic strategies. Moreover, we accentuate critical developments in understanding tumor behavior, focusing on recent research breakthroughs in treatment responses and the elucidation of cellular signaling pathways. Analyzing the most novel transcriptomic studies, we investigate the variations in gene expression patterns in glioma cells, assessing the prognostic and therapeutic implications of these genetic alterations. Furthermore, the role of epigenetic modifications in the pathogenesis of gliomas is underscored, suggesting that such changes are fundamental to tumor evolution and possible therapeutic advancements. In the end, this comparative oncological analysis situates GBM within the wider context of neoplasms, delineating both distinct and shared characteristics with other types of tumors.

Genetic and clinical landscape of ER + /PR- breast cancer in China

BACKGROUND

Estrogen receptor-positive and progesterone receptor-negative (ER + /PR-) breast cancer comprise a special type. More than 10% breast cancer patients belonged to ER + /PR-.

METHODS

In order to better understand this patient population, we utilized a unique dataset from China, examining the clinicopathological features and genomic profiles of ER + /PR- breast cancers. Our study involved three cohorts: Cohort 1 included 2120 unselected ER-positive female patients with re-evaluated clinicopathological and survival data; Cohort 2 comprised 442 ER-positive females who underwent genetic testing; and Cohort 3 consisted of 77 ER-positive/HER2-negative females tested with MammaPrint and BluePrint.

RESULTS

Patients were stratified into four categories based on the PR/ER ratio. Clinically, ER + /PR- tumors (PR/ER ratio = 0) showed the lowest proportion of T1 tumors (10.88%) and highest proportion of HER2-positive tumors (28.36%) than did other ER + /PR + tumors groups. The ER + /PR- group contained a higher number of underweight patients (20.20%). Independently of HER2 status, ER + /PR- patients demonstrated the poorest prognosis. Genomically, the most prevalent mutations were PIK3CA (50%) in ER + /PR + tumors and TP53 (65%) in ER + /PR- tumors. ER + /PR- tumors presented more frequent mutations in TP53, ERBB2, CDK12, SPEN, and NEB, with mutation rates of 65%, 42%, 27%, 13%, and 10%, respectively. Additionally, the Tumor Mutational Burden (TMB) was higher in the ER + /PR- group compared to the ER + /PR + group. The MammaPrint score for the ER + /PR-/HER2- group was significantly lower than that of other groups. In the BluePrint analysis, only four patients were classified as Basal-Type, all of whom were ER + /PR-/HER2-.

CONCLUSIONS

In this study, we identified the clinical and genetic characteristics of ER + /PR- breast cancer patients in China. Distinct PR statuses indicated different biological processes of ER + breast cancer and survival outcomes. Future treatment strategies may need to be tailored for ER + /PR- patients.

Novel approaches targeting ferroptosis in treatment of glioma

Glioma is a malignant brain tumor with a high mortality rate; hence novel treatment approaches are being explored to improve patient outcomes. Ferroptosis, a newly described form of regulated cell death, is emerging as a potential therapeutic target in glioma. Ferroptosis is characterized by the accumulation of lipid peroxides due to a loss of intracellular antioxidant systems represented by the depletion of glutathione and decreased activity of glutathione peroxidase 4 (GPX4). Since glioma cells have a high demand for iron and lipid metabolism, modulation of ferroptosis may represent a promising therapeutic approach for this malignancy. Recent studies indicate that ferroptosis inducers like erastin and RSL3 display potent anticancer activity in a glioma model. In addition, therapeutic strategies, including GPX4 targeting, lipid metabolism modulation, inhibition of amino acid transporters, and ferroptosis targeting natural compounds, have shown positive results in preclinical studies. This review will provide an overview of the functions of ferroptosis in glioma and its potential as a suitable target for glioma therapy.

MDM2 Inhibition in the Treatment of Glioblastoma: From Concept to Clinical Investigation

Inhibition of the interaction between MDM2 and p53 has emerged as a promising strategy for combating cancer, including the treatment of glioblastoma (GBM). Numerous MDM2 inhibitors have been developed and are currently undergoing rigorous testing for their potential in GBM therapy. Encouraging results from studies conducted in cell culture and animal models suggest that MDM2 inhibitors could effectively treat a specific subset of GBM patients with wild-type TP53 or functional p53. Combination therapy with clinically established treatment modalities such as radiation and chemotherapy offers the potential to achieve a more profound therapeutic response. Furthermore, an increasing array of other molecularly targeted therapies are being explored in combination with MDM2 inhibitors to increase the effects of individual treatments. While some MDM2 inhibitors have progressed to early phase clinical trials in GBM, their efficacy, alone and in combination, is yet to be confirmed. In this article, we present an overview of MDM2 inhibitors currently under preclinical and clinical investigation, with a specific focus on the drugs being assessed in ongoing clinical trials for GBM patients.

Deep DNA sequencing of MGMT, TP53 and AGT in Mexican astrocytoma patients identifies an excess of genetic variants in women and a predictive biomarker

PURPOSE

Astrocytomas are a type of malignant brain tumor with an unfavorable clinical course. The impact of AGT and MGMT somatic variants in the prognosis of astrocytoma is unknown, and it is controversial for TP53. Moreover, there is a lack of knowledge regarding the molecular characteristics of astrocytomas in Mexican patients.

METHODS

We studied 48 Mexican patients, men and women, with astrocytoma (discovery cohort). We performed DNA deep sequencing in tumor samples, targeting AGT, MGMT and TP53, and we studied MGMT gene promoter methylation status. Then we compared our findings to a cohort which included data from patients with astrocytoma from The Cancer Genome Atlas (validation cohort).

RESULTS

In the discovery cohort, we found a higher number of somatic variants in AGT and MGMT than in the validation cohort (10.4% vs < 1%, p < 0.001), and, in both cohorts, we observed only women carried variants AGT variants. We also found that the presence of either MGMT variant or promoter methylation was associated to better survival and response to chemotherapy, and, in conjunction with TP53 variants, to progression-free survival.

CONCLUSIONS

The occurrence of AGT variants only in women expands our knowledge about the molecular differences in astrocytoma between men and women. The increased prevalence of AGT and MGMT variants in the discovery cohort also points towards possible distinctions in the molecular landscape of astrocytoma among populations. Our findings warrant further study.

P53-negative status and gross total resection as predictive factors for autologous tumor vaccine treatment in newly diagnosed glioblastoma patients

Background

Among primary brain tumors, glioblastoma (GBM) is the most common and aggressive in adults, with limited treatment options. Our previous study showed that autologous formalin-fixed tumor vaccine (AFTV) contributed to prognostic improvements in newly diagnosed GBM patients. However, some patients died early despite the treatment. The discovery of predictive factors in the treatment was warranted for efficient patient recruitment and studies to overcome resistance mechanisms. Identifying prognostic factors will establish AFTV guidelines for patients who may respond to the therapy.

Methods

Data from 58 patients with newly diagnosed GBM, including 29 who received standard therapy plus AFTV (AFTV group) and 29 who received standard treatment (control group) were analyzed. Several data including patient age, sex, the extent of removal, and various cell immunohistochemistry (IHC) parameters were also included in the analysis.

Results

Both univariate and multivariate analyses revealed that gross total resection (GTR) and negative p53 were associated with a better prognosis only in the AFTV group. In the IHC parameters, CD8 staining status was also one of the predictive factors in the univariate analysis. For blood cell-related data, lymphocyte counts of 1100 or more and monocyte counts of 280 or more before chemo-radiotherapy were significant factors for good prognosis in the univariate analysis.

Conclusions

A p53-negative status in IHC and GTR were the predictive factors for AFTV treatment in newly diagnosed GBM patients. Microenvironment-targeted treatment and pretreatment blood cell status may be key factors to enhance therapy effects.

robustica: customizable robust independent component analysis

BACKGROUND

Independent Component Analysis (ICA) allows the dissection of omic datasets into modules that help to interpret global molecular signatures. The inherent randomness of this algorithm can be overcome by clustering many iterations of ICA together to obtain robust components. Existing algorithms for robust ICA are dependent on the choice of clustering method and on computing a potentially biased and large Pearson distance matrix.

RESULTS

We present robustica, a Python-based package to compute robust independent components with a fully customizable clustering algorithm and distance metric. Here, we exploited its customizability to revisit and optimize robust ICA systematically. Of the 6 popular clustering algorithms considered, DBSCAN performed the best at clustering independent components across ICA iterations. To enable using Euclidean distances, we created a subroutine that infers and corrects the components' signs across ICA iterations. Our subroutine increased the resolution, robustness, and computational efficiency of the algorithm. Finally, we show the applicability of robustica by dissecting over 500 tumor samples from low-grade glioma (LGG) patients, where we define two new gene expression modules with key modulators of tumor progression upon IDH1 and TP53 mutagenesis.

CONCLUSION

robustica brings precise, efficient, and customizable robust ICA into the Python toolbox. Through its customizability, we explored how different clustering algorithms and distance metrics can further optimize robust ICA. Then, we showcased how robustica can be used to discover gene modules associated with combinations of features of biological interest. Taken together, given the broad applicability of ICA for omic data analysis, we envision robustica will facilitate the seamless computation and integration of robust independent components in large pipelines.

Risk stratification and pathway analysis based on graph neural network and interpretable algorithm

Background

Pathway-based analysis of transcriptomic data has shown greater stability and better performance than traditional gene-based analysis. Until now, some pathway-based deep learning models have been developed for bioinformatic analysis, but these models have not fully considered the topological features of pathways, which limits the performance of the final prediction result.

Results

To address this issue, we propose a novel model, called PathGNN, which constructs a Graph Neural Networks (GNNs) model that can capture topological features of pathways. As a case, PathGNN was applied to predict long-term survival of four types of cancer and achieved promising predictive performance when compared to other common methods. Furthermore, the adoption of an interpretation algorithm enabled the identification of plausible pathways associated with survival.

Conclusion

PathGNN demonstrates that GNN can be effectively applied to build a pathway-based model, resulting in promising predictive power.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04950-1.