Biomarker Discovery In Patients Research Articles

Integrating knowledge graphs into machine learning models for survival prediction and biomarker discovery in patients with non–small-cell lung cancer

Accurate survival prediction for Non-Small Cell Lung Cancer (NSCLC) patients remains a significant challenge for the scientific and clinical community despite decades of advanced analytics. Addressing this challenge not only helps inform the critical aspects of clinical study design and biomarker discovery but also ensures that the ‘right patient’ receives the ‘right treatment’. However, survival prediction is a highly complex task, given the large number of ‘omics; and clinical features, as well as the high degree of freedom that drive patient survival. Prior knowledge could play a critical role in uncovering the complexity of a disease and understanding the driving factors affecting a patient’s survival. We introduce a methodology for incorporating prior knowledge into machine learning–based models for prediction of patient survival through Knowledge Graphs, demonstrating the advantage of such an approach for NSCLC patients. Using data from patients treated with immuno-oncologic therapies in the POPLAR (NCT01903993) and OAK (NCT02008227) clinical trials, we found that the use of knowledge graphs yielded significantly improved hazard ratios, including in the POPLAR cohort, for models based on biomarker tumor mutation burden compared with those based on knowledge graphs. Use of a model-defined mutational 10-gene signature led to significant overall survival differentiation for both trials. We provide parameterized code for incorporating knowledge graphs into survival analyses for use by the wider scientific community.

A study of serum proteome expression in patients with severe hand-foot-mouth disease.

Although numerous studies have been conducted on hand-foot-mouth disease (HFMD), the diagnosis of severe HFMD has not been fully clarified. Hence, it is important to further clarify the diagnosis of severe HFMD. In this study, we conducted a clinical biomarker discovery in patients with severe HFMD. In this study, serum samples were isolated from severe HFMD, HFMD, and healthy controls. Each group consisted of 32 cases. Isobaric tagging for relative and absolute quantitation (iTRAQ) combined with liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to detect proteome expression in the serum samples. Then, candidate proteins were screened and verified by ELISA. Protein expressions were significantly different between the HFMD group, severe HFMD group, and healthy control group. Comparison of the proteins between the three groups showed that serum amyloid A-1 protein (P0DJ18), C-reactive protein (P02741), fibronectin (P02751), plasminogen (P00747) and apolipoprotein A (P08519) were different, so they were selected as candidate proteins. However, the results of ELISA showed that the expression levels of serum amyloid A-1 protein, C-reactive protein, fibronectin, and apolipoprotein a in the severe HFMD group were significantly different from those in the other two groups (p<0.05). In conclusion, the results showed that serum amyloid A-1, C-reactive protein, fibronectin, and apolipoprotein A may be potential biomarkers for clinical diagnosis of severe HFMD.

Niemann-Pick type C disease as proof-of-concept for intelligent biomarker panel selection in neurometabolic disorders.

Using Niemann-Pick type C disease (NPC) as a paradigm, we aimed to improve biomarker discovery in patients with neurometabolic disorders. Using a multiplexed liquid chromatography tandem mass spectrometry dried bloodspot assay, we developed a selective intelligent biomarker panel to monitor known biomarkers N-palmitoyl-O-phosphocholineserine and 3β,5α,6β-trihydroxy-cholanoyl-glycine as well as compounds predicted to be affected in NPC pathology. We applied this panel to a clinically relevant paediatric patient cohort (n=75; 35 males, 40 females; mean age 7 years 6 months, range 4 days-19 years 8 months) presenting with neurodevelopmental and/or neurodegenerative pathology, similar to that observed in NPC. The panel had a far superior performance compared with individual biomarkers. Namely, NPC-related established biomarkers used individually had 91% to 97% specificity but the combined panel had 100% specificity. Moreover, multivariate analysis revealed long-chain isoforms of glucosylceramide were elevated and very specific for patients with NPC. Despite advancements in next-generation sequencing and precision medicine, neurological non-enzymatic disorders remain difficult to diagnose and lack robust biomarkers or routine functional testing for genetic variants of unknown significance. Biomarker panels may have better diagnostic accuracy than individual biomarkers in neurometabolic disorders, hence they can facilitate more prompt disease identification and implementation of emerging targeted, disease-specific therapies. Intelligent biomarker panel design can help expedite diagnosis in neurometabolic disorders. In Niemann-Pick type C disease, such a panel performed better than individual biomarkers. Biomarker panels are easy to implement and widely applicable to neurometabolic conditions.

Biomarker Discovery in Patients with Immunotherapy-Treated Melanoma with Imaging Mass Cytometry.

Imaging mass cytometry (IMC) is among the first tools with the capacity for multiplex analysis of more than 40 targets, which provides a novel approach to biomarker discovery. Here, we used IMC to characterize the tumor microenvironment of patients with metastatic melanoma who received immunotherapy in efforts to find indicative factors of treatment response. In spite of the new power of IMC, the image analysis aspects are still limited by the challenges of cell segmentation. Here, rather than segment, we performed image analysis using a newly designed version of the AQUA software to measure marker intensity in molecularly defined compartments: tumor cells, stroma, T cells, B cells, and macrophages. IMC data were compared with quantitative immunofluorescence (QIF) and digital spatial profiling. Validation of IMC results for immune markers was confirmed by regression with additional multiplexing methods and outcome assessment. Multivariable analyses by each compartment revealed significant associations of 12 markers for progression-free survival and seven markers for overall survival (OS). The most compelling indicative biomarker, beta2-microglobulin (B2M), was confirmed by correlation with OS by QIF in the discovery cohort and validated in an independent published cohort profiled by mRNA expression. Using digital image analysis based on pixel colocalization to assess IMC data allowed us to quantitively measure 25 markers simultaneously on formalin-fixed, paraffin-embedded tissue microarray samples. In addition to showing high concordance with other multiplexing technologies, we identified a series of potentially indicative biomarkers for immunotherapy in metastatic melanoma, including B2M.

P.739 High-throughput proteomics for suicide biomarker discovery in patients with bipolar disorder

The genetic basis of suicide attempts (SA) remains unclear. Especially the role of copy number variations (CNVs) remains to be elucidated. The present study aimed to identify susceptibility variants associated with SA among Chinese with major depressive disorder (MDD), covering both CNVs and single-nucleotide polymorphisms (SNPs).We conducted a genome-wide association study (GWAS) on MDD patients with and without SA and top results were tested in a replication study. A genome-wide CNV study was also performed. Subsequently, a validation assay using qRT-PCR technology was performed to confirm any associated CNVs and then applied to the entire cohort to examine the association.Although GWAS did not identify any SNPs reaching genome-wide significance, we identified TPH2 as the top susceptibility gene (p-value = 2.75e−05) in gene-based analysis, which is a strong biological candidate for its role in the serotonergic system. As for CNV analysis, we found that the global rate of CNV was higher in SA than that in non-SA subjects (p-value = 0.023). Genome-wide CNV study revealed an SA-associated CNV region that achieved genome-wide significance (corrected p-value = 0.014). The associated CNV was successfully validated with a more rigorous qRT-PCR assay and identified to be a common variant in this cohort. Its deletion rate was higher in SA subjects [OR = 2.05 (1.02–4.12), adjusted p-value = 0.045]. Based on the GTEx database, genetic variants that probed this CNV were significantly associated with the expression level of ZNF33B in two brain regions (p-value < 4.2e−05). In stratified analysis, the CNV showed a significant effect [OR = 2.58 (1.06–6.27), p-value = 0.039] in those with high neuroticism but not in those with average or low neuroticism.We identified a new common CNV likely involved in the etiology of SA. This finding sheds light on an important role of common CNVs in the pathophysiology of SA, suggesting a new promising avenue for investigating its genetic architecture.

Cerebrospinal fluid lipidomic biomarker signatures of demyelination for multiple sclerosis and Guillain\u2013Barr\xe9 syndrome

Multiple sclerosis (MS) and Guillain–Barré syndrome (GBS) are demyelinating disorders affecting the central nervous system and peripheral nervous system (PNS), respectively. Cerebrospinal fluid (CSF) is one of the most valuable sources of diagnostic biomarkers in neurological diseases. In the present study high sensitivity shotgun mass spectrometry was used to characterise the CSF lipidome of patients with MS, GBS and controls with non-demyelinating diseases. The quantification of 222 CSF lipid molecular species revealed characteristic changes in the absolute and relative lipid concentrations in MS and GBS compared to the controls. For the GBS group, the fourfold elevation in the total lipid content was a discriminatory and a newly identified feature of PNS demyelination. In contrast, in MS, the accumulation of the myelin-derived cerebrosides represented a specific feature of demyelination. As a common feature of demyelination, we identified upregulated levels of lipid metabolic intermediates. We found strong positive correlation between total protein content and lipid concentrations in both diseases. By exploring the CSF lipidome we demonstrate usefulness of broad-range shotgun lipidomic analysis as a fast and reliable method of biomarker discovery in patients with demyelinating neurological disorders that might be a valuable diagnostic complement to existing examinations.

Plasma Biomarker for Post-concussive Syndrome: A Pilot Study Using an Alternating Current Electro-Kinetic Platform

Background: Technology platforms that afford biomarker discovery in patients suffering from traumatic brain injury (TBI) remain an unmet medical need. Here, we describe an observational pilot study to explore the utility of an alternating current electrokinetic (ACE) microchip device in this context.Methods: Blood samples were collected from participating subjects with and without minor TBI. Plasma levels of glial fibrillary acidic protein (GFAP), Tau, ubiquitin C-terminal hydrolase L1 (UCH-L1), and cell-free DNA (cfDNA) were determined in subjects with and without minor TBI using ACE microchip device followed by on-chip immunofluorescent analysis. Post-concussive symptoms were assessed using the Rivermead Post Concussion Symptoms Questionnaire (RPCSQ) at one-month follow-up.Results: Highest levels of GFAP, UCH-L1, and Tau were seen in two minor TBI subjects with abnormality on head computed tomography (CT). In patients without abnormal head CT, Tau and GFAP levels discriminated between plasma from minor-TBI and non-TBI patients, with sensitivity and specificity of 64–72 and 50%, respectively. Plasma GFAP, UCH-L1, and Tau strongly correlated with the cumulative RPCSQ score. Plasma UCH-L1 and GFAP exhibited highest correlation to sensitivity to noise and light (r = 0.96 and 0.91, respectively, p < 0.001). Plasma UCH-L1 and Tau showed highest correlation with headache (r = 0.74 and 0.78, respectively, p < 0.001), sleep disturbance (r = 0.69 and 0.84, respectively, p < 0.001), and cognitive symptoms, including forgetfulness (r = 0.76 and 0.74, respectively, p < 0.001), poor concentration (r = 0.68 and 0.76, respectively, p < 0.001), and time required for information processing (r = 0.77 and 0.81, respectively, p < 0.001). cfDNA exhibited a strong correlation with depression (r = 0.79, p < 0.01) and dizziness (r = 0.69, p < 0.01). While cfDNA demonstrated positive correlation with dizziness and depression (r = 0.69 and 0.79, respectively, p < 0.001), no significant correlation was observed between cumulative RPCSQ and cfDNA (r = 0.07, p = 0.81).Conclusion: We provide proof-of-principle results supporting the utility of ACE microchip for plasma biomarker analysis in patients with minor TBI.

MicroRNA-328 in exosomes derived from M2 macrophages exerts a promotive effect on the progression of pulmonary fibrosis via FAM13A in a rat model

Currently, exosome-enclosed microRNAs (miRs) in exhaled breath have potential for biomarker discovery in patients with pulmonary diseases. This study was performed to investigate the roles of M2 macrophage-derived exosomes expressing miR-328 in pulmonary fibrosis (PF). Microarray-based analysis was used to screen differentially expressed genes (DEGs) and regulatory miRs in PF. The miR-target relationship between FAM13A and miR-328 was confirmed. The expression of FAM13A and miR-328 was measured in PF rats, and gain- and loss-of-function assays were conducted to determine the regulatory effects of FAM13A and miR-328 on PF. In addition, exosomes derived from M2 macrophages were isolated and then cocultured with pulmonary interstitial fibroblasts to identify the role of these exosomes in PF. Furthermore, the effects of M2 macrophage-derived exosomes overexpressing miR-328 on pulmonary fibroblast proliferation and the progression of PF were assessed in vivo. miR-328 might perform a vital function in PF by regulating FAM13A. FAM13A expression was downregulated while miR-328 expression was upregulated in rats with PF, and a miR-target relationship between miR-328 and FAM13A was observed. Additionally, miR-328 overexpression and FAM13A silencing each were suggested to promote pulmonary interstitial fibroblast proliferation and the expression of Collagen 1A, Collagen 3A and α-SMA. Then, in vitro experiments demonstrated that M2 macrophage-derived exosomes overexpressing miR-328 contributed to enhanced pulmonary interstitial fibroblast proliferation and promoted PF. Furthermore, in vivo experiments confirmed the promotive effects of M2 macrophage-derived exosomes overexpressing miR-328 on the progression of PF. Collectively, the results showed that M2 macrophage-derived exosomes overexpressing miR-328 aggravate PF through the regulation of FAM13A.

Exosome-enclosed microRNAs in exhaled breath hold potential for biomarker discovery in patients with pulmonary diseases

Exosome-enclosed microRNAs in exhaled breath hold potential for biomarker discovery in patients with pulmonary diseases

Investigation of autoantibody profiles for cerebrospinal fluid biomarker discovery in patients with relapsing–remitting multiple sclerosis

Using the UNIarray® marker technology platform, cerebrospinal fluid immunoglobulin G reactivities of 15 controls and 17 RRMS patients against human recombinant proteins were investigated. Patient cerebrospinal fluids were oligoclonal band positive and reactivities were compared to that of sex- and age-matched controls. We hereby aimed at the characterization of autoreactivity in patients with RRMS. Differences in autoreactivities between control and RRMS samples were identified comprising autoantigens identified in this study only and previously reported autoantigens as well. A combination of the 10–15 most significant proteins may be investigated further as autoantigens for diagnostic purposes. Additional investigations may include minimizing the number of proteins used in such diagnostic tests.

Biomarker discovery with SELDI-TOF MS in human urine associated with early renal injury: evaluation with computational analytical tools

Urine proteomics is one of the key emerging technologies to discover new biomarkers for renal disease, which may be used in the early diagnosis, prognosis and treatment of patients. In the present study, we validated surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) for biomarker discovery in patients with mild ischaemic kidney injury. We used first-morning mid-stream urine samples from healthy volunteers, and from intensive care unit patients we collected urine 12-24 h after coronary artery bypass graft (CABG) surgery. Samples of 50 volunteers were mixed to establish a reference sample (master pool). Urine samples were analysed with constant creatinine levels. The average intra- and interchip variation was found to be in the normal experimental range (CV of 10 to 30%). Computational analysis revealed (i) low intra-individual day-to-day variation in individual healthy volunteers; (ii) high concordance between the master pool sample and individual samples. Machine learning techniques for classification of CABG condition vs healthy patients showed that (iii) in the 3-20 kDa range, the joint activity of four protein peaks effectively discriminated the two classes, (iv) in the 20-70 kDa range, a single m/z marker was sufficient to achieve perfect separation. Our results substantiate the effectiveness of Seldi-TOF MS-based computational analysis as a tool for discovering potential biomarkers in urine samples associated with early renal injury.