Liquid biopsy is a method sampled from body fluids, such as blood, saliva, urine, pleural effusion, cerebrospinal fluid, and so on. It is minimally invasive and reproducible, and therefore can build a dynamic, real-time monitoring of oral squamous cell carcinoma patient's conditions and treatment responses. Circulating tumor cells, circulating tumor DNA and exosomes are three main detection objects of liquid biopsy, having different detection methods and features involving cost, sensitivity, specificity and output. Blood and saliva are the options of liquid biopsy in oral cancer. Then we reviewed the studies of liquid biopsy in oral cancer, integrating multiomics analysis of these results. The multiomics analysis of genomics, transcriptomics, proteomics, metabolomics, and DNA methylation, have shown potential for the early screening, diagnosis, staging, prognosis, personalized medicine therapy, and monitoring of recurrence (minimal residual disease). Besides, we concluded some problems to be solved, such as the lack of the standard of the measurement methods and procedures of samples, the insufficient connection among different omics, and how to improve the sensitivity and specificity. And we also put up rough assumptions to these problems. However, the analysis of multiomics of liquid biopsy in oral cancer still shows great clinical value in the diagnosis and treatment of oral squamous cell carcinoma.

Updates of liquid biopsy in oral cancer and multiomics analysis.

Differentiation of high-grade glioma and primary central nervous system lymphoma: Multiparametric imaging of the enhancing tumor and peritumoral regions based on hybrid 18F-FDG PET/MRI.

BACKGROUND
The utility of liquid biopsies is well documented in several extracranial and intracranial (brain/leptomeningeal metastases, gliomas) tumors.


METHODS
The RANO (Response Assessment in Neuro-Oncology) group has set up a multidisciplinary Task Force to critically review the role of blood and CSF-liquid biopsy in central nervous system lymphomas, with a main focus on primary central nervous system lymphomas (PCNSL).


RESULTS
Several clinical applications are suggested: diagnosis of PCNSL in critical settings (elderly or frail patients, deep locations, steroids responsiveness), definition of minimal residual disease, early indication of tumor response or relapse following treatments and prediction of outcome.


CONCLUSIONS
Thus far, no clinically validated circulating biomarkers for managing both primary and secondary CNS lymphomas exist. There is need of standardization of biofluid collection, choice of analytes and type of technique to perform the molecular analysis. The various assays should be evaluated through well organized central testing within clinical trials.

Liquid biopsy for improving diagnosis and monitoring of CNS lymphomas: a RANO review.


 
 
 The prognostic roles of clinical and laboratory markers have been exploited to model risk in patients with primary CNS lymphoma, but these approaches do not fully explain the observed variation in outcome. To date, neuroimaging or molecular information is not used. The aim of this study was to determine the utility of radiomic features to capture clinically relevant phenotypes, and to link those to molecular profiles for enhanced risk stratification.
 
 
 
 In this retrospective study, we investigated 133 patients across 9 sites in Austria (2005 to 2018) and an external validation site in South Korea (44 patients, 2013 to 2016). We used T1-weighted contrast-enhanced MRI and an L1-norm regularized Cox proportional hazard model to derive a radiomic risk score. We integrated radiomic features with DNA methylation profiles using machine learning-based prediction, and validated the most relevant biological associations in tissues and cell lines.
 
 
 
 The radiomic risk score, consisting of twenty mostly textural features, was a strong and independent predictor of survival (multivariate HR=6.56 [3.64-11.81]) that remained valid in the external validation cohort. Radiomic features captured gene-regulatory differences such as in BCL6 binding activity, which was put forth as testable treatment target for a subset of patients.
 
 
 
 The radiomic risk score was a robust and complementary predictor of survival and reflected characteristics in underlying DNA methylation patterns. Leveraging imaging phenotypes to assess risk and inform epigenetic treatment targets provides a concept on which to advance prognostic modeling and precision therapy for this aggressive cancer.


Radiomic features define risk and are linked to DNA methylation attributes in primary CNS lymphoma

ABSTRACT In this study, our goal was to determine probe-specific thresholds for identifying aberrant, or outlying, DNA methylation and to provide guidance on the relative merits of using continuous or outlier methylation data. To construct a reference database, we downloaded Illumina Human 450K array data for more than 2,000 normal samples, characterized the distribution of DNA methylation and derived probe-specific thresholds for identifying aberrations. We made the decision to restrict our reference database to solid normal tissue and morphologically normal tissue found adjacent to solid tumours, excluding blood which has very distinctive patterns of DNA methylation. Next, we explored the utility of our outlier thresholds in several analyses that are commonly performed on DNA methylation data. Outliers are as effective as the full continuous dataset for simple tasks, like distinguishing tumour tissue from normal, but becomes less useful as the complexity of the problem increases. We developed an R package called OutlierMeth containing our thresholds, as well as functions for applying them to data.

https://www.tandfonline.com/doi/pdf/10.1080/15592294.2023.2213874?needAccess=true&role=button

Detecting aberrant DNA methylation in Illumina DNA methylation arrays: a toolbox and recommendations for its use

Definitive diagnosis of primary central nervous system lymphoma (PCNSL) requires invasive surgical brain biopsy, causing treatment delays. In this paper, we identified and validated tumor-specific markers that can distinguish PCNSL from other CNS tumors in tissues. In a pilot study, we tested these newly identified markers in plasma. The Methylation Outlier Detector program was used to identify markers in TCGA dataset of 48 diffuse large B-cell lymphoma (DLBCL) and 656 glioblastomas and lower-grade gliomas. Eight methylated markers clearly distinguished DLBCL from gliomas. Marker performance was verified (ROC-AUC of ≥ 0.989) in samples from several GEO datasets (95 PCNSL; 2112 other primary CNS tumors of 11 types). Next, we developed a novel, efficient assay called Tailed Amplicon Multiplexed-Methylation-Specific PCR (TAM-MSP), which uses two of the methylation markers, cg0504 and SCG3 triplexed with ACTB. FFPE tissue sections (25 cases each) of PCNSL and eight types of other primary CNS tumors were analyzed using TAM-MSP. TAM-MSP distinguished PCNSL from the other primary CNS tumors with 100% accuracy (AUC = 1.00, 95% CI 0.95–1.00, P < 0.001). The TAM-MSP assay also detected as few as 5 copies of fully methylated plasma DNA spiked into 0.5 ml of healthy plasma. In a pilot study of plasma from 15 PCNSL, 5 other CNS tumors and 6 healthy individuals, methylation in cg0504 and SCG3 was detectable in 3/15 PCNSL samples (20%). The Methylation Outlier Detector program identified methylated markers that distinguish PCNSL from other CNS tumors with accuracy. The high level of accuracy achieved by these markers was validated in tissues by a novel method, TAM-MSP. These studies lay a strong foundation for a liquid biopsy-based test to detect PCNSL-specific circulating tumor DNA.

/pdf/methylated-markers-accurately-distinguish-primary-central-40bra6se22.pdf

Methylated markers accurately distinguish primary central nervous system lymphomas (PCNSL) from other CNS tumors.

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Methylated markers accurately distinguish primary central nervous system lymphomas (PCNSL) from other CNS tumors.