Blood-sampling collection prior to surgery may have a significant influence upon biomarker concentrations measured.

BACKGROUND: Biomarkers can be subtle tools to aid the diagnosis, prognosis and monitoring of therapy and disease progression. The validation of biomarkers is a cumbersome process involving many steps. Serum samples from lung cancer patients were collected in the framework of a larger study for evaluation of biomarkers for early detection of lung cancer. The analysis of biomarker levels measured revealed a noticeable difference in certain biomarker values that exhibited a dependence of the time point and setting of the sampling. Biomarker concentrations differed significantly if taken before or after the induction of anesthesia and if sampled via venipuncture or arterial catheter. METHODS: To investigate this observation, blood samples from 13 patients were drawn 1-2 days prior to surgery (T1), on the same day by venipuncture (T2) and after induction of anesthesia via arterial catheter (T3). The biomarkers Squamous Cell Carcinoma antigen (CanAG SCC EIA, Fujirebio Diagnostics, Malvern, USA), Carcinoembrionic Antigen (CEA), and CYFRA 21-1 (Roche Diagnostics GmbH, Mannheim, Germany) were analyzed. RESULTS: SCC showed a very strong effect in relation to the sampling time and procedure. While the first two points in time (T1; T2) were highly comparable (median fold-change: 0.84; p = 0.7354; correlation ρ = 0.883), patients showed a significant increase (median fold-change: 4.96; p = 0.0017; correlation ρ = -0.036) in concentration when comparing T1 with the sample time subsequent to anesthesia induction (T3). A much weaker increase was found for CYFRA 21-1 at T3 (median fold-change: 1.40; p = 0.0479). The concentration of CEA showed a very small, but systematic decrease (median fold-change: 0.72; p = 0.0039). CONCLUSIONS: In this study we show the unexpectedly marked influence of blood withdrawal timing (before vs. after anesthesia) and procedure (venous versus arterial vessel puncture) has on the concentration of the protein biomarker SCC and to a less extent upon CYFRA21-1. The potential causes for these effects remain to be elucidated in subsequent studies, however these findings highlight the importance of a standardized, controlled blood collection protocol for biomarker detection.

Association of Serum Metabolites with Serum Indices and Preanalytical Factors of Biobanked Serum Samples.

Background: Serum indices (hemolysis, icterus, and lipemia; HIL) are known to impact clinical chemistry assay results. This study aimed to investigate the impact of HIL indices on serum metabolite profiles and the association of serum metabolite levels with pre-analytical factors of serum samples. Methods: A cohort of serum samples (n = 12,196) from the Korean Genome and Epidemiology Study (KoGES) was analyzed for HIL indices and the pre-analytical variables (SPRECs) which were generated in the process of serum collection. We further performed targeted metabolomics on a subset comprising hemolyzed (n = 60), icteric (n = 60), lipemic (n = 60) groups, and a common control group of non-HIL samples (n = 60) using the Absolute IDQ p180 kit. Results: We found 22 clinical chemistry analytes significantly associated with hemolysis, 25 with icterus, and 24 with lipemia (p < 0.0001). Serum metabolites (n = 27) were associated with all of hemolysis, icterus, and lipemia (p < 0.05). The PC ae C36 2 had exhibited a significant association with pre-analytical factors corresponding to the third (pre-centrifugation delay between processing) and sixth (post-centrifugation) elements of the SPREC. Conclusions: This study showed the association of the serum index and pre-analytical factors with serum metabolite profiles. In addition, the association of pre-analytical factors with serum metabolite concentrations would corroborate the utility of SPRECs for the quality control of biobanked serum samples.

Standardising the preanalytical reporting of biospecimens to improve reproducibility in extracellular vesicle research - A GEIVEX study.

The standardization of clinical studies using extracellular vesicles (EVs) has mainly focused on the procedures employed for their isolation and characterization; however, preanalytical aspects of sample collection, handling and storage also significantly impact the reproducibility of results. We conducted an online survey based on SPREC (Standard PREanalytical Code) among members of GEIVEX (Grupo Español de Investigación en Vesiculas Extracelulares) to explore how different laboratories handled fluid biospecimens destined for EV analyses. We received 70 surveys from forty-three different laboratories: 44% focused on plasma, 9% on serum and 16% on urine. The survey indicated that variability in preanalytical approaches reaches 94%. Moreover, in some cases, researchers had no access to all relevant preanalytical details of samples, with some sample aspects with potential impact on EV isolation/characterisation not coded within the current version of SPREC. Our study highlights the importance of working with common standard operating procedures (SOP) to control preanalytical conditions. The application of SPREC represents a suitable approach to codify and register preanalytical conditions. Integrating SPREC into the SOPs of laboratories/biobanks will provide a valuable source of information and constitute an advance for EV research by improving reproducibility and credibility.

Real-life data from standardized preanalytical coding (SPREC) in tissue biobanking and its dual use for sample characterization and process optimization.

The standardized preanalytical code (SPREC) aggregates warm ischemia (WIT), cold ischemia (CIT), and fixation times (FIT) in a precise format. Despite its growing importance underpinned by the European in vitro diagnostics regulation or broad preanalytical programs by the National Institutes of Health, little is known about its empirical occurrence in biobanked surgical specimen. In several steps, the Tissue Bank Bern achieved a fully informative SPREC code with insights from 10,555 CIT, 4,740 WIT, and 3,121 FIT values. During process optimization according to LEAN six sigma principles, we identified a dual role of the SPREC code as a sample characteristic and a traceable process parameter. With this preanalytical study, we outlined real-life data in a variety of organs with specific differences in WIT, CIT, and FIT values. Furthermore, our FIT data indicate the potential to adapt the SPREC fixation toward concrete paraffin-embedding time points and to extend its categories beyond 72 h due to weekend delays. Additionally, we identified dependencies of preanalytical variables from workload, daytime, and clinics that were actionable with LEAN process management. Thus, streamlined biobanking workflows during the day were significantly resilient to workload peaks, diminishing the turnaround times of native tissue processing (i.e. CIT) from 74.6 to 46.1 min under heavily stressed conditions. In conclusion, there are surgery-specific preanalytics that are surgico-pathologically limited even under process optimization, which might affect biomarker transfer from one entity to another. Beyond sample characteristics, SPREC coding is highly beneficial for tissue banks and Institutes of Pathology to track WIT, CIT, and FIT for process optimization and monitoring measurements.

Quality Control of Breast Cancer Surgery Samples: Introducing Time Stamp Checking.

Background: Analytical information obtained from clinical tissue samples has recently become more important due to recent advancements in the clinical practice of medicine, for example, gene panel testing. However, acquiring and managing the sample quality, which greatly influences the analyses, are not sufficient and hence requires immediate attention. We introduced time stamp (TS) recording and documentation using the Standard PREanalytical Code (SPREC) for breast cancer surgery samples to monitor and control their quality. Materials and Methods: The TS recording used SPREC for quality control of each sample by recording seven factors: type of sample, type of collection, warm ischemia time (WIT), cold ischemia time (CIT), fixation type, fixation time (FT), and long-term storage. The responsibilities to record each factor were assigned among group members (breast surgeons, anesthesiologists, pathologists, operating room nurses, and medical technologists in pathology). Results: Records based on SPREC were recorded for 393 surgical cases of first-time breast cancer patients performed at the Kanagawa Cancer Center from May 2018 to April 2019. The vascular clamp time was defined as when skin flap formation was completed, regardless of the surgical procedure. An anesthesiologist recorded the vascular clamp time and sample collection time, and the pathologist recorded the fixation start time and fixation end time. WIT was 23 (3-116) minutes (breast-conserving surgery, 11 [3-38] minutes; mastectomy, 26 [5-116] minutes; and nipple-sparing mastectomy, 39 [31-43] minutes), CIT was 37 (3-1052) minutes, and FT was 43 (17-115) hours. The median CIT and FT were significantly shortened after introducing the TS system, and the variabilities were reduced. Conclusion: A TS system for quality control of breast cancer surgical sample functions well due to the establishment of highly versatile WIT and a working group consisting of multiple members of different occupations who shared roles.

Quality and reporting practices in an Australian cancer biobank cohort.

OBJECTIVES: Inadequate research biospecimen quality may adversely impact research translation to clinical practice. Despite the development and endorsement of external quality assurance (QA) programs and biospecimen quality reporting tools, there has been little examination of relevant biobank practices. DESIGN AND METHODS: An online survey was designed to describe the use and communication of QA and quality control (QC) measures within an Australian cancer biobank cohort (n=21), classified according to access policy. Survey questions examined the development and maintenance of Standard Operating Procedures (SOPs), other specific QA and biospecimen QC activities, and communication of biospecimen QC results to researchers. RESULTS: Over three quarters of biobanks utilised regularly-reviewed, best-practice-referenced SOPs, and most biobanks undertook at least one QC activity. Whereas all open-access biobanks (n=11) utilised SOPs and undertook at least one QC activity, these practices were significantly less frequent in restricted-access biobanks (n=10). There were overall low rates of recording the SPREC code, with increased but incomplete recording of Tier 1 BRISQ data. Open-access biobanks were significantly more likely to provide biospecimen QC results to researchers, and to report receiving requests for QC results or additional sample data. CONCLUSIONS: Improved resourcing and education may be required to boost current levels of QA and QC activities and reporting by cancer biobanks.