Clinical Utility Of Biomarkers Research Articles

Challenges in IBD Research 2024: Precision Medicine.

Precision medicine is part of 5 focus areas of the Challenges in IBD Research 2024 research document, which also includes preclinical human IBD mechanisms, environmental triggers, novel technologies, and pragmatic clinical research. Building on Challenges in IBD Research 2019, the current Challenges aims to provide a comprehensive overview of current gaps in inflammatory bowel diseases (IBDs) research and deliver actionable approaches to address them with a focus on how these gaps can lead to advancements in interception, remission, and restoration for these diseases. The document is the result of multidisciplinary input from scientists, clinicians, patients, and funders, and represents a valuable resource for patient-centric research prioritization. In particular, the precision medicine section is focused on the main research gaps in elucidating how to bring the best care to the individual patient in IBD. Research gaps were identified in biomarker discovery and validation for predicting disease progression and choosing the most appropriate treatment for each patient. Other gaps were identified in making the best use of existing patient biosamples and clinical data, developing new technologies to analyze large datasets, and overcoming regulatory and payer hurdles to enable clinical use of biomarkers. To address these gaps, the Workgroup suggests focusing on thoroughly validating existing candidate biomarkers, using best-in-class data generation and analysis tools, and establishing cross-disciplinary teams to tackle regulatory hurdles as early as possible. Altogether, the precision medicine group recognizes the importance of bringing basic scientific biomarker discovery and translating it into the clinic to help improve the lives of IBD patients.

Evaluating the diagnostic accuracy of heat shock proteins and their combination with Alpha-Fetoprotein in the detection of hepatocellular carcinoma: a meta-analysis

BackgroundA growing body of research suggests that heat shock proteins (HSPs) may serve as diagnostic biomarkers for hepatocellular carcinoma (HCC), but their results are still controversial. This meta-analysis endeavors to evaluate the diagnostic accuracy of HSPs both independently and in conjunction with alpha-fetoprotein (AFP) as novel biomarkers for HCC detection.MethodsPooled statistical indices, including sensitivity, specificity, diagnostic odds ratio (DOR), positive likelihood ratio (PLR), and negative likelihood ratio (NLR) with 95% confidence intervals (CI), were computed to assess the diagnostic accuracy of HSPs, AFP, and their combinations. Additionally, the area under the summary receiver operating characteristic (SROC) curve (AUC) was determined.ResultsA total of 2013 HCC patients and 1031 control subjects from nine studies were included in this meta-analysis. The summary estimates for HSPs and AFP are as follows: sensitivity of 0.78 (95% CI: 0.69–0.85) compared to 0.73 (95% CI: 0.65–0.80); specificity of 0.89 (95% CI: 0.81–0.95) compared to 0.86 (95% CI: 0.77–0.91); PLR of 7.4 (95% CI: 3.7–14.9) compared to 5.1 (95% CI: 3.3–8.1); NLR of 0.24 (95% CI: 0.16–0.37) compared to 0.31 (95% CI: 0.24–0.41); DOR of 30.19 (95% CI: 10.68–85.37) compared to 16.34 (95% CI: 9.69–27.56); and AUC of 0.90 (95% CI: 0.87–0.92) compared to 0.85 (95% CI: 0.82–0.88). The pooled sensitivity, specificity, PLR, NLR, DOR and AUC were 0.90 (95% CI: 0.82–0.95), 0.94 (95% CI: 0.82–0.98), 14.5 (95% CI: 4.6–45.4), 0.11 (95% CI: 0.06–0.20), 133.34 (95% CI: 29.65–599.61), and 0.96 (95% CI: 0.94–0.98) for the combination of HSPs and AFP.ConclusionOur analysis suggests that HSPs have potential as a biomarker for clinical use in the diagnosis of HCC, and the concurrent utilization of HSPs and AFP shows notable diagnostic effectiveness for HCC.

Abstract 3633: Multi-parametric characterization of extracellular vesicles in the aqueous humor of uveal melanoma eyes undergoing brachytherapy

Abstract Introduction: Aqueous humor (AH), fluid in the anterior chamber of the eye, is a liquid biopsy source of tumor information in uveal melanoma (UM). Extracellular vesicles (EVs) have recently been characterized in the AH of retinoblastoma eyes, however, have not been investigated in UM. EVs are small membrane-enclosed particles secreted by virtually every cell type with a role in intercellular communications. We hypothesize that tumor-derived EVs are detectable in the AH of UM eyes and that understanding the type of vesicles present may help us better understand their function, particularly for cancer. Small extracellular vesicles (sEVs) have been shown to promote tumorigenesis, treatment resistance, and metastasis, leading to the discovery of clinically-relevant biomarkers for multiple cancer types. As the diversity and role of sEVs in the AH of UM patients has never previously been reported, this project is a first step towards investigating new biomarkers of clinical utility. Our aim is to use single particle analysis to characterize sEV subpopulations in the AH of UM patients by quantifying their size, concentration, and phenotypes based on cell surface markers, specifically tetraspanin co-expression patterns. Methods: sEVs were analyzed from paired pre- and post-brachytherapy AH samples collected from 19 UM patients. AH samples from 5 glaucoma patients served as the control cohort. Unprocessed AH underwent Nanoparticle Tracking Analysis for size quantification and Single Particle-Interferometric Reflectance Imaging Sensor analysis for fluorescent particle count and tetraspanin immunophenotyping; counts were subsequently converted to percentages for analysis. Statistics were performed using Mann-Whitney U, ANOVA, and Tukey’s tests. Results: AH derived from UM eyes (pre-brachytherapy) displayed a broader sEV size distribution than glaucoma eyes, with more diverse sEV subpopulations. Both tumor and nontumor AH samples demonstrated a dominant CD63+ sEV subpopulation. A significantly higher percentage of CD63/81+ sEVs (P= 0.0217) and CD9/63/81+ sEVs (P= 0.0146) were found in post-brachytherapy samples compared to pre-brachytherapy. Conclusions: sEV subpopulations in UM eyes demonstrate increased heterogeneity in comparison to nontumor eyes. Our results are consistent with prior studies reporting that the CD63+ sEV subtype is an AH-specific biomarker enriched across multiple ocular diseases. The increase in CD63/81+ sEVs and CD9/63/81+ sEVs after brachytherapy is presumably a result of radiation-induced release of these vesicles, suggesting a tumor-derived origin of these subpopulations. CD63/81+ sEV subpopulation was previously reported to also be a tumor-derived subpopulation in AH of retinoblastoma eyes. Further study of the cargo carried by these sEV subpopulations may uncover molecular mechanisms pivotal to UM and other ocular cancers. Citation Format: Shreya Sirivolu, Chen-Ching Peng, Paolo Neviani, Jesse L. Berry, Liya Xu. Multi-parametric characterization of extracellular vesicles in the aqueous humor of uveal melanoma eyes undergoing brachytherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3633.

Tau Protein in Biological Fluids and Its Clinical Value

The tau protein is one of the neurocytoskeletal proteins that participate in the pathogenesis of serious neurological diseases, especially Alzheimer's disease. The tau protein is characterized by considerable structural variability, which is reflected in the existence of its numerous proteoforms. This review aims to provide brief information on the structure of tau protein and its proteoforms, which seem promising biomarkers for clinical use. Bio­logical fluids, suitable for laboratory examination in clinical practice, i.e., cerebrospinal fluid and blood (plasma/serum), are discussed. Total tau protein and its phosphorylated forms (mainly the pT181-tau protein, phosphorylated at the threonine residue 181) have already found clinical application in diagnosis of Alzheimer's disease.

Unlocking the potential of oncology biomarkers: advancements in clinical theranostics.

Cancer biomarkers have revolutionized the field of oncology by providing valuable insights into tumor changes and aiding in screening, diagnosis, prognosis, treatment prediction, and risk assessment. The emergence of "omic" technologies has enabled biomarkers to become reliable and accurate predictors of outcomes during cancer treatment. In this review, we highlight the clinical utility of biomarkers in cancer identification and motivate researchers to establish a personalized/precision approach in oncology. By extending a multidisciplinary technology-based approach, biomarkers offer an alternative to traditional techniques, fulfilling the goal of cancer therapeutics to find a needle in a haystack. We target different forms of cancer to establish a dynamic role of biomarkers in understanding the spectrum of malignancies and their biochemical and molecular characterization, emphasizing their prospective contribution to cancer screening. Biomarkers offer a promising avenue for the early detection of human cancers and the exploration of novel technologies to predict disease severity, facilitating maximum survival and minimum mortality rates. This review provides a comprehensive overview of the potential of biomarkers in oncology and highlights their prospects in advancing cancer diagnosis and treatment.

Biomarkers vs Machines: The Race to Predict Acute Kidney Injury.

Acute kidney injury (AKI) is a serious complication affecting up to 15% of hospitalized patients. Early diagnosis is critical to prevent irreversible kidney damage that could otherwise lead to significant morbidity and mortality. However, AKI is a clinically silent syndrome, and current detection primarily relies on measuring a rise in serum creatinine, an imperfect marker that can be slow to react to developing AKI. Over the past decade, new innovations have emerged in the form of biomarkers and artificial intelligence tools to aid in the early diagnosis and prediction of imminent AKI. This review summarizes and critically evaluates the latest developments in AKI detection and prediction by emerging biomarkers and artificial intelligence. Main guidelines and studies discussed herein include those evaluating clinical utilitiy of alternate filtration markers such as cystatin C and structural injury markers such as neutrophil gelatinase-associated lipocalin and tissue inhibitor of metalloprotease 2 with insulin-like growth factor binding protein 7 and machine learning algorithms for the detection and prediction of AKI in adult and pediatric populations. Recommendations for clinical practices considering the adoption of these new tools are also provided. The race to detect AKI is heating up. Regulatory approval of select biomarkers for clinical use and the emergence of machine learning algorithms that can predict imminent AKI with high accuracy are all promising developments. But the race is far from being won. Future research focusing on clinical outcome studies that demonstrate the utility and validity of implementing these new tools into clinical practice is needed.

A scenario-drafting study to explore potential future implementation pathways of circulating tumor DNA testing in oncology.

Circulating tumor DNA (ctDNA) detection has multiple promising applications in oncology, but the road toward implementation in clinical practice is unclear. We aimed to support the implementation process by exploring potential future pathways of ctDNA testing. To do so, we studied four ctDNA-testing applications in two cancer types and elicited opinions from 30 ctDNA experts in the Netherlands. Our results showed that the current available evidence differed per application and cancer type. Tumor profiling and monitoring treatment response were found most likely to be implemented in non-small cell lung cancer (NSCLC) within 5 years. For colorectal cancer, applications of ctDNA testing were found to be at an early stage in the implementation process. Demonstrating clinical utility was found a key aspect for successful implementation, but there was no consensus regarding the evidence requirements. The next step toward implementation is to define how clinical utility of biomarkers should be evaluated. Finally, these data indicate that specific challenges for each clinical application and tumor type should be appropriately addressed in a deliberative process involving all stakeholders to ensure implementation of ctDNA testing and timely access for patients.

Exploring Optimal Biomarker Sources: A Comparative Analysis of Exosomes and Whole Plasma in Fasting and Non-Fasting Conditions for Liquid Biopsy Applications.

The study of liquid biopsy with plasma samples is being conducted to identify biomarkers for clinical use. Exosomes, containing nucleic acids and metabolites, have emerged as possible sources for biomarkers. To evaluate the effectiveness of exosomes over plasma, we analyzed the small non-coding RNAs (sncRNAs) and metabolites extracted from exosomes in comparison to those directly extracted from whole plasma under both fasting and non-fasting conditions. We found that sncRNA profiles were not affected by fasting in either exosome or plasma samples. Our results showed that exosomal sncRNAs were found to have more consistent profiles. The plasma miRNA profiles contained high concentrations of cell-derived miRNAs that were likely due to hemolysis. We determined that certain metabolites in whole plasma exhibited noteworthy concentration shifts in relation to fasting status, while others did not. Here, we propose that (1) fasting is not required for a liquid biopsy study that involves both sncRNA and metabolomic profiling, as long as metabolites that are not influenced by fasting status are selected, and (2) the utilization of exosomal RNAs promotes robust and consistent findings in plasma samples, mitigating the impact of batch effects derived from hemolysis. These findings advance the optimization of liquid biopsy methodologies for clinical applications.

The impact of misclassification errors on the performance of biomarkers based on next-generation sequencing, a simulation study

ABSTRACT The development of next-generation sequencing (NGS) opens opportunities for new applications such as liquid biopsy, in which tumor mutation genotypes can be determined by sequencing circulating tumor DNA after blood draws. However, with highly diluted samples like those obtained with liquid biopsy, NGS invariably introduces a certain level of misclassification, even with improved technology. Recently, there has been a high demand to use mutation genotypes as biomarkers for predicting prognosis and treatment selection. Many methods have also been proposed to build classifiers based on multiple loci with machine learning algorithms as biomarkers. How the higher misclassification rate introduced by liquid biopsy will affect the performance of these biomarkers has not been thoroughly investigated. In this paper, we report the results from a simulation study focused on the clinical utility of biomarkers when misclassification is present due to the current technological limit of NGS in the liquid biopsy setting. The simulation covers a range of performance profiles for current NGS platforms with different machine learning algorithms and uses actual patient genotypes. Our results show that, at the high end of the performance spectrum, the misclassification introduced by NGS had very little effect on the clinical utility of the biomarker. However, in more challenging applications with lower accuracy, misclassification could have a notable effect on clinical utility. The pattern of this effect can be complex, especially for machine learning-based classifiers. Our results show that simulation can be an effective tool for assessing different scenarios of misclassification.

Machine learning strategy for identifying altered gut microbiomes for diagnostic screening in myasthenia gravis.

Myasthenia gravis (MG) is a neuromuscular junction disease with a complex pathophysiology and clinical variation for which no clear biomarker has been discovered. We hypothesized that because changes in gut microbiome composition often occur in autoimmune diseases, the gut microbiome structures of patients with MG would differ from those without, and supervised machine learning (ML) analysis strategy could be trained using data from gut microbiota for diagnostic screening of MG. Genomic DNA from the stool samples of MG and those without were collected and established a sequencing library by constructing amplicon sequence variants (ASVs) and completing taxonomic classification of each representative DNA sequence. Four ML methods, namely least absolute shrinkage and selection operator, extreme gradient boosting (XGBoost), random forest, and classification and regression trees with nested leave-one-out cross-validation were trained using ASV taxon-based data and full ASV-based data to identify key ASVs in each data set. The results revealed XGBoost to have the best predicted performance. Overlapping key features extracted when XGBoost was trained using the full ASV-based and ASV taxon-based data were identified, and 31 high-importance ASVs (HIASVs) were obtained, assigned importance scores, and ranked. The most significant difference observed was in the abundance of bacteria in the Lachnospiraceae and Ruminococcaceae families. The 31 HIASVs were used to train the XGBoost algorithm to differentiate individuals with and without MG. The model had high diagnostic classification power and could accurately predict and identify patients with MG. In addition, the abundance of Lachnospiraceae was associated with limb weakness severity. In this study, we discovered that the composition of gut microbiomes differed between MG and non-MG subjects. In addition, the proposed XGBoost model trained using 31 HIASVs had the most favorable performance with respect to analyzing gut microbiomes. These HIASVs selected by the ML model may serve as biomarkers for clinical use and mechanistic study in the future. Our proposed ML model can identify several taxonomic markers and effectively discriminate patients with MG from those without with a high accuracy, the ML strategy can be applied as a benchmark to conduct noninvasive screening of MG.

MiR-124 as a Liquid Biopsy Prognostic Biomarker in Small Extracellular Vesicles from NSCLC Patients.

Despite advances in non-small cell lung cancer (NSCLC) research, this is still the most common cancer type that has been diagnosed up to date. microRNAs have emerged as useful clinical biomarkers in both tissue and liquid biopsy. However, there are no reliable predictive biomarkers for clinical use. We evaluated the preclinical use of seven candidate miRNAs previously identified by our group. We collected a total of 120 prospective samples from 88 NSCLC patients. miRNA levels were analyzed via qRT-PCR from tissue and blood samples. miR-124 gene target prediction was performed using RNA sequencing data from our group and interrogating data from 2952 NSCLC patients from two public databases. We found higher levels of all seven miRNAs in tissue compared to plasma samples, except for miR-124. Our findings indicate that levels of miR-124, both free-circulating and within exosomes, are increased throughout the progression of the disease, suggesting its potential as a marker of disease progression in both advanced and early stages. Our bioinformatics approach identified KPNA4 and SPOCK1 as potential miR-124 targets in NSCLC. miR-124 levels can be used to identify early-stage NSCLC patients at higher risk of relapse.

C-reactive protein is a prognostic biomarker in pancreatic ductal adenocarcinoma patients.

The 5-year survival rate of pancreatic ductal adenocarcinoma (PDAC) is approximately 11% and has only improved marginally over the last three decades. For operable PDAC, resection and adjuvant FOLFIRINOX chemotherapy is standard of care. There is growing interest in perioperative regimens to improve outcomes. The non-randomized Phase II study "Gemcitabine and Abraxane for resectable Pancreatic cancer" (GAP) demonstrated the feasibility of perioperative gemcitabine/abraxane. Long-term survival in PDAC requires an effective immune response; hence, we undertook this translational study of the GAP trial cohort to identify immune-oncology biomarkers for clinical use. We combined Nanostring nCounter technology with immunohistochemistry to investigate the correlation between gene expression and overall patient survival. Findings were investigated in samples from the International Cancer Genome Consortium (ICGC, n=88) and the Australian Pancreatic Genome Initiative (APGI, n=227). We confirmed that human equilibrative nucleoside transporter 1 (hENT1) expression was not a prognostic marker in PDAC but patients with high levels of hENT1 were more likely to live longer than 24 months post-surgery. Additionally, CD274 (PD-L1) and two novel biomarkers of survival, cathepsin W (CTSW) and C-reactive protein (CRP), were identified in the GAP cohort (n=19). CRP expression was confirmed in data from the ICGC. Although PD-L1 and CTSW proteins were not significant across all three cohorts, results show that low CRP mRNA and protein expression are associated with longer overall survival in all three patient groups. PDAC patients with long survival have higher hENT1 expression levels. Furthermore, CRP expression is a biomarker of poor prognosis following perioperative chemotherapy and resection in PDAC patients and thus may be useful for identifying patients who could benefit from more aggressive adjuvant strategies.

Head-to-head comparison of plasma and PET imaging ATN markers in subjects with cognitive complaints

BackgroundGaining more information about the reciprocal associations between different biomarkers within the ATN (Amyloid/Tau/Neurodegeneration) framework across the Alzheimer’s disease (AD) spectrum is clinically relevant. We aimed to conduct a comprehensive head-to-head comparison of plasma and positron emission tomography (PET) ATN biomarkers in subjects with cognitive complaints.MethodsA hospital-based cohort of subjects with cognitive complaints with a concurrent blood draw and ATN PET imaging (18F-florbetapir for A, 18F-Florzolotau for T, and 18F-fluorodeoxyglucose [18F-FDG] for N) was enrolled (n = 137). The β-amyloid (Aβ) status (positive versus negative) and the severity of cognitive impairment served as the main outcome measures for assessing biomarker performances.ResultsPlasma phosphorylated tau 181 (p-tau181) level was found to be associated with PET imaging of ATN biomarkers in the entire cohort. Plasma p-tau181 level and PET standardized uptake value ratios of AT biomarkers showed a similarly excellent diagnostic performance for distinguishing between Aβ+ and Aβ− subjects. An increased tau burden and glucose hypometabolism were significantly associated with the severity of cognitive impairment in Aβ+ subjects. Additionally, glucose hypometabolism – along with elevated plasma neurofilament light chain level – was related to more severe cognitive impairment in Aβ− subjects.ConclusionPlasma p-tau181, as well as 18F-florbetapir and 18F-Florzolotau PET imaging can be considered as interchangeable biomarkers in the assessment of Aβ status in symptomatic stages of AD. 18F-Florzolotau and 18F-FDG PET imaging could serve as biomarkers for the severity of cognitive impairment. Our findings have implications for establishing a roadmap to identifying the most suitable ATN biomarkers for clinical use.

DNA methyltransferase inhibitor exposure-response: Challenges and opportunities.

Although DNA methyltransferase inhibitors (DNMTis), such as azacitidine and decitabine, are used extensively in the treatment of myelodysplastic syndromes and acute myeloid leukemia, there remain unanswered questions about DNMTi's mechanism of action and predictors of clinical response. Because patients often remain on single-agent DNMTis or DNMTi-containing regimens for several months before knowing whether clinical benefit can be achieved, the development and clinical validation of response-predictive biomarkers represents an important unmet need in oncology. In this review, we will summarize the clinical studies that led to the approval of azacitidine and decitabine, as well as the real-world experience with these drugs. We will then focus on biomarker development for DNMTis-specifically, efforts at determining exposure-response relationships and challenges that remain impacting the broader clinical translation of these methods. We will highlight recent progress in liquid-chromatography tandem mass spectrometry technology that has allowed for the simultaneous measurement of decitabine genomic incorporation and global DNA methylation, which has significant potential as a mechanism-of-action based biomarker in patients on DNMTis. Last, we will cover important research questions that need to be addressed in order to optimize this potential biomarker for clinical use.

Brain functional connectivity based on phase lag index of electroencephalography for automated diagnosis of schizophrenia using residual neural networks.

The complexity of symptoms of schizophrenia (SZ) complicate traditional and effective diagnoses based on clinical signs. Moreover, clinical diagnosis of SZ is manual, time-consuming, and error-prone. Thus, there is a requirement to develop automated systems for timely and accurate diagnosis of SZ. This paper proposes an automated SZ diagnosis pipeline based on residual neural networks (ResNet). To exploit the superior image processing capabilities of the ResNet models, multi-channel electroencephalogram (EEG) signals were converted into functional connectivity representations (FCRs). The functional connectivity of multiple regions in the cerebral cortex is critical for a better understanding of the mechanisms of SZ. In creating the FCR input images, the phase lag index (PLI) was calculated based on 16-channel EEG signals from 45 SZ patients and 39 healthy control (HC) subjects to reduce and avoid the volume conduction effect. The experimental results showed that satisfactory classification performance (accuracy=96.02%, specificity=94.85%, sensitivity=97.03%, precision=95.70%, and F1-score=96.33%) was achieved by combining FCR inputs of beta oscillatory and the ResNet-50 model. The statistical analyses also confirmed that there is a significant difference between SZ patients and HC subjects (p<0.001, one-way ANOVA). More specifically, the average connectivity strengths between nodes in the parietal cortex and those in the central, occipital, and temporal regions were significantly reduced in SZ patients compared to HC subjects. Overall results demonstrated that this paper not only provided an automated diagnostic model whose classification performance is superior to most previous studies but also valuable biomarkers for clinical use.

Transferable structural, static and dynamic functional MRI features for predicting conversion from mild cognitive impairment to Alzheimer’s disease in Shanghai Memory Study and ADNI cohorts

AbstractBackgroundMild cognitive impairment (MCI) is a critical prodromal stage of Alzheimer’s disease (AD) with high risk of conversion. It is of great challenge to construct reliable biomarkers for predicting conversion from MCI to AD, while the underlying mechanism is still not fully explored. Inter‐dataset generalizability is a prerequisite for clinical use of biomarkers and always a shortage of neuroimaging‐based studies.MethodIn this study, we propose a novel framework by integrating structural MRI (sMRI) and both static and dynamic resting‐state functional MRI (fMRI) measurements to investigate the differences between MCI converters (MCI_C) and non‐converters (MCI_NC), and then utilized support vector machine (SVM) to construct the prediction models based on selected features. A total of 186 MCI patients with both MRI and three‐year outcome data were selected from two independent cohorts: Shanghai Memory Study (SMS) cohort for selection of MRI predictors and internal cross‐validation, and ADNI cohort for external validation on the generalizability of these MRI predictors.ResultIn comparison with MCI_NC, the MRI converters were mainly characterized by alterations of medial temporal lobe (MTL) with atrophy extending to lateral temporal and regional hyperactivity and instability, posterior parietal cortex (PPC) with atrophy and inter‐regional hypo‐connectivity and connectional instability, and occipital cortex with functional instability. All of the imaging‐based prediction models achieved an AUC above 0.7 and ACC above 70% in both SMS and ADNI cohorts. The combination of static and dynamic fMRI features resulted in overall good performance as relative to static or dynamic fMRI solely, supporting the contribution of dynamic features into the fMRI model. In both cohorts, the best imaging model was the multi‐modality MRI model which provided excellent performance with AUC above 0.85 and average ACC/sensitivity/specificity around 80%.ConclusionThis inter‐cohort validation study provides a new insight into the mechanisms of MCI conversion and paves a way for eventual clinical use of MRI biomarkers.

Normalization methods for RT‐qPCR assessment of circulating microRNAs in Alzheimer’s and other aging‐related diseases

AbstractBackgroundCirculating miRNAs in blood plasma present significant potential for use as minimally‐invasive biomarkers for diagnosis of aging‐related diseases, such as cancer or Alzheimer’s disease (AD). The most sensitive and inexpensive analysis method of circulating miRNA is RT‐qPCR. However, lack of RT‐qPCR standardisation is one of the major causes of data inconsistency across different studies and it hinders progress towards widespread implementation of miRNA biomarkers in clinical use. Normalisation of RT‐qPCR data to stably expressed miRNAs is a key step in data analysis. Current methods of identifying optimal normalisers are lacking in their evaluation of the stability of normalisers and their combinations in aging populations. To address these needs, we created a novel, transparent, method for selecting optimal normalisers in aging populations, using the Python programming language (v. 3.7.3).MethodOur method uses the Python packages: pandas (v. 1.2.3), numpy (v. 1.17.1), scipy (v. 1.3.1). The method uses three measures to evaluate the miRNA Cq values for potential normalisers: Kolmogorov‐Smirnov score (KS‐score), average displacement from a mean of zero and mean standard deviation. In each case, the comparators are two distinct biological groups of interest (such as AD and cognitively normal controls). Before comparison, the group of potential normalisers were normalised according to the log2(2−ΔΔCq) method. The miRNA data were collected from Polish subjects including 44 AD patients and 70 similar‐aged, cognitively normal, controls). Circulating miRNA molecules were isolated from plasma, followed by RT‐qPCR with a selection of 7 miRNAs tested as potential normalisers, along with suitable controls for mRNA isolation and reverse transcription.ResultWe provide a novel method for identification of optimal normalisers with the advantages of: assessments of a greater number of combined potential normalisers (7 in this study), transparency of decision making and the ability for end users to weight that decision making according to their judgment of relative importance.ConclusionWe recommend assessment of plasma miRNA levels in an aging population employing a novel set of normalisers.Work supported by the Polish National Science Centre grant OPUS 2018/29/B/NZ7/02757and by the EU Horizon2020 FETOPEN grant no 737390 (ArrestAD).

Biomarkers for clinical use in psychiatry: where are we and will we ever get there?

Biomarkers for clinical use in psychiatry: where are we and will we ever get there?

Abstract 2209: A proteomic-based prognostic signature of pancreatic adenocarcinoma

Abstract Introduction: Pancreatic cancer has a very poor prognosis, with no established prognostic biomarkers in clinical use. This project aims to identify a prognostic proteomic-based signature for pancreatic adenocarcinomas. Methods: Fresh frozen tumors and matched normal samples from 125 patients were prepared for proteomic analyses using data-independent acquisition mass spectrometry (DIA-MS). Differential expression analyses were conducted on the normalized protein matrix to identify the top differentially expressed proteins (DEP) within the tumor samples. DEP were subjected to crosstalk and pathway enrichment analysis (PEA). Survival analysis based on initial univariate and subsequent 100 runs of multivariate Cox regression with Least Absolute Shrinkage and Selection Operator (LASSO) was performed to obtain a reduced list of candidate proteins associated with Overall Survival (OS). The proteins that appeared in greater than 95% of the LASSO runs were then used in a multivariate Cox model with recursive feature selection, which yielded the final 29 proteins. A risk score was built from the final 29 proteins. Consensus clustering was performed on the median absolute deviation-based top 20% highly variable proteins in tumor samples to identify proteomic-based subtypes. Results: Proteomic analyses revealed 5614 proteins identified from 599 sample runs. Differential expression analyses revealed 398 DEP in tumor samples (FDR-adjusted p-value &amp;lt;0.05, and |logFC|&amp;gt;1). PEA showed that these proteins were related to focal adhesion, extracellular matrix interaction (ECM), angiogenesis, and PI3K signaling pathways. A total of 803 proteins were significantly associated with OS in a univariate Cox regression analysis (p&amp;lt;0.05). PEA on the top 200 proteins associated with poorer OS revealed pathways related to focal adhesion, PI3K signaling, ECM and hypoxia-induced factor-1. Using LASSO multivariate Cox regression modeling, a 29-protein signature was identified, from which a risk score was calculated that dichotomized patients into high- and low-risk groups in terms of OS (Hazard ratio (HR) 2.8, 95% Confidence Interval (CI) [2.3, 3.3], concordance index of 0.91). This risk score was also prognostic for recurrence and three-year survival (both p&amp;lt;0.0001). A multivariate Cox regression model adjusted for other clinical variables revealed a significant association of the risk score with OS (HR 2.91, 95% CI [2.4, 3.5], p&amp;lt;0.001) while maintaining the concordance index (0.907). Consensus clustering analyses revealed four proteomic-based clusters, with cluster 3 showing the worst OS (p&amp;lt;0.001), independent of other clinical variables. PEA on the DEP within cluster 3 showed upregulation of proteins related to cell adhesion, angiogenesis, and immune-related pathways. Conclusion: A 29-protein signature identified a sub-group of patients with pancreatic adenocarcinoma with a poorer prognosis independent of clinical variables. Citation Format: Adel T. Aref, AKM Azad, Asim Anees, Mohashin Pathan, Jason Grealey, Daniela-Lee Smith, Erin M. Humphries, Daniel Bucio-Noble, Jennifer M. Koh, Erin Sykes, Steven G. Williams, Ruth Lyons, Natasha Lucas, Dylan Xavier, Sumit Sahni, Anubhav Mittal, Jaswinder S. Samra, John V. Pearson, Nicola Waddell, Peter G. Hains, Phil J. Robinson, Qing Zhong, Roger R. Reddel, Anthony J. Gill. A proteomic-based prognostic signature of pancreatic adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2209.

Profiling of circRNA expressions in radiation-treated head and neck cancer cells and the potential role of circPVT1

ObjectiveRadiotherapy is an indispensable treatment modality for head and neck cancers (HNCs). Due to their stable structure, circular RNAs (circRNA) have been implicated as potential biomarkers for clinical use in cancers. The purpose of this study was profiling the circRNA in radiation-treated head and neck cancer cells to identify potential differentially expressed circRNAs. DesignThe effects of radiation on the expression level of circRNAs were investigated in HNCs cells, compared to healthy cell lines. To predict the potential role of circRNAs in HNC patients, tissue expression levels, survival analyses of circRNAs, and circRNA-miRNA network were evaluated using TCGA/CPTAC datasets. Based on expression level in irradiated cells, circPVT1 (plasmacytoma variant translocation 1) was further investigated by sequence analysis. ResultsThe study revealed the characterization of differentially expressed circRNAs in cancer cells and that irradiation made significant changes in the expression of circRNAs. These findings suggest that certain circRNAs, especially circPVT1, may be potential biomarkers to monitor radiotherapy effects in patients with HNCs. ConclusionsCircRNAs may be promising molecules for improving and understanding radiotherapy efficacy in HNCs.