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.

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.

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.

Discrimination Between Pre- and Postcapillary Pulmonary Hypertension Using Platelet RNA.

Background Appropriate treatment of pulmonary hypertension (PH) is critically dependent on accurate discrimination between pre- and postcapillary PH. However, clinical discrimination is challenging and frequently requires a right heart catheterization. Existing risk scores to detect postcapillary PH have suboptimal discriminatory strength. We have previously shown that platelet-derived RNA profiles may have diagnostic value for PH detection. Here, we hypothesize that platelet-derived RNAs can be employed to select unique biomarker panels for the discrimination between pre- and postcapillary PH. Methods and Results Blood platelet RNA from whole blood was isolated and sequenced from 50 patients with precapillary PH (with different PH subtypes) as well as 50 patients with postcapillary PH. RNA panels were calculated by ANOVA statistics, and classifications were performed using a support vector machine algorithm, supported by particle swarm optimization. We identified in total 4279 different RNAs in blood platelets from patients with pre- and postcapillary PH. A particle swarm optimization-selected RNA panel of 1618 distinctive RNAs with differential levels together with a trained support vector machine algorithm accurately discriminated patients with precapillary PH from patients with postcapillary PH with 100% sensitivity, 60% specificity, 80% accuracy, and 0.95 (95% CI, 0.86-1.00) area under the curve in the independent validation series (n=20). Conclusions This proof-of-concept study demonstrates that particle swarm optimization/support vector machine-enhanced classification of platelet RNA panels may be able to discriminate precapillary PH from postcapillary PH. This research provides a foundation for the development of a blood test with a high negative predictive value that would improve early diagnosis of precapillary PH and prevents unnecessary invasive testing in patients with postcapillary PH.

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.

Platelet RNA enables accurate detection of ovarian cancer: an intercontinental, biomarker identification study.

Platelets are reprogrammed by cancer via a process called education, which favors cancer development. The transcriptional profile of tumor-educated platelets (TEPs) is skewed and therefore practicable for cancer detection. This intercontinental, hospital-based, diagnostic study included 761 treatment-naïve inpatients with histologically confirmed adnexal masses and 167 healthy controls from nine medical centers (China, n = 3; Netherlands, n = 5; Poland, n = 1) between September 2016 and May 2019. The main outcomes were the performance of TEPs and their combination with CA125 in two Chinese (VC1 and VC2) and the European (VC3) validation cohorts collectively and independently. Exploratory outcome was the value of TEPs in public pan-cancer platelet transcriptome datasets. The AUCs for TEPs in the combined validation cohort, VC1, VC2, and VC3 were 0.918 (95% CI 0.889-0.948), 0.923 (0.855-0.990), 0.918 (0.872-0.963), and 0.887 (0.813-0.960), respectively. Combination of TEPs and CA125 demonstrated an AUC of 0.922 (0.889-0.955) in the combined validation cohort; 0.955 (0.912-0.997) in VC1; 0.939 (0.901-0.977) in VC2; 0.917 (0.824-1.000) in VC3. For subgroup analysis, TEPs exhibited an AUC of 0.858, 0.859, and 0.920 to detect early-stage, borderline, non-epithelial diseases and 0.899 to discriminate ovarian cancer from endometriosis. TEPs had robustness, compatibility, and universality for preoperative diagnosis of ovarian cancer since it withstood validations in populations of different ethnicities, heterogeneous histological subtypes, and early-stage ovarian cancer. However, these observations warrant prospective validations in a larger population before clinical utilities.

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.

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.

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.

Distinct Platelet Ribonucleic Acid Signatures in Patients with Pulmonary Hypertension.

Rationale: Pulmonary hypertension encompasses progressive disorders leading to right ventricular dysfunction and early death. Late detection is an important cause of poor clinical outcomes. However, biomarkers that accurately predict the presence of pulmonary hypertension are currently lacking. Objectives: In this study, we provide evidence that blood platelets contain a distinctive ribonucleic acid (RNA) profile that may be exploited for the detection of pulmonary hypertension. Methods: Blood platelet RNA was isolated prospectively from 177 prevalent patients with different subtypes of pulmonary hypertension as well as 195 control subjects clinically not suspected of pulmonary hypertension. Sequencing libraries were created using SMARTer (Switching Mechanism at 5' end of RNA Template) copy desoxyribonucleic acid amplification and sequenced on the Illumina High Throughput Sequencing platform. RNA-sequencing reads were mapped to the human reference genome, and intron-spanning spliced RNA reads were selected. Differential spliced RNA panels were calculated by analysis of variance statistics. A particle swarm optimization-enhanced classification algorithm was built employing a development (n = 213 samples) and independent validation series (n = 159 samples). Results: We detected a total of 4,014 different RNAs in blood platelets from patients with pulmonary hypertension (n = 177) and asymptomatic control subjects (n = 195). Gene ontology analysis revealed enhanced RNA concentrations for genes related to RNA processing, translation, and mitochondrial function. A particle swarm optimization-selected RNA panel of 408 distinctive differentially spliced RNAs mediated detection of pulmonary hypertension with 93% sensitivity, 62% specificity, 77% accuracy, 0.89 (95% confidence interval, 0.83-0.93) area under the curve, and a negative predictive value of 91% in the independent validation series. The prediction score was independent of age, sex, smoking, pulmonary hypertension subtype, and the use of pulmonary hypertension-specific medication or anticoagulants. Conclusions: A platelet RNA panel may accurately discriminate patients with pulmonary hypertension from asymptomatic control subjects. In the light of current diagnostic delays, this study is the starting point for further development and evaluation of a platelet RNA-based blood test to ultimately improve early diagnosis and clinical outcomes in patients with pulmonary hypertension.

Uncommon presentations of a neurosurgical site infection: impaired wound healing with hypergranulation and crust formation.

Hypergranulation and crust formation after cranial neurosurgery is rare. We report three patients with an uncommon form of hypergranulation with extensive crust formation after cranial neurosurgery, associated with a St. Aureus infection of the scalp, and propose that this is a form of pyogenic dermatitis, as is commonly seen among domestic animals with a coat of fur. It can be treated conservatively. We propose a treatment algorithm.

Diagnostic Accuracy of Liquid Biopsy in Endometrial Cancer.

BACKGROUND: Liquid biopsy is a minimally invasive collection of a patient body fluid sample. In oncology, they offer several advantages compared to traditional tissue biopsies. However, the potential of this method in endometrial cancer (EC) remains poorly explored. We studied the utility of tumor educated platelets (TEPs) and circulating tumor DNA (ctDNA) for preoperative EC diagnosis, including histology determination. METHODS: TEPs from 295 subjects (53 EC patients, 38 patients with benign gynecologic conditions, and 204 healthy women) were RNA-sequenced. DNA sequencing data were obtained for 519 primary tumor tissues and 16 plasma samples. Artificial intelligence was applied to sample classification. RESULTS: Platelet-dedicated classifier yielded AUC of 97.5% in the test set when discriminating between healthy subjects and cancer patients. However, the discrimination between endometrial cancer and benign gynecologic conditions was more challenging, with AUC of 84.1%. ctDNA-dedicated classifier discriminated primary tumor tissue samples with AUC of 96% and ctDNA blood samples with AUC of 69.8%. CONCLUSIONS: Liquid biopsies show potential in EC diagnosis. Both TEPs and ctDNA profiles coupled with artificial intelligence constitute a source of useful information. Further work involving more cases is warranted.

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.

The brain-derived neurotrophic factor prompts platelet aggregation and secretion.

Brain-derived neurotrophic factor (BDNF) has both autocrine and paracrine roles in neurons, and its release and signaling mechanisms have been extensively studied in the central nervous system. Large quantities of BDNF have been reported in circulation, essentially stored in platelets with concentrations reaching 100- to 1000-fold those of neurons. Despite this abundance, the function of BDNF in platelet biology has not been explored. At low concentrations, BDNF primed platelets, acting synergistically with classical agonists. At high concentrations, BDNF induced complete biphasic platelet aggregation that in part relied on amplification from secondary mediators. Neurotrophin-4, but not nerve growth factor, and an activating antibody against the canonical BDNF receptor tropomyosin-related kinase B (TrkB) induced similar platelet responses to BDNF, suggesting TrkB could be the mediator. Platelets expressed, both at their surface and in their intracellular compartment, a truncated form of TrkB lacking its tyrosine kinase domain. BDNF-induced platelet aggregation was prevented by inhibitors of Ras-related C3 botulinum toxin substrate 1 (Rac1), protein kinase C, and phosphoinositide 3-kinase. BDNF-stimulated platelets secreted a panel of angiogenic and inflammatory cytokines, which may play a role in maintaining vascular homeostasis. Two families with autism spectrum disorder were found to carry rare missense variants in the BDNF gene. Platelet studies revealed defects in platelet aggregation to low concentrations of collagen, as well as reduced adenosine triphosphate secretion in response to adenosine diphosphate. In summary, circulating BDNF levels appear to regulate platelet activation, aggregation, and secretion through activation of a truncated TrkB receptor and downstream kinase-dependent signaling.

Transcriptomic landscape of blood platelets in healthy donors.

Blood platelet RNA-sequencing is increasingly used among the scientific community. Aberrant platelet transcriptome is common in cancer or cardiovascular disease, but reference data on platelet RNA content in healthy individuals are scarce and merit complex investigation. We sought to explore the dynamics of platelet transcriptome. Datasets from 204 healthy donors were used for the analysis of splice variants, particularly with regard to age, sex, blood storage time, unit of collection or library size. Genes B2M, PPBP, TMSB4X, ACTB, FTL, CLU, PF4, F13A1, GNAS, SPARC, PTMA, TAGLN2, OAZ1 and OST4 demonstrated the highest expression in the analysed cohort, remaining substantial transcription consistency. CSF3R gene was found upregulated in males (fold change 2.10, FDR q < 0.05). Cohort dichotomisation according to the median age, showed upregulated KSR1 in the older donors (fold change 2.11, FDR q < 0.05). Unsupervised hierarchical clustering revealed two clusters which were irrespective of age, sex, storage time, collecting unit or library size. However, when donors are analysed globally (as vectors), sex, storage time, library size, the unit of blood collection as well as age impose a certain degree of between- and/or within-group variability. Healthy donor platelet transcriptome retains general consistency, with very few splice variants deviating from the landscape. Although multidimensional analysis reveals statistically significant variability between and within the analysed groups, biologically, these changes are minor and irrelevant while considering disease classification. Our work provides a reference for studies working both on healthy platelets and pathological conditions affecting platelet transcriptome.

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.

imPlatelet classifier: image-converted RNA biomarker profiles enable blood-based cancer diagnostics.

Liquid biopsies offer a minimally invasive sample collection, outperforming traditional biopsies employed for cancer evaluation. The widely used material is blood, which is the source of tumor-educated platelets. Here, we developed the imPlatelet classifier, which converts RNA-sequenced platelet data into images in which each pixel corresponds to the expression level of a certain gene. Biological knowledge from the Kyoto Encyclopedia of Genes and Genomes was also implemented to improve accuracy. Images obtained from samples can then be compared against standard images for specific cancers to determine a diagnosis. We tested imPlatelet on a cohort of 401 non-small cell lung cancer patients, 62 sarcoma patients, and 28 ovarian cancer patients. imPlatelet provided excellent discrimination between lung cancer cases and healthy controls, with accuracy equal to 1 in the independent dataset. When discriminating between noncancer cases and sarcoma or ovarian cancer patients, accuracy equaled 0.91 or 0.95, respectively, in the independent datasets. According to our knowledge, this is the first study implementing an image-based deep-learning approach combined with biological knowledge to classify human samples. The performance of imPlatelet considerably exceeds previously published methods and our own alternative attempts of sample discrimination. We show that the deep-learning image-based classifier accurately identifies cancer, even when a limited number of samples are available.

Circular RNA Sequencing of Maternal Platelets: A Novel Tool for the Identification of Pregnancy-Specific Biomarkers.

BACKGROUND: In the first trimester of pregnancy, the maternal platelet is directly involved in a positive feedback mechanism that facilitates invasion of the extravillous trophoblast into the maternal spiral arteries. Dysfunctional trophoblast invasion with defective deep placentation is primordial in the etiology of the "great obstetrical syndromes." METHODS: In this proof-of-concept study, using transcriptome analysis of circular RNA (circRNA) following RNA sequencing of maternal platelets, we tested whether pregnancy-specific circRNA markers could be identified in the first trimester of normal pregnancies. Differential transcript expression analysis of circRNAs, as predicted by Accurate CircRNA Finder Suite, CircRNA Identifier (version 2), and Known and Novel Isoform Explorer, was done using thromboSeq.R with variation of multiple settings. Test performance was checked for (a) de novo circRNA identification using the novel platelet-specific Plt-circR4 as a positive control, (b) complete segregation of groups (pregnant vs nonpregnant) after heat map-dendrogram clustering, (c) identification of pregnancy-specific circRNA markers at a false discovery rate (FDR) <0.05, and (d) confirmation of differentially expressed circRNA markers with an FDR <0.05 by an independent method, reverse transcription-quantitative PCR. RESULTS: Of the differentially expressed circRNAs with P values <0.05, 41 circRNAs were upregulated (logFC >2), and 52 circRNAs were downregulated (logFC less than -2) in first-trimester platelet RNA. Of these, nuclear receptor-interacting protein 1 circRNA covering exons 2 and 3 of the 5'-untranslated region was pregnancy specific with upregulation in first-trimester maternal platelets compared to nonpregnant controls. CONCLUSION: CircRNA sequencing of first-trimester maternal platelets permits the identification of novel pregnancy-specific RNA biomarkers. Future use could include the assessment of maternal and fetal well-being.

The bivariate NRIP1/ZEB2 RNA marker permits non-invasive presymptomatic screening of pre-eclampsia.

Using genome-wide transcriptome analysis by RNA sequencing of first trimester plasma RNA, we tested whether the identification of pregnancies at risk of developing pre-eclampsia with or without preterm birth or growth restriction is possible between weeks 9-14, prior to the appearance of clinical symptoms. We implemented a metaheuristic approach in the self-learning SVM algorithm for differential gene expression analysis of normal pregnancies (n = 108), affected pregnancies (n = 34) and non-pregnant controls (n = 19). Presymptomatic candidate markers for affected pregnancies were validated by RT-qPCR in first trimester samples (n = 34) from an independent cohort. PRKG1 was significantly downregulated in a subset of pregnancies with birth weights below the 10thpercentile as shared symptom. The NRIP1/ZEB2 ratio was found to be upregulated in pregnancies with pre-eclampsia or trisomy 21. Complementary quantitative analysis of both the linear and circular forms of NRIP1 permitted discrimination between pre-eclampsia and trisomy 21. Pre-eclamptic pregnancies showed an increase in linear NRIP1 compared to circular NRIP1, while trisomy 21 pregnancies did not.

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

Tumor-educated platelets (TEPs) are potential biomarkers for cancer diagnostics. We employ TEP-derived RNA panels, determined by swarm intelligence, to detect and monitor glioblastoma. We assessed specificity by comparing the spliced RNA profile of TEPs from glioblastoma patients with multiple sclerosis and brain metastasis patients (validation series, n = 157; accuracy, 80%; AUC, 0.81 [95% CI, 0.74-0.89; p < 0.001]). Second, analysis of patients with glioblastoma versus asymptomatic healthy controls in an independent validation series (n = 347) provided a detection accuracy of 95% and AUC of 0.97 (95% CI, 0.95-0.99; p < 0.001). Finally, we developed the digitalSWARM algorithm to improve monitoring of glioblastoma progression and demonstrate that the TEP tumor scores of individual glioblastoma patients represent tumor behavior and could be used to distinguish false positive progression from true progression (validation series, n = 20; accuracy, 85%; AUC, 0.86 [95% CI, 0.70-1.00; p < 0.012]). In conclusion, TEPs have potential as a minimally invasive biosource for blood-based diagnostics and monitoring of glioblastoma patients.