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Sonobiopsy for minimally invasive, spatiotemporally-controlled, and sensitive detection of glioblastoma-derived circulating tumor DNA.

ABSTRACT: Though surgical biopsies provide direct access to tissue for genomic characterization of brain cancer, they are invasive and pose significant clinical risks. Brain cancer management via blood-based liquid biopsies is a minimally invasive alternative; however, the blood-brain barrier (BBB) restricts the release of brain tumor-derived molecular biomarkers necessary for sensitive diagnosis. Methods: A mouse glioblastoma multiforme (GBM) model was used to demonstrate the capability of focused ultrasound (FUS)-enabled liquid biopsy (sonobiopsy) to improve the diagnostic sensitivity of brain tumor-specific genetic mutations compared with conventional blood-based liquid biopsy. Furthermore, a pig GBM model was developed to characterize the translational implications of sonobiopsy in humans. Magnetic resonance imaging (MRI)-guided FUS sonication was performed in mice and pigs to locally enhance the BBB permeability of the GBM tumor. Contrast-enhanced T₁-weighted MR images were acquired to evaluate the BBB permeability change. Blood was collected immediately after FUS sonication. Droplet digital PCR was used to quantify the levels of brain tumor-specific genetic mutations in the circulating tumor DNA (ctDNA). Histological staining was performed to evaluate the potential for off-target tissue damage by sonobiopsy. Results: Sonobiopsy improved the detection sensitivity of EGFRvIII from 7.14% to 64.71% and TERT C228T from 14.29% to 45.83% in the mouse GBM model. It also improved the diagnostic sensitivity of EGFRvIII from 28.57% to 100% and TERT C228T from 42.86% to 71.43% in the porcine GBM model. Conclusion: Sonobiopsy disrupts the BBB at the spatially-targeted brain location, releases tumor-derived DNA into the blood circulation, and enables timely collection of ctDNA. Converging evidence from both mouse and pig GBM models strongly supports the clinical translation of sonobiopsy for the minimally invasive, spatiotemporally-controlled, and sensitive molecular characterization of brain cancer.

SUBMITTER: Pacia CP

PROVIDER: S-EPMC8690937 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Treatment of aggressive glioblastoma multiforme (GBM) must be based on very precise histological and molecular diagnostic of GBM type. According to the WHO guidelines, only tissue biopsy is a relevant source of cellular material evaluated in the diagnostic process to specify the tumor features. Nevertheless, obtaining a GBM biopsy is complicated and relies mostly on resection surgery. Evaluating circulating free DNA and/or circulating tumor cells (CTCs) in the clinic, using a liquid biopsy could represent a non-invasive cancer care optimization. In the present study, the peripheral blood of patients undergoing GBM resection (n = 18) was collected and examined for CTCs. The feasibility of GBM molecular diagnostics from a simple non-invasive peripheral blood withdrawal was evaluated. The size-based enriched CTCs were analyzed using cytomorphology and their origin confirmed based on mutational analysis. In addition, shared DNA mutations in CTCs and in primary tumor tissue were searched. For the identification of CTCs, next generation sequencing (NGS) was used. The GeneReader™ sequencing platform enables targeted sequencing of a 12-gene panel and direct evaluation of detected gene variations using QIAGEN Clinical Insight Analyze (QCI-A) software with a special algorithm for liquid biopsy sequencing analysis. Herein, we present a standard operating procedure for CTC enrichment in GBM patients, CTC in vitro culture, CTC cytomorphological evaluation, and NGS analysis of CTCs using the QIAGEN Actionable Insights Tumor (ATP) Panel. CTCs were present in all tested patients (18/18). The NGS data generated for formalin-fixed paraffin-embedded (FFPE) primary tumor tissues and CTCs reached significantly high-quality parameters. The comparisons between different sample types (CTCs vs. primary tumors) and sampling area (different primary tumor regions) showed a significant level of concordance, indicating CTC testing could be used for patient monitoring and recurrence awareness. Notably, more mutations were detected when analyzing CTC samples compared with the paired primary tumors (n = 3). The results confirm the feasibility of using CTCs as a source of tumor DNA in a diagnostic process, especially when evaluating the molecular characteristics of GBMs. A major advantage of the presented NGS approach for detecting CTCs is the simultaneous identification of several markers relevant for GBM diagnostics, allowing molecular diagnostics on cytological specimens and potential administration of innovative targeted therapies.

Project description:BackgroundPancreatic cancer (PC) is among the most lethal cancers. The lack of effective tools for early detection results in late tumor detection and, consequently, high mortality rate. Precision oncology aims to develop targeted individual treatments based on advanced computational approaches of omics data. Biomarkers, such as global alteration of cytosine (CpG) methylation, can be pivotal for these objectives. In this study, we performed DNA methylation profiling of pancreatic cancer patients using circulating cell-free DNA (cfDNA) and artificial intelligence (AI) including Deep Learning (DL) for minimally invasive detection to elucidate the epigenetic pathogenesis of PC.MethodsThe Illumina Infinium HD Assay was used for genome-wide DNA methylation profiling of cfDNA in treatment-naïve patients. Six AI algorithms were used to determine PC detection accuracy based on cytosine (CpG) methylation markers. Additional strategies for minimizing overfitting were employed. The molecular pathogenesis was interrogated using enrichment analysis.ResultsIn total, we identified 4556 significantly differentially methylated CpGs (q-value < 0.05; Bonferroni correction) in PC versus controls. Highly accurate PC detection was achieved with all 6 AI platforms (Area under the receiver operator characteristics curve [0.90-1.00]). For example, DL achieved AUC (95% CI): 1.00 (0.95-1.00), with a sensitivity and specificity of 100%. A separate modeling approach based on logistic regression-based yielded an AUC (95% CI) 1.0 (1.0-1.0) with a sensitivity and specificity of 100% for PC detection. The top four biological pathways that were epigenetically altered in PC and are known to be linked with cancer are discussed.ConclusionUsing a minimally invasive approach, AI, and epigenetic analysis of circulating cfDNA, high predictive accuracy for PC was achieved. From a clinical perspective, our findings suggest that that early detection leading to improved overall survival may be achievable in the future.

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Sonobiopsy for minimally invasive, spatiotemporally-controlled, and sensitive detection of glioblastoma-derived circulating tumor DNA.

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