scholarly journals Electrochemical aptasensor for lung cancer-related protein detection in crude blood plasma samples

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Galina S. Zamay ◽  
Tatiana N. Zamay ◽  
Vasilii A. Kolovskii ◽  
Alexandr V. Shabanov ◽  
Yury E. Glazyrin ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Blood Plasma ◽  
Protein Detection ◽  
Plasma Samples ◽  
Electrochemical Aptasensor ◽  
Related Protein

scholarly journals Pan-cancer circulating tumor DNA detection in over 10,000 Chinese patients

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Yongliang Zhang ◽  
Yu Yao ◽  
Yaping Xu ◽  
Lifeng Li ◽  
Yan Gong ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Circulating Tumor Dna ◽  
Small Cell ◽  
Chinese Patients ◽  
Plasma Samples ◽  
Small Cell Lung ◽  
Pan Cancer ◽  
Tumor Dna

AbstractCirculating tumor DNA (ctDNA) provides a noninvasive approach to elucidate a patient’s genomic landscape and actionable information. Here, we design a ctDNA-based study of over 10,000 pan-cancer Chinese patients. Using parallel sequencing between plasma and white blood cells, 14% of plasma cell-free DNA samples contain clonal hematopoiesis (CH) variants, for which detectability increases with age. After eliminating CH variants, ctDNA is detected in 73.5% of plasma samples, with small cell lung cancer (91.1%) and prostate cancer (87.9%) showing the highest detectability. The landscape of putative driver genes revealed by ctDNA profiling is similar to that in a tissue-based database (R2 = 0.87, p < 0.001) but also shows some discrepancies, such as higher EGFR (44.8% versus 25.2%) and lower KRAS (6.8% versus 27.2%) frequencies in non-small cell lung cancer, and a higher TP53 frequency in hepatocellular carcinoma (53.1% versus 28.6%). Up to 41.2% of plasma samples harbor drug-sensitive alterations. These findings may be helpful for identifying therapeutic targets and combined treatment strategies.

scholarly journals Alterations in lipid profile upon uterine fibroids and its recurrence

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Narine M. Tonoyan ◽  
Vitaliy V. Chagovets ◽  
Natalia L. Starodubtseva ◽  
Alisa O. Tokareva ◽  
Konstantin Chingin ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Blood Plasma ◽  
Lipid Profile ◽  
Plasma Levels ◽  
Uterine Fibroids ◽  
Control Group ◽  
Cholesterol Esters ◽  
Plasma Samples ◽  
Diagnostic Models ◽  
First Time

AbstractUterine fibroids (UF) is the most common (about 70% cases) type of gynecological disease, with the recurrence rate varying from 11 to 40%. Because UF has no distinct symptomatology and is often asymptomatic, the specific and sensitive diagnosis of UF as well as the assessment for the probability of UF recurrence pose considerable challenge. The aim of this study was to characterize alterations in the lipid profile of tissues associated with the first-time diagnosed UF and recurrent uterine fibroids (RUF) and to explore the potential of mass spectrometry (MS) lipidomics analysis of blood plasma samples for the sensitive and specific determination of UF and RUF with low invasiveness of analysis. MS analysis of lipid levels in the myometrium tissues, fibroids tissues and blood plasma samples was carried out on 66 patients, including 35 patients with first-time diagnosed UF and 31 patients with RUF. The control group consisted of 15 patients who underwent surgical treatment for the intrauterine septum. Fibroids and myometrium tissue samples were analyzed using direct MS approach. Blood plasma samples were analyzed using high performance liquid chromatography hyphened with mass spectrometry (HPLC/MS). MS data were processed by discriminant analysis with projection into latent structures (OPLS-DA). Significant differences were found between the first-time UF, RUF and control group in the levels of lipids involved in the metabolism of glycerophospholipids, sphingolipids, lipids with an ether bond, triglycerides and fatty acids. Significant differences between the control group and the groups with UF and RUF were found in the blood plasma levels of cholesterol esters, triacylglycerols, (lyso) phosphatidylcholines and sphingomyelins. Significant differences between the UF and RUF groups were found in the blood plasma levels of cholesterol esters, phosphotidylcholines, sphingomyelins and triacylglycerols. Diagnostic models based on the selected differential lipids using logistic regression showed sensitivity and specificity of 88% and 86% for the diagnosis of first-time UF and 95% and 79% for RUF, accordingly. This study confirms the involvement of lipids in the pathogenesis of uterine fibroids. A diagnostically significant panel of differential lipid species has been identified for the diagnosis of UF and RUF by low-invasive blood plasma analysis. The developed diagnostic models demonstrated high potential for clinical use and further research in this direction.

scholarly journals Metabolic Signatures of Lung Cancer in Biofluids: NMR-Based Metabonomics of Blood Plasma

2011 ◽  
Vol 10 (9) ◽  
pp. 4314-4324 ◽  
Author(s):  
Cláudia M. Rocha ◽  
Joana Carrola ◽  
António S. Barros ◽  
Ana M. Gil ◽  
Brian J. Goodfellow ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Blood Plasma ◽  
Metabolic Signatures

Epidermal Growth Factor Receptor Mutations in Plasma DNA Samples Predict Tumor Response in Chinese Patients With Stages IIIB to IV Non–Small-Cell Lung Cancer

2009 ◽  
Vol 27 (16) ◽  
pp. 2653-2659 ◽  
Author(s):  
Hua Bai ◽  
Li Mao ◽  
hang Shu Wang ◽  
Jun Zhao ◽  
Lu Yang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Egfr Mutation ◽  
Tumor Response ◽  
Growth Factor Receptor ◽  
Small Cell ◽  
Egfr Mutations ◽  
Plasma Samples ◽  
Plasma Dna ◽  
Small Cell Lung ◽  
Epidermal Growth

Purpose Mutations in the epidermal growth factor receptor (EGFR) kinase domain can predict tumor response to tyrosine kinase inhibitors (TKIs) in non–small-cell lung cancer (NSCLC). However, obtaining tumor tissues for mutation analysis is challenging. We hypothesized that plasma-based EGFR mutation analysis is feasible and has value in predicting tumor response in patients with NSCLC. Patients and Methods Plasma DNA samples and matched tumors from 230 patients with stages IIIB to IV NSCLC were analyzed for EGFR mutations in exons 19 and 21 by using denaturing high-performance liquid chromatography. We compared the mutations in the plasma samples and the matched tumors and determined an association between EGFR mutation status and the patients' clinical outcomes prospectively. Results In 230 patients, we detected 81 EGFR mutations in 79 (34.3%) of the patients' plasma samples. We detected the same mutations in 63 (79.7%) of the matched tumors. Sixteen plasma (7.0%) and fourteen tumor (6.1%) samples showed unique mutations. The mutation frequencies were significantly higher in never-smokers and in patients with adenocarcinomas (P = .012 and P = .009, respectively). In the 102 patients who failed platinum-based treatment and who were treated with gefitinib, 22 (59.5%) of the 37 with EGFR mutations in the plasma samples, whereas only 15 (23.1%) of the 65 without EGFR mutations, achieved an objective response (P = .002). Patients with EGFR mutations had a significantly longer progression-free survival time than those without mutations (P = .044) in plasma. Conclusion EGFR mutations can be reliably detected in plasma DNA of patients with stages IIIB to IV NSCLC and can be used as a biomarker to predict tumor response to TKIs.

scholarly journals Parathyroid hormone-related protein detection and interaction with NO and cyclic AMP in the renovascular system

1996 ◽  
Vol 50 (5) ◽  
pp. 1591-1603 ◽  
Author(s):  
Thierry Massfelder ◽  
Andrew F. Stewart ◽  
Karlhans Endlich ◽  
Neil Soifer ◽  
Clément Judes ◽  
...  
Keyword(s):  
Parathyroid Hormone ◽  
Cyclic Amp ◽  
Protein Detection ◽  
Related Protein ◽  
Parathyroid Hormone Related Protein

120 Expression of lung resistance-related protein (LRP) in lung cancer

1995 ◽  
Vol 31 ◽  
pp. S28-S29 ◽  
Author(s):  
A.M.C. Dingemans ◽  
J. van Ark-Otte ◽  
P. van der Valk ◽  
R.J. Scheper ◽  
P.E. Postmus ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Related Protein ◽  
Lung Resistance

Integration of proteomic and clinical data for the prediction of response to immune checkpoint inhibitor therapy in non-small cell lung cancer.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e21110-e21110
Author(s):  
Yuval Shaked ◽  
Michal Harel ◽  
Coren Lahav ◽  
Eyal Jacob ◽  
Itamar Sela ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Clinical Trial ◽  
Host Response ◽  
Immune Checkpoint Inhibitor ◽  
Checkpoint Inhibitor ◽  
Resistance Mechanisms ◽  
Small Cell ◽  
Plasma Samples ◽  
Small Cell Lung ◽  
Nsclc Patients

e21110 Background: Immune checkpoint inhibitor (ICI) therapy represents one of the most promising cancer treatments to date. However, despite unprecedented rates of durable response, only a small proportion of patients benefits from this approach. Major efforts are therefore required to characterize treatment resistance mechanisms, as well as to identify reliable biomarkers for response. We have previously shown that in response to various types of cancer therapy, including ICIs, the host may induce pro-tumorigenic processes that can promote therapy resistance. Here we examined systemic host-response proteomic profiles in non-small cell lung cancer (NSCLC) patients, aiming to discover biomarkers for response to ICI therapy and to unravel underlying resistance mechanisms. Methods: As part of our ongoing PROPHETIC clinical trial (NCT04056247), plasma samples were obtained at baseline (T0) and early-on treatment (T1; following the first treatment) from 120 NSCLC patients receiving ICI therapy. Proteomic profiling of the plasma samples was performed using proximity-extension assay (PEA) technology; validation was carried out for a fraction of the samples using ELISA-based arrays. To identify a proteomic signature that predicts clinical outcome, machine learning algorithms were applied following a random separation of the cohort into a discovery set and a validation set. Results: A proteomic signature predictive of response to treatment was identified and validated. Bioinformatic analysis identified potential mechanisms of resistance based on differentially expressed proteins associated with pro-tumorigenic biological processes. Statistical analysis of the clinical data identified multiple novel differential clinical parameters between responders and non-responders, either at baseline or by comparing T0 to T1, which may suggest host-mediated effects. Conclusions: Our study demonstrates the potential clinical utility of analyzing the host response to ICI therapy, in particular for the discovery of novel predictive biomarkers for NSCLC patient stratification. Clinical trial information: NCT04056247.

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