Interpretation of sequential measurements of cancer antigen 125 (CA 125), carcinoembryonic antigen (CEA), and tissue polypeptide antigen (TPA) based on analytical imprecision and biological variation in the monitoring of ovarian cancer

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.

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

OBJECTIVES

Reliable biological variation (BV) data are required for the clinical use of tumor markers in the diagnosis and monitoring of treatment effects in cancer. The European Biological Variation Study (EuBIVAS) was established by the EFLM Biological Variation Working Group to deliver BV data for clinically important measurands. In this study, EuBIVAS-based BV estimates are provided for cancer antigen (CA) 125, CA 15-3, CA 19-9, carcinoembryonic antigen, cytokeratin-19 fragment, alpha-fetoprotein and human epididymis protein 4.

METHODS

Subjects from five European countries were enrolled in the study, and weekly samples were collected from 91 healthy individuals (53 females and 38 males; 21-69 years old) for 10 consecutive weeks. All samples were analyzed in duplicate within a single run. After excluding outliers and homogeneity analysis, the BVs of tumor markers were determined by CV-ANOVA on trend-corrected data, when relevant (Røraas method).

RESULTS

Marked individuality was found for all tumor markers. CYFRA 21-1 was the measurand with the highest index of individuality (II) at 0.67, whereas CA 19-9 had the lowest II at 0.07. The CV _I s of HE4, CYFRA 21-1, CA 19-9, CA 125 and CA 15-3 of pre- and postmenopausal females were significantly different from each other.

CONCLUSIONS

This study provides updated BV estimates for several tumor markers, and the findings indicate that marked individuality is characteristic. The use of reference change values should be considered when monitoring treatment of patients by means of tumor markers.

Performance of seven criteria to assess CA125 increments among ovarian cancer patients monitored during first-line chemotherapy and the post-therapy follow-up period

Aim:

To investigate seven CA125 criteria to monitor progressive ovarian cancer among patients with stage IC–IV disease.

Materials & methods:

Four criteria were used to asses CA125 increments starting from concentrations ≥35 U/ml and three criteria to asses increments starting from concentrations <35 U/ml.

Results:

A total of 231 patients were allocated to CA125 monitoring. The performances of the CA125 criteria were similar with sensitivities of 30–55%, negative predictive values of 28–46%, positive predictive values of 90–100% and median lead times of 26–87 days.

Conclusion:

The criteria showed low sensitivity and inability to exclude progressive ovarian cancer. The study suggests that CA125 information cannot stand alone but should be considered used in conjunction with other investigative procedures.

Few have validated the CA125 progression criteria proposed by Rustin et al. in 1996 and in 2001. These two criteria were officially recommended by the Gynecological Cancer Intergroup to be incorporated into clinical trials in 2010. Among other criteria, we validated the Gynecological Cancer Intergroup CA125 progression criteria in an ovarian cancer population regarding sensitivity, specificity, positive predictive value and negative predictive value. We found that the criteria had low sensitivity and ability to exclude tumor growth. We believe that the clinical utility of CA125 may be questioned, and there is a need for supplementary markers and alternative assessment criteria.

Calculation of limits for significant bidirectional changes in two or more serial results of a biomarker based on a computer simulation model

Background

Reference change values provide objective tools to assess the significance of a change in two consecutive results of a biomarker from an individual. However, in practice, more results are usually available and using the reference change value concept on more than two results will increase the number of false positive results.

Methods

A computer simulation model was developed using Excel. Based on 10,000 simulated measurements among healthy individuals, a series of up to 20 results of a biomarker from each individual was generated using different values for the within-subject biological variation plus the analytical variation. Each new result in this series was compared to the initial result. These successive serial differences were computed to give limits for significant bidirectional changes with constant cumulated maximum probabilities of 95% (p < 0.05) and 99% (p < 0.01).

Results

From an individual factors used to multiply the first result were calculated to create limits for constant cumulated significant changes. The factors were shown to become a function of the number of results included and the total coefficient of variation.

Conclusions

The first result should be multiplied by the appropriate factors for increase and decrease to give the limits for a significant bidirectional change in several consecutive measurements.

Calculation of limits for significant unidirectional changes in two or more serial results of a biomarker based on a computer simulation model

Background

Reference change values (RCVs) were introduced more than 30 years ago and provide objective tools for assessment of the significance of differences in two consecutive results from an individual. However, in practice, more results are usually available for monitoring, and using the RCV concept on more than two results will increase the number of false-positive results. Therefore, a simple method is needed to interpret the significance of a difference when all available serial biomarker results are considered.

Methods

A computer simulation model using Excel was developed. Based on 10,000 simulated data from healthy individuals, a series of up to 20 results from an individual was generated using different values for the within-subject biological variation plus the analytical variation. Each new result in this series was compared to the initial measurement result. These successive serial relative differences were computed to give limits for significant unidirectional differences with a constant cumulated maximum probability of both 95% ( P < 0.05) and 99% ( P < 0.01).

Results

Factors used to multiply the first result from an individual were calculated to create the limits for constant cumulated significant differences. The factors were shown to become a simple function of the number of results and the total coefficient of variation.

Conclusions

To interpret unidirectional differences in two or more serial results of a biomarker, the limits for significances are easily calculated using the presented factors. The first result is multiplied by the appropriate factor for increase or decrease, which gives the limits for a significant difference.

Development of multiplexed bead-based immunoassays for the detection of early stage ovarian cancer using a combination of serum biomarkers

CA125 as a biomarker of ovarian cancer is ineffective for the general population. The aim of this study was to evaluate multiplexed bead-based immunoassay of multiple ovarian cancer-associated biomarkers such as transthyretin and apolipoprotein A1, together with CA125, to improve the identification and evaluation of prognosis of ovarian cancer. We measured the serum levels of CA125, transthyretin, and apolipoprotein A1 from the serum of 61 healthy individuals, 84 patients with benign ovarian disease, and 118 patients with ovarian cancer using a multiplex liquid assay system, Luminex 100. The results were then analyzed according to healthy and/or benign versus ovarian cancer subjects. When CA125 was combined with the other biomarkers, the overall sensitivity and specificity were significantly improved in the ROC curve, which showed 95% and 97% sensitivity and specificity, respectively. At 95% specificity for all stages the sensitivity increased to 95.5% compared to 67% for CA125 alone. For stage I+II, the sensitivity increased from 30% for CA125 alone to 93.9%. For stage III+IV, the corresponding values were 96.5% and 91.6%, respectively. Also, the three biomarkers were sufficient for maximum separation between noncancer (healthy plus benign group) and stage I+II or all stages (I-IV) of disease. The new combination of transthyretin, and apolipoprotein A1 with CA125 improved both the sensitivity and the specificity of ovarian cancer diagnosis compared with those of individual biomarkers. These findings suggest the benefit of the combination of these markers for the diagnosis of ovarian cancer.

Serum Protein S-100 Predicts Clinical Outcome in Patients with Melanoma Treated with Adjuvant Interferon – Comparison with Tyrosinase RT-PCR

OBJECTIVE

To study the clinical value of the determination of serum S-100 protein as a single tumor marker or in combination with tyrosinase RT-PCR in patients with melanoma receiving adjuvant interferon.

PATIENTS AND METHODS

Patients were tested for serum S-100 protein luminoimmunometric assay and for blood tyrosinase mRNA (RT-PCR), before starting interferon and every 2-3 months thereafter.

RESULTS

One hundred and six patients (stage IIA, 27; IIB, 19; III, 49; and IV, 11) were included in the study. Median follow-up was 51 months (range 2-76). In the univariate analysis, under treatment S-100 > or =0.15 microg/l and a positive RT-PCR correlated with a lower disease-free survival and overall survival (OS). In the multivariate analysis, clinical stage, under therapy positive RT-PCR and S-100 levels > or =0.15 mug/ml, were independent prognostic factors for OS. The hazard ratio for OS was 3.9 (95% CI, 1.67-9.15; p = 0.004) and 2.2 (95% CI, 1.05-4.6; p = 0.016) for S-100 > or =0.15 microg/l and positive RT-PCR, respectively. When both techniques where combined, a positive RT-PCR indicated a poorer clinical outcome only in patients with S-100 <0.15 microg/l.

CONCLUSIONS

S-100 > or =0.15 microg/l and a positive RT-PCR during adjuvant interferon therapy indicate a high risk of death in resected melanoma patients. S-100 determination has a higher positive predictive value than RT-PCR, while tyrosinase RT-PCR adds prognostic information in patients with S-100 <0.15 microg/l.

Reference change values and power functions

Repeated samplings and measurements in the monitoring ofpatients to look for changes are common clinical problems. The “reference change value”, calculated as z

Biological variation of soluble CD163

A soluble plasma form of CD163 (sCD163) was recently identified. The protein has anti-inflammatory effects in vitro and is elevated in patients with myelo-monocytic leukaemia and infection. For rational use and evaluation of this potential new quantity it is important to have knowledge of its biological variability and to use a methodology that has a sufficiently analytical quality. The day-to-day and diurnal biological variabilities of sCD163 were studied in 12 healthy people using a sandwich ELISA. The within-run-, between run- and total analytical coefficients of variation were estimated to 3.6%, 4.8% and 6.0%, respectively. The day-to-day within-subject biological variation was estimated to 9.0%, and the between-subject biological variation to 35.9%. A diurnal variation in sCD163 concentrations with 14% lower values in the night (supine position) was observed. The ratio between total analytical variation and within-subject biological variation was 0.67. The index of individuality, calculated as the ratio between within-subject biological variation and between-subject biological variation, was low and similar to complement factors and immunoglobulins. A low index of individuality is important in a monitoring situation where small changes from the set point of the individual can be detected in serial measurements. For this purpose, the critical difference for a series of results in the same patient (significant at p < 0.05) was calculated to 30% for samples taken on different days and measured in separate runs.