Combination Of Biomarkers Research Articles

Development and validation of a tumor marker-based model for the prediction of lung cancer: an analysis of a multicenter retrospective study in Shanghai, China

BackgroundThe incidence and mortality rates of cancer are the highest globally. Developing novel methodologies that precisely, safely, and economically differentiate between benign and malignant lung conditions holds immense clinical importance. This research seeks to construct a predictive model utilizing a combination of diverse biomarkers to effectively discriminate between benign and malignant lung diseases.MethodsThis retrospective study included patients admitted to the two general hospitals in Shanghai from 2014 to 2015. This study was developed using five tumor markers: carcinoembryonic antigen (CEA), carbohydrate antigen 199 (CA199), cytokeratin fragment 21-1 (CA211), squamous cell carcinoma antigen (SCC), and neuron specific enolase (NSE). The entire sample was divided into two groups according to the hospital: 1033 cases were included in the development cohort and 300 cases in the validation cohort. Logistic regression analysis was used for univariate analysis to explore individual correlations between each selected clinical variable and lung cancer diagnostic outcome. Diagnostic prediction models were constructed and validated based on independent prognostic factors identified using multifactorial analysis. A nomogram was created using these tumor markers (age and sex were additionally included) and validated using the concordance index and calibration curves. Clinical prediction models were evaluated using decision curve analysis.ResultsFully adjusted multivariate analysis showed that the risk of lung cancer was 2.38 times higher in men than in women. CEA positivity was associated with an 13.41-fold increased risk in lung cancer. The area under the curve (AUC) values for the development cohort and validation cohort models were 0.907 and 0.954, respectively. In the established nomogram, the AUC for the receiver operating characteristic curve was 0.907 (95% CI, 0.889–0.925). The validation model confirmed the strong discriminative power of the nomogram (AUC = 0.954). The described calibration curves demonstrated good fit predictions and observation probabilities. In addition, decision curve analysis concluded that the newly established nomogram has important implications for clinical decision making.ConclusionsCombined prediction models based on CEA, CA199, CA211, SCC, and NSE biomarkers could significantly the differentiation between benign and malignant lung diseases, thus facilitating better clinical decision making.

Screening and Predictive Biomarkers for Down Syndrome Through Amniotic Fluid Metabolomics.

Down syndrome (DS) is a congenital disorder caused by the presence of an extra copy of all or part of chromosome 21. It is characterized by significant intellectual disability, distinct facial features, and growth and developmental challenges. The utilization of metabolomics to analyze specific metabolic markers in maternal amniotic fluid may provide innovative tools and screening methods for investigating the early pathophysiology of trisomy 21 at the functional level. Amniotic fluid samples were obtained via amniocentesis from 57 pregnancies with DS and 55 control pregnancies between 173/7 and 240/7weeks of gestation. The targeted metabolomics focused on 34 organic acids, 17 amino acids, and 5 acylcarnitine metabolites. The untargeted metabolomics analysis concentrated on lipid profiles and included 602 metabolites that met quality control standards. Principal Component Analysis, Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA), and false discovery rate (FDR) adjustments were applied. MetaboAnalystR 5.0 was used to perform the metabolic pathway analysis on the identified differential metabolites. Fifty differential metabolites, including L-glutamine, eight organic acids, and 41 lipids, were significantly altered in DS based on three criteria: VIP > 1 in the OPLS-DA model, FDR-adjusted p-value < 0.05, and |log2FC|>log2(1.5) from a volcano plot of all detected metabolites. An analysis of 212 differential metabolites, selected from both targeted and untargeted approaches (VIP > 1 in the OPLS-DA model and FDR-adjusted p-value < 0.05), revealed significant changes in nine metabolic pathways. Fourteen key metabolites were identified to establish a screening model for DS, achieving an area under the curve of 1.00. Our results underscore the potential of metabolomics approaches in identifying concise and reliable biomarker combinations that demonstrate promising screening performance in DS.

Virtual patient analysis identifies strategies to improve the performance of predictive biomarkers for PD-1 blockade

Patients with metastatic triple-negative breast cancer (TNBC) show variable responses to PD-1 inhibition. Efficient patient selection by predictive biomarkers would be desirable but is hindered by the limited performance of existing biomarkers. Here, we leveraged in silico patient cohorts generated using a quantitative systems pharmacology model of metastatic TNBC, informed by transcriptomic and clinical data, to explore potential ways to improve patient selection. We evaluated and quantified the performance of 90 biomarker candidates, including various cellular and molecular species, at different cutoffs by a cutoff-based biomarker testing algorithm combined with machine learning-based feature selection. Combinations of pretreatment biomarkers improved the specificity compared to single biomarkers at the cost of reduced sensitivity. On the other hand, early on-treatment biomarkers, such as the relative change in tumor diameter from baseline measured at two weeks after treatment initiation, achieved remarkably higher sensitivity and specificity. Further, blood-based biomarkers had a comparable ability to tumor- or lymph node-based biomarkers in identifying a subset of responders, potentially suggesting a less invasive way for patient selection.

The characterization of serum proteomics and metabolomics across the cancer trajectory in chronic hepatitis B‐related liver diseases

AbstractHepatocellular carcinoma (HCC) is a deadly cancer that emerges from a continuous progression of liver cells from normal to abnormal, often following infections by hepatitis B/C viruses (HBV/HCV), liver fibrosis, and liver cirrhosis (LC), ultimately culminating in cancer. However, there is currently limited systematic molecular analysis of biomarkers at different stages of HCC progression using multi‐omics approaches. We carried out an innovative pipeline by utilizing targeted proteomics and metabolomics to identify potential biomarkers for early detection of HCC in 316 participants, including healthy adults and patients diagnosed with HBV, HCV, LC, and HCC from three independent cohorts. We first established a detailed database of candidate biomarkers for HCC containing 3059 proteins and 103 metabolites, and identified pivotal candidates implicated in the progressive trajectory of liver cancers. Through our developed DeepPRM, scheduled multiple reaction monitoring (MRM)‐targeted approach, and machine learning‐based computational pipeline, we identified an eight‐biomolecular‐based combination with an accuracy rate of 91.43% for early diagnosis of HCC, and a 12‐biomolecular‐based combination with an accuracy rate of 80.00% for detecting changes in HBV–LC progression. These two biomarker combinations significantly improved accuracy compared to traditional tumor biomarkers. Our extensive analysis provides valuable proteomic and metabolomic data resources that will contribute to a deeper understanding of liver disease progression and enhance the identification of potential therapeutic targets.

Biomarker Assessment in Parkinson’s Disease Dementia and Dementia with Lewy Bodies by the Immunomagnetic Reduction Assay and Clinical Measures

Background: Plasma biomarker assays provide an opportunity to reassess whether Alzheimer’s disease, Parkinson’s disease dementia (PDD), and dementia with Lewy bodies (DLB) plasma biomarkers are diagnostically useful. Objective: We hypothesized that immunomagnetic reduction (IMR) of plasma biomarkers could differentiate between patients with PDD and DLB and healthy patients when combined with established clinical testing measures. Methods: Plasma samples from 61 participants (12 PDD, 12 DLB, 37 controls) were analyzed using IMR to quantify amyloid-β 42 (Aβ42), total tau (t-tau), phosphorylated tau at threonine 181 (p-tau181), and α-synuclein (α-syn). Receiver operating characteristic curve (ROC) analysis was used to obtain sensitivity, specificity, and area under the ROC curve. Biomarker results were combined with clinical measures from the Unified Parkinson’s Disease Rating Scale (UPDRS), Montreal Cognitive Assessment, and Hoehn-Yahr stage to optimize diagnostic test performance. Results: Participants with PDD had higher α-syn than those with DLB and healthy participants and were distinguishable by their biomarker products Aβ42×p-tau181 and Aβ42×α-syn. Patients with DLB had higher p-tau181 than those with PDD and healthy participants and were distinguishable by their concentrations of α-syn×p-tau181. Plasma α-syn plus UPDRS versus either test alone increased sensitivity, specificity, and AUC when healthy patients were compared with those with PDD and DLB. Combined clinical examination scores and plasma biomarker products demonstrated utility in differentiating PDD from DLB when p-tau181 was combined with UPDRS, α-syn was combined with UPDRS, and α-syn×p-tau181 was combined with UPDRS. Conclusions: In this pilot study, IMR plasma p-tau181 and α-syn may discriminate between PDD and DLB when used in conjunction with clinical testing.

Proteomic and serological markers for diagnosing cardia gastric cancer and precursor lesions in a Chinese population

Cardia gastric cancer (CGC) is prevalent in East Asia, and noninvasive, cost-effective screening methods are needed. This study investigated the diagnostic value of serum pepsinogen (PG), gastrin-17 (G-17), Helicobacter pylori (H. pylori) antibodies, and proteomic profiling for CGC and precancerous lesions. We conducted a case-control study involving biopsy-confirmed patients with CGC (n = 60), low-grade intraepithelial neoplasia (CLGD, n = 60), high-grade intraepithelial neoplasia (CHGD, n = 64), and healthy controls (n = 120) matched for age and sex from high-incidence areas in China. Serological markers including PGI, PGII, G-17, and H. pylori were measured using ELISA and Western blot, while plasma protein markers were assessed using Olink® technology. The VSOLassoBag algorithm and nine machine learning (ML) algorithms were employed to identify crucial features and construct predictive models. Various evaluation metrics, including the area under the receiver-operating-characteristic curve (AUC), were utilized to compare predictive performance. Elevated PGII levels, decreased PGR, and H. pylori infection were significantly associated with an increased risk of CGC and precancerous lesions (P for trend < 0.05). The eXtreme Gradient Boosting (XGBoost) model performed best in discriminative ability among the 9 ML models. Following feature reduction based on predictive performance, a final explainable XGBoost model was developed, incorporating five protein biomarkers (CDHR2, ICAM4, PTPRM, CDC27, and FLT1). This model exhibited excellent performance in distinguishing individuals with CGC and precancerous lesions from healthy controls (AUC = 0.931 for CGC, 0.867 for CHGD, and 0.763 for CLGD), surpassing the traditional serological marker-based model. This study underscores the diagnostic potential of serological markers and proteomic profiling in the detection of CGC. Further validation and exploration of combined biomarker approaches are warranted to enhance early diagnosis and improve outcomes in high-risk populations.

Evaluation of GFAP/UCH-L1 biomarkers for computed tomography exclusion in mild traumatic brain injury (mTBI)

IntroductionMild traumatic brain injury (mTBI) represents a major public health concern and affects millions of people worldwide every year. Diagnosis mainly relies on clinical criteria and computed tomography (CT) scans. GFAP (glial fibrillary acidic protein) and UCH-L1 (ubiquitin carboxyl-terminal hydrolase-L1) have been recently studied as potential biomarkers of mTBI. This study retrospectively evaluated the possible use of these combined biomarkers as negative predictors for excluding brain injuries in patients with suspected mTBI in the emergency department.MethodsAdult patients (n = 130) enrolled at Tor Vergata University Hospital (Rome, Italy), consecutively registered at the triage of the emergency department between October 2022 and January 2023, with non-penetrating TBI and Glasgow Coma Scale (GCS) score of 13–15, were considered. All eligible patients underwent intracranial CT scans and blood tests, within 12 h after trauma, for GFAP and UCH-L1 serum concentrations.ResultsIntracranial CT detected injuries in only seven patients (5%); GFAP and UCH-L1 tested positive in 96 patients and negative in 34 patients (74% vs. 26%). Combined biomarkers had a sensitivity equal to 1.00 (95% CI 0.64-1.00) and a negative predictive value (NPV) of 1.00 (0.99-1.00) in mTBI diagnosis with a negative CT.ConclusionsCombined laboratory tests for GFAP and UCH-L1 biomarkers might play a potential clinical role in avoiding unnecessary head CT scans after mTBI in emergency departments.

LC-MS/MS analysis reveals plasma protein signatures associated with lymph node metastasis in colorectal cancer

ObjectivesColorectal cancer (CRC) is a major global health concern, ranking as the third most common cancer and the fourth leading cause of cancer-related deaths worldwide. Currently, the diagnostic accuracy of Lymph node metastasis (LNM) is currently unsatisfactory. Therefore, there is an urgent need to develop a reliable tool that can accurately predict lymph node metastasis (LNM) in patients diagnosed with CRC.MethodsWe conducted an extensive proteomics investigation aimed at examining lymph node metastasis (LNM) in individuals diagnosed with colorectal cancer (CRC). In the discovery stage, employing a mass spectrometry-based proteomic approach, we analyzed a cohort of 60 colorectal cancer patients (NM=30, LNM=30), identifying distinct molecular profiles that differentiate patients with and without lymph node metastasis (LNM). Subsequently, we validated the protein classifier associated with lymph node metastasis.ResultsWe elucidated a combinatorial predictive protein biomarker that can distinguish patients with and without lymph node metastasis by LC-MS/MS. The classifier achieved an area under the curve (AUC) of 0.892 (95% CI, 0.842-0.941), while in the testing cohort, it attained an AUC of 0.929 (95% CI, 0.824-1.000). Furthermore, the four protein markers demonstrated an AUC of 0.84 (95% CI, 0.783–0.890) in the validation cohort. Additionally, we categorized patients into three types based on immunophenotyping. Type 1 primarily consisted of patients with negative lymph node metastasis (NM), characterized by immune cells such as NK cells, CD4 T effector memory cells, and memory B cells. Type 2 mainly included patients with positive lymph node metastasis (LNM), characterized by immune cells such as mesangial cells, epithelial cells, and mononuclear cells. In Type 1, a prominent upregulation observed in immune inflammation, as well as in glucose and lipid metabolism. In Type 2, significant upregulation was evident in pathways such as pyrimidine metabolism and cell cycle regulation. The findings of this study suggest that immune mechanisms may exert a pivotal role in the process of lymph node metastasis in CRC.ConclusionsHere, we present plasma protein signatures associated with lymph node metastasis in colorectal cancer (CRC). However, further validation across multiple centers is necessary to generalize these findings.

Free fatty acids: independent predictors of long-term adverse cardiovascular outcomes in heart failure patients

BackgroundThe association between plasma free fatty acid (FFA) and the outcomes in the heart failure (HF) patients remains unclear.MethodsA cohort study among HF patients was performed. Plasma FFA was analyzed as both a continuous and a categorical variable (grouped by tertiles) to assess its association with composite cardiovascular (CV) death and HF hospitalization (CV death & HHP), CV death alone, and all-cause mortality (ACM) using Cox regression models. Subgroup analyses of HF patients with preserved ejection fraction (HFpEF) and mildly reduced/reduced ejection fraction (HFmrEF/HFrEF) were performed. This work also assessed the effectiveness of combining FFA and NT-pro BNP biomarkers for risk stratification by calculating the concordance index (C-index).ResultsAmong the 4,109 HF patients, FFA levels exceeding 0.4–0.42 mmol/L were associated with increased risks of the three outcomes. Patients in the highest FFA tertile faced greater risks than those in the lowest tertile. Adjusted hazard ratios were 1.32 (95% CI: 1.11–1.58) for CV death & HHP, 1.45 (95% CI: 1.16–1.82) for CV death, and 1.39 (95% CI: 1.15–1.68) for ACM, with a maximum follow-up of 8 years (median: 25 months). Subgroup analyses revealed that elevated FFA levels consistently predicted worse outcomes in both HFmrEF/HFrEF and HFpEF patients. The C-index for predicting outcomes was significantly greater when NT-pro BNP and FFA were combined than when NT-pro BNP was used alone (P < 0.01).ConclusionIncreased plasma FFA concentrations were independently associated with greater risks of CV death & HHP, CV death, and ACM among HF patients, irrespective of the ejection fraction. The combination of FFA and NT-pro BNP biomarkers significantly improved risk stratification in HF patients.

Temporal ordering of cognitive impairment in Parkinson's disease patients based on disease progression models

IntroductionIdentifying Parkinson's disease (PD) patients at risk of cognitive decline is crucial for enhancing clinical interventions. While several models predicting cognitive decline in PD exist, a new machine learning framework called disease progression models (DPMs) offers a data-driven approach to understand disease evolution. MethodsWe enrolled 423 PD patients and 196 healthy controls from the Parkinson's Progression Markers Initiative (PPMI). Our study encompassed a range of biomarkers, including motor, neurocognitive, and neuroimaging evaluations at baseline and annually. A methodology was employed to select optimal combinations of biomarkers for constructing DPMs with superior predictive capabilities for both diagnosing and estimating conversion times toward cognitive decline. ResultsAt baseline, the approach showed excellent performance in identifying individuals at high risk of cognitive decline within the first five years. Furthermore, the proposed timeline from cognitive impairment to dementia was also used to explore clinical events such as the onset of cognitive impairment, the development of dementia or amyloid pathology. The presence of amyloid pathology did not alter the progression of cognitive impairment among PD patients. ConclusionsNeuropsychological measures and certain biomarkers, including cerebrospinal fluid (CSF) amyloid beta 42 (Aβ42) and dopamine transporter deficits, can be used to predict cognitive decline and estimate a timeline from cognitive impairment to dementia, with amyloid pathology preceding the onset of dementia in many cases. Our DPMs suggested that the profiles of CSF Aβ42 and phosphorylated tau in PD patients may differ from those in aging patients and those with Alzheimer's disease.

From a solitary blood-derived biomarker to combined biomarkers of sarcopenia: Experiences from the Korean Frailty and Aging Cohort Study.

Sarcopenia is recognized as a complex and multifactorial disorder that includes nutritional deficiency, inactivity, proinflammatory status, hormonal changes, neurological degeneration, and metabolic disturbances. Its' pathogenesis is not fully understood. Therefore, identifying specific biomarkers of sarcopenia will help us understand its pathophysiology. The most frequently reported blood-derived biomarkers of sarcopenia are growth factors, neuromuscular junctions, endocrine systems, mitochondrial dysfunction, inflammation-mediated and redox processes, muscle protein turnover, blood metabolomics, and behavior-mediated biomarkers. Here, we address the implications of sarcopenia biomarkers based on our research experience with KFACS cohort data. It includes free testosterone, myostatin, fibroblast growth factor 21 (FGF-21), growth differentiation factor 15 (GDF-15), procollagen type III N-terminal peptide (P3NP), creatinine-based biomarkers, eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), brain-derived neurotrophic factor (BDNF), metabolites (proline, alanine, tryptophan), and multi-biomarker risk score. We attempted to explain the paradoxical findings of myostatin and FGF-21 levels in relation to sarcopenia. GDF-15 levels were associated with sarcopenia prevalence but not its incidence. Plasma P3NP and BDNF levels may be biomarkers of muscle quality rather than quantity. Lower erythrocyte EPA and DHA levels were associated with slow gait speed, and erythrocyte EPA levels were associated with low handgrip strength. We developed a multi-biomarker risk score for sarcopenia and found that its accuracy in diagnosing sarcopenia was higher than that of any single biomarker.

Head-to-head comparison of leading blood tests for Alzheimer's disease pathology.

Blood tests have the potential to improve the accuracy of Alzheimer's disease (AD) clinical diagnosis, which will enable greater access to AD-specific treatments. This study compared leading commercial blood tests for amyloid pathology and other AD-related outcomes. Plasma samples from the Alzheimer's Disease Neuroimaging Initiative were assayed with AD blood tests from C2N Diagnostics, Fujirebio Diagnostics, ALZPath, Janssen, Roche Diagnostics, and Quanterix. Outcomes measures were amyloid positron emission tomography (PET), tau PET, cortical thickness, and dementia severity. Logistic regression models assessed the classification accuracies of individual or combined plasma biomarkers for binarized outcomes, and Spearman correlations evaluated continuous relationships between individual plasma biomarkers and continuous outcomes. Measures of plasma p-tau217, either individually or in combination with other plasma biomarkers, had the strongest relationships with all AD outcomes. This study identified the plasma biomarker analytes and assays that most accurately classified amyloid pathology and other AD-related outcomes. Plasma p-tau217 measures most accurately classified amyloid and tau status. Plasma Aβ42/Aβ40 had relatively low accuracy in classification of amyloid status. Plasma p-tau217 measures had higher correlations with cortical thickness than NfL. Correlations of plasma biomarkers with dementia symptoms were relatively low.

Blood-Based Biomarkers in Alzheimer's Disease: Advancing Non-Invasive Diagnostics and Prognostics.

Alzheimer's disease (AD), the most prevalent form of dementia, is expected to rise dramatically in incidence due to the global population aging. Traditional diagnostic approaches, such as cerebrospinal fluid analysis and positron emission tomography, are expensive and invasive, limiting their routine clinical use. Recent advances in blood-based biomarkers, including amyloid-beta, phosphorylated tau, and neurofilament light, offer promising non-invasive alternatives for early AD detection and disease monitoring. This review synthesizes current research on these blood-based biomarkers, highlighting their potential to track AD pathology and enhance diagnostic accuracy. Furthermore, this review uniquely integrates recent findings on protein-protein interaction networks and microRNA pathways, exploring novel combinations of proteomic, genomic, and epigenomic biomarkers that provide new insights into AD's molecular mechanisms. Additionally, we discuss the integration of these biomarkers with advanced neuroimaging techniques, emphasizing their potential to revolutionize AD diagnostics. Although large-scale validation is still needed, these biomarkers represent a critical advancement toward more accessible, cost-effective, and early diagnostic tools for AD.

Predictive Blood Biomarkers of Targeted Intervention for Chronic Mental Health Symptoms following Traumatic Brain Injury.

The purpose of this study was to assess the performance of predictive blood biomarkers for responsiveness to targeted treatments for chronic psychological issues years after traumatic brain injury (TBI). Targeted Evaluation Action and Monitoring of TBI was a prospective 6-month interventional trial of participants with chronic TBI sequelae (n = 95). Plasma biomarkers were analyzed pre-intervention: glial fibrillary acidic protein (GFAP), tau, hyperphosphorylated tau Thr231 (p-Tau), von Willebrand factor (vWF), brain lipid-binding protein (BLBP), ubiquitin C-terminal hydrolase-L1 (UCH-L1), vascular endothelial growth factor-a (VEGFa), and claudin-5 (CLDN5). Clinical outcomes included the Post-Traumatic Stress Disorder (PTSD)Checklist for DSM-5 (PCL-5) and Brief Symptom Inventory-18 (BSI-18). Regression models were built for change in PCL5/BSI-18. Biomarkers and covariates were included. Two models were built to identify responders (improved beyond the minimum clinically important difference). The model to predict change in PCL5 (R2=0.64; p < 0.001) included vWF (p = 0.032), BLBP (p = 0.001), tau (p = 0.002), VEGFa (p = 0.015), female sex (p = 0.06), and military status (p = 0.014). The model to predict change in BSI-18 (R2=0.42; p = 0.003) included vWF (p = 0.042), VEGFa (p = 0.09), BLBP (p = 0.01), CLDN5 (p < 0.001), female sex (p = 0.012), and military status (p = 0.004) as predictors. The model to differentiate participants who improved for PCL5 (R2=0.68; p < 0.001; AUC = 0.93) included vWF (p = 0.02), VEGFa (p = 0.008), and BLBP (p = 0.006). The model to differentiate participants who improved for BSI-18 (R2=0.25; p = 0.04; AUC = 0.75) included UCH-L1 (p = 0.03), GFAP (p = 0.06), and vWF (p = 0.03). Combinations of pre-intervention blood biomarkers were able to differentiate responders from nonresponders in both post-traumatic stress and overall psychological health domains.

Evaluating Combinations of Biological and Clinicopathologic Factors Linked to Poor Outcomes in Resected Colorectal Liver Metastasis: An External Validation Study.

Recent studies have suggested that certain combinations of KRAS or BRAF biomarkers with clinical factors are associated with poor outcomes and may indicate that surgery could be "biologically" futile in otherwise technically resectable colorectal liver metastasis (CRLM). However, these combinations have yet to be validated through external studies. We conducted a systematic search to identify these studies. The overall survival (OS) of patients with these combinations was evaluated in a cohort of patients treated at 11 tertiary centers. Additionally, the study investigated whether using high-risk KRAS point mutations in these combinations could be associated with particularly poor outcomes. The recommendations of four studies were validated in 1661 patients. The first three studies utilized KRAS, and their validation showed the following median and 5-year OS: (1) 30 months and 16.9%, (2) 24.3 months and 21.6%, and (3) 46.8 months and 44.4%, respectively. When analyzing only patients with high-risk KRAS mutations, median and 5-year OS decreased to: (1) 26.2 months and 0%, (2) 22.3 months and 15.1%, and (3) not reached and 44.9%, respectively. The fourth study utilized BRAF, and its validation showed a median OS of 10.4 months, with no survivors beyond 21 months. The combinations of biomarkers and clinical factors proposed to render surgery for CRLM futile, as presented in studies 1 (KRAS high-risk mutations) and 4, appear justified. In these studies, there were no long-term survivors, and survival was similar to that of historic cohorts with similar mutational profiles that received systemic therapies alone for unresectable disease.

Modified furosemide responsiveness index and biomarkers for AKI progression and prognosis: a prospective observational study.

Modified furosemide responsiveness index (mFRI) is a novel biomarker for assessing diuretic response and AKI progression in patients with early AKI. However, the comparative predictive performance of mFRI and novel renal biomarkers for adverse renal outcomes remains unclear. In a single-center prospective study, we aimed to evaluate the discriminatory abilities of mFRI and other novel renal biomarkers in predicting AKI progression and prognosis in patients with initial mild and moderate AKI (KDIGO stage 1 to 2). Patients with initial mild and moderate AKI within 48h following cardiac surgery were included in this study. The mFRI, renal biomarkers (including serum or urinary neutrophil gelatinase-associated lipocalin [sNGAL or uNGAL], serum cystatin C, urinary N-acetyl-beta-D-glycosaminidase [uNAG], urinary albumin-to-creatinine ratio) and cytokines (TNF, IL-1β, IL-2R, IL-6, IL-8, and IL-10) were measured at AKI diagnosis. The mFRI was calculated for each patient, which was defined as 2-hour urine output divided by furosemide dose and body weight. Of 1013 included patients, 154 (15.2%) experienced AKI progression, with 59 (5.8%) progressing to stage 3 and 33 (3.3%) meeting the composite outcome of hospital mortality or receipt of renal replacement therapy (RRT). The mFRI showed non-inferiority or potential superiority to renal biomarkers and cytokines in predicting AKI progression (area under the curve [AUC] 0.80, 95% confidence interval [CI] 0.77-0.82), progression to stage 3 (AUC 0.87, 95% CI 0.85-0.89), and composite outcome of death and receipt of RRT (AUC 0.85, 95% CI 0.82-0.87). Furthermore, the combination of a functional biomarker (mFRI) and a urinary injury biomarker (uNAG or uNGAL) resulted in a significant improvement in the prediction of adverse renal outcomes than either individual biomarker (all P < 0.05). Moreover, incorporating these panels into clinical model significantly enhanced its predictive capacity for adverse renal outcomes, as demonstrated by the C index, integrated discrimination improvement, and net reclassification improvement (all P < 0.05). As a rapid, cost-effective and easily accessible biomarker, mFRI, exhibited superior or comparable predictive capabilities for AKI progression and prognosis compared to renal biomarkers in cardiac surgical patients with mild to moderate AKI. Clinicaltrials.gov, NCT04962412. Registered July 15, 2021, https://clinicaltrials.gov/ct2/show/NCT04962412?cond=NCT04962412&draw=2&rank=1 .

Combining Biomarkers to Improve Diagnostic Accuracy in Detecting Diseases With Group-Tested Data.

We consider the problem of combining multiple biomarkers to improve the diagnostic accuracy of detecting a disease when only group-tested data on the disease status are available. There are several challenges in addressing this problem, including unavailable individual disease statuses, differential misclassification depending on group size and number of diseased individuals in the group, and extensive computation due to a large number of possible combinations of multiple biomarkers. To tackle these issues, we propose a pairwise model fitting approach to estimating the distribution of the optimal linear combination of biomarkers and its diagnostic accuracy under the assumption of a multivariate normal distribution. The approach is evaluated in simulation studies and applied to data on chlamydia detection and COVID-19 diagnosis.

Towards a framework for predicting immunotherapy outcome: a hybrid multiscale mathematical model of immune response to vascular tumor growth.

Studying tumor immune microenvironment (TIME) is pivotal to understand the mechanism and predict the outcome of cancer immunotherapy. Systems biology mathematical models can consider and control various factors of TIME and therefore explore the anti-tumor immune response meticulously. However, the role of tumor vasculature in the recruitment of T cells and the mechanism of T cell migration through TIME have not been studied comprehensively. In this work, we developed a hybrid discrete-continuum multi-scale model to study TIME. The mathematical model includes angiogenesis and T cell recruitment via tumor vasculature. Moreover, solid tumor growth, vascular growth and remodeling, interstitial fluid flow, hemodynamics, and blood rheology are all considered in the model. In addition, different aspects of T cells, including their migration, proliferation, subtype conversion, and interaction with tumor cells are thoroughly included. The model reproduces spatiotemporal distribution of tumor infiltrating T cells that mimics histopathological patterns. Furthermore, TIME model robustly recapitulates different phases of tumor immunoediting. We also examined a number of biomarkers to predict the outcome of immune checkpoint blockade (ICB) treatment. The results demonstrated that although tumor mutational burden (TMB) may predict non-responders to ICB, a combination of different biomarkers is essential to predict the majority of the responders. Based on our results, the ICB response rate varies significantly from 28 to 89% depending on the values of different parameters, even in the cases with high TMB.

5156 Circulating MicroRNA-based Biomarker Combinations for Differentiating Uni- and Bilateral Primary Aldosteronism Using a Machine Learning Approach

Abstract Disclosure: B. Vekony: None. G. Nyiro: None. Z. Herold: None. J. Fekete: None. F. Ceccato: None. S. Gruber: None. L. Kurzinger: None. M. Parasiliti-Caprino: None. B. Szeredas: None. S.K. Nazri: None. V. Fell: None. M. Bassiony: None. E.A. Azizan: None. I. Bancos: None. F. Beuschlein: None. P. Igaz: None. Background: The differentiation of uni- and bilateral forms of primary aldosteronism (PA) is of pivotal clinical relevance. Whereas unilateral PA warrants surgical treatment, bilateral PA is treated by aldosterone antagonists. The most reliable way to differentiate these two entities is adrenal venous sampling (AVS) that is an invasive method with limited availability and requiring great expertise. Aim: To develop a circulating blood-borne microRNA-based approach using machine learning for the differentiation of uni- and bilateral PA. Methods: Circulating microRNA profiling on an Illumina MiSeq platform was performed on total RNA samples isolated from EDTA-anticoagulated blood specimens taken by AVS from 18 pairs of right and left suprarenal veins (10 uni-, 8 bilateral PA). Significantly differentially expressed microRNAs were analyzed by a neural network (90-10% learner-tester cross-validation simulation). From the top 29 significantly differentially expressed microRNAs, 9 were selected for validation by Taqman RT-qPCR on a separate cohort of 30 AVS-confirmed samples (15-15 uni- and bilateral forms) both on plasma samples from adrenal as well as from peripheral veins. The method was finally tested on another independent cohort of peripheral blood samples from 80 patients (40 uni- and 40 bilateral PA). Results: Regarding the 9 microRNAs selected for validation, microRNA abundance was non-significantly lower in peripheral samples compared to AVS samples. By analyzing peripheral plasma samples, ten combinations of 4-8 microRNAs from the pool of 9 microRNAs turned out to have maximal sensitivity and specificity values over 85 %. The best combination included 6 microRNAs with a sensitivity of 86.3 % and a specificity of 87.91 % (AUC = 0.871). The inclusion of further parameters such as imaging, potassium and renin levels did not improve the model’s performance. Conclusion: The analysis of circulating blood-borne microRNAs represents a minimally invasive and potentially reliable way of differentiating uni- and bilateral PA. If bilateral PA is confirmed by the model, no more localization efforts would be necessary thus simplifying the management of PA patients. This would be relevant both from the patient’s and a public health perspective. Presentation: 6/3/2024

Affinity-based 3D-printed microfluidic chip for clinical sepsis detection with CD69, CD64, and CD25

Sepsis is a life-threatening immune response to infection in the body, eventually resulting in fatal organ failure. Current methods utilize blood cultures and quick-Sequential-Organ-Failure-Assessment (qSOFA), but there is a need for more accurate and time-sensitive diagnostic methods to improve survival rates. We present a 3D-printed microfluidic chip that bioconjugates antibodies CD69, CD64, and CD25 to channel surfaces to capture sepsis cells in blood samples and validate it with clinical samples (n = 125 septic, n = 10 healthy). Other variables were taken such as healthy volunteer blood samples and patient demographics to validate and confirm our device’s diagnostic ability. Statistical differences were found between healthy volunteer and sepsis patient antigen cell counts (CD69 p-value < 0.001, CD64 p-value < 0.004, CD25 p-value < 0.0009), and were confirmed using principal component analysis. Demographics such as length of stay, age, culture results, and need for surgery also factored into sepsis detection on a smaller scale than the antigen cell counts. The receiver operating characteristic (ROC) analysis showed an area under the curve (AUC) of 0.989, 0.988, and 0.992 for CD69, CD64, and CD25, respectively, and a combined biomarker panel of 0.997. Overall, the device performed within a shorter time frame of 4 h compared to standard blood culture tests and was validated for use in detecting sepsis in patients.