Blood Biomarkers Research Articles

Blood Gene Expression Profiling and Donor-derived Cell-free DNA to Noninvasively Diagnose Clinical and Subclinical Kidney Transplant Rejection: A Real-life Appraisal Study

Background. Peripheral blood biomarkers aim to noninvasively diagnose kidney allograft rejection, but most lack robust independent validation. TruGraf is intended to exclude subclinical cellular rejection (TCMR), whereas donor-derived cell-free DNA Viracor-TRAC has proven value in excluding antibody-mediated rejection (AMR). We aim to validate both biomarkers for accurate rejection diagnosis in a real-world clinical setting. Methods. We prospectively included 230 unselected, consecutive kidney transplants from 6 centers undergoing for-cause and protocol biopsies with paired blood samples from December 2021 to 2022. TruGraf and Viracor-TRAC were blindly run by a central laboratory. Results. The incidence of rejection was 22.6% (17.3% surveillance; 27% for-cause biopsies). Inflammation was associated with higher TRAC levels, with AMR/mixed and microvascular inflammation (MVI) showing the highest levels (P < 0.05). TruGraf did not associate with any specific allograft injury. No biomarkers, individually or combined, accurately diagnosed any rejection (area under the receiver operating characteristic curve [AUROC] < 0.65). However, high TRAC levels, when combined with DSA in for-cause biopsies, predicted AMR/mixed rejection or MVI (AUROC = 0.817; P < 0.001), outperforming serum creatinine and DSA (AUROC < 0.65). Conclusions. In this large, prospective, observational real-life study, we were unable to validate TruGraf and TRAC to diagnose rejection but found a useful context of use for TRAC to noninvasively diagnose AMR/mixed or MVI in conjunction with DSA in dysfunctioning graft.

Multiomics biomarkers were not superior to clinical variables for pan-cancer screening

Abstract Background Cancer screening tests are considered pivotal for early diagnosis and survival. However, the efficacy of these tests for improving survival has recently been questioned. This study aims to test if cancer screening could be improved by biomarkers in peripheral blood based on multi-omics data. Methods We utilize multi-omics data from 500,000 participants in the UK Biobank. Machine learning is applied to search for proteins, metabolites, genetic variants, or clinical variables to diagnose cancers collectively and individually. Results Here we show that the overall performance of the potential blood biomarkers do not outperform clinical variables for collective diagnosis. However, we observe promising results for individual cancers in close proximity to peripheral blood, with an Area Under the Curve (AUC) greater than 0.8. Conclusions Our findings suggest that the identification of blood biomarkers for cancer might be complicated by variable overlap between molecular changes in tumor tissues and peripheral blood. This explanation is supported by local proteomics analyses of different tumors, which all show high AUCs, greater than 0.9. Thus, multi-omics biomarkers for the diagnosis of individual cancers may potentially be effective, but not for groups of cancers.

Longitudinal evaluation of hemodynamic blood and echocardiographic biomarkers for the prediction of BPD and BPD-related pulmonary hypertension in very-low-birth-weight preterm infants.

Mainly at an early stage of postnatal neonatal care in VLBW preterm infants, several biomarkers were found to be associated with the combined endpoint BPD/death and BPD-PH. New candidates of blood biomarkers (NTproBNPZlog, ET-1, and CA125) and echocardiographic markers (TAPSE, PAAT/RVET) might serve as innovative predictors for BPD, BPD-PH, and adverse outcomes in VBLW infants. • VLBW infants are at risk for the development of BPD and BPD-related PH, which both are main contributors for short and long-term morbidity and mortality. Several studies in the past focused on the evaluation of circulating blood biomarkers and biomarkers from echocardiographic assessment of these infants. But to date, there is still a lack on longitudinal prospective studies especially in VLBW infants. • For the first time, this set of selected blood biomarkers (with the first description of Zlog-transformed NTproBNP and CA125 in preterm infants) and several echocardiographic markers were analyzed in a prospective longitudinal study from birth until 36 weeks post menstrual age in VLBW infants. Our data help clinicians to identify preterm infants at risk for BPD, BPD-PH and death and to offer new candidates of biomarkers. This might help to facilitate decision making and guidance of therapy in these highly vulnerable patients.

Integration of Single-Cell and Spatial Transcriptomic Data Reveals Spatial Architecture and Potential Biomarkers in Alzheimer's Disease.

Alzheimer's disease (AD) is a complex neurodegenerative disorder characterized by the gradual loss of neurons and the accumulation of amyloid plaques and neurofibrillary tangles. Despite advancements in the understanding of AD's pathophysiology, the cellular organization and interactions in the prefrontal cortex (PFC) remain elusive. Eight single-cell RNA sequencing (scRNA-seq) datasets from both normal controls and individuals with AD were harmonized. Stringent preprocessing protocols were implemented to uphold dataset integrity. Unsupervised clustering and annotation revealed 22 distinct cell clusters corresponding to 19 unique cell types. The spatial architecture of the PFC region was constructed using the CARD tool. Further analyses encompassed trajectory examination of Oligodendrocyte subtypes, evaluation of regulon activity scores, and spot clustering within white matter regions (WM). Differential expression analysis and functional enrichment assays unveiled molecular signatures linked to AD progression and were validated using microarray data sourced from neurodegenerative disorder patients. Our investigation employs scRNA-seq and spatial transcriptomics to uncover the cellular atlas and spatial architecture of the human PFC in AD. Moreover, our results indicate that Oligodendrocytes are more prevalent in AD patients, showcasing diverse subtypes and spatial organization within WM regions. Each subtype appears to be associated with distinct biological processes and transcriptional regulators, shedding light on their involvement in AD pathology. Notably, the Oligodendrocyte_C6 subtype is linked to neurological damage in AD patients, characterized by heightened expression of genes involved in cell-cell connections, cell membrane stability, and myelination. Additionally, 12 target genes regulated by NFIA were identified, which are upregulated in AD patients and associated with disease progression. Elevated PLXDC2 expression in peripheral blood was also identified, suggesting its potential as a non-invasive biomarker for early AD detection. Our study provides novel insights into the role of Oligodendrocytes in AD and highlights the potential of PLXDC2 as a blood biomarker for non-invasive diagnosis and monitoring of AD patients.

Development of a prognostic signature for overall survival using peripheral blood biomarkers in head and neck squamous cell carcinoma treated with immune checkpoint inhibitors.

We previously reported in recurrent/metastatic head and neck squamous cell carcinoma (R/M HNSCC) treated with immune checkpoint inhibitors (ICIs), pretreatment higher lactate dehydrogenase (LDH) and absolute (abx) neutrophils as well as lower percent (%) lymphocytes correlated with worse overall survival (OS). In this study we aimed to develop a prognostic signature for HNSCC treated with ICIs using these peripheral blood biomarkers (PBBMs). Adults with R/M HNSCC treated with ICIs at our institution from 08/2012 to 03/2021 with pretreatment PBBMs were included. Follow-up continued until 02/15/2022. The cohort (n = 151) was randomly split into training (n = 100) and testing (n = 51) datasets. A prognostic score incorporating LDH, % lymphocytes, and abx neutrophils was developed from the training dataset using Cox proportional hazards regression. In the training dataset, a grid search identified the optimal cutpoints classifying patients into high, medium, and low-risk groups (trichotomized signature) as well as high vs. low-risk groups (dichotomized signature). The prognostic score, dichotomized and trichotomized signatures were then validated in the testing dataset. Training and testing datasets showed no clinically meaningful differences in clinicodemographic characteristics or PBBMs. An OS prognostic model was developed from the training dataset: Risk score = 1.24*log10(LDH) - 1.95*log10(% lymphocytes) + 0.47*log10(abx neutrophils). Optimal risk score cutpoints for the dichotomized and trichotomized signatures were defined in the training dataset, and Kaplan-Meier curves for both dichotomized and trichotomized signatures showed good separation between risk groups. Risk scores were calculated in the testing dataset, where the trichotomized signature demonstrated overlap between low and medium-risk groups but good separation from the high-risk group while the dichotomized signature showed clear separation between low and high-risk groups. Higher risk score correlated with worse OS (HR 2.08, [95%CI 1.17-3.68], p = 0.012). Progression-free survival Kaplan-Meier curves likewise showed excellent separation between dichotomized risk groups in the training and testing datasets. We developed a prognostic signature for OS based on 3 previously identified PBBMs for HNSCC treated with ICIs and identified a high-risk group of patients least likely to have survival benefit from ICIs. This signature may improve ICI patient selection and warrants validation in an independent cohort as well as correlation with CPS.

Large-scale genome-wide association studies identified causal relationship between multiple blood biomarkers and risk of acute pancreatitis.

Observational studies have shown that there is a connection between blood biomarkers and the occurrence of acute pancreatitis (AP). Nevertheless, the causal relationships are still not clear. The purpose of this study was to evaluate causal association between biomarkers and AP. A bidirectional two-sample Mendelian randomization (MR) analysis was applied to investigate the causal association between blood biomarkers and AP. Summary statistics obtained from genome-wide association studies were utilized for this analysis. The primary statistical approach employed was the inverse variance weighted (IVW) method, complemented by sensitivity analyses aimed at assessing heterogeneity and pleiotropy. Furthermore, a multivariable MR (MVMR) analysis was performed to adjust for confounders. A total of 11 red blood cell (RBC) traits, 6 white blood cell traits, platelet count, and 30 blood biomarkers were analyzed in this study. Genetically predicted RBC count (IVW odds ratio [OR]=1.144, P=0.004), the high light scatter reticulocyte count (HLSR) (OR=1.127, P=0.022), blood glucose (BG) (OR=1.480, P=0.019), and leptin (OR=1.234, P=0.050) were suggestively associated with an increased risk of AP. Reverse MR analysis showed no causal effect of AP on RBC, HLSR, BG, and leptin (IVW P>0.05). Sensitivity analyses and MVMR analysis still supported the earlier causality. Our findings provide evidence of a suggestive association between RBC count, HLSR, BG, and leptin with an increased susceptibility to AP. These findings aid in our comprehension of the cause of AP and may be used as potential prognostic markers or predictors of severity with AP.

Artificial intelligence modeling of biomarker-based physiological age: Impact on phase 1 drug-metabolizing enzyme phenotypes.

Age and aging are important predictors of health status, disease progression, drug kinetics, and effects. The purpose was to develop ensemble learning-based physiological age (PA) models for evaluating drug metabolism. National Health and Nutrition Examination Survey (NHANES) data were modeled with ensemble learning to obtain two PA models, PA-M1 and PA-M2. PA-M1 included body composition, blood and urine biomarkers, and disease variables as predictors. PA-M2 had blood and urine-derived variables as predictors. Activity phenotypes for cytochrome-P450 (CYP) CYP2E1, CYP1A2, CYP2A6, xanthine oxidase (XO), and N-acetyltransferase-2 (NAT-2) and telomere attrition were assessed. Bayesian networks were used to obtain mechanistic systems pharmacology model structures for PA. The study included n = 22,307 NHANES participants (51.5% female, mean age 46.0 years, range: 18-79 years). The PA-M1 and PA-M2 distributions had greater dispersion across age strata with a right skew for younger age strata and a left skew for older age strata. There was no evidence of algorithmic bias based on sex or race/ethnicity. Klotho, lean body mass, glycohemoglobin, and systolic blood pressure were the top four predictors for PA-M1. Glycohemoglobin, serum creatinine, total cholesterol, and urine creatinine were the top four predictors for PA-M2. The models also performed satisfactorily in independent validation. Model-predicted PA was associated with CYP2E1, CYP1A2, CYP2A6, XO, and NAT-2 activity. Telomere attrition was associated with greater PA-M1 and PA-M2. Ensemble learning models provide robust assessments of PA from easily obtained blood and urine biomarkers. PA is associated with Phase I drug-metabolizing enzyme phenotypes.

Is underweight associated with poorer diet, nutrient status, bone and cardiometabolic health, and school performance in Danish 8-11-year-olds?

Underweight, i.e. low body mass index for age and sex, may indicate undernutrition, but despite high prevalence, this aspect is largely overlooked in children in high-income countries. We explored if dietary intake, nutrient status, body composition, bone mineralization, cardiometabolic markers and school performance differed in schoolchildren with underweight compared to normal- and overweight. We used cross-sectional data from 815 Danish 8-11-year-old children collected in 2011. Intake of foods, macronutrients and key micronutrients (vitamin D, vitamin B12, calcium, iron, zinc and selenium) was assessed by 7-day dietary records. Measurements included anthropometry, dual-energy X-ray absorptiometry and tests of attention and reading skills. Fasting blood samples were analyzed for biomarkers of iron, long-chain n-3 fatty acids and vitamin D status as well as blood lipids, insulin and growth markers. Eighty-three (10.2%) children had underweight and were shown to have a lower intake of energy, red meat, protein and zinc and higher intake of added sugar than children with normal- and overweight. They also had higher fish intake relative to overweight, but blood biomarkers did not differ between groups. Children with underweight had lower fat percent and bone mineralization compared to peers with normalweight, but apart from lower insulin, they did not differ in overall cardiometabolic health or school performance. Although we found some differences in diet, there were no considerable differences in nutrient status, cardiometabolic health or school performance between children with underweight and their normalweight peers. However, the lower bone mineralization is a concern and needs further investigation.

Blood biomarkers in Down syndrome: Facilitating Alzheimer's disease detection and monitoring.

Blood-based biomarkers continue to be explored for disease detection, monitoring of progression, and therapeutic outcomes as the diagnostic determination of Alzheimer's Disease in Down Syndrome (DS-AD) remains challenging in clinical settings. This perspective highlights the current status of this effort. Overall, amyloid (A), tau (T), and neurodegeneration (AT[N]) blood-based biomarkers have been shown to increase with disease pathology for individuals with DS. Phosphorylated tau biomarkers (p-tau217, p-tau181) have been consistently shown to track disease progression for DS-AD and are likely good candidates for use in clinical settings. Biomarkers of inflammation (glial fibrillary acidic protein) also show promise; however, additional work is needed. Findings from stability work of blood-based biomarkers conducted among non-DS also supportthe potential longitudinal utility of biomarkers such as neurofilament light chain and p-tau181 in DS. Gaps in our knowledge are highlighted, and a potential role for sex differences in biomarker outcomes is noted, along with recommendations for determining the appropriate context of use when translating biomarkers into clinical applications. HIGHLIGHTS: An overview of blood-based biomarkers for Alzheimer's disease (AD) was provided for consideration of their utility among individuals with Down syndrome when looking toward potential clinical applications. Longitudinal stability of many blood biomarkers and improvement in detection sensitivity make blood such as plasma a viable source for exploring AD pathology. Variability in reviewed findings regarding the application of blood biomarkers highlights the importance of understanding and defining the appropriate context of use, particularly when translating them into clinical practice.

Estimation of Selected Metals in the Tissues of Prostate Carcinoma Patients

It Indicated that the Prostate cancer is common it affects nearly one-third of male over the age of 45. For two fundamental reasons, prostate cancer (PCa) is a serious health issue and a good prospect for tissue-scale, individualized modeling of cancer progression. In vivo validation might be allowed due to its compact size and the ability to measure serum PSA, a for prostate cancer blood biomarker can be diagnosed with support if certain markers identified by immunohistochemistry on tissue sections can aid in the diagnosis of adenocarcinoma that is primary in the prostate gland or metastatic. The biology of prostate cancer can be deeply understood by the gene identification and gene patterns expression. Using the Affymetrix Uranium95a, U95b, and Uranium95c chip sets (37,777 genes and expression sequence tags), Comprehensive gene expression is performed study on 152 hompsapien samples, which were from men of all ages and included cancer tissues, prostate tissues next to tumors, and prostate tissue of donor. In the prostate tissues, the majority of metals show a random distribution; nevertheless, patients show comparatively more unpredictability than controls. However, our findings indicate that the pattern of gene expression in cancer-related tissues is altered to such an extent that it resembles the effects of a cancer field. In contrast to controls, PCA and CA demonstrated how hazardous metals interfered with the critical metals' normal physiological and metabolic roles in the prostate tissues of both patient groups. In comparison to hyperthyroid patients and controls, hypothyroid patients' prostate tissues had greater mean levels of Fe, Ni, Cr, and Hg. Additionally; we discovered that by employing a unique model, Prostate cancer aggressiveness can be predicted by gene expression patterns.

Abstract 4134460: Application of Principal Component Analysis to Heterogenous Fontan Registry Data to Identify Independent Contributing Factors to Decline

Introduction: Single ventricle heart disease is a severe and life-threatening illness, and improvements in clinical outcomes of those with Fontan circulation have not yet yielded acceptable survival over the past two decades. Patients are at risk of developing a diverse variety of Fontan-associated comorbidities that may ultimately require heart transplant. Goal: Our study goal was to determine if principal component analysis (PCA), which determines unique directions of data variance, applied to data collected from a pediatric Fontan cohort can predict functional decline (N=140). Methods: Heterogeneous data broadly consisting of measures of cardiac and vascular function, exercise (VO 2max ), lymphatic biomarkers, and blood biomarkers were collected over 9 years at a single site as part of a prospectively applied clinical care pathway; in that time, 16 events occurred that are considered here in a composite adverse outcome measure. After standardization and PCA, principal components (PCs) representing >5% of total variance were thematically labeled based on their constituents and tested for association with the composite outcome. Results: Our main findings suggest that the 6 th PC (PC6), representing 7.1% percent of the total variance in the set, is greatly influenced by albumin, alkaline phosphatase, total protein, BUN and BNP max and is a superior measure of proportional hazard compared to EF; it displayed the greatest accuracy for classifying Fontan patients as determined by AUC. In bivariate hazard analysis, we found that models combining systolic function (EF or PC5) and blood biomarkers (PC6) were most predictive, with the former having the greatest AIC, and the latter having the highest c-statistic. Conclusion: Our findings support the hypothesis that a broad, prospectively collected, multiorgan system model analyzed by way of unsupervised machine learning methods will improve prognosis of adverse outcomes in a Fontan cohort.

Abstract 4139115: Genomic, Phenomic, and Geographic Relationships with Leukocyte Telomere Length in the U.S. Yields New Insights for Neoplasms and Cardiometabolic Disease

Background: Leukocyte telomere length (LTL) is an age-related marker strongly associated with neoplasms and cardiometabolic disease. LTL is also intimately linked with clonal hematopoiesis of indeterminate potential (CHIP), a recently described risk factor for cardiovascular disease (CVD). However, population-scale investigations of LTL in diverse backgrounds are limited, and geolocational variance has not been studied. Method: LTL and CHIP were derived using blood-derived whole genome sequencing data from 244,819 diverse U.S. residents in the NIH All of Us Research Program (AoU). We performed association analyses of LTL with CHIP and other available health-related traits, Phecode, as well as geolocation data by ZIP code. Genome-wide association study (GWAS) of LTL in AoU and meta-analysis with UK Biobank were performed. All association tests were adjusted for age, sex, sequencing site, and the first 10 genetic principal components with multiple test corrections. Mediation analysis was adjusted for additional risk factors for CVD. Result: Among 244,819 AoU participants, 146,717 (59.9%) were female with mean (standard deviation) age 51.8 (±16) years. Estimated LTL correlated strongly with age, sex, and genetic ancestry in AoU. Socioeconomic status, lifestyle variables, anthropometry, blood biomarkers (e.g., lipids and glucose), and vital signs were associated with LTL in AoU. Phecode analysis showed LTL is associated with neoplasms and age-related conditions, including CVDs. LTL mediated some of the observed increased coronary artery disease risk among individuals with hematologic malignancies ( P < 2 × 10 -16 ). Significantly longer LTL clustered in the West Coast and Central Midwest ( P = 0.001 and 0.033, respectively), while significantly shorter LTL clustered in the Southeast ( P = 0.001) (Figure). GWAS meta-analyses identified 197 loci, of which 36 (18%) were novel, including ancestry-specific loci. Rare variant aggregation test found 9 novel associated genes. Conclusions: LTL is a marker of biological age that is associated and interacts with neoplasms, CVDs, and its various risk factors, with notable geographic variability within the U.S. Germline genetic analyses of LTL yield new insights into drivers of LTL.

Identification of a 5-Plex Cytokine Signature that Differentiates Patients with Multiple Systemic Inflammatory Diseases.

Patients with non-infectious systemic inflammation may suffer from one of many diseases, including hyperinflammation (HI), autoinflammatory disorders (AID), and systemic autoimmune disease (AI). Despite their clinical overlap, the pathophysiology and patient management differ between these disorders. We aimed to investigate blood biomarkers able to discriminate between patient groups. We included 44 patients with active clinical and/or genetic systemic inflammatory disease (9 HI, 27 AID, 8 systemic AI) and 16 healthy controls. We quantified 55 serum proteins and combined multiple machine learning algorithms to identify five proteins (CCL26, CXCL10, ICAM-1, IL-27, and SAA) that maximally separated patient groups. High ICAM-1 was associated with HI. AID was characterized by an increase in SAA and decrease in CXCL10 levels. A trend for higher CXCL10 and statistically lower SAA was observed in patients with systemic AI. Principal component analysis and unsupervised hierarchical clustering confirmed separation of disease groups. Logistic regression modelling revealed a high statistical significance for HI (P = 0.001), AID, and systemic AI (P < 0.0001). Predictive accuracy was excellent for systemic AI (AUC 0.94) and AID (0.91) and good for HI (0.81). Further research is needed to validate findings in a larger prospective cohort. Results will contribute to a better understanding of the pathophysiology of systemic inflammatory disorders and can improve diagnosis and patient management.

Tracking inflammation resolution signatures in lungs after SARS-CoV-2 omicron BA.1 infection of K18-hACE2 mice.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes Coronavirus Disease 2019 (COVID-19), which can result in severe disease, often characterised by a 'cytokine storm' and the associated acute respiratory distress syndrome. However, many infections with SARS-CoV-2 are mild or asymptomatic throughout the course of infection. Although blood biomarkers of severe disease are well studied, less well understood are the inflammatory signatures in lung tissues associated with mild disease or silent infections, wherein infection and inflammation are rapidly resolved leading to sequelae-free recovery. Herein we described RNA-Seq and histological analyses of lungs over time in an omicron BA.1/K18-hACE2 mouse infection model, which displays these latter features. Although robust infection was evident at 2 days post infection (dpi), viral RNA was largely cleared by 10 dpi. Acute inflammatory signatures showed a slightly different pattern of cytokine signatures compared with severe infection models, and where much diminished 30 dpi and absent by 66 dpi. Cellular deconvolution identified significantly increased abundance scores for a number of anti-inflammatory pro-resolution cell types at 5/10 dpi. These included type II innate lymphoid cells, T regulatory cells, and interstitial macrophages. Genes whose expression trended downwards over 2-66 dpi included biomarkers of severe disease and were associated with 'cytokine storm' pathways. Genes whose expression trended upward during this period were associated with recovery of ciliated cells, AT2 to AT1 transition, reticular fibroblasts and innate lymphoid cells, indicating a return to homeostasis. Very few differentially expressed host genes were identified at 66 dpi, suggesting near complete recovery. The parallels between mild or subclinical infections in humans and those observed in this BA.1/K18-hACE2 mouse model are discussed with reference to the concept of "protective inflammation".

CTNI-42. A BIOMARKER-GUIDED PHASE 2 STUDY OF DB107-RRV (A RETROVIRAL REPLICATING VECTOR) COMBINED WITH DB107-FC (FLUCYTOSINE EXTENDED-RELEASE TABLETS) IN PATIENTS WITH RECURRENT GLIOBLASTOMA OR ANAPLASTIC ASTROCYTOMA: A CLINICAL PROTOCOL

Abstract INTRODUCTION Despite significant advances in understanding the basic pathogenesis of glioblastoma, the median survival of patients has minimally changed in the past 25 years with a 5-year survival rate of &amp;lt;7%. Because of the dismal prognosis, attention has shifted to alternative adjuvant treatment modalities including gene/viral-based therapy. The first clinically tested replicating retroviral vector DB107-RRV (vocimagene amiretrorepvec, formerly Toca511) was assessed in a randomized Phase 3 study (NCT02414165) to evaluate the intracranial administration of DB107-RRV with subsequent oral administration of DB107-FC (5-fluocytosine extended-release tablets) in patients with recurrent HGG. Although the primary endpoint of overall survival (OS) in the intent-to-treat population was not met (median OS=11.07 months, DB107-RRV+ DB107-FC arm and 12.22 months for the SOC arm (HR=1.06; 95% CI:[0.83, 1.35], p=0.6154)), post-hoc analysis identified a subgroup of patients (responders) who demonstrated a marked survival benefit from the Phase III clinical trial (Toca 5, NCT02414165). Using a non-invasive genotyping pipeline, a novel biomarker, DGM7, was identified among responders that confers an improved prognosis: (DB107-RRV+DB107-FC is 18.3 months vs. 12.0 months in the SOC group (HR=0.50; 95% CI:[0.28, 0.89], p= 0.0167)). DGM7 is present in the blood of approximately 20-30% of HGG patients. METHODS This is a Phase 2, single-site, open-label, single-arm study to assess the oral administration of DB107-FC in combination with DB107-RRV in DGM7+ patients with recurrent/progressive HGG. Key inclusion criteria include presence of the DGM7 biomarker in blood, patient age 18–75 with histologically proven, measurable (MRI) recurrent/progressive HGG, and no radiation therapy for at least 12 weeks without histopathologic/MRI progression confirmation. The primary endpoint of this study will be progression-free survival at 6 months (PFS6) and OS of DB107-RRV+DB107-FC in DGM7+ patients with recurrent/progressive HGG compared to the historical study arm SOC/DGM7+ from NCT02414165.

BIOM-62. CIRCULATING TUMOR CELLS IN MEDULLOBLASTOMA: A NOVEL PERIPHERAL BIOMARKER FOR CNS DISEASE

Abstract BACKGROUND Medulloblastoma is an embryonal tumor of the cerebellum and the most common pediatric malignancy of the central nervous system (CNS). Diagnosis, staging, and disease monitoring require invasive neurosurgical biopsy or MRI. No blood or cerebrospinal fluid (CSF) biomarkers exist. We demonstrate the presence of circulating tumor cell clusters (CTCCs) in patients with medulloblastoma and use a transcriptomic approach to describe their gene expression. Design/Method: We enrolled 34 total patients in a longitudinal study: 24 with medulloblastoma and 10 without malignancy. CTCCs are captured from unprocessed whole blood using a physics-based label-free approach to CTCC isolation. RNA sequencing was performed on 21 samples at time of diagnostic resection from 6 patients with medulloblastoma. Sequencing reads were assessed for quality control, trimmed, and aligned to the human genome. Differential gene expression (DGE) was performed against whole blood from healthy controls. Reactome pathway database of curated, peer-reviewed gene sets are used for gene set enrichment analysis (GSEA). RESULTS The presence of peripherally CTCC patients with medulloblastoma is not previously known and was detected in all patients with medulloblastoma whereas none were observed from patients with non-malignant hematological conditions. RNA-sequencing demonstrate separation on principal component analysis and gene-expression heatmap against whole blood healthy control. DGE shows upregulation of genes indicating stem-like state. GSEA show enrichment of medulloblastoma molecular pathways and gene sets defining integrin, extracellular matrix remodeling, and cell adhesion pathways. Transient elevation of peripheral CTCC followed by return to baseline levels was associated with durable disease response to therapy, whereas persistence or elevation of CTCC in CSF correlated with disease recurrence. CONCLUSION The universal and specific finding of peripherally captured CTCC arising from parent tumor in the CNS within our patient cohort suggests a utility as a tumor biomarker. Future sequencing is planned comparing CTCC transcriptome against parent tumor.

CNSC-06. NAVIGATING BRAIN CANCER: PATIENT STORIES AND MEDICAL BREAKTHROUGH

Abstract The complex interplay of genetic mutation, dys regulated signalling pathways, Epigenetic modifications and Interaction with tumor microenvironment are the major aggravating factors for Brain Tumors. Neurological complications involve Increased Intracranial pressure, Seizures, Motor and Sensory deficits, Aphasia, Hydrocephaly, Behavioral and personality changes, Cerebellar Symptoms. It is diagnosed in a sequential steps. Collecting good history from patient in primary then examining the patient all through and making some investigations to come to a final diagnosis. On asking history to a patient he complains of recurrent convulsions, Personality changes and some motor activity deficit and loss of few senses. On examination we can reveal defect in cranial nerve functions, Motor or sensory deficits, loss of reflexes and coordination. Then Investigations being the next step for diagnosis, We ask the patient to undergo for a test for Blood Biomarkers and CSF analysis in case of suspection of metastatic Brain tumors. Biopsy to obtain for Histopathological Examination. Histopathological examination is done for analysis of genetic mutations and molecular markers. The other imaging techniques like MRI, MRS, CT, PET, EEG are done. This gives a clear picture of what actually caused tumor and shows a way for treatment. Now the treatment involves both Medical and Surgical treatment. Medical treatment includes Chemotherapy along with radiation therapy where the drugs given are Temozolomide and targeted therapy included EGFR BRAF inhibitors for glioblastoma and low grade gliomas respectively. Tumor treating fields- Optune is given. Surgical treatment involves Craniotomy for removal of primary Brain tumors, Neuro Endoscopy if tumor is present in ventricles or base of Brain, Laser interstitial Thermal therapy for deap seated and recurrent brain tumors. This is all about the treatment and now the patient management is very important for further clinical help. Physical, occupational and speech therapy helps patients recover. Symptomatic approach for inoperable tumors Provide comprehensive support for patients nearing end of life, Emphasise confidence and Quality of life.

Exploiting blood-based biomarkers to align preclinical models with human traumatic brain injury.

Rodent models are important research tools for studying the pathophysiology of traumatic brain injury (TBI) and developing new therapeutic interventions for this devastating neurological disorder. However, the failure rate for the translation of drugs from animal testing to human treatments for TBI is 100%. While there are several potential explanations for this, previous clinical trials have relied on extrapolation from preclinical studies for critical design considerations, including drug dose optimization, post-injury drug treatment initiation and duration. Incorporating clinically relevant biomarkers in preclinical studies may provide an opportunity to calibrate preclinical models to identical (or similar) measurements in humans, link to human TBI biomechanics and pathophysiology, and guide therapeutic decisions. To support this translational goal, we conducted a systematic literature review of preclinical TBI studies in rodents measuring blood levels of clinically used GFAP, UCH-L1, NfL, t-Tau, or p-Tau, published in PubMed/EMBASE up to April 10th, 2024. Although many factors influence clinical TBI outcomes, many of those cannot routinely be assessed in rodent studies (e.g., ICP monitoring), thus we focused on blood biomarkers' temporal trajectories and discuss our findings in the context of the latest clinical TBI biomarker data. Out of the 805 original preclinical studies, 74 met the inclusion criteria, with a median quality score of 5 (25th-75th percentiles: 4-7) on the CAMARADES checklist. GFAP was measured in 43 studies, UCH-L1 in 21, NfL in 20, t-Tau in 19, and p-Tau in seven. Data in rodent models indicate that all biomarkers exhibited injury severity-dependent elevations with distinct temporal profiles. GFAP and UCH-L1 peaked within the first day after TBI (30- and 4-fold increases, respectively, in moderate-to-severe TBI versus sham) with the highest levels observed in the contusion TBI model. NfL peaked within days (18-fold increase) and remained elevated up to 6 months post-injury. GFAP and NfL show a pharmacodynamic response in 64.7% and 60%, respectively, of studies evaluating neuroprotective therapies in preclinical models. However, GFAP's rapid decline post-injury may limit its utility for understanding the response to new therapeutics beyond the hyperacute phase after experimental TBI. Furthermore, as in humans, subacute NfL levels inform on chronic white matter loss after TBI. t-Tau and p-Tau levels increased over weeks after TBI (up to 6- and 16-fold, respectively); however, their relationship with underlying neurodegeneration has yet to be addressed. Further investigation into biomarker levels in the subacute and chronic phases after TBI will be needed to fully understand the pathomechanisms underpinning blood biomarkers' trajectories and select the most suitable experimental model to optimally relate preclinical mechanistic studies to clinical observations in humans. This new approach could accelerate the translation of neuroprotective treatments from laboratory experiments to real-world clinical practices.

Exploring multi-omics and clinical characteristics linked to accelerated biological aging in Asian women of reproductive age: insights from the S-PRESTO study

BackgroundPhenotypic age (PhenoAge), a widely used marker of biological aging, has been shown to be a robust predictor of all-cause mortality and morbidity in different populations. Existing studies on biological aging have primarily focused on individual domains, resulting in a lack of a comprehensive understanding of the multi-systemic dysregulation that occurs in aging.MethodsPhenoAge was evaluated based on a linear combination of chronological age (CA) and 9 clinical biomarkers in 952 multi-ethnic Asian women of reproductive age. Phenotypic age acceleration (PhenoAgeAccel), an aging biomarker, represents PhenoAge after adjusting for CA. This study conducts an in-depth association analysis of PhenoAgeAccel with clinical, nutritional, lipidomic, gut microbiome, and genetic factors.ResultsHigher adiposity, glycaemia, plasma saturated fatty acids, kynurenine pathway metabolites, GlycA, riboflavin, nicotinamide, and insulin-like growth factor binding proteins were positively associated with PhenoAgeAccel. Conversely, a healthier diet and higher levels of pyridoxal phosphate, all-trans retinol, betaine, tryptophan, glutamine, histidine, apolipoprotein B, and insulin-like growth factors were inversely associated with PhenoAgeAccel. Lipidomic analysis found 132 lipid species linked to PhenoAgeAccel, with PC(O-36:0) showing the strongest positive association and CE(24:5) demonstrating the strongest inverse association. A genome-wide association study identified rs9864994 as the top genetic variant (P = 5.69E-07) from the ZDHHC19 gene. Gut microbiome analysis revealed that Erysipelotrichaceae UCG-003 and Bacteroides vulgatus were inversely associated with PhenoAgeAccel. Integrative network analysis of aging-related factors underscored the intricate links among clinical, nutritional and lipidomic variables, such as positive associations between kynurenine pathway metabolites, amino acids, adiposity, and insulin resistance. Furthermore, potential mediation effects of blood biomarkers related to inflammation, immune response, and nutritional and energy metabolism were observed in the associations of diet, adiposity, genetic variants, and gut microbial species with PhenoAgeAccel.ConclusionsOur findings provide a comprehensive analysis of aging-related factors across multiple platforms, delineating their complex interconnections. This study is the first to report novel signatures in lipidomics, gut microbiome and blood biomarkers specifically associated with PhenoAgeAccel. These insights are invaluable in understanding the molecular and metabolic mechanisms underlying biological aging and shed light on potential interventions to mitigate accelerated biological aging by targeting modifiable factors.

Associations between milk infrared-predicted plasma biomarkers of stress resilience and fertility in dairy cattle: insights for enhancing breeding programs and herd management

Fertility is a crucial aspect of dairy herd efficiency and sustainability. Among factors influencing fertility in dairy cattle, metabolic stress and systemic inflammation of animals are of main relevance, especially in the postpartum stage when ovarian activity begins and cows are inseminated. Our study aimed to infer the associations between milk infrared-predicted blood biomarkers of stress resilience and fertility traits, namely the interval from calving to first service (iCF), days open (DO), and the pregnancy rate at first service (PRF) in a multi-breed population of 89,097 dairy cows. The blood metabolites (15 blood biomarkers related to hepatic damage and function, oxidative stress, and inflammation/innate immunity) were predicted using milk Fourier-transform mid-infrared (MIR) spectroscopy. A gradient boosting machine (GBM) approach with leave-one-batch-out cross-validation (R2 range from 0.45 to 0.82) was implemented to an independent calibration database of 1,367 lactating cows reared in 5 herds. Calibration equations were then applied to a population database of 1,799,186 MIR milk spectral data, that were then merged with fertility data collected by the Breeders Federation of Alto Adige (Bolzano province, Italy) generating a final database of 285,145 records. The 2 databases were merged according to the milk test day (and thus the MIR spectrum) closest to the date of insemination. The interval fertility traits were fitted as the hazard of either receiving the first service after calving at time t for iCF or becoming pregnant after calving at time t for DO in a Cox proportional-hazards model. Statistical analyses were performed including in the model the number of lactations, year of calving, and herd as fixed effects. The independent effect of the MIR-based predictions of metabolites was also included with each metabolite evaluated separately and discretized into 7 levels based on percentiles. Pregnancy rate at first service, on the other hand, was analyzed using logistic regression and the same explanatory variables. The metabolites linked to liver function and damage, such as aspartate aminotransferase, total bilirubin, and alkaline phosphatase, had a relevant influence on iCF and DO in terms of the hazard ratio (HR). Relevant results were also obtained for the biomarkers related to oxidative stress and inflammation/innate immunity. Specifically, increasing levels of ceruloplasmin, total reactive oxygen metabolites, and advanced oxidation protein products resulted in a relevant decrease in the HR of cows becoming pregnant. The logistic regression analysis did not reveal any significant effect of the aforementioned biomarkers on PRF, indicating that the effects of the stress response mainly concern the resumption of the ovarian cycle after calving. The results for the associations of the predicted biomarkers of the stress response with iCF and DO were consistent with expected physiological patterns. In conclusion, the predicted biomarkers investigated revealed to be promising novel phenotypes for assessing animal health and welfare, in the view of enhancing fertility in dairy cattle also through selective breeding, thus improving the overall efficiency of dairy herds.