Lung cancer is the second leading cause of death in the United States. Lung cancer is often diagnosed in its late stage leading to a poor prognosis. Lung nodules are often described as indeterminate from CT scans resulting in lung biopsies that are invasive and may lead to complications. The need for noninvasive methods to assess malignancy risk in lung nodules is great. The lung nodule risk reclassifier assay consists of 7 protein biomarkers: Carcinoembryonic Antigen (CEA), C-X-C Motif Chemokine Ligand 10 (CXCL10), Epidermal Growth Factor Receptor (EGFR), Neutrophil Activating Protein-2 (NAP2), Pro-surfactant Protein B (ProSB), Receptor for Advanced Glycation Endproducts (RAGE), and Tissue Inhibitor of Metalloproteinase Inhibitor 1 (TIMP1) and 6 clinical factors (subject age, smoking pack years, and sex, and lung nodule size, location, and spiculated appearance). The protein biomarker assays comprise a multiplex immunoassay panel printed on giant magnetoresistance (GMR) sensor chips as components of a printed circuit board (PCB) run on the MagArray MR-813 instrument system. The analytical validation consisted of imprecision, accuracy, linearity, limits of blank, and limits of detection studies for each biomarker. Several reagents, as well as PCBs, were used in these studies. The entire validation study also assessed multiple users. This laboratory-developed test (LDT), using the MagArray platform, meets the manufacturer's specifications for imprecision, analytical sensitivity, linearity, and recovery. Common biological interferents are known to interfere with the detection of each biomarker. The lung nodule risk reclassifier assay performed as required to be offered as an LDT in the MagArray CLIA-certified laboratory.

AbstractBackgroundProtein biomarker profiling in biological fluids will soon become an established procedure in clinical diagnosis of neurodegenerative disease subtypes. Observed differences between technology platforms in performances can be attributed to technology principles and their development status, selection and characterization of antibody combinations, reproducibility of test results, and pre‐analytical factors and processing of samples. Combination of advantages of emerging technologies, together with availability of precision‐qualified assay formats and innovative sample processing procedures, will create additional possibilities for individual patient management using clinically validated protein biomarker panels.MethodThe Giant Magneto Resistance (GMR) technology platform was used to develop multiplex immunoassays for different biomarker proteoforms in blood (e.g., Aß, (phospho)Tau, NF‐L, and GFAP).ResultWe have developed and optimized performance of multiplex assays using a Design of Experiment (DOE) approach to generate reproducible results. The selectivity and specificity of each antibody combination was confirmed with multiplex assay designs. We have used clinical samples instead of calibrators in buffer to shorten the development time. Process optimization through DOE was done with biotinylated peptides. After optimization, reproducibility was improved by approximately 4‐fold to < 5% CV for each analyte. We have included 3D6 as a capture antibody on the printed circuit boards (PCBs), while analyzing Aß1‐42 & Aß1‐40 using 21F12 and 2G3 as capture antibodies, respectively, to identify the presence of Aß oligomers. PCB selection, printing conditions including environmental factors, buffer selections, and conjugation process were optimized in a stepwise manner while frequently checking biomarker levels in clinical samples. Sample processing is part of our roadmap to generate clinically valuable results. The diagnostic potential for each assay format to identify amyloidopathy in an early phase of the disease will be presented using blood samples from cognitively normal, MCI, and AD subjects, characterized by CSF biomarker profiles and/or amyloid PET status.ConclusionThe GMR technology allows screening of performance of antibodies in multiplex formats, shortening the assay development time. The biomarker panels and novel methods for analysis are uniquely positioned to help the healthcare community. This study was funded by Alzheimer’s Drug Discovery Foundation's Diagnostics Accelerator (DxA) Program.

Deficiencies in risk assessment for patients with pulmonary nodules (PNs) contribute to unnecessary invasive testing and delays in diagnosis. What is the accuracy of a novel PN risk model that includes plasma proteins and clinical factors? How does the accuracy compare with that of an established risk model? Based on technology using magnetic nanosensors, assays were developed with seven plasma proteins. In a training cohort (n= 429), machine learning approaches were used to identify an optimal algorithm that subsequently was evaluated in a validation cohort (n= 489), and its performance was compared with the Mayo Clinic model. In the training set, we identified a support vector machine algorithm that included the seven plasma proteins and six clinical factors that demonstrated an area under the receiver operating characteristic curve of 0.87 and met other selection criteria. The resulting risk reclassification model (RRM) was used to recategorize patients with a pretest risk of between 10%and 84%, and its performance was assessed across five risk strata (low,≤ 10%; moderate, 10%-34%; intermediate, 35%-70%; high, 71%-84%; very high, > 85%). Stratification by the RRM decreased the proportion of intermediate-risk patients from 26.7%to 10.8%(P< .001) and increased the low-risk and high-risk strata from 16.8%to 21.9%(P< .001) and from 3.7%to 12.1%(P< .001), respectively. Among patients classified as low risk by the RRM and Mayo Clinic model, the corresponding true-negative to false-negative ratios were 16.8 and 19.5, respectively. Among patients classified as very high risk by the RRM and Mayo Clinic model, the corresponding true-positive to false-positive ratios were 28.5 and 17.0, respectively. Compared with the Mayo Clinic model, the RRM provided higher specificity at the low-risk threshold and higher sensitivity at the very high-risk threshold. The RRM accurately reclassified some patients into low-risk and very high-risk categories, suggesting the potential to improve PN risk assessment.

&amp;lt;p&amp;gt;Active and past subduction systems influence the interpretation and understanding of current tectonics and velocity structures of the upper mantle of the Alps and Apennines. Computational advances over the years made it possible to identify remnant and active slabs up to great depths. SKS splitting measurements revealed mostly clockwise rotation in the Alpine region and mostly splitting parameters parallel to the Apennines (with new measurements in Central Italy). More than 700 stations were used in this study to calculate splitting intensities and with those similar but more stable fast polarization directions were recovered compared to SKS measurements. Splitting intensity measurements support a possible mantle material flowing through a tear in the Central Apennines. In the Po Plain region as well as east of the Apennine mountains anisotropy seems to be weaker. Moreover the complexity of layered anisotropy, upper mantle flow through possible slab detachments, and subduction related anisotropy with a dipping axis of symmetry are difficult to recover. Due to directional dependency of splitting intensity measurements, they can be used in tomographic inversions to get depth dependent horizontal anisotropy. So far we are able to recover the most prominent splitting patterns and see some changes with depth, especially for anisotropic strength. In this study we intend to use our results to improve tomographic images of the upper mantle by mapping and comparing existing and new anisotropy measurements (e.g., SKS, Pn anisotropy, azimuthal anisotropy from surface waves tomography, and splitting intensities).&amp;lt;/p&amp;gt;

&amp;lt;p&amp;gt;Transversely isotropic lower crust of the Bohemian Massif (BM) has been revealed by an ambient noise tomography (ANT) of the BM (Kvapil et al., Solid Earth 2021). The significant feature of this 3D v&amp;lt;sub&amp;gt;SV&amp;lt;/sub&amp;gt; model is the low velocity layer in the lower part of the crust at depth between 18-30 km and the Moho. The upper interface is characterized by a velocity drop in the 1D velocity models retrieved by the ANT. The interface is interrupted around boundaries of major tectonic units of the BM. The lower interface (Moho) exhibits a sharp velocity increase at 26-40km depths through the massif.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this work we test whether we are able to detect azimuthal anisotropy in the lower crust, approximated up to now by anisotropic VTI model. We use Rayleigh wave dispersion curves evaluated from station pairs sampling the BM in the period range sensitive to the lower crust. First, we analyze seasonal variations of noise sources and their effect on quality and repeatability of dispersion curve measurements. Then we remove the effect of local heterogeneities by subtraction of synthetic dispersion curves calculated for the 3D v&amp;lt;sub&amp;gt;SV &amp;lt;/sub&amp;gt;model along each station-pair raypath. Retrieved variations of azimuthal anisotropy are period-dependent with the fast velocity directions around NE-SW. We interpret the lower crust anisotropy layer as an imprint of the Variscan orogenic processes such as the NW-SE shortening of the crust and the late-Variscan strike-slip movements along boundaries of the crustal unit recorded in the interruptions of velocity drop interface in zones where anisotropic fabric of the lower crust was modified or erased.&amp;lt;/p&amp;gt;

AbstractThe ability to characterize the binding kinetics of drug-target interactions in a biologically relevant matrix, such as serum or plasma, remains a fundamental challenge in drug discovery. We apply a novel label-based giant magnetoresistance (GMR) biosensor platform to measure protein binding kinetics and affinities of drug-target pairs in buffer and different levels of serum. Specifically, we evaluate three well-established immune checkpoint inhibitors, pembrolizumab, nivolumab and atezolizumab and compare the results with label-free kinetic platforms: surface plasmon resonance (SPR) and bio-layer interferometry (BLI). Labeling of analytes does not affect their association and dissociation rates (on and off rates) from GMR biosensors which enables kinetic measurements in biologically relevant matrices. Only the GMR biossensors is consistently suitable for measuring binding kinetics in up to 80% serum. The faster and different off-rates of the three immune checkpoint inhibitors in the presence of serum should be considered when modeling their pharmacological performance.TeaserWe reveal the effects of serum on binding kinetics of antibody drugs, relevant to the pharmacological performance of immunotherapeutic.

We demonstrated that effects of serum matrix on molecular interactions between drugs and target proteins can be investigated in real time using magnetic bio-sensing techniques. A giant magneto-resistive (GMR) sensor was used on which target proteins were fixed and superparamagnetic nanoparticles (diameter: 50 nm) conjugated with drug were used in phosphate buffer, with and without serum. In this study, the following drug-protein pairs were investigated: quercetin and cAMP-dependent protein kinase A (PKA), Infliximab and tumor necrosis factor alpha (TNFα), and Bevacizumab and vascular endothelial growth factor (VEGF). For the quercetin and PKA pair, the time profile of the signal from the GMR sensor due to binding between quercetin and PKA clearly changed before and after the addition of serum. Moreover, it was revealed that not only the association process, but also the dissociation process was influenced by the addition of serum, suggesting that the quercetin and PKA complex may partially contain serum proteins, which affect the formation and stability of the complex. For antibody drugs, little effects of serum matrix were observed on both the association and dissociation processes. These clear differences may be attributed to the hydrophobic and electrostatic character of the drug molecule, target protein, and serum proteins. The real-time monitoring of molecular interactions in a biological matrix enabled by the GMR bio-sensing technique is a powerful tool to investigate such complicated molecular interactions. Understanding the molecular interactions that occur in a biological matrix is indispensable for determining the mechanism of action of the drugs and pharmacokinetics/pharmacodynamics inside the body. Additionally, this method can be applied for the analysis of the influence of any kind of third molecule that may have some interaction between two molecules, for example, an inhibitor drug against the interaction between two kinds of proteins.

To reduce overdiagnosis and overtreatment of non-cancerous pulmonary nodules found on CT scans, a noninvasive and easily administered test is needed to assess clinically significant disease risk. Such an assay should also accurately inform whether additional aggressive evaluation, including lung biopsy or thoracic surgery, is warranted. Objective: To determine the performance of a novel, plasma-based multiplexed protein test model when compared to the Veterans Affairs Clinical Factors Model (VA model) for discriminating between a lung cancer diagnosis established pathologically and an Indeterminate Pulmonary Nodule (IPN) found to be clinically and radiographically stable for at least one year. The protein biomarker-based risk model had been trained and tested with a cohort of 277 subjects at high risk of lung cancer, aged 25-85, who were current smokers with an indeterminate lung nodule 4-30mm in diameter (121 subject training set; 59 subject test set) from eight medical centers across the US. Using retrospective plasma samples, we compared the protein biomarker model results with the malignant or benign outcomes in an independent validation cohort comprised of 97 subjects from the Vanderbilt University medical center. Among the 97 validation study subjects (average age 60.1 years, range 42-83; average nodule size 16.1mm), the protein biomarker model correctly identified as benign or malignant an additional 44 of the 68 (65%) indeterminate pulmonary nodules classified as having intermediate risk by the VA model. Negative predictive value was 0.94. Only three patients with malignant disease were missed (94% sensitivity) while an additional 28 intermediate risk samples (41%) were properly classified as true positive, thus potentially avoiding aggressive interventions in those subjects with benign disease. This study evaluated a novel plasma protein biomarker assay model as a noninvasive risk assessment aid for characterizing indeterminate pulmonary nodules. When the model results are combined with the VA model, risk stratification for benign nodules is improved compared to current methods in clinical practice. We hypothesize patients with benign disease may benefit the most from this assay by avoiding unnecessary lung biopsy and subsequent overtreatment, while improving patient quality of care and reducing risks from these procedures. Providers and their patients in whom they suspect lung cancer may consider using this novel assay prior to proceeding with more aggressive interventions.

Background:The increase in lung cancer screening is intensifying the need for a noninvasive test to characterize the many indeterminate pulmonary nodules (IPN) discovered. Correctly identifying non-cancerous nodules is needed to reduce overdiagnosis and overtreatment. Alternatively, early identification of malignant nodules may represent a potentially curable form of lung cancer.Objective:To develop and validate a plasma-based multiplexed protein assay for classifying IPN by discriminating between those with a lung cancer diagnosis established pathologically and those found to be clinically and radiographically stable for at least one year.Methods:Using a novel technology, we developed assays for plasma proteins associated with lung cancer into a panel for characterizing the risk that an IPN found on chest imaging is malignant. The assay panel was evaluated with a cohort of 277 samples, all from current smokers with an IPN 4–30 mm. Subjects were divided into training and test sets to identify a Support Vector Machine (SVM) model for risk classification containing those proteins and clinical factors that added discriminatory information to the Veteran’s Affairs (VA) Clinical Factors Model. The algorithm was then evaluated in an independent validation cohort.Results:Among the 97 validation study subjects, 68 were grouped as having intermediate risk by the VA model of which the SVM model correctly identified 44 (65%) of these intermediate-risk samples as low (n=16) or high risk (n=28). The SVM model negative predictive value (NPV) was 94% and its sensitivity was 94%.Conclusion:The performance of the novel plasma protein biomarker assay supports its use as a noninvasive risk assessment aid for characterizing IPN. The high NPV of the SVM model suggests its application as a rule-out test to increase the confidence of providers to avoid aggressive interventions for their patients for whom the VA model result is an inconclusive, intermediate risk.