Sex- and mutation-specific p53 gain-of-function activity in gliomagenesis

Sex-specific molecular differences in glioblastoma: assessing the clinical significance of genetic variants

Introduction

Glioblastoma multiforme (GBM) is one of the most aggressive types of brain cancer, and despite rigorous research, patient prognosis remains poor. The characterization of sex-specific differences in incidence and overall survival (OS) of these patients has led to an investigation of the molecular mechanisms that may underlie this dimorphism.

Methods

We reviewed the published literature describing the gender specific differences in GBM Biology reported in the last ten years and summarized the available information that may point towards a patient-tailored GBM therapy.

Results

Radiomics analyses have revealed that imaging parameters predict OS and treatment response of GBM patients in a sex-specific manner. Moreover, gender-based analysis of the transcriptome GBM tumors has found differential expression of various genes, potentially impacting the OS survival of patients in a sex-dependent manner. In addition to gene expression differences, the timing (subclonal or clonal) of the acquisition of common GBM-driver mutations, metabolism requirements, and immune landscape of these tumors has also been shown to be sex-specific, leading to a differential therapeutic response by sex. In male patients, transformed astrocytes are more sensitive to glutaminase 1 (GLS1) inhibition due to increased requirements for glutamine uptake. In female patients, GBM is more sensitive to anti-IL1β due to an increased population of circulating granulocytic myeloid-derived suppressor cells (gMDSC).

Conclusion

Moving forward, continued elucidation of GBM sexual dimorphism will be critical in improving the OS of GBM patients by ensuring that treatment plans are structured to exploit these sex-specific, molecular vulnerabilities in GBM tumors.

Sex-related incidence and survival differences in pediatric high-grade glioma subtypes: A population-based cohort study

Not much is known on sex differences in incidence, survival, and treatment characteristics for midline and hemispheric pHGGs. This population-based study confirms previously reported study results that found worse survival outcomes for malignant diffuse gliomas in girls in the age group 0-9 years. Additionally, in our study we pinpoint this difference to girls with midline pHGGs aged 0-4 years. We provide insight in the possible underlying mechanisms contributing to sex survival differences in pHGG patients. With first line treatment having no impact on the higher risk of dying for girls, but age and tumor characteristics having a neutralizing effect. The results of this population-based study serve as a basis for future pre-clinical and clinical studies to further unravel the underlying mechanisms responsible for the survival gap between sexes in midline pHGG.

Prediction of p53 mutation status in rectal cancer patients based on magnetic resonance imaging-based nomogram: a study of machine learning

BACKGROUND

The current study aimed to construct and validate a magnetic resonance imaging (MRI)-based radiomics nomogram to predict tumor protein p53 gene status in rectal cancer patients using machine learning.

METHODS

Clinical and imaging data from 300 rectal cancer patients who underwent radical resections were included in this study, and a total of 166 patients with p53 mutations according to pathology reports were included in these patients. These patients were allocated to the training (n = 210) or validation (n = 90) cohorts (7:3 ratio) according to the examination time. Using the training data set, the radiomic features of primary tumor lesions from T2-weighted images (T2WI) of each patient were analyzed by dimensionality reduction. Multivariate logistic regression was used to screen predictive features, which were combined with a radiomics model to construct a nomogram to predict p53 gene status. The accuracy and reliability of the nomograms were assessed in both training and validation data sets using receiver operating characteristic (ROC) curves.

RESULTS

Using the radiomics model with the training and validation cohorts, the diagnostic efficacies were 0.828 and 0.795, the sensitivities were 0.825 and 0.891, and the specificities were 0.722 and 0.659, respectively. Using the nomogram with the training and validation data sets, the diagnostic efficacies were 0.86 and 0.847, the sensitivities were 0.758 and 0.869, and the specificities were 0.833 and 0.75, respectively.

CONCLUSIONS

The radiomics nomogram based on machine learning was able to predict p53 gene status and facilitate preoperative molecular-based pathological diagnoses.

Neuro-Oncology in Women

Sex differences play a large role in oncology. It has long been discussed that the incidence of different types of tumors varies by sex, and this holds in neuro-oncology. There are also profound survival sex differences, biologic factors, and treatment effects. This review aims to summarize some of the main sex differences observed in primary brain tumors and goes on to focus specifically on gliomas and meningiomas, as these are two commonly encountered primary brain tumors in clinical practice. Additionally, considerations unique to female individuals, including pregnancy and breastfeeding, are explored. This review sheds light on many of the unique attributes that must be considered when diagnosing and treating female patients with primary brain tumors in clinical practice.

The spectrum of sex differences in cancer

Sex differences in cellular and systems biology have been evolutionarily selected to optimize reproductive success in all species with little (sperm) and big (ova) gamete producers. They are evident from the time of fertilization and accrue throughout development through genetic, epigenetic, and circulating sex hormone-dependent mechanisms. Among other effects, they significantly impact on chromatin organization, metabolism, cell cycle regulation, immunity, longevity, and cancer risk and survival. Sex differences in cancer should be expected and accounted for in basic, translational, and clinical oncology research.