The Influence of Blood Collection Tubes in Biomarkers' Screening by Mass Spectrometry

Establishing reference ranges for circulating biomarkers of drug-induced liver injury in healthy human volunteers

AIMS

The potential of mechanistic biomarkers to improve prediction of drug-induced liver injury (DILI) and hepatic regeneration is widely acknowledged. We sought to determine reference intervals for new biomarkers of DILI and regeneration, as well as to characterize their natural variability and impact of diurnal variation.

METHODS

Serum samples from 227 healthy volunteers were recruited as part of a cross-sectional study; of these, 25 subjects had weekly serial sampling over 3 weeks, while 23 had intensive blood sampling over a 24h period. Alanine aminotransferase (ALT), MicroRNA-122 (miR-122), High Mobility Group Box-1 (HMGB1), total Keratin-18 (K18), caspase-cleaved Keratin-18 (ccK18), Glutamate Dehydrogenase (GLDH) and Macrophage Colony-Stimulating Factor-1 (CSF-1) were assayed.

RESULTS

Reference intervals were established for each biomarker based on the 97.5% quantile (90% CI) following the assessment of fixed effects in univariate and multivariable models. Intra-individual variability was found to be non-significant, and there was no significant impact of diurnal variation.

CONCLUSION

Reference intervals for novel DILI biomarkers have been described. An upper limit of a reference range might represent the most appropriate mechanism to utilize these data. These data can now be used to interpret data from exploratory clinical DILI studies and to assist their further qualification as required by regulatory authorities.

Influence of preanalytical and analytical factors on the quantification of six regulatory serum proteins

Background: We analyzed differences in protein concentrations in human blood serum depending on the tube material and the immunoassay platform used. Materials & methods: Blood samples from study participants were collected in glass and polypropylene tubes (n = 292). Serum concentrations of six proteins (BDNF, IGF-1, VEGF-A, TGF-β1, MCP-1 and IL-18) were assessed by using ELISAs (all biomarkers), as well as a novel fully automated immunoassay platform (all but IGF-1, n = 211). Bland-Altman analyses were conducted to investigate intrasample variability of protein concentrations. Results: Tube comparison resulted in mean biases of between -0.45 and -70.64%. Platform comparison revealed mean biases of between 21.04 and -128.10%. Conclusion: Protein concentrations can vary significantly depending on the types of tube and immunoassay used, with protein-specific differences.

Biomarkers and Lung Cancer Early Detection: State of the Art

Simple Summary

Lung cancer is the leading cause of cancer death worldwide. Detecting lung malignancies promptly is essential for any anticancer treatment to reduce mortality and morbidity, especially in high-risk individuals. The use of liquid biopsy to detect circulating biomarkers such as RNA, microRNA, DNA, proteins, autoantibodies in the blood, as well as circulating tumor cells (CTCs), can substantially change the way we manage lung cancer patients by improving disease stratification using intrinsic molecular characteristics, identification of therapeutic targets and monitoring molecular residual disease. Here, we made an update on recent developments in liquid biopsy-based biomarkers for lung cancer early diagnosis, and we propose guidelines for an accurate study design, execution, and data interpretation for biomarker development.

Abstract

Lung cancer burden is increasing, with 2 million deaths/year worldwide. Current limitations in early detection impede lung cancer diagnosis when the disease is still localized and thus more curable by surgery or multimodality treatment. Liquid biopsy is emerging as an important tool for lung cancer early detection and for monitoring therapy response. Here, we reviewed recent advances in liquid biopsy for early diagnosis of lung cancer. We summarized DNA- or RNA-based biomarkers, proteins, autoantibodies circulating in the blood, as well as circulating tumor cells (CTCs), and compared the most promising studies in terms of biomarkers prediction performance. While we observed an overall good performance for the proposed biomarkers, we noticed some critical aspects which may complicate the successful translation of these biomarkers into the clinical setting. We, therefore, proposed a roadmap for successful development of lung cancer biomarkers during the discovery, prioritization, and clinical validation phase. The integration of innovative minimally invasive biomarkers in screening programs is highly demanded to augment lung cancer early detection.