Biomarker Panel
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Oncology ◽  
2021 ◽  
pp. 1-11
Axel Wein ◽  
Robert Stoehr ◽  
Stephan Kersting ◽  
Jürgen Siebler ◽  
Susanne Merkel ◽  

<b><i>Objective:</i></b> The study aimed to prospectively evaluate a new molecular biomarker panel (<i>KRAS</i>, <i>NRAS</i>, <i>BRAF</i>, <i>PIK3CA</i>, and <i>ERBB2</i>) for palliative first-line treatment of colorectal cancer (CRC), including a multidisciplinary treatment approach. The rate of secondary metastasis resections was assessed. <b><i>Patients and Methods:</i></b> A total of 40 patients with definitively nonresectable metastatic CRC were enrolled from 10 centers before the interim analysis (June 2019) of the IVOPAK II trial (Interdisciplinary Care with Quality Control in Palliative Treatment of Colorectal Cancer). After determination of 5 molecular biomarkers in the tumor (<i>KRAS</i>, exons 2–4; <i>NRAS</i>, exons 2–4; <i>BRAF</i> V600E; <i>PIK3CA</i>; and <i>ERBB2</i>), patients in the IVOPAK II study received FOLFIRI plus cetuximab for all-<i>RAS</i>/quintuple-wildtype disease and FOLFIRI plus bevacizumab in the case of <i>RAS</i> mutations. The current article presents the early description of the clinical outcome of the interim analysis of IVOPAK II comparing the all-<i>RAS</i>/quintuple-wildtype and <i>RAS</i>-mutations populations, including a multidisciplinary-treated case report of a quintuple-wildtype patient. <b><i>Results:</i></b> The quintuple-wildtype population treated with FOLFIRI plus cetuximab in first-line exhibited a significantly higher response rate and enhanced early tumor shrinkage in the interim analysis than the <i>RAS</i>-mutations population, as well as a high rate of secondary metastatic resections. <b><i>Conclusion:</i></b> Initial results of this new biomarker panel (quintuple-wildtype) are promising for anti-EGFR therapy with cetuximab plus doublet chemotherapy (FOLFIRI) in first-line treatment of metastatic CRC. These results warrant confirmation with higher case numbers in the IVOPAK II trial.

2021 ◽  
Vol 11 ◽  
Shreya Mehta ◽  
Nazim Bhimani ◽  
Anthony J. Gill ◽  
Jaswinder S. Samra ◽  
Sumit Sahni ◽  

2021 ◽  
Vol 42 (Supplement_1) ◽  
J T Neumann ◽  
N A Sorenson ◽  
C P McCarthy ◽  
C A Magaret ◽  
R F Rhyne ◽  

Abstract Background Undetected obstructive coronary artery disease (oCAD) is a global health problem associated with significant morbidity and mortality. A need exists for an accurate and easily accessible diagnostic test for oCAD. Using machine learning, a multi-biomarker blood diagnostic test for oCAD based on high-sensitivity cardiac troponin-I (hs-cTnI) has been developed. Purpose To validate the performance of a previously developed, algorithmically weighted, multiple protein diagnostic panel to diagnose oCAD in a pooled multi-national cohort and to compare the diagnostic panel's performance to predict oCAD to hs-cTnI alone. Methods Three clinical factors (sex, age, and previous coronary percutaneous intervention) and three biomarkers (hs-cTnI, Adiponectin, and Kidney Injury Molecule-1) were combined. hs-cTnI blood samples were assayed on the Siemens Atellica and Abbott Diagnostics ARCHITECT immunoassay platforms. Adiponectin and Kidney Injury Molecule-1 were measured with a multiplex assay on blood samples via the Luminex 100/200 xMAP platform. Individual data from a total of 924 patients with a mixture of acute and lesser acute presentations from three centers were pooled (Table 1). oCAD was defined as &gt;50% coronary obstruction in at least one coronary artery (for the University Hospital Hamburg-Eppendorf cohort) or &gt;70% coronary obstruction in at least one coronary artery (for the other two cohorts). The multiple biomarker diagnostic panel's performance to predict oCAD was also compared to hs-cTnI alone. Results The multiple protein panel had an area under the receiver-operating characteristic curve of 0.80 (95% CI, 0.77, 0.83, p&lt;0.001) for the presence of oCAD (Figure 1). At optimal cutoff, the score had 74% sensitivity, 72% specificity, and a positive predictive value of 81% for oCAD. The multiple biomarker panel had a diagnostic odds ratio of 7.48 (95% CI 5.55, 10.09, p&lt;0.001). In comparison, in patients without an acute MI, hs-cTnI alone had an area under the receiver-operating characteristic curve of 0.63 (95% CI, 0.60, 0.67, p&lt;0.001)) for oCAD (Figure 1). Conclusions In this multinational pooled cohort, a previously described novel machine learning, multiple biomarker panel provided high accuracy to diagnose patients for oCAD. FUNDunding Acknowledgement Type of funding sources: Private company. Main funding source(s): Prevencio, Inc. Table 1. Pooled Variable Data Figure 1. ROC for HART CADhs and hs-cTnI

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Jin Zhang ◽  
Fei Xiao ◽  
Guangliang Qiang ◽  
Zhenrong Zhang ◽  
Qianli Ma ◽  

Background. The competitive endogenous RNA (ceRNA) mechanism has been discovered recently and regulating cancer-related gene expressions. The ceRNA network participates in multiple processes, such as cell proliferation and metastasis, and potentially drives the progression of cancer. In this study, we focus on the ceRNA networks of esophageal squamous cell carcinoma and discovered a novel biomarker panel for cancer prognosis. Methods. RNA expression data of esophageal carcinoma from the TCGA database were achieved and constructed ceRNA network in esophageal carcinoma using R packages. Results. Four miRNAs were discovered as the core of the ceRNA model, including miR-93, miR-191, miR-99b, and miR-3615. Moreover, we constructed a ceRNA network in esophageal carcinoma, which included 4 miRNAs and 6 lncRNAs. After ceRNA network modeling, we investigated six lncRNAs which could be taken together as a panel for prognosis prediction of esophageal cancer, including LINC02575, LINC01087, LINC01816, AL136162.1, AC012073.1, and AC117402.1. Finally, we tested the predictive power of the panel in all TCGA samples. Conclusions. Our study discovered a new biomarker panel which may have potential values in the prediction of prognosis of esophageal carcinoma.

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Nino Guy Cassuto ◽  
David Piquemal ◽  
Florence Boitrelle ◽  
Lionel Larue ◽  
Nathalie Lédée ◽  

Choosing spermatozoa with an optimum fertilizing potential is one of the major challenges in assisted reproductive technologies (ART). This selection is mainly based on semen parameters, but the addition of molecular approaches could allow a more functional evaluation. To this aim, we used sixteen fresh sperm samples from patients undergoing ART for male infertility and classified them in the high- and poor-quality groups, on the basis of their morphology at high magnification. Then, using a DNA sequencing method, we analyzed the spermatozoa methylome to identify genes that were differentially methylated. By Gene Ontology and protein–protein interaction network analyses, we defined candidate genes mainly implicated in cell motility, calcium reabsorption, and signaling pathways as well as transmembrane transport. RT-qPCR of high- and poor-quality sperm samples allowed showing that the expression of some genes, such as AURKA, HDAC4, CFAP46, SPATA18, CACNA1C, CACNA1H, CARHSP1, CCDC60, DNAH2, and CDC88B, have different expression levels according to sperm morphology. In conclusion, the present study shows a strong correlation between morphology and gene expression in the spermatozoa and provides a biomarker panel for sperm analysis during ART and a new tool to explore male infertility.

2021 ◽  
Fiona A. Hagenbeek ◽  
Jenny van Dongen ◽  
René Pool ◽  
Peter J Roetman ◽  
Amy C Harms ◽  

This study introduces and illustrates the potential of an integrated multi-omics approach in investigating the underlying biology of complex traits such as childhood aggressive behavior. Using multivariate statistical methods, we integrated 45 polygenic scores (PGSs) based on genome-wide SNP data, 78,772 CpGs, and 90 metabolites for 645 twins (cases=42.0%, controls=58.0%). The single-omics models selected 31 PGSs, 1614 CpGs, and 90 metabolites, and the multi-omics biomarker panel comprised 44 PGSs, 746 CpGs, and 90 metabolites. The predictive accuracy in the test (N=277, cases=42.2%, controls=57.8%) and validation data (N=142 participants from a clinical cohort, cases=45.1%, controls=54.9%) ranged from 43.0% to 57.0% for the single- and multi-omics models. The average correlations across omics layers of omics traits selected for aggression in single-omics models ranged from 0.18 to 0.28. In the multi-omics model higher correlations were found and we describe five sets of correlational patterns with high absolute correlations (|r| ≥ 0.60) of aggression-related omics traits selected into the multi-omics model, providing novel biological insights.

Jeremiah R. Brown ◽  
Devin Parker ◽  
Meagan E. Stabler ◽  
Marshall L. Jacobs ◽  
Jeffrey P. Jacobs ◽  

2021 ◽  
Vol 350 ◽  
pp. S60
G.E. Conway ◽  
S.V. Llewellyn ◽  
P. Nymark ◽  
U.B. Vogel ◽  
S. Halappanavar ◽  

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