Within- and between-subject biological variation data for tumor markers based on the European Biological Variation Study

Variations in tumor growth, intra-individual biological variability, and the interpretation of changes

OBJECTIVES

The identification of changes in tumor markers (TMs) in cancer patients that indicate response to treatment, stabilization or disease progression is a challenge for laboratory medicine. Several approaches have been proposed: assessing percentage increases, applying discriminant values, and estimating half-life (t1/2) or doubling time (DT). In all of them it is assumed that the TM is a surrogate of the variation in tumor size. In general this variation is time-dependent, but this is not the case of intraindividual biological variability (CVi), which can range from 6 % in CA15-3 to 22 % in CA125. When decisions are made on the basis of DT or t1/2, these values can be affected by the CVi; if it is very large, the growth rate very slow and the period of time between determinations very short, the result obtained for DT may be due mainly to the CVi. The aim of this study is to establish the relationship between the CVi and temporal variables.

METHODS

We related equations for calculating DT and t1/2 to the reference change values in tumor markers.

RESULTS

The application of the formula obtained allows the calculation of the optimal time between measurements to ensure that the influence of the CVi is minimal in different types of tumors and different scenarios.

CONCLUSIONS

Intraindividual variation affects the calculation of DT and t1/2. It is necessary to establish the minimum time between two measurements to ensure that the CVi does not affect their calculation or lead to misinterpretation.

Investigating the Current Harmonization Status of Tumor Markers Using Global External Quality Assessment Programs: A Feasibility Study

BACKGROUND

The harmonization status of most tumor markers (TMs) is unknown. We report a feasibility study performed to determine whether external quality assessment (EQA) programs can be used to obtain insights into the current harmonization status of the tumor markers α-fetoprotein (AFP), prostate specific antigen (PSA), carcinoembryonic antigen (CEA), cancer antigen (CA)125, CA15-3 and CA19-9.

METHODS

EQA sample results provided by 6 EQA providers (INSTAND [Germany], Korean Association of External Quality Assessment Service [KEQAS, South Korea], National Center for Clinical Laboratories [NCCL, China], United Kingdom National External Quality Assessment Service [UK NEQAS, United Kingdom], Stichting Kwaliteitsbewaking Medische Laboratoriumdiagnostiek [SKML, the Netherlands], and the Royal College of Pathologists of Australasia Quality Assurance Programs [RCPAQAP, Australia]) between 2020 and 2021 were used. The consensus means, calculated from the measurement procedures present in all EQA programs (Abbott Alinity, Beckman Coulter DxI, Roche Cobas, and Siemens Atellica), was used as reference values. Per measurement procedure, the relative difference between consensus mean for each EQA sample and the mean of all patient-pool-based EQA samples were calculated and compared to minimum, desirable, and optimal allowable bias criteria based on biological variation.

RESULTS

Between 19040 (CA15-3) and 25398 (PSA) individual results and 56 (PSA) to 76 (AFP) unique EQA samples were included in the final analysis. The mean differences with the consensus mean of patient-pool-based EQA samples for all measurement procedures were within the optimum bias criterion for AFP, the desirable bias for PSA, and the minimum bias criterion for CEA. However, CEA results <8 µg/L exceeded the minimum bias criterion. For CA125, CA15-3, and CA19-9, the harmonization status was outside the minimum bias criterion, with systematic differences identified.

CONCLUSIONS

This study provides relevant information about the current harmonization status of 6 tumor markers. A pilot harmonization investigation for CEA, CA125, CA15-3, and CA19-9 would be desirable.

The impact of physiological variations on personalized reference intervals and decision limits: an in-depth analysis

The interpretation of laboratory data is a comparative procedure. Physicians typically need reference values to compare patients' laboratory data for clinical decisions. Therefore, establishing reliable reference data is essential for accurate diagnosis and patient monitoring. Human metabolism is a dynamic process. Various types of systematic and random fluctuations in the concentration/activity of biomolecules are observed in response to internal and external factors. In the human body, several biomolecules are under the influence of physiological rhythms and are therefore subject to ultradian, circadian and infradian fluctuations. In addition, most biomolecules are also characterized by random biological variations, which are referred to as biological fluctuations between subjects and within subjects/individuals. In routine practice, reference intervals based on population data are used, which by nature are not designed to capture physiological rhythms and random biological variations. To ensure safe and appropriate interpretation of patient laboratory data, reference intervals should be personalized and estimated using individual data in accordance with systematic and random variations. In this opinion paper, we outline (i) the main variations that contribute to the generation of personalized reference intervals (prRIs), (ii) the theoretical background of prRIs and (iii) propose new methods on how to harmonize prRIs with the systematic and random variations observed in metabolic activity, based on individuals' demography.

Pre-analytical stability of the CEA, CYFRA 21.1, NSE, CA125 and HE4 tumor markers

BACKGROUND

For lung cancer, circulating tumor markers (TM) are available to guide clinical treatment decisions. To ensure adequate accuracy, pre-analytical instabilities need to be known and addressed in the pre-analytical laboratory protocols.

OBJECTIVE

This study investigates the pre-analytical stability of CA125, CEA, CYFRA 21.1, HE4 and NSE for the following pre-analytical variables and procedures; i) whole blood stability, ii) serum freeze-thaw cycles, iii) electric vibration mixing and iv) serum storage at different temperatures.

METHODS

Left-over patient samples were used and for every investigated variable six patient samples were used and analysed in duplicate. Acceptance criteria were based on analytical performance specifications based on biological variation and significant differences with baseline.

RESULTS

Whole blood was stable for at least 6 hours for all TM except for NSE. Two freeze-thaw cycles were acceptable for all TM except CYFRA 21.1. Electric vibration mixing was allowed for all TM except for CYFRA 21.1. Serum stability at 4°C was 7 days for CEA, CA125, CYFRA 21.1 and HE4 and 4 hours for NSE.

CONCLUSIONS

Critical pre-analytical processing step conditions were identified that, if not taken into account, will result in reporting of erroneous TM results.

Lung cancer tumor marker analysis: A clinical laboratory perspective

 Clinical laboratories are responsible for performing lung cancer tumor marker testing as part of routine clinical care. It is their responsibility to guarantee that the reported tumor marker results are reliable and meet the necessary quality standards for proper clinical use. During the different laboratory phases, pre-analytical, analytical and post-analytical, specific steps and processes can introduce errors and generate incorrect clinical interpretation. This editorial briefly outlines critical laboratory issues related to lung cancer tumor markers, specific for each of these three laboratory phases.

Practical delta check limits for tumour markers in different clinical settings

OBJECTIVES

Few studies have reported on delta checks for tumour markers, even though these markers are often evaluated serially. Therefore, this study aimed to establish a practical delta check limit in different clinical settings for five tumour markers: alpha-fetoprotein, cancer antigen 19-9, cancer antigen 125, carcinoembryonic antigen, and prostate-specific antigen.

METHODS

Pairs of patients' results (current and previous) for five tumour markers between 2020 and 2021 were retrospectively collected from three university hospitals. The data were classified into three subgroups, namely: health check-up recipient (subgroup H), outpatient (subgroup O), and inpatient (subgroup I) clinics. The check limits of delta percent change (DPC), absolute DPC (absDPC), and reference change value (RCV) for each test were determined using the development set (the first 18 months, n=179,929) and then validated and simulated by applying the validation set (the last 6 months, n=66,332).

RESULTS

The check limits of DPC and absDPC for most tests varied significantly among the subgroups. Likewise, the proportions of samples requiring further evaluation, calculated by excluding samples with both current and previous results within the reference intervals, were 0.2-2.9% (lower limit of DPC), 0.2-2.7% (upper limit of DPC), 0.3-5.6% (absDPC), and 0.8-35.3% (RCV99.9%). Furthermore, high negative predictive values >0.99 were observed in all subgroups in the in silico simulation.

CONCLUSIONS

Using real-world data, we found that DPC was the most appropriate delta-check method for tumour markers. Moreover, Delta-check limits for tumour markers should be applied based on clinical settings.

Personalized and Population-Based Reference Intervals for 48 Common Clinical Chemistry and Hematology Measurands: A Comparative Study

BACKGROUND

Personalized reference intervals (prRIs) have the potential to improve individual patient follow-up as compared to population-based reference intervals (popRI). In this study, we estimated popRI and prRIs for 48 clinical chemistry and hematology measurands using samples from the same reference individuals and explored the effect of using group-based and individually based biological variation (BV) estimates to derive prRIs.

METHODS

143 individuals (median age 28 years) were included in the study and had fasting blood samples collected once. From this population, 41 randomly selected subjects had samples collected weekly for 5 weeks. PopRIs were estimated according to Clinical Laboratory Standards Institute EP28 and within-subject BV (CVI) were estimated by CV-ANOVA. Data were assessed for trends and outliers prior to calculation of individual prRIs, based on estimates of (a) within-person BV (CVP), (b) CVI derived in this study, and (c) publically available CVI estimates.

RESULTS

For most measurands, the individual prRI ranges were smaller than the popRI range, but overall about half the study participants had a prRI wider than the popRI for 5 or more out of 48 measurands. The dispersion of prRIs based on CVP was wider than that of prRIs based on CVI.

CONCLUSION

The prRIs derived in our study varied significantly between different individuals, especially if based on CVP. Our results highlight the limitations of popRIs in interpreting test results of individual patients. If sufficient data from a steady-state situation are available, using prRI based on CVP estimates will provide a RI most specific for an individual patient.

Biological variation of CA 15-3, CA 125 and HE 4 on lithium heparinate plasma in apparently healthy Caucasian volunteers

OBJECTIVES

Tumor markers are well-known for being important tools in the support of diagnosis, monitoring of treatment efficacy and follow-up of cancers. CA 125, CA 15-3 and HE 4 have demonstrated potential efficacy in other clinical indications. The main objective was to evaluate the biological variation of these glycoproteins using two different immunoassays in an apparently healthy Caucasian population.

METHODS

Nineteen healthy volunteers including 11 women and 8 men were sampled weekly for 5 consecutive weeks. Samples were analyzed in duplicate on Lumipulse® G600II (Fujirebio) and on the Cobas e602 (Roche Diagnostics) analyzers. After assessment of normality, exclusion of outliers and analysis of homogeneity of variance, analytical variation (CVA), within-subject biological variation (CVI) and between-subject biological variation (CVG) were determined using a nested ANOVA.

RESULTS

CVA, CVI and CVG were determined on both analyzers and both genders. For CA 125, the CVA ranges from 1.0 to 3.4%, the CVI from 5.7 to 13.8% and the CVG from 32.2 to 42.9%. For CA 15-3, the CVA is between 1.1 and 3.4%, the CVI between 3.9 and 6.5% and the CVG between 43.7 and 196.9%. Lastly, HE 4 has CVA values between 1.4 and 2.4%, CVI between 5.1 and 10.5% and CVG between 7.1 and 12.6%.

CONCLUSIONS

Our study provided updated data on the biological variation of CA 125, HE 4 and CA 15-3. These data allow to improve the clinical interpretation and thus the management of the patient.

Longitudinal Evaluation of AFP and CEA External Proficiency Testing Reveals Need for Method Harmonization

The glycoproteins alpha-fetoprotein (AFP) and carcinoembryonic antigen (CEA) have long been approved as biomarkers for diagnosing and monitoring tumors. International Reference Preparations (IRPs) have been around since 1975. Nevertheless, manufacturer-dependent differences have been reported, indicating a lack of harmonization. This paper analyzes data from 15 external quality assessment (EQA) surveys conducted worldwide between 2018 and 2022. The aim was to gain insight into the longitudinal development of manufacturer-dependent differences for CEA and AFP. In each survey, participating laboratories received two samples with different tumor marker levels. Inter- and intra-assay variability was analyzed and the mean 80% and 90% of the manufacturer collectives were compared to the evaluation criteria of the German Medical Association (RiliBÄK). The median EQA results for CEA revealed manufacturer-dependent differences between the highest and lowest collective of up to 100%; for AFP, the median differences mostly remained below 40%. The coefficients of variation were predominantly low for both markers. We concluded that the current assays for AFP and CEA detection are better harmonized than previously reported. The assays displayed a good robustness; however, a narrowing of the current assessment limits in EQA schemes could further enhance the quality of laboratory testing.

Practical application of European biological variation combined with Westgard Sigma Rules in internal quality control

OBJECTIVES

Westgard Sigma Rules is a statistical tool available for quality control. Biological variation (BV) can be used to set analytical performance specifications (APS). The European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) regularly updates BV data. However, few studies have used robust BV data to determine quality goals and design a quality control strategy for tumor markers. The aim of this study was to derive APS for tumor markers from EFLM BV data and apply Westgard Sigma Rules to establish internal quality control (IQC) rules.

METHODS

Precision was calculated from IQC data, and bias was obtained from the relative deviation of the External quality assurance scheme (EQAS) group mean values and laboratory-measured values. Total allowable error (TEa) was derived using EFLM BV data. After calculating sigma metrics, the IQC strategy for each tumor marker was determined according to Westgard Sigma Rules.

RESULTS

Sigma metrics achieved for each analyte varied with the level of TEa. Most of these tumor markers except neuron-specific enolase reached 3σ or better based on TEa_min. With TEa_des and TEa_opt set as the quality goals, almost all analytes had sigma values below 3. Set TEa_min as quality goal, each analyte matched IQC muti rules and numbers of control measurements according to sigma values.

CONCLUSIONS

Quality goals from the EFLM BV database and Westgard Sigma Rules can be used to develop IQC strategy for tumor markers.

Critical review and meta-analysis of biological variation estimates for tumor markers

OBJECTIVES

Biological variation data (BV) can be used for different applications, but this depends on the availability of robust and relevant BV data. In this study, we aimed to summarize and appraise BV studies for tumor markers, to examine the influence of study population characteristics and concentrations on BV estimates and to discuss the applicability of BV data for tumor markers in clinical practice.

METHODS

Studies reporting BV data for tumor markers related to gastrointestinal, prostate, breast, ovarian, haematological, lung, and dermatological cancers were identified by a systematic literature search. Relevant studies were evaluated by the Biological Variation Data Critical Appraisal Checklist (BIVAC) and meta-analyses were performed for BIVAC compliant studies to deliver global estimates of within-subject (CV_I) and between-subject (CV_G) BV with 95% CI.

RESULTS

The systematic review identified 49 studies delivering results for 22 tumor markers; four papers received BIVAC grade A, 3 B, 27 C and 15 D. Out of these, 29 CV_I and 29 CV_G estimates met the criteria to be included in the meta-analysis. Robust data are lacking to conclude on the relationship between BV and different disease states and tumor marker concentrations.

CONCLUSIONS

This review identifies a lack of high-quality BV studies for many tumor markers and a need for delivery of BIVAC compliant studies, including in different, disease states and tumor marker concentrations. As of yet, the state-of-the-art may still be the most appropriate model to establish analytical performance specifications for the majority of tumor markers.

Personalized reference intervals - statistical approaches and considerations

For many measurands, physicians depend on population-based reference intervals (popRI), when assessing laboratory test results. The availability of personalized reference intervals (prRI) may provide a means to improve the interpretation of laboratory test results for an individual. prRI can be calculated using estimates of biological and analytical variation and previous test results obtained in a steady-state situation. In this study, we aim to outline statistical approaches and considerations required when establishing and implementing prRI in clinical practice. Data quality assessment, including analysis for outliers and trends, is required prior to using previous test results to estimate the homeostatic set point. To calculate the prRI limits, two different statistical models based on 'prediction intervals' can be applied. The first model utilizes estimates of 'within-person biological variation' which are based on an individual's own data. This model requires a minimum of five previous test results to generate the prRI. The second model is based on estimates of 'within-subject biological variation', which represents an average estimate for a population and can be found, for most measurands, in the EFLM Biological Variation Database. This model can be applied also when there are lower numbers of previous test results available. The prRI offers physicians the opportunity to improve interpretation of individuals' test results, though studies are required to demonstrate if using prRI leads to better clinical outcomes. We recommend that both popRIs and prRIs are included in laboratory reports to aid in evaluating laboratory test results in the follow-up of patients.

The European Biological Variation Study (EuBIVAS): a summary report

Biological variation (BV) data have many important applications in laboratory medicine. Concerns about quality of published BV data led the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) 1st Strategic Conference to indicate need for new studies to generate BV estimates of required quality. In response, the EFLM Working Group on BV delivered the multicenter European Biological Variation Study (EuBIVAS). This review summarises the EuBIVAS and its outcomes. Serum/plasma samples were taken from 91 ostensibly healthy individuals for 10 consecutive weeks at 6 European centres. Analysis was performed by Siemens ADVIA 2400 (clinical chemistry), Cobas Roche 8000, c702 and e801 (proteins and tumor markers/hormones respectively), ACL Top 750 (coagulation parameters), and IDS iSYS or DiaSorin Liaison (bone biomarkers). A strict preanalytical and analytical protocol was applied. To determine BV estimates with 95% CI, CV-ANOVA after analysis of outliers, homogeneity and trend analysis or a Bayesian model was applied. EuBIVAS has so far delivered BV estimates for 80 different measurands. Estimates for 10 measurands (Non-HDL Cholesterol, S100-β protein, neuron-specific enolase, soluble transferrin receptor, intact fibroblast growth-factor-23, uncarboxylated-unphosphorylated matrix-Gla protein, human epididymis protein-4, free, conjugated and %free prostate-specific antigen), prior to EuBIVAS, have not been available. BV data for creatinine and troponin I were obtained using two analytical methods in each case. The EuBIVAS has delivered high-quality BV data for a wide range of measurands. The BV estimates are for many measurands lower than those previously reported, having an impact on the derived analytical performance specifications and reference change values.