Sex-Specific Differences in Low Grade Glioma Presentation and Outcome

Unlocking new horizons: advances in treating IDH-mutant, 1p/19q-codeleted oligodendrogliomas

Oligodendrogliomas are a distinct subtype of diffuse gliomas characterized by IDH mutations and 1p/19q codeletion, classified as grade 2 or 3 based on histological features. This review examines current advancements in the diagnosis, treatment, and prognosis of oligodendrogliomas, with an emphasis on personalized approaches driven by molecular insights. Surgery remains the cornerstone of treatment, aiming for maximal safe resection to obtain tissue for diagnosis and alleviate symptoms. For grade 2 tumors with residual disease but no symptomatic progression, the IDH inhibitor vorasidenib has emerged as a promising option to delay the need for radiation therapy (RT) and chemotherapy. For grade III oligodendrogliomas, postoperative combined-modality therapy with RT and chemotherapy, such as the PCV regimen, demonstrates significant survival benefits, while temozolomide is an alternative due to its ease of administration and reduced toxicity. Recurrent oligodendrogliomas present therapeutic challenges, necessitating tailored strategies based on prior treatments and the interval since initial therapy. Options include repeat surgery, reirradiation, or novel targeted therapies. Advances in molecular diagnostics, such as homozygous CDKN2A/B deletion as a prognostic marker, have refined risk stratification and informed treatment decisions. Despite these strides, further research is needed to optimize long-term outcomes and address resistance mechanisms. This review underscores the importance of integrating molecular diagnostics with clinical management to achieve personalized, evidence-based care for patients with oligodendrogliomas.

Oncology: Brain asymmetries in language-relevant brain tumors

Brain tumors are classified as rare diseases, with an annual occurrence of 300,000 cases and account for an annual loss of 241,000 lives, highlighting their devastating nature. Recent advancements in diagnosis and treatment have significantly improved the management and care of brain tumors. This chapter provides an overview of the common types of primary brain tumors affecting language functions-gliomas and meningiomas. Techniques for identifying and mapping critical language areas, including the white matter language system, such as awake brain tumor surgery and diffusion-weighted tractography, are pivotal for understanding language localization and informing personalized treatment approaches. Numerous studies have demonstrated that gliomas in the dominant hemisphere can lead to (often subtle) impairments across various cognitive domains, with a particular emphasis on language. Recently, increased attention has been directed toward (nonverbal) cognitive deficits in patients with gliomas in the nondominant hemisphere, as well as cognitive outcomes in patients with meningiomas, a group historically overlooked. A patient-tailored approach to language and cognitive functions across the pre-, intra-, and postoperative phases is mandatory for brain tumor patients to preserve quality of life. Continued follow-up studies, in conjunction with advanced imaging techniques, are crucial for understanding the brain's potential for neuroplasticity and optimizing patient outcomes.

The construction of a novel prognostic prediction model for glioma based on GWAS-identified prognostic-related risk loci

Backgrounds

Glioma is a highly malignant brain tumor with a grim prognosis. Genetic factors play a role in glioma development. While some susceptibility loci associated with glioma have been identified, the risk loci associated with prognosis have received less attention. This study aims to identify risk loci associated with glioma prognosis and establish a prognostic prediction model for glioma patients in the Chinese Han population.

Methods

A genome-wide association study (GWAS) was conducted to identify risk loci in 484 adult patients with glioma. Cox regression analysis was performed to assess the association between GWAS-risk loci and overall survival as well as progression-free survival in glioma. The prognostic model was constructed using LASSO Cox regression analysis and multivariate Cox regression analysis. The nomogram model was constructed based on the single nucleotide polymorphism (SNP) classifier and clinical indicators, enabling the prediction of survival rates at 1-year, 2-year, and 3-year intervals. Additionally, the receiver operator characteristic (ROC) curve was employed to evaluate the prediction value of the nomogram. Finally, functional enrichment and tumor-infiltrating immune analyses were conducted to examine the biological functions of the associated genes.

Results

Our study found suggestive evidence that a total of 57 SNPs were correlated with glioma prognosis (p < 5 × 10-5). Subsequently, we identified 25 SNPs with the most significant impact on glioma prognosis and developed a prognostic model based on these SNPs. The 25 SNP-based classifier and clinical factors (including age, gender, surgery, and chemotherapy) were identified as independent prognostic risk factors. Subsequently, we constructed a prognostic nomogram based on independent prognostic factors to predict individualized survival. ROC analyses further showed that the prediction accuracy of the nomogram (AUC = 0.956) comprising the 25 SNP-based classifier and clinical factors was significantly superior to that of each individual variable.

Conclusion

We identified a SNP classifier and clinical indicators that can predict the prognosis of glioma patients and established a prognostic prediction model in the Chinese Han population. This study offers valuable insights for clinical practice, enabling improved evaluation of patients' prognosis and informing treatment options.

A single-institution retrospective analysis of pathologically determined malignant transformation in IDH mutant glioma patients

Background

Lower-grade IDH mutant glioma patients frequently undergo malignant transformation (MT), with apparent worse prognosis. Many studies examine MT in mixed IDH status cohorts and define MT using imaging, not histopathology. Our study examines the timing, predictors, and prognostic implications of pathologically determined MT in a large, exclusively IDH mutant cohort.

Methods

We identified 193 IDH mutant lower-grade glioma patients at UCLA who received multiple surgeries. We examined the outcomes of pathologically determined MT patients.

Results

Time to MT is longer in grade 2 oligodendroglioma (G2 Oligo) than in grade 2 astrocytoma (G2 Astro) (HR = 0.46, P = .0007). The grade 3 astrocytoma (G3 Astro) to grade 4 astrocytoma (G4 Astro) interval is shorter in stepwise MT (G2 to G3 to G4 Astro) patients than in initial G3 Astro patients (P = .03). Novel contrast enhancement had 65% positive predictivity, 67% negative predictivity, 75% sensitivity, and 55% specificity in indicating pathologically defined MT. In G2 Astro, initial gross total resection delayed MT (HR = 0.50, P = .02) and predicted better overall survival (OS) (HR = 0.34, P = .009). In G2 Oligo, spontaneous MT occurred earlier than treated MT (HR = 11.43, P = .0002), but treatment did not predict improved OS (P = .8). MT patients (n = 126) exhibited worse OS than non-MT patients (n = 67) in All (HR = 2.54, P = .0009) and G2 Astro (HR = 4.26, P = .02).

Conclusion

Our study expands the understanding of MT to improve IDH mutant lower-grade glioma management.

The Next Frontier in Health Disparities-A Closer Look at Exploring Sex Differences in Glioma Data and Omics Analysis, from Bench to Bedside and Back

Sex differences are increasingly being explored and reported in oncology, and glioma is no exception. As potentially meaningful sex differences are uncovered, existing gender-derived disparities mirror data generated in retrospective and prospective trials, real-world large-scale data sets, and bench work involving animals and cell lines. The resulting disparities at the data level are wide-ranging, potentially resulting in both adverse outcomes and failure to identify and exploit therapeutic benefits. We set out to analyze the literature on women's data disparities in glioma by exploring the origins of data in this area to understand the representation of women in study samples and omics analyses. Given the current emphasis on inclusive study design and research, we wanted to explore if sex bias continues to exist in present-day data sets and how sex differences in data may impact conclusions derived from large-scale data sets, omics, biospecimen analysis, novel interventions, and standard of care management.