Tumor-Educated Platelet RNA for the Detection and (Pseudo)progression Monitoring of Glioblastoma

The impact of platelets on the metastatic potential of tumour cells

In cancer, activation of platelets by tumor cells is critical to disease progression. Development of precise antiplatelet targeting may improve outcomes from anticancer therapy. Alongside a distinct shift in functionality such as pro-metastatic and pro-coagulant properties, platelet production is often accelerated significantly early in carcinogenesis and the cancer-associated thrombocytosis increases the risk of metastasis formation and thromboembolic events. Tumor-activated platelets facilitate the proliferation of migrating tumor cells and shield them from immune surveillance and physical stress during circulation. Additionally, platelet-tumor cell interactions promote tumor cell intravasation, intravascular arrest, and extravasation through a repertoire of adhesion molecules, growth factors and angiogenic factors. Particularly, the presence of circulating tumor cell (CTC) clusters in association with platelets is a negative prognostic indicator. The contribution of platelets to the metastatic process is an area of intense investigation and this review provides an overview of the advances in understanding platelet-tumor cell interactions and their contribution to disease progression. Also, we review the potential of targeting platelets to interfere with the metastatic process.

Improving platelet-RNA-based diagnostics: a comparative analysis of machine learning models for cancer detection and multiclass classification

Liquid biopsy demonstrates excellent potential in patient management by providing a minimally invasive and cost-effective approach to detecting and monitoring cancer, even at its early stages. Due to the complexity of liquid biopsy data, machine-learning techniques are increasingly gaining attention in sample analysis, especially for multidimensional data such as RNA expression profiles. Yet, there is no agreement in the community on which methods are the most effective or how to process the data. To circumvent this, we performed a large-scale study using various machine-learning techniques. First, we took a closer look at existing datasets and filtered out some patients to assert data collection quality. The final data collection included platelet RNA samples acquired from 1397 cancer patients (17 types of cancer) and 354 asymptomatic, presumed healthy, donors. Then, we assessed an array of different machine-learning models and techniques (e.g., feature selection of RNA transcripts) in pan-cancer detection and multiclass classification. Our results show that simple logistic regression performs the best, reaching a 68% cancer detection rate at a 99% specificity level, and multiclass classification accuracy of 79.38% when distinguishing between five cancer types. In summary, by revisiting classical machine-learning models, we have exceeded the previously used method by 5% and 9.65% in cancer detection and multiclass classification, respectively. To ease further research, we open-source our code and data processing pipelines (https://gitlab.com/jopekmaksym/improving-platelet-rna-based-diagnostics), which we hope will serve the community as a strong baseline.

The use of platelets as a clinical tool in oncology: opportunities and challenges

Platelets are small circulating anucleated cells mainly involved in thrombosis and hemostasis processes. Moreover, platelets play an active role in tumorigenesis and cancer progression, stimulating angiogenesis and vascular remodelling, and protecting circulating cancer cells from shear forces and immune surveillance. Several reports indicate that platelet number in the blood circulation of cancer patients is associated with prognosis and response to treatment. However, the mechanisms of platelets "education" by cancer cells and the crosstalk between platelets and tumor are still unclear, and the role of "tumor educated platelets" (TEPs) is achieving growing interest in cancer research. TEPs are a biological source of cancer-derived biomarkers, especially RNAs that are protected by platelets membrane from circulating RNases, and could serve as a non-invasive tool for tumor detection, molecular profiling and evolution during therapy in clinical practice. Moreover, short platelet lifespan offers the possibility to get a snapshot assessment of cancer molecular profile, providing a real-time tool. We review and discuss the potential and the clinical utility, in terms of cancer diagnosis and monitoring, of platelet count together with other morphological parameters and of the more recent and innovative TEP profiling.

Prospects of liquid biopsy in the prognosis and clinical management of gastrointestinal cancers

Gastrointestinal (GI) cancers account for one-fourth of the global cancer incidence and are incriminated to cause one-third of cancer-related deaths. GI cancer includes esophageal, gastric, liver, pancreatic, and colorectal cancers, mostly diagnosed at advanced stages due to a lack of accurate markers for early stages. The invasiveness of diagnostic methods like colonoscopy for solid biopsy reduces patient compliance as it cannot be frequently used to screen patients. Therefore, minimally invasive approaches like liquid biopsy may be explored for screening and early identification of gastrointestinal cancers. Liquid biopsy involves the qualitative and quantitative determination of certain cancer-specific biomarkers in body fluids such as blood, serum, saliva, and urine to predict disease progression, therapeutic tolerance, toxicities, and recurrence by evaluating minimal residual disease and its correlation with other clinical features. In this review, we deliberate upon various tumor-specific cellular and molecular entities such as circulating tumor cells (CTCs), tumor-educated platelets (TEPs), circulating tumor DNA (ctDNA), cell-free DNA (cfDNA), exosomes, and exosome-derived biomolecules and cite recent advances pertaining to their use in predicting disease progression, therapy response, or risk of relapse. We also discuss the technical challenges associated with translating liquid biopsy into clinical settings for various clinical applications in gastrointestinal cancers.

Assessing the complementary information from an increased number of biologically relevant features in liquid biopsy-derived RNA-Seq data

Liquid biopsy-derived RNA sequencing (lbRNA-seq) exhibits significant promise for clinic-oriented cancer diagnostics due to its non-invasiveness and ease of repeatability. Despite substantial advancements, obstacles like technical artefacts and process standardisation impede seamless clinical integration. Alongside addressing technical aspects such as normalising fluctuating low-input material and establishing a standardised clinical workflow, the lack of result validation using independent datasets remains a critical factor contributing to the often low reproducibility of liquid biopsy-detected biomarkers. Considering the outlined drawbacks, our objective was to establish a workflow/methodology characterised by: 1. Harness the rich diversity of biological features accessible through lbRNA-seq data, encompassing a holistic range of molecular and functional attributes. These components are seamlessly integrated via a Machine Learning-based Ensemble Classification framework, enabling a unified and comprehensive analysis of the intricate information encoded within the data. 2. Implementing and rigorously benchmarking intra-sample normalisation methods to heighten their relevance within clinical settings. 3. Thoroughly assessing its efficacy across independent test sets to ascertain its robustness and potential utility. Using ten datasets from several studies comprising three different sources of biological material, we first show that while the best-performing normalisation methods depend strongly on the dataset and coupled Machine Learning method, the rather simple Counts Per Million method is generally very robust, showing comparable performance to cross-sample methods. Subsequently, we demonstrate that the innovative biofeature types introduced in this study, such as the Fraction of Canonical Transcript, harbour complementary information. Consequently, their inclusion consistently enhances prediction power compared to models relying solely on gene expression-based biofeatures. Finally, we demonstrate that the workflow is robust on completely independent datasets, generally from different labs and/or different protocols. Taken together, the workflow presented here outperforms generally employed methods in prediction accuracy and may hold potential for clinical diagnostics application due to its specific design.

TEP RNA: a new frontier for early diagnosis of NSCLC

BACKGROUND

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer (LC), which is the leading cause of tumor mortality. In recent years, compared with tissue biopsy, which is the diagnostic gold standard for tumor diagnosis, Liquid biopsy (LB) is considered to be a more minimally invasive, sensitive, and safer alternative or auxiliary diagnostic method. However, the current value of LB in early diagnosis of LC is not ideal, so it is particularly important to study the changes in blood composition during the process of tumorigenesis and find more sensitive biomarkers.

PURPOSE

Platelets are a type of abundant blood cells that carry a large amount of RNA. In the LC regulatory network, activated platelets play an important role in the process of tumorigenesis, development, and metastasis. In order to identify predictive liquid biopsy biomarkers for the diagnosis of NSCLC, we summarized the development and function of platelets, the interaction between platelets and tumors, the value of TEP RNA in diagnosis, prognosis, and treatment of NSCLC, and the method for detecting TEP RNA of NSCLC in this article.

CONCLUSION

The application of platelets in the diagnosis and treatment of NSCLC remains at a nascent stage. In addition to the drawbacks of low platelet count and complex experimental processes, the diagnostic accuracy of TEP RNA-seq for cancer in different populations still needs to be improved and validated. At present, a large number of studies have confirmed significant differences in the expression of TEP RNA in platelets between NSCLC patients and healthy individuals. Continuous exploration of the diagnostic value of TEP RNA in NSCLC is of utmost importance. The integration of NSCLC platelet-related markers with other NSCLC markers can improve current tumor diagnosis and prognostic evaluation systems, providing broad prospects in tumor screening, disease monitoring, and prognosis assessment.

SYSTEMIC INFLAMMATORY INDICES IN PATIENTS WITH MALIGNANT GLIOMAS AND EFFECTS OF PLATELET SECRETOME IN VITRO

BACKGROUND

To date, no significant clinical progress has been achieved in the treatment of brain malignant gliomas (MG), and the active search for non-invasive circulating biomarkers continues. The prognostic significance of the ratio of the main peripheral blood cell populations of patients with MG is evaluated. Considerable attention is paid to the secretome of platelets (Pt) of peripheral blood.

AIM

To evaluate the indicators of the peripheral blood cell population ratios in patients with brain MG and to study the influence of the secretome of Pt (SPt) of the peripheral blood of patients with brain MG in cell cultures in vitro.

MATERIALS AND METHODS

We studied samples of peripheral blood from patients with glioma CNS WHO grade G2 (n = 5), G3 (n = 12), and G4 (n = 20). The peripheral blood cell counts were analyzed in the preoperative period on an automatic hematology analyzer. The in vitro study of SPt was performed on the U251 human glioblastoma cell line cultured with SPt from MG patients or SPt pre-incubated with anti-TGF-β1 antibody. Cell cultures were observed for 72 h, and mitotic index (MI) was calculated.

RESULTS

In MG patients, the count of peripheral blood leukocytes and neutrophils increased (p < 0.05). The neutrophil-to-lymphocyte ratio (NLR) and systemic immune-inflammation index (SII) increased by 2-3 times compared to control. Nevertheless, correlation analysis did not reveal significant relationships between quantitative indicators of peripheral blood cells and the tumor malignancy degree in MG patients. The MI in U251 cells increased under the influence of SPt from patients with MG (p < 0.021), correlated with the tumor degree of malignancy (r = 0.246, p = 0.014). Pre-incubation of SPt with anti-TGF-β1 antibody tends to neutralize this promitotic effect.

CONCLUSION

In MG patients, the integral indicators of NLR and SII increased but no significant relationship with the degree of tumor malignancy was found. In U251 cells, promitotic effects of SPt of MG patients partially decreased by anti-TGF-β1 antibody.

Heterogeneity of platelets and their responses

There has been increasing recognition of heterogeneity in blood platelets and their responses, particularly in recent years, where next-generation technologies and advanced bioinformatic tools that interrogate "big data" have enabled large-scale studies of RNA and protein expression across a growing list of disease states. However, pioneering platelet biologists and clinicians were already hypothesizing upon and investigating heterogeneity in platelet (and megakaryocyte) activity and platelet metabolism and aggregation over half a century ago. Building on their foundational hypotheses, in particular Professor Marian A. Packham's pioneering work and a State of the Art lecture in her memoriam at the 2023 International Society on Thrombosis and Haemostasis Congress by Anandi Krishnan, this review outlines the key features that contribute to the heterogeneity of platelets between and within individuals. Starting with important epidemiologic factors, we move stepwise through successively smaller scales down to heterogeneity revealed by single-cell technologies in health and disease. We hope that this overview will urge future scientific and clinical studies to recognize and account for heterogeneity of platelets and aim to apply methods that capture that heterogeneity. Finally, we summarize other exciting new data presented on this topic at the 2023 International Society on Thrombosis and Haemostasis Congress.

Blood platelet RNA profiles do not enable for nivolumab response prediction at baseline in patients with non-small cell lung cancer

BACKGROUND

Anti-PD-(L)1 immunotherapy has emerged as a promising treatment approach for non-small cell lung cancer (NSCLC), though the response rates remain low. Pre-treatment response prediction may improve patient allocation for immunotherapy. Blood platelets act as active immune-like cells, thereby constraining T-cell activity, propagating cancer metastasis, and adjusting their spliced mRNA content.

OBJECTIVE

We investigated whether platelet RNA profiles before start of nivolumab anti-PD1 immunotherapy may predict treatment responses.

METHODS

We performed RNA-sequencing of platelet RNA samples isolated from stage III-IV NSCLC patients before treatment with nivolumab. Treatment response was scored by the RECIST-criteria. Data were analyzed using a predefined thromboSeq analysis including a particle-swarm-enhanced support vector machine (PSO/SVM) classification algorithm.

RESULTS

We collected and processed a 286-samples cohort, separated into a training/evaluation and validation series and subjected those to training of the PSO/SVM-classification algorithm. We observed only low classification accuracy in the 107-samples validation series (area under the curve (AUC) training series: 0.73 (95% -CI: 0.63-0.84, n = 88 samples), AUC evaluation series: 0.64 (95% -CI: 0.51-0.76, n = 91 samples), AUC validation series: 0.58 (95% -CI: 0.45-0.70, n = 107 samples)), employing a five-RNAs biomarker panel.

CONCLUSIONS

We concluded that platelet RNA may have minimally discriminative capacity for anti-PD1 nivolumab response prediction, with which the current methodology is insufficient for diagnostic application.

A brain metastasis liquid biopsy: Where are we now?

Brain metastases remain a challenging and feared complication for patients with cancer and research in this area has lagged behind research into metastases to other organs. Due to their location and the risks associated with neurosurgical biopsies, the biology underpinning brain metastases response to treatment and evolution over time remains poorly understood. Liquid biopsies are proposed to overcome many of the limitations present with tissue biopsies, providing a better representation of tumor heterogeneity, facilitating repeated sampling, and providing a noninvasive assessment of tumor biology. Several different liquid biopsy approaches have been investigated including circulating tumor cells, circulating tumor DNA, extracellular vesicles, and tumor-educated platelets; however, these have generally been less effective in assessing brain metastases compared to metastases to other organs requiring improved techniques to investigate these approaches, studies combining different liquid biopsy approaches and/or novel liquid biopsy approaches. Through this review, we highlight the current state of the art and define key unanswered questions related to brain metastases liquid biopsies.

The application value of deep learning in the background of precision medicine in glioblastoma

Introduction: Glioblastoma is a highly malignant central nervous system tumor, World Health Organization Ⅳ, glioblastoma is the most common primary malignancy, due to its own specificity and complexity, different patients often benefit from the current conventional treatment regimen because of different molecular subtypes, in the context of precision medicine, the application of deep learning to identify the salient features of tumors on brain imaging, prognostic predictive assessment combined with clinical data to maximize the benefits of each patient from the treatment regimen is a non-invasive and feasible regimen. Methods: We conducted a comprehensive review of the existing literature on the role of deep learning in glioblastomas, covering molecular classification and diagnosis, prognosis assessment. Results: Data based on a variety of magnetic resonance imaging sequences, genetic information, and clinical combinations enable noninvasive predictive tumor diagnosis of glioblastoma and assess overall survival and treatment response accuracy. For images, standardized image acquisition and data extraction techniques can be effectively translated into learning models for clinical practice. However, it must be recognized that interventions in the treatment of glioblastoma using deep learning are still in their infancy, and the robustness of the model is challenged, as the current total number of glioblastoma samples is insufficient for large-scale experimental methods, which is directly related to the difficulty of application of the model. Conclusion: Compared to radiomics and shallow machine learning, deep learning can be a more robust, non-invasive, and effective approach, providing more valuable information as clinicians develop personalized medical protocols for glioblastoma patients.

Recent Developments in Blood Biomarkers in Neuro-oncology

PURPOSE OF REVIEW

Given the invasive and high-risk nature of brain surgery, the need for non-invasive biomarkers obtained from the peripheral blood is greatest in tumors of the central nervous system (CNS). In this comprehensive review, we highlight recent advances in blood biomarker development for adult and pediatric brain tumors.

RECENT FINDINGS

We summarize recent blood biomarker development for CNS tumors across multiple key analytes, including peripheral blood mononuclear cells, cell-free DNA, cell-free RNA, proteomics, circulating tumor cells, and tumor-educated platelets. We also discuss methods for enhancing blood biomarker detection through transient opening of the blood-brain barrier. Although blood-based biomarkers are not yet used in routine neuro-oncology practice, this field is advancing rapidly and holds great promise for improved and non-invasive management of patients with brain tumors. Prospective and adequately powered studies are needed to confirm the clinical utility of any blood biomarker prior to widespread clinical implementation.

Decoding the role of platelets in tumour metastasis: enigmatic accomplices and intricate targets for anticancer treatments

The canonical role of platelets as central players in cardiovascular disease by way of their fundamental role in mediating thrombosis and haemostasis is well appreciated. However, there is now a large body of experimental evidence demonstrating that platelets are also pivotal in various physiological and pathophysiological processes other than maintaining haemostasis. Foremost amongst these is the emerging data highlighting the key role of platelets in driving cancer growth, metastasis and modulating the tumour microenvironment. As such, there is significant interest in targeting platelets therapeutically for the treatment of cancer. Therefore, the purpose of this review is to provide an overview of how platelets contribute to the cancer landscape and why platelets present as valuable targets for the development of novel cancer diagnosis tools and therapeutics.

Liquid biopsy: creating opportunities in brain space

In recent years, liquid biopsy has emerged as an alternative method to diagnose and monitor tumors. Compared to classical tissue biopsy procedures, liquid biopsy facilitates the repetitive collection of diverse cellular and acellular analytes from various biofluids in a non/minimally invasive manner. This strategy is of greater significance for high-grade brain malignancies such as glioblastoma as the quantity and accessibility of tumors are limited, and there are collateral risks of compromised life quality coupled with surgical interventions. Currently, blood and cerebrospinal fluid (CSF) are the most common biofluids used to collect circulating cells and biomolecules of tumor origin. These liquid biopsy analytes have created opportunities for real-time investigations of distinct genetic, epigenetic, transcriptomics, proteomics, and metabolomics alterations associated with brain tumors. This review describes different classes of liquid biopsy biomarkers present in the biofluids of brain tumor patients. Moreover, an overview of the liquid biopsy applications, challenges, recent technological advances, and clinical trials in the brain have also been provided.

Bleeding and Thrombosis in Multiple Myeloma: Platelets as Key Players during Cell Interactions and Potential Use as Drug Delivery Systems

Multiple myeloma (MM) is a hematological malignancy originated in the bone marrow and characterized by unhindered plasma cell proliferation that results in several clinical manifestations. Although the main role of blood platelets lies in hemostasis and thrombosis, platelets also play a pivotal role in a number of other pathological conditions. Platelets are the less-explored components from the tumor microenvironment in MM. Although some studies have recently revealed that MM cells have the ability to activate platelets even in the premalignant stage, this phenomenon has not been widely investigated in MM. Moreover, thrombocytopenia, along with bleeding, is commonly observed in those patients. In this review, we discuss the hemostatic disturbances observed in MM patients and the dynamic interaction between platelets and myeloma cells, along with present and future potential avenues for the use of platelets for diagnostic and therapeutic purposes.

In vitro cross-talk between metastasis-competent circulating tumor cells and platelets in colon cancer: a malicious association during the harsh journey in the blood

Background: Platelets are active players in hemostasis, coagulation and also tumorigenesis. The cross-talk between platelets and circulating tumor cells (CTCs) may have various pro-cancer effects, including promoting tumor growth, epithelial-mesenchymal transition (EMT), metastatic cell survival, adhesion, arrest and also pre-metastatic niche and metastasis formation. Interaction with CTCs might alter the platelet transcriptome. However, as CTCs are rare events, the cross-talk between CTCs and platelets is poorly understood. Here, we used our established colon CTC lines to investigate the colon CTC-platelet cross-talk in vitro and its impact on the behavior/phenotype of both cell types. Methods: We exposed platelets isolated from healthy donors to thrombin (positive control) or to conditioned medium from three CTC lines from one patient with colon cancer and then we monitored the morphological and protein expression changes by microscopy and flow cytometry. We then analyzed the transcriptome by RNA-sequencing of platelets indirectly (presence of a Transwell insert) co-cultured with the three CTC lines. We also quantified by reverse transcription-quantitative PCR the expression of genes related to EMT and cancer development in CTCs after direct co-culture (no Transwell insert) with platelets. Results: We observed morphological and transcriptomic changes in platelets upon exposure to CTC conditioned medium and indirect co-culture (secretome). Moreover, the expression levels of genes involved in EMT (p < 0.05) were decreased in CTCs co-cultured with platelets, but not of genes encoding mesenchymal markers (FN1 and SNAI2). The expression levels of genes involved in cancer invasiveness (MYC, VEGFB, IL33, PTGS2, and PTGER2) were increased. Conclusion: For the first time, we studied the CTC-platelet cross-talk using our unique colon CTC lines. Incubation with CTC conditioned medium led to platelet aggregation and activation, supporting the hypothesis that their interaction may contribute to preserve CTC integrity during their journey in the bloodstream. Moreover, co-culture with platelets influenced the expression of several genes involved in invasiveness and EMT maintenance in CTCs.

Liquid biopsy: an examination of platelet RNA obtained from head and neck squamous cell carcinoma patients for predictive molecular tumor markers

Aim

Recently, a tumor cell-platelet interaction was identified in different tumor entities, resulting in a transfer of tumor-derived RNA into platelets, named further "tumor-educated platelets (TEP)". The present pilot study aims to investigate whether such a tumor-platelet transfer of RNA occurs also in patients suffering from head and neck squamous cell carcinoma (HNSCC).

Methods

Sequencing analysis of RNA derived from platelets of tumor patients (TPs) and healthy donors (HDs) were performed. Subsequently, quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was used for verification of differentially expressed genes in platelets from TPs and HDs in a second cohort of patients and HDs. Data were analyzed by applying bioinformatic tools.

Results

Sequencing of RNA derived from the tumor as well as from platelets of TPs and HDs revealed 426 significantly differentially existing RNA, at which 406 RNA were more and 20 RNA less abundant in platelets from TPs in comparison to that of HDs. In TPs' platelets, abundantly existing RNA coding for 49 genes were detected, characteristically expressed in epithelial cells and RNA, the products of which are involved in tumor progression. Applying bioinformatic tools and verification on a second TP/HD cohort, collagen type I alpha 1 chain (COL1A1) and zinc finger protein 750 (ZNF750) were identified as the strongest potentially platelet-RNA-sequencing (RNA-seq)-based biomarkers for HNSCC.

Conclusions

These results indicate a transfer of tumor-derived messenger RNA (mRNA) into platelets of HNSCC patients. Therefore, analyses of a patient's platelet RNA could be an efficient option for liquid biopsy in order to diagnose HNSCC or to monitor tumorigenesis as well as therapeutic responses at any time and in real time.

Tumor-educated platelet blood tests for Non-Small Cell Lung Cancer detection and management

Liquid biopsy approaches offer a promising technology for early and minimally invasive cancer detection. Tumor-educated platelets (TEPs) have emerged as a promising liquid biopsy biosource for the detection of various cancer types. In this study, we processed and analyzed the TEPs collected from 466 Non-small Cell Lung Carcinoma (NSCLC) patients and 410 asymptomatic individuals (controls) using the previously established thromboSeq protocol. We developed a novel particle-swarm optimization machine learning algorithm which enabled the selection of an 881 RNA biomarker panel (AUC 0.88). Herein we propose and validate in an independent cohort of samples (n = 558) two approaches for blood samples testing: one with high sensitivity (95% NSCLC detected) and another with high specificity (94% controls detected). Our data explain how TEP-derived spliced RNAs may serve as a biomarker for minimally-invasive clinical blood tests, complement existing imaging tests, and assist the detection and management of lung cancer patients.

Is There a Role for Machine Learning in Liquid Biopsy for Brain Tumors? A Systematic Review

The paucity of studies available in the literature on brain tumors demonstrates that liquid biopsy (LB) is not currently applied for central nervous system (CNS) cancers. The purpose of this systematic review focused on the application of machine learning (ML) to LB for brain tumors to provide practical guidance for neurosurgeons to understand the state-of-the-art practices and open challenges. The herein presented study was conducted in accordance with the PRISMA-P (preferred reporting items for systematic review and meta-analysis protocols) guidelines. An online literature search was launched on PubMed/Medline, Scopus, and Web of Science databases using the following query: "((Liquid biopsy) AND (Glioblastoma OR Brain tumor) AND (Machine learning OR Artificial Intelligence))". The last database search was conducted in April 2023. Upon the full-text review, 14 articles were included in the study. These were then divided into two subgroups: those dealing with applications of machine learning to liquid biopsy in the field of brain tumors, which is the main aim of this review (n = 8); and those dealing with applications of machine learning to liquid biopsy in the diagnosis of other tumors (n = 6). Although studies on the application of ML to LB in the field of brain tumors are still in their infancy, the rapid development of new techniques, as evidenced by the increase in publications on the subject in the past two years, may in the future allow for rapid, accurate, and noninvasive analysis of tumor data. Thus making it possible to identify key features in the LB samples that are associated with the presence of a brain tumor. These features could then be used by doctors for disease monitoring and treatment planning.

Tumor-educated platelet SNORA58, SNORA68 and SNORD93 as novel diagnostic biomarkers for esophageal cancer

Aim: The purpose of this study was to evaluate whether tumor-educated platelet (TEP) snoRNAs could be used as a diagnostic biomarker for esophageal cancer (ESCA). Methods: Platelet precipitates were obtained from platelet-rich plasma by low-speed centrifugation, and total RNA was extracted from platelets using Trizol™ reagent. RT-qPCR was used to detect snoRNA expression, and the receiver operating characteristic was used to assess its diagnostic potential. Results: SNORA58, SNORA68 and SNORD93 were significantly upregulated in TEPs from ESCA patients and early-stage patients compared with healthy controls. Importantly, the three snoRNAs were capable of serving as circulating biomarkers of diagnostics and early diagnosis of ESCA, possessing areas under the curve of 0.846 and 0.857, respectively. Conclusion: TEP SNORA58, SNORA68 and SNORD93 could potentially serve as noninvasive biomarkers for diagnosis and early diagnosis of ESCA.

Platelet-Based Liquid Biopsies through the Lens of Machine Learning

Liquid biopsies offer minimally invasive diagnosis and monitoring of cancer disease. This biosource is often analyzed using sequencing, which generates highly complex data that can be used using machine learning tools. Nevertheless, validating the clinical applications of such methods is challenging. It requires: (a) using data from many patients; (b) verifying potential bias concerning sample collection; and (c) adding interpretability to the model. In this work, we have used RNA sequencing data of tumor-educated platelets (TEPs) and performed a binary classification (cancer vs. no-cancer). First, we compiled a large-scale dataset with more than a thousand donors. Further, we used different convolutional neural networks (CNNs) and boosting methods to evaluate the classifier performance. We have obtained an impressive result of 0.96 area under the curve. We then identified different clusters of splice variants using expert knowledge from the Kyoto Encyclopedia of Genes and Genomes (KEGG). Employing boosting algorithms, we identified the features with the highest predictive power. Finally, we tested the robustness of the models using test data from novel hospitals. Notably, we did not observe any decrease in model performance. Our work proves the great potential of using TEP data for cancer patient classification and opens the avenue for profound cancer diagnostics.

Combinatorial Blood Platelets-Derived circRNA and mRNA Signature for Early-Stage Lung Cancer Detection

Despite the diversity of liquid biopsy transcriptomic repertoire, numerous studies often exploit only a single RNA type signature for diagnostic biomarker potential. This frequently results in insufficient sensitivity and specificity necessary to reach diagnostic utility. Combinatorial biomarker approaches may offer a more reliable diagnosis. Here, we investigated the synergistic contributions of circRNA and mRNA signatures derived from blood platelets as biomarkers for lung cancer detection. We developed a comprehensive bioinformatics pipeline permitting an analysis of platelet-circRNA and mRNA derived from non-cancer individuals and lung cancer patients. An optimal selected signature is then used to generate the predictive classification model using machine learning algorithm. Using an individual signature of 21 circRNA and 28 mRNA, the predictive models reached an area under the curve (AUC) of 0.88 and 0.81, respectively. Importantly, combinatorial analysis including both types of RNAs resulted in an 8-target signature (6 mRNA and 2 circRNA), enhancing the differentiation of lung cancer from controls (AUC of 0.92). Additionally, we identified five biomarkers potentially specific for early-stage detection of lung cancer. Our proof-of-concept study presents the first multi-analyte-based approach for the analysis of platelets-derived biomarkers, providing a potential combinatorial diagnostic signature for lung cancer detection.

Liquid biopsies: the future of cancer early detection

Cancer is a worldwide pandemic. The burden it imposes grows steadily on a global scale causing emotional, physical, and financial strains on individuals, families, and health care systems. Despite being the second leading cause of death worldwide, many cancers do not have screening programs and many people with a high risk of developing cancer fail to follow the advised medical screening regime due to the nature of the available screening tests and other challenges with compliance. Moreover, many liquid biopsy strategies being developed for early detection of cancer lack the sensitivity required to detect early-stage cancers. Early detection is key for improved quality of life, survival, and to reduce the financial burden of cancer treatments which are greater at later stage detection. This review examines the current liquid biopsy market, focusing in particular on the strengths and drawbacks of techniques in achieving early cancer detection. We explore the clinical utility of liquid biopsy technologies for the earlier detection of solid cancers, with a focus on how a combination of various spectroscopic and -omic methodologies may pave the way for more efficient cancer diagnostics.

Tumour-educated platelets for breast cancer detection: biological and technical insights

BACKGROUND

Studies have shown that blood platelets contain tumour-specific mRNA profiles tumour-educated platelets (TEPs). Here, we aim to train a TEP-based breast cancer detection classifier.

METHODS

Platelet mRNA was sequenced from 266 women with stage I-IV breast cancer and 212 female controls from 6 hospitals. A particle swarm optimised support vector machine (PSO-SVM) and an elastic net-based classifier (EN) were trained on 71% of the study population. Classifier performance was evaluated in the remainder (29%) of the population, followed by validation in an independent set (37 cases and 36 controls). Potential confounding was assessed in post hoc analyses.

RESULTS

Both classifiers reached an area under the curve (AUC) of 0.85 upon internal validation. Reproducibility in the independent validation set was poor with an AUC of 0.55 and 0.54 for the PSO-SVM and EN classifier, respectively. Post hoc analyses indicated that 19% of the variance in gene expression was associated with hospital. Genes related to platelet activity were differentially expressed between hospitals.

CONCLUSIONS

We could not validate two TEP-based breast cancer classifiers in an independent validation cohort. The TEP protocol is sensitive to within-protocol variation and revision might be necessary before TEPs can be reconsidered for breast cancer detection.

Platelet RNA sequencing for cancer screening in patients with unprovoked venous thromboembolism: a prospective cohort study

BACKGROUND

Platelet RNA sequencing has been shown to accurately detect cancer in previous studies.

OBJECTIVES

To compare the diagnostic accuracy of platelet RNA sequencing with standard-of-care limited cancer screening in patients with unprovoked venous thromboembolism (VTE).

METHODS

Patients aged ≥40 years with unprovoked VTE were recruited at 13 centers and followed for 12 months for cancer. Participants underwent standard-of-care limited cancer screening, and platelet RNA sequencing analysis was performed centrally at study end for cases and selected controls. Sensitivity and specificity were calculated, using the predefined primary positivity threshold of 0.54 for platelet RNA sequencing aiming at 86% test sensitivity, and an additional predefined threshold of 0.89 aiming at 99% test specificity.

RESULTS

A total of 476 participants were enrolled, of whom 25 (5.3%) were diagnosed with cancer during 12-month follow-up. For each cancer patient, 3 cancer-free patients were randomly selected for the analysis. The sensitivity of limited screening was 72% (95% CI, 52-86) at a specificity of 91% (95% CI, 82-95). The area under the receiver operator characteristic for platelet RNA sequencing was 0.54 (95% CI, 0.41-0.66). At the primary positivity threshold, all patients had a positive test, for a sensitivity estimated at 100% (95% CI, 87-99) and a specificity of 8% (95% CI, 3.7-16.4). At the secondary threshold, sensitivity was 68% (95% CI, 48-83; p value compared with limited screening 0.71) at a specificity of 36% (95% CI, 26-47).

CONCLUSION

Platelet RNA sequencing had poor diagnostic accuracy for detecting occult cancer in patients with unprovoked VTE with the current algorithm.

Contents in tumor-educated platelets as the novel biosource for cancer diagnostics

Liquid biopsy, a powerful non-invasive test, has been widely used in cancer diagnosis and treatment. Platelets, the second most abundant cells in peripheral blood, are becoming one of the richest sources of liquid biopsy with the capacity to systematically and locally respond to the presence of cancer and absorb and store circulating proteins and different types of nucleic acids, thus called "tumor-educated platelets (TEPs)". The contents of TEPs are significantly and specifically altered, empowering them with the potential as cancer biomarkers. The current review focuses on the alternation of TEP content, including coding and non-coding RNA and proteins, and their role in cancer diagnostics.

Liquid biopsies and minimal residual disease in myeloid malignancies

Minimal residual disease (MRD) assessment through blood component sampling by liquid biopsies (LBs) is increasingly being investigated in myeloid malignancies. Blood components then undergo molecular analysis by flow cytometry or sequencing techniques and can be used as a powerful tool for prognostic and predictive purposes in myeloid malignancies. There is evidence and more is evolving about the quantification and identification of cell-based and gene-based biomarkers in myeloid malignancies to monitor treatment response. MRD based acute myeloid leukemia protocol and clinical trials are currently incorporating LB testing and preliminary results are encouraging for potential widespread use in clinic in the near future. MRD monitoring using LBs are not standard in myelodysplastic syndrome (MDS) but this is an area of active investigation. In the future, LBs can replace more invasive techniques such as bone marrow biopsies. However, the routine clinical application of these markers continues to be an issue due to lack of standardization and limited number of studies investigating their specificities. Integrating artificial intelligence (AI) could help simplify the complex interpretation of molecular testing and reduce errors related to operator dependency. Though the field is rapidly evolving, the applicability of MRD testing using LB is mostly limited to research setting at this time due to the need for validation, regulatory approval, payer coverage, and cost issues. This review focuses on the types of biomarkers, most recent research exploring MRD and LB in myeloid malignancies, ongoing clinical trials, and the future of LB in the setting of AI.

Application of tumor-educated platelets as new fluid biopsy markers in various tumors

The incidence of malignant tumors is increasing year by year. Early detection and diagnosis of malignant tumors can improve the prognosis of patients and prolong their life. Pathological biopsy is the current gold standard for diagnosis, but the results of pathological biopsy are affected by the sampling site and cannot fully reflect the nature of the disease. Moreover, the invasive nature of pathological biopsy limits repeated detection. Liquid biopsies are non-invasive and can be used for early detection and monitoring of tumors, which considered to represent a promising tool. Platelets make themselves to be one of the richest liquid biopsy sources by the capacity to take up proteins and nucleic acids and alter their megakaryocyte-derived transcripts and proteins in response to external signals, which are called tumor-educated platelets (TEPs). In this article, we will review the application of tumor-educated platelets in various malignancies (nasopharyngeal carcinoma, prostate cancer, lung cancer, glioblastoma, colorectal cancer, pancreas cancer, ovarian cancer, sarcoma, breast cancer and hepatocellular carcinoma) and provide theoretical basis for the research of TEPs in tumor diagnosis, monitoring and treatment.

A narrative review for platelets and their RNAs in cancers: New concepts and clinical perspectives

Recent years have witnessed a growing body of evidence suggesting that platelets are involved in several stages of the metastatic process via direct or indirect interactions with cancer cells, contributing to the progression of neoplastic malignancies. Cancer cells can dynamically exchange components with platelets in and out of blood vessels, and directly phagocytose platelets to hijack their proteome, transcriptome, and secretome, or be remotely regulated by metabolites or microparticles released by platelets, resulting in phenotypic, genetic, and functional modifications. Moreover, platelet interactions with stromal and immune cells in the tumor microenvironment lead to alterations in their components, including the ribonucleic acid (RNA) profile, and complicate the impact of platelets on cancers. A deeper understanding of the roles of platelets and their RNAs in cancer will contribute to the development of anticancer strategies and the optimization of clinical management. Encouragingly, advances in high-throughput sequencing, bioinformatics data analysis, and machine learning have allowed scientists to explore the potential of platelet RNAs for cancer diagnosis, prognosis, and guiding treatment. However, the clinical application of this technique remains controversial and requires larger, multicenter studies with standardized protocols. Here, we integrate the latest evidence to provide a broader insight into the role of platelets in cancer progression and management, and propose standardized recommendations for the clinical utility of platelet RNAs to facilitate translation and benefit patients.

Blood platelets as an RNA biomarker platform for neuro-oncological diseases

Blood-based liquid biopsies are an upcoming approach for earlier cancer detection, diagnostics, prognostics, therapy-response prediction, and therapy monitoring, including in patients with tumors of the central nervous system. Among these, liquid biopsies are plasma-derived markers such as cell-free DNA, RNA and proteins, extracellular vesicles, circulating glioma cells, immune cells, and blood platelets. Blood platelets are involved in the local and systemic response to the presence of cancer, thereby sequestering and splicing RNAs, which may be clinically useful as blood-based biomarkers. In this review, we discuss the available literature regarding the role of blood platelets in gliomas and provide suggestions for future research efforts.

Bioplatforms in liquid biopsy: advances in the techniques for isolation, characterization and clinical applications

Tissue biopsy analysis has conventionally been the gold standard for cancer prognosis, diagnosis and prediction of responses/resistances to treatments. The existing biopsy procedures used in clinical practice are, however, invasive, painful and often associated with pitfalls like poor recovery of tumor cells and infeasibility for repetition in single patients. To circumvent these limitations, alternative non-invasive, rapid and economical, yet sturdy, consistent and dependable, biopsy techniques are required. Liquid biopsy is an emerging technology that fulfills these criteria and potentially much more in terms of subject-specific real-time monitoring of cancer progression, determination of tumor heterogeneity and treatment responses, and specific identification of the type and stages of cancers. The present review first briefly revisits the state-of-the-art technique of liquid biopsy and then proceeds to address in detail, the advances in the potential clinical applications of four major biological agencies present in liquid biopsy samples (circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), exosomes and tumor-educated platelets (TEPs)). Finally, the authors conclude with the limitations that need to be addressed in order for liquid biopsy to effectively replace the conventional invasive biopsy methods in the clinical settings.

Whole transcriptome profiling of liquid biopsies from tumour xenografted mouse models enables specific monitoring of tumour-derived extracellular RNA

While cell-free DNA (cfDNA) is widely being investigated, free circulating RNA (extracellular RNA, exRNA) has the potential to improve cancer therapy response monitoring and detection due to its dynamic nature. However, it remains unclear in which blood subcompartment tumour-derived exRNAs primarily reside. We developed a host-xenograft deconvolution framework, exRNAxeno, with mapping strategies to either a combined human-mouse reference genome or both species genomes in parallel, applicable to exRNA sequencing data from liquid biopsies of human xenograft mouse models. The tool enables to distinguish (human) tumoural RNA from (murine) host RNA, to specifically analyse tumour-derived exRNA. We applied the combined pipeline to total exRNA sequencing data from 95 blood-derived liquid biopsy samples from 30 mice, xenografted with 11 different tumours. Tumoural exRNA concentrations are not determined by plasma platelet levels, while host exRNA concentrations increase with platelet content. Furthermore, a large variability in exRNA abundance and transcript content across individual mice is observed. The tumoural gene detectability in plasma is largely correlated with the RNA expression levels in the tumour tissue or cell line. These findings unravel new aspects of tumour-derived exRNA biology in xenograft models and open new avenues to further investigate the role of exRNA in cancer.

Detection and localization of early- and late-stage cancers using platelet RNA

Cancer patients benefit from early tumor detection since treatment outcomes are more favorable for less advanced cancers. Platelets are involved in cancer progression and are considered a promising biosource for cancer detection, as they alter their RNA content upon local and systemic cues. We show that tumor-educated platelet (TEP) RNA-based blood tests enable the detection of 18 cancer types. With 99% specificity in asymptomatic controls, thromboSeq correctly detected the presence of cancer in two-thirds of 1,096 blood samples from stage I-IV cancer patients and in half of 352 stage I-III tumors. Symptomatic controls, including inflammatory and cardiovascular diseases, and benign tumors had increased false-positive test results with an average specificity of 78%. Moreover, thromboSeq determined the tumor site of origin in five different tumor types correctly in over 80% of the cancer patients. These results highlight the potential properties of TEP-derived RNA panels to supplement current approaches for blood-based cancer screening.

Novel Markers for Liquid Biopsies in Cancer Management: Circulating Platelets and Extracellular Vesicles

Although radiologic imaging and histologic assessment of tumor tissues are classic approaches for diagnosis and monitoring of treatment response, they have many limitations. These include challenges in distinguishing benign from malignant masses, difficult access to the tumor, high cost of the procedures, and tumor heterogeneity. In this setting, liquid biopsy has emerged as a potential alternative for both diagnostic and monitoring purposes. The approaches to liquid biopsy include cell-free DNA/circulating tumor DNA, long and micro noncoding RNAs, proteins/peptides, carbohydrates/lectins, lipids, and metabolites. Other approaches include detection and analysis of circulating tumor cells, extracellular vesicles, and tumor-activated platelets. Ultimately, reliable use of liquid biopsies requires bioinformatics and statistical integration of multiple datasets to achieve approval in a Clinical Laboratory Improvement Amendments setting. This review provides a balanced and critical assessment of recent discoveries regarding tumor-derived biomarkers in liquid biopsies along with the potential and pitfalls for cancer detection and longitudinal monitoring.

Liquid biopsy: early and accurate diagnosis of brain tumor

Noninvasive examination is an emerging area in the field of neuro-oncology. Liquid biopsy captures the landscape of genomic alterations of brain tumors and revolutionizes the traditional diagnosis approaches. Rapidly changing sequencing technologies and more affordable prices put the screws on more application of liquid biopsy in clinical settings. In the past few years, extensive application of liquid biopsy has been seen throughout the whole diagnosis and treatment process of brain tumors, including early and accurate detection, characterization and dynamic monitoring. Here, we summarized and compared the most advanced techniques and target molecules or macrostructures related to brain tumor liquid biopsy. We further reviewed and emphasized recent progression in different clinical settings for brain tumors in blood and CSF. The preferred protocol, potential novel biomarkers and future development are discussed in the last part.

PltDB: a blood platelets-based gene expression database for disease investigation

MOTIVATION

Molecular profiling of blood-based liquid biopsies is a promising disease detection method, which overcomes the limitations of invasive diagnostic strategies. Recently, gene expression profiling of platelets reportedly provides valuable resource for developing new biomarkers for the detection of diseases, including cancer. However, there is no database containing RNAs in platelets.

RESULTS

In this study, we constructed PltDB (http://www.pltdb-hust.com), a platelets-based gene expression database featuring integration and visualization of RNA expression profiles based on RNA-seq and microarray data spanning both normal individuals and patients with different diseases. PltDB currently contains the expression landscape of mRNAs, lncRNAs, circRNAs, and miRNAs in platelets from patients with different disease types and healthy controls. Moreover, PltDB provides users with the tools for visualizing results of comparison and correlation analysis and for downloading expression profiles and analysis results. A submission interface for the scientific community is also embraced for uploading novel RNA expression profiles derived from platelet samples. PltDB will offer a comprehensive review of the clinical use of platelets, overcome technical problems when analyzing data from diverse studies and serve as a powerful platform for developing new blood biomarkers.

AVAILABILITY

PltDB is accessible at http://www.pltdb-hust.com.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

MRI-based contrast clearance analysis shows high differentiation accuracy between radiation-induced reactions and progressive disease after cranial radiotherapy

Background

Pseudoprogression (PsP) or radiation necrosis (RN) may frequently occur after cranial radiotherapy and show a similar imaging pattern compared with progressive disease (PD). We aimed to evaluate the diagnostic accuracy of magnetic resonance imaging-based contrast clearance analysis (CCA) in this clinical setting.

Patients and methods

Patients with equivocal imaging findings after cranial radiotherapy were consecutively included into this monocentric prospective study. CCA was carried out by software-based automated subtraction of imaging features in late versus early T1-weighted sequences after contrast agent application. Two experienced neuroradiologists evaluated CCA with respect to PsP/RN and PD being blinded for histological findings. The radiological assessment was compared with the histopathological results, and its accuracy was calculated statistically.

Results

A total of 33 patients were included; 16 (48.5%) were treated because of a primary brain tumor (BT), and 17 (51.1%) because of a secondary BT. In one patient, CCA was technically infeasible. The accuracy of CCA in predicting the histological result was 0.84 [95% confidence interval (CI) 0.67-0.95; one-sided P = 0.051; n = 32]. Sensitivity and specificity of CCA were 0.93 (95% CI 0.66-1.00) and 0.78 (95% CI 0.52-0.94), respectively. The accuracy in patients with secondary BTs was 0.94 (95% CI 0.71-1.00) and nonsignificantly higher compared with patients with primary BT with an accuracy of 0.73 (95% CI 0.45-0.92), P = 0.16.

Conclusions

In this study, CCA was a highly accurate, easy, and helpful method for distinguishing PsP or RN from PD after cranial radiotherapy, especially in patients with secondary tumors after radiosurgical treatment.

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CCA is accurate in distinguishing treatment reactions from true PD.

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CCA was more accurate for irradiated metastases than primary BTs.

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CCA is not feasible for lesions with no contrast media uptake.

Liquid biopsy at the frontier of detection, prognosis and progression monitoring in colorectal cancer

Colorectal cancer (CRC) is one of the most common cancers worldwide and a leading cause of carcinogenic death. To date, surgical resection is regarded as the gold standard by the operator for clinical decisions. Because conventional tissue biopsy is invasive and only a small sample can sometimes be obtained, it is unable to represent the heterogeneity of tumor or dynamically monitor tumor progression. Therefore, there is an urgent need to find a new minimally invasive or noninvasive diagnostic strategy to detect CRC at an early stage and monitor CRC recurrence. Over the past years, a new diagnostic concept called “liquid biopsy” has gained much attention. Liquid biopsy is noninvasive, allowing repeated analysis and real-time monitoring of tumor recurrence, metastasis or therapeutic responses. With the advanced development of new molecular techniques in CRC, circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), exosomes, and tumor-educated platelet (TEP) detection have achieved interesting and inspiring results as the most prominent liquid biopsy markers. In this review, we focused on some clinical applications of CTCs, ctDNA, exosomes and TEPs and discuss promising future applications to solve unmet clinical needs in CRC patients.

Tumor-Educated Platelets as a Promising Biomarker for Blood-Based Detection of Renal Cell Carcinoma

Purpose

Tumor-educated platelets (TEPs) are a promising liquid biopsy in many cancers. However, their role in renal cell carcinoma (RCC) is unknown. Thus, this study explored the diagnostic value of TEPs in RCC patients.

Methods

Platelets were prospectively collected from 24 RCC patients and 25 controls. RNA-seq was performed to identify the differentially expressed genes (DEGs) between RCC patients and controls. Besides, RNA-seq data of pan-cancer TEPs were downloaded and randomly divided into training and validation sets. A pan-cancer TEP model was developed in the training set using the support vector machine (SVM) and validated in the validation set and our RCC dataset. Finally, an RCC-based TEP model was developed and optimized through the SVM algorithms and recursive feature elimination (RFE) method.

Result

Two hundred three DEGs, 64 (31.5%) upregulated and 139 (68.5%) downregulated, were detected in the platelets of RCC patients compared with controls. The pan-cancer TEP model had a high accuracy in detecting cancer in the internal validation (training set, accuracy 98.8%, AUC: 0.999; validation set, accuracy 95.4%, AUC: 0.972; different tumor subtypes, accuracy 86.6%–96.1%, AUC: 0.952–1.000). However, the pan-cancer TEP model in the external validation had a scarce diagnostic value in RCC patients (accuracy 48.7%, AUC: 0.615). Therefore, to develop the RCC-based TEP model, the gene biomarkers mostly contributing to the model were selected using the RFE method. The RCC-based TEP model containing 68 gene biomarkers reached a diagnostic accuracy of 100% (AUC: 1.000) in the training set, 88.9% (AUC: 0.963) in the validation set, and 95.9% (AUC: 0.988) in the overall cohort.

Conclusion

TEPs could function as a minimally invasive blood biomarker in the detection of RCC.

Tannic Acid Attenuates Peripheral and Brain Changes in a Preclinical Rat Model of Glioblastoma by Modulating Oxidative Stress and Purinergic Signaling

Glioblastoma (GB) is a highly aggressive and invasive brain tumor; its treatment remains palliative. Tannic acid (TA) is a polyphenol widely found in foods and possesses antitumor and neuroprotective activities. This study aimed to investigate the effect of TA on oxidative stress parameters and the activity of ectonucleotidases in the serum, platelets, and lymphocytes and/or in the brain of rats with preclinical GB. Rats with GB were treated intragastrically with TA (50 mg/kg/day) for 15 days or with a vehicle. In the platelets of the animals with glioma, the adenosine triphosphate (ATP) and adenosine monophosphate (AMP) hydrolysis and the catalase (CAT) activity decreased. Besides, the adenosine diphosphate (ADP) hydrolysis, adenosine (Ado) deamination, and the reactive oxygen species (ROS) and nitrite levels were increased in glioma animals; however, TA reversed ROS and nitrite levels and AMP hydrolysis alterations. In lymphocytes from animals with glioma, the ATP and ADP hydrolysis, as well as Ado deamination were increased; TA treatment countered this increase. In the brain of the animals with glioma, the ROS, nitrite, and thiobarbituric acid reactive substance (TBARS) levels increased and the thiol (SH) levels and CAT and superoxide dismutase (SOD) activities were decreased; TA treatment decreased the ROS and TBARS levels and restored the SOD activity. In the serum of the animals with glioma, the ATP hydrolysis decreased; TA treatment restored this parameter. Additionally, the ROS levels increased and the SH and SOD activity decreased by glioma implant; TA treatment enhanced nitrite levels and reversed SOD activity. Altogether, our results suggest that TA is an important target in the treatment of GB, as it modulates purinergic and redox systems.

Cell-free DNA technologies for the analysis of brain cancer

Survival for glioma patients has shown minimal improvement over the past 20 years. The ability to detect and monitor gliomas relies primarily upon imaging technologies that lack sensitivity and specificity, especially during the post-surgical treatment phase. Treatment-response monitoring with an effective liquid-biopsy paradigm may also provide the most facile clinical scenario for liquid-biopsy integration into brain-tumour care. Conceptually, liquid biopsy is advantageous when compared with both tissue sampling (less invasive) and imaging (more sensitive and specific), but is hampered by technical and biological problems. These problems predominantly relate to low concentrations of tumour-derived DNA in the bloodstream of glioma patients. In this review, we highlight methods by which the neuro-oncological scientific and clinical communities have attempted to circumvent this limitation. The use of novel biological, technological and computational approaches will be explored. The utility of alternate bio-fluids, tumour-guided sequencing, epigenomic and fragmentomic methods may eventually be leveraged to provide the biological and technological means to unlock a wide range of clinical applications for liquid biopsy in glioma.

Toward platelet transcriptomics in cancer diagnosis, prognosis and therapy

Widespread adoption of next-generation techniques such as RNA-sequencing (RNA-seq) has enabled research examining the transcriptome of anucleate blood platelets in health and disease, thus revealing a rich platelet transcriptomic signature that is reprogrammed in response to disease. Platelet signatures not only capture information from parent megakaryocytes and progenitor hematopoietic stem cells but also the bone marrow microenvironment, and underlying disease states. In cancer, the substantive body of research in patients with solid tumours has identified distinct signatures in 'tumour-educated platelets', reflecting influences of the tumour, stroma and vasculature on splicing, sequestration of tumour-derived RNAs, and potentially cytokine and microvesicle influences on megakaryocytes. More recently, platelet RNA expression has emerged as a highly sensitive approach to profiling chronic progressive haematologic malignancies, where the combination of large data cohorts and machine-learning algorithms enables precise feature selection and potential prognostication. Despite these advances, however, our ability to translate platelet transcriptomics toward clinical diagnostic and prognostic efforts remains limited. In this Perspective, we present a few actionable steps for our basic, translational and clinical research communities in advancing the utility of the platelet transcriptome as a highly sensitive biomarker in cancer and collectively enable efforts toward clinical translation and patient benefit.

Advantages and Challenges of Using ctDNA NGS to Assess the Presence of Minimal Residual Disease (MRD) in Solid Tumors

The ability to detect minimal residual disease (MRD) after a curative-intent surgery or treatment is of paramount importance, because it offers the possibility to help guide the clinical decisions related adjuvant therapy. Thus, the earlier MRD is detected, the earlier potentially beneficial treatment can be proposed to patients who might need it. Liquid biopsies, and in particular the next-generation sequencing of circulating tumor DNA (ctDNA) in the blood, have been the focus of an increasing amount of research in the past years. The ctDNA detection at advanced cancer stages is practicable for several solid tumors, and complements molecular information on acquired therapy resistance. In the context of MRD, it is by definition more challenging to detect ctDNA, but it is technically achievable and provides information on treatment response and probability of relapse significantly earlier than standard imaging methods. The clinical benefit of implementing this new technique in the routine is being tested in interventional clinical trials at the moment. We propose here an update of the current use of ctDNA detection by NGS as a tool to assess the presence of MRD and improve adjuvant treatment of solid tumors. We also discuss the main limitations and medium-term perspectives of this process in the clinic.

Tumor-Educated Platelets: A Review of Current and Potential Applications in Solid Tumors

In this current era of precision medicine, liquid biopsy poses a unique opportunity for an easily accessible, comprehensive molecular profile that would allow for the identification of therapeutic targets and sequential monitoring. Solid tumors are definitively diagnosed by analyzing primary tumor tissue, but surgical sampling is not always sufficient to generate a comprehensive genetic fingerprint at the time of diagnosis, or an appropriate means for continued monitoring. Platelets are known to have a dynamic, bidirectional relationship with tumors, acting beyond their role of hemostasis. Tumor-educated platelets (TEP) are modified by the tumor in multiple ways and act as a carrier and protector of metastasis. Data so far have shown that the mRNA in TEP can be harnessed for cancer diagnostics, with many potential applications.

Liquid Biopsy in Glioblastoma Management: From Current Research to Future Perspectives

Glioblastoma (GBM) is the most common primary tumor of the central nervous system. Arising from neuroepithelial glial cells, GBM is characterized by invasive behavior, extensive angiogenesis, and genetic heterogeneity that contributes to poor prognosis and treatment failure. Currently, there are several molecular biomarkers available to aid in diagnosis, prognosis, and predicting treatment outcomes; however, all require the biopsy of tumor tissue. Nevertheless, a tissue sample from a single location has its own limitations, including the risk related to the procedure and the difficulty of obtaining longitudinal samples to monitor treatment response and to fully capture the intratumoral heterogeneity of GBM. To date, there are no biomarkers in blood or cerebrospinal fluid for detection, follow-up, or prognostication of GBM. Liquid biopsy offers an attractive and minimally invasive solution to support different stages of GBM management, assess the molecular biology of the tumor, identify early recurrence and longitudinal genomic evolution, predict both prognosis and potential resistance to chemotherapy or radiotherapy, and allow patient selection for targeted therapies. The aim of this review is to describe the current knowledge regarding the application of liquid biopsy in glioblastoma, highlighting both benefits and obstacles to translation into clinical care. IMPLICATIONS FOR PRACTICE: To translate liquid biopsy into clinical practice, further prospective studies are required with larger cohorts to increase specificity and sensitivity. With the ever-growing interest in RNA nanotechnology, microRNAs may have a therapeutic role in brain tumors.

The Analysis of Platelet-Derived circRNA Repertoire as Potential Diagnostic Biomarker for Non-Small Cell Lung Cancer

Tumor-educated Platelets (TEPs) have emerged as rich biosources of cancer-related RNA profiles in liquid biopsies applicable for cancer detection. Although human blood platelets have been found to be enriched in circular RNA (circRNA), no studies have investigated the potential of circRNA as platelet-derived biomarkers for cancer. In this proof-of-concept study, we examine whether the circRNA signature of blood platelets can be used as a liquid biopsy biomarker for the detection of non-small cell lung cancer (NSCLC). We analyzed the total RNA, extracted from the platelet samples collected from NSCLC patients and asymptomatic individuals, using RNA sequencing (RNA-Seq). Identification and quantification of known and novel circRNAs were performed using the accurate CircRNA finder suite (ACFS), followed by the differential transcript expression analysis using a modified version of our thromboSeq software. Out of 4732 detected circRNAs, we identified 411 circRNAs that are significantly (p-value < 0.05) differentially expressed between asymptomatic individuals and NSCLC patients. Using the false discovery rate (FDR) of 0.05 as cutoff, we selected the nuclear receptor-interacting protein 1 (NRIP1) circRNA (circNRIP1) as a potential biomarker candidate for further validation by reverse transcription-quantitative PCR (RT-qPCR). This analysis was performed on an independent cohort of platelet samples. The RT-qPCR results confirmed the RNA-Seq data analysis, with significant downregulation of circNRIP1 in platelets derived from NSCLC patients. Our findings suggest that circRNAs found in blood platelets may hold diagnostic biomarkers potential for the detection of NSCLC using liquid biopsies.

Serum Extracellular Vesicle-Derived circHIPK3 and circSMARCA5 Are Two Novel Diagnostic Biomarkers for Glioblastoma Multiforme

Glioblastoma multiforme (GBM) is the most frequent and deadly human brain cancer. Early diagnosis through non-invasive biomarkers may render GBM more easily treatable, improving the prognosis of this currently incurable disease. We suggest the use of serum extracellular vesicle (sEV)-derived circular RNAs (circRNAs) as highly stable minimally invasive diagnostic biomarkers for GBM diagnosis. EVs were isolated by size exclusion chromatography from sera of 23 GBM and 5 grade 3 glioma (GIII) patients, and 10 unaffected controls (UC). The expression of two candidate circRNAs (circSMARCA5 and circHIPK3) was assayed by droplet digital PCR. CircSMARCA5 and circHIPK3 were significantly less abundant in sEVs from GBM patients with respect to UC (fold-change (FC) of -2.15 and -1.92, respectively) and GIII (FC of -1.75 and -1.4, respectively). Receiver operating characteristic curve (ROC) analysis, based on the expression of sEV-derived circSMARCA5 and circHIPK3, allowed us to distinguish GBM from UC (area under the curve (AUC) 0.823 (0.667-0.979) and 0.855 (0.704 to 1.000), with a 95% confidence interval (CI), respectively). Multivariable ROC analysis, performed by combining the expression of sEV-derived circSMARCA5 and circHIPK3 with preoperative neutrophil to lymphocyte (NLR), platelet to lymphocyte (PLR) and lymphocyte to monocyte (LMR) ratios, three known diagnostic and prognostic GBM markers, allowed an improvement in the GBM diagnostic accuracy (AUC 0.901 (0.7912 to 1.000), 95% CI). Our data suggest sEV-derived circSMARCA5 and circHIPK3 as good diagnostic biomarkers for GBM, especially when associated with preoperative NLR, PLR and LMR.

Blood-Based Biomarkers for Glioma in the Context of Gliomagenesis: A Systematic Review

BACKGROUND

Gliomas are the most common and aggressive tumors of the central nervous system. A robust and widely used blood-based biomarker for glioma has not yet been identified. In recent years, a plethora of new research on blood-based biomarkers for glial tumors has been published. In this review, we question which molecules, including proteins, nucleic acids, circulating cells, and metabolomics, are most promising blood-based biomarkers for glioma diagnosis, prognosis, monitoring and other purposes, and align them to the seminal processes of cancer.

METHODS

The Pubmed and Embase databases were systematically searched. Biomarkers were categorized in the identified biomolecules and biosources. Biomarker characteristics were assessed using the area under the curve (AUC), accuracy, sensitivity and/or specificity values and the degree of statistical significance among the assessed clinical groups was reported.

RESULTS

7,919 references were identified: 3,596 in PubMed and 4,323 in Embase. Following screening of titles, abstracts and availability of full-text, 262 articles were included in the final systematic review. Panels of multiple biomarkers together consistently reached AUCs >0.8 and accuracies >80% for various purposes but especially for diagnostics. The accuracy of single biomarkers, consisting of only one measurement, was far more variable, but single microRNAs and proteins are generally more promising as compared to other biomarker types.

CONCLUSION

Panels of microRNAs and proteins are most promising biomarkers, while single biomarkers such as GFAP, IL-10 and individual miRNAs also hold promise. It is possible that panels are more accurate once these are involved in different, complementary cancer-related molecular pathways, because not all pathways may be dysregulated in cancer patients. As biomarkers seem to be increasingly dysregulated in patients with short survival, higher tumor grades and more pathological tumor types, it can be hypothesized that more pathways are dysregulated as the degree of malignancy of the glial tumor increases. Despite, none of the biomarkers found in the literature search seem to be currently ready for clinical implementation, and most of the studies report only preliminary application of the identified biomarkers. Hence, large-scale validation of currently identified and potential novel biomarkers to show clinical utility is warranted.