New perspectives in liquid biopsy for glioma patients

Advancements in Image-Based Models for High-Grade Gliomas Might Be Accelerated

The first half of 2022 saw the publication of several major research advances in image-based models and artificial intelligence applications to optimize treatment strategies for high-grade gliomas, the deadliest brain tumors. We review them and discuss the barriers that delay their entry into clinical practice; particularly, the small sample size and the heterogeneity of the study designs and methodologies used. We will also write about the poor and late palliation that patients suffering from high-grade glioma can count on at the end of life, as well as the current legislative instruments, with particular reference to Italy. We suggest measures to accelerate the gradual progress in image-based models and end of life care for patients with high-grade glioma.

Cerebrospinal Fluid Liquid Biopsies in the Evaluation of Adult Gliomas

PURPOSE OF REVIEW

This review aims to discuss recent research regarding the biomolecules explored in liquid biopsies and their potential clinical uses for adult-type diffuse gliomas.

RECENT FINDINGS

Evaluation of tumor biomolecules via cerebrospinal fluid (CSF) is an emerging technology in neuro-oncology. Studies to date have already identified various circulating tumor DNA, extracellular vesicle, micro-messenger RNA and protein biomarkers of interest. These biomarkers show potential to assist in multiple avenues of central nervous system (CNS) tumor evaluation, including tumor differentiation and diagnosis, treatment selection, response assessment, detection of tumor progression, and prognosis. In addition, CSF liquid biopsies have the potential to better characterize tumor heterogeneity compared to conventional tissue collection and CNS imaging. Current imaging modalities are not sufficient to establish a definitive glioma diagnosis and repeated tissue sampling via conventional biopsy is risky, therefore, there is a great need to improve non-invasive and minimally invasive sampling methods. CSF liquid biopsies represent a promising, minimally invasive adjunct to current approaches which can provide diagnostic and prognostic information as well as aid in response assessment.

IDH1 mutation is detectable in plasma cell-free DNA and is associated with survival outcome in glioma patients

BACKGROUND

Circulating cell-free DNA (cfDNA, liquid biopsy) is a powerful tool to detect molecular alterations. However, depending on tumor characteristics, biology and anatomic localization, cfDNA detection and analysis may be challenging. Gliomas are enclosed into an anatomic sanctuary, which obstacles the release of cfDNA into the peripheral blood. Therefore, the advantages of using liquid biopsy for brain tumors is still to be confirmed. The present study evaluates the ability of liquid biopsy to detect IDH1 mutations and its correlation with survival and clinical characteristics of glioma patients.

METHODS

Blood samples obtained from glioma patients were collected after surgery prior to the adjuvant therapy. cfDNA was extracted from plasma and IDH1 p.R132H mutation analysis was performed on a digital droplet PCR. χ2-test and Cohen k were used to assess the correlation between plasma and tissue IDH1 status, while Kaplan Meier curve and Cox regression analysis were applied to survival analysis. Statistical calculations were performed by MedCalc and GraphPad Prism software.

RESULTS

A total of 67 samples were collected. A concordance between IDH1 status in tissue and in plasma was found (p = 0.0024), and the presence of the IDH1 mutation both in tissue (138.8 months vs 24.4, p < 0.0001) and cfDNA (116.3 months vs 35.8, p = 0.016) was associated with longer median OS. A significant association between IDH1 mutation both in tissue and cfDNA, age, tumor grade and OS was demonstrated by univariate Cox regression analysis. No statistically significant association between IDH1 mutation and tumor grade was found (p = 0.10).

CONCLUSIONS

The present study demonstrates that liquid biopsy may be used in brain tumors to detect IDH1 mutation which represents an important prognostic biomarker in patients with different types of gliomas, being associated to OS.

Cancer Stem Cells and Glioblastoma: Time for Innovative Biomarkers of Radio-Resistance?

Despite countless papers in the field of radioresistance, researchers are still far from clearly understanding the mechanisms triggered in glioblastoma. Cancer stem cells (CSC) are important to the growth and spread of cancer, according to many studies. In addition, more recently, it has been suggested that CSCs have an impact on glioblastoma patients' prognosis, tumor aggressiveness, and treatment outcomes. In reviewing this new area of biology, we will provide a summary of the most recent research on CSCs and their role in the response to radio-chemotherapy in GB. In this review, we will examine the radiosensitivity of stem cells. Moreover, we summarize the current knowledge of the biomarkers of stemness and evaluate their potential function in the study of radiosensitivity.

The impact of survivorship bias in glioblastoma research

Despite advances in the therapy of Central Nervous System (CNS) malignancies, treatment of glioblastoma (GB) poses significant challenges due to GB resistance and high recurrence rates following post-operative radio-chemotherapy. The majority of prognostic and predictive GB biomarkers are currently developed using tumour samples obtained through surgical interventions. However, the selection criteria adopted by different neurosurgeons to determine which cases are suitable for surgery make operated patients not representative of all GB cases. Particularly, geriatric and frail individuals are excluded from surgical consideration in some cancer centers. Such selection generates a survival (or selection) bias that introduces limitations, rendering the patients or data chosen for downstream analyses not representative of the entire community. In this review, we discuss the implication of survivorship bias on current and novel biomarkers for patient selection, stratification, therapy, and outcome analyses.