Diagnostic delays in patients with brain cancer are common and can impact patient outcome. Development of a blood-based assay for detection of brain cancers could accelerate brain cancer diagnosis. In this study, we analyzed genome-wide cell-free (cfDNA) fragmentomes, including fragmentation profiles and repeat landscapes, from the plasma of individuals with (n = 148) or without (n = 357) brain cancer. Machine learning analyses of cfDNA fragmentome features detected brain cancer across all-grade gliomas (AUC = 0.90; 95% confidence interval, 0.87–0.93), and these results were validated in an independent prospectively collected cohort. cfDNA fragmentome changes in patients with gliomas represented a combination of fragmentation profiles from glioma cells and altered white blood cell populations in the circulation. These analyses reveal the properties of cfDNA in patients with brain cancer and open new avenues for noninvasive detection of these individuals. Significance: Brain cancer is one of the deadliest and most challenging cancers to detect with liquid biopsy approaches in blood, hampering efforts for earlier noninvasive diagnosis. We have developed a machine learning genome-wide cfDNA fragmentation method that provides a sensitive and accessible approach for brain cancer detection.

Glioblastoma, a highly aggressive brain tumor, poses significant treatment challenges. A deeper investigation into its molecular complexity is essential for the identification of novel prognostic biomarkers and therapeutic strategies, potentially improving patient outcomes in terms of survival and quality of life. While nuclear DNA mutations have been extensively studied, the role of mitochondrial DNA (mtDNA) mutations, specifically in the D-loop region, remains poorly understood. This prospective case-control study aimed to assess the prognostic significance of the mtDNA D-loop m.16126T>C variant in glioblastoma patients. Immunohistochemistry and droplet digital PCR (ddPCR) were employed for mutation analysis, complemented by statistical analyses and a literature review. The study cohort comprised 22 glioblastoma patients (mean age 59.36 ± 14.17, 12 (54.55%) females), and 25 controls (59.48 ± 13.22, 12 (80%) females). The D-loop m.16126T>C variant was observed in four (18%) of the glioblastoma samples and was associated with shorter median survival (9.5 vs. 18 months; p = 0.016, log-rank test). This study underscores the importance of investigating mtDNA, especially D-loop variants, in glioblastoma, suggesting its potential as a prognostic biomarker and, therefore, its possible therapeutic targets, warranting further exploration.

We aimed to: identify independent risk factors of 30-day mortality in patients with surgically treated spontaneous supratentorial ICH, validate the SwICH Score within Polish Healthcare System, and compare the SwICH Score to the ICH Score. We carried out a single-centre retrospective analysis of the medical data juxtaposed with CT scans of 136 ICH patients treated surgically between 2008 and 2022. Statistical analysis was performed using the same characteristics as in the SwICH Score and the ICH Score. Backward stepwise logistic regression with both 5-fold cross-validation and 1000x bootstrap procedure was used to create new scoring system. Finally predictive potential of these scales were compared. The most important predictors of 30-days mortality were: ICH volume (p<0.01), GCS at admission (p<0.01), anticoagulant status (p=0.03), and age (p<0.01). The SwICH score appears to have a better predictive potential than the ICH score, although this did not reach statistical significance [AUC: 0.789 (95% CI: 0.715-0.863) vs. AUC: 0.757 (95% CI: 0.677-0.837)]. Moreover, based on the analysed characteristics, we developed our score (encompassing: age, ICH volume, anticoagulants status, GCS at admission), [AUC of 0.872 (95% CI: 0.815-0.929)]. This score was significantly better than previous ones. Differences in healthcare systems seem to affect the accuracy of prognostic scales for patients with ICH, including possible differences in indications for surgery and postoperative care. Thus, it is important to validate assessment tools before they can be applied in a new setting and develop population-specific scores. This may improve the effectiveness of risk stratification in patients with ICH.

Schematic overview of the study. A, Schematic overview of the study design, including cohort selection and experimental and statistical approaches. TE, transposable element, SINE, short interspersed nuclear elements, LINE, long interspersed nuclear elements, PNS, peripheral nervous system. B, Schematic overview of the systemic and local pathophysiologic alterations in HGGs relevant to cfDNA fragmentation and liquid biopsies. HGGs establish a systemic and local immune-suppressive environment that leads to systemic lymphopenia and infiltration of myeloid-derived suppressor cells in the tumor microenvironment. Apoptotic lymphocytes release cfDNA in the circulation, increasing the contribution of lymphocytes to the total pool of cfDNA fragments compared with individuals without cancer. In the local microenvironment, the blood–brain barrier (BBB) is broken down in the tumor, thus allowing nucleosomal DNA from apoptotic tumor cells and other supporting cells to exit into the circulation.

CfDNA fragmentation and mutations in plasma of patients with HGGs. A, The table shows the number and percentage of patients analyzed and detected with the ARTEMIS–DELFI assay, the tumor-guided mutational panel approach, or both. B, Histogram representation of the sensitivity of HGG detection by the mutation panel or the ARTEMIS–DELFI approach. The score threshold corresponding to 90% specificity is applied to determine the samples detected by ARTEMIS–DELFI approach. The analytical specificity of the tumor-informed mutation panel assay is 99.9%. C, Fragment length distribution of tumor-specific mutant fragments vs. fragments with no mutations, germline mutations or known clonal hematopoiesis of indeterminate potential (CHIP) mutations. Tumor-specific mutations are residing in shorter fragments than wild-type (WT) or nontumor mutant fragments (median length of 146 bp vs. 167 bp, Wilcoxon P < 0.0001).

Primitive intracranial arteries, including the trigeminal, proatlantal, otic, hypoglossal, and stapedial arteries, are embryonic vascular structures that temporarily connect the anterior and posterior, or intracranial and extracranial, circulations before typically regressing during development. Their persistence into adulthood, although rare, may alter local haemodynamics, contributing to aneurysm formation, vessel stenosis, or cranial nerve dysfunction. However, given the low incidence of associated pathologies, the mere presence of a persistent primitive anastomosis is not considered a concern for aneurysm formation. The persistent trigeminal artery is the most commonly observed persistent intracranial artery, with an incidence rate of up to 0.6%. It is followed by the persistent hypoglossal artery, present in 0.02–0.09% of cases, and the persistent proatlantal artery, with an incidence of 0.03%, while others, such as the otic and stapedial arteries, are exceptionally rare. Each persistent anastomosis has distinct embryological origins, anatomical variants, and potential roles in cerebrovascular compensation. Although persistent arteries may serve as collateral pathways in cases of cerebrovascular stenosis or occlusion, they can also pose risks during neurosurgical and endovascular procedures. Given their potential association with vascular pathologies in some cases, precise recognition of these arteries via imaging is essential to prevent misdiagnosis and optimise therapeutic strategies. This study provides a comprehensive review of the embryology, anatomy, and clinical relevance of persistent primitive intracranial arteries, aiming to enhance understanding, facilitate diagnosis, and improve treatment planning in cerebrovascular interventions.