Is serum human epididymis protein 4 ready for prime time?

Abstract

The National Institute for Health and Clinical Excellence (NICE) guidelines have sparked hot debate regarding the role of carbohydrate antigen 125 (CA-125) for ovarian cancer (OC) detection. Recent literature and evidence calls into question the use of CA-125 in diagnostic algorithms, given the better performance of human epididymis protein 4 (HE4) vs. CA-125 to rule OC. This is an important consideration since combined measurements are not cost-effective. The quality of this evidence is, however, threatened by important gaps related to study design, enrolled populations and analytical issues. For instance, despite the clinical need to prioritize the evaluation of biomarker performance in early stage tumours, sound evidence on this cannot be provided. In addition, results should be cautiously interpreted due to wide differences in the type of employed assays and in adopted diagnostic thresholds for HE4. Comparability among results obtained by different commercially available HE4 assays, together with an objective establishment of analytical goals is essential for the optimal clinical application of this marker.

Making new biomarkers a reality: the case of serum human epididymis protein 4

“…A CA125 value above the threshold of 35 kU/L should be followed by an ultrasound scan (US) of the abdomen and pelvis in order to decide if there is a need to urgently refer or not the symptomatic woman to the specialist. Considering that the actual diagnostic sensitivity and specificity of CA125 amount to less than 80% [15] and the disease prevalence in women presenting with symptoms to primary care is 0.23%, the estimated PPV of CA125 results to be <1%, far from 10% value expected by oncologists [8,9]. The Prostate, Lung, Colorectal and Ovarian Cancer Screening Randomized Clinical Trial, enrolling 78,216 women (median follow-up of 12.4 years), showed that screening by CA125 and transvaginal US was unable to provide diagnostic anticipation when compared with usual care [16].…”

“…In general, the early diagnosis of malignancy in the population at risk is challenging, because the very low OvCa incidence (<40 per 100,000 women) compels setting as desirable, for a diagnostic test, a specificity of >99.5% to finally achieve a positive predictive value (PPV) of 10% [8]. This PPV, implying nine false positive test results for each true positive detecting OvCa, is the goal that currently gynecological oncologists consider as cost-effective for a diagnostic test [9].…”

“…Lastly, although statistics assign the highest baseline risk index for OvCa to post-menopausal women, there was a scarce focus on menopausal status. In a following paper, we further listed what information was still needed before deciding for the clinical introduction of HE4 [8]: -to verify the status of harmonization of commercially available assays and the comparability of HE4 results as well as to assess the possible effects on patients' clinical classification; -to identify biological and lifestyle factors influencing HE4 concentrations in serum; -to define the most adequate interpretation strategy (i.e. age-related reference limits, fixed decision limits or reference change value [RCV]); -to decide if CA125 should be replaced or whether HE4 should be offered in combination with it; -to know if HE4 measurement impacts better on patient outcome and leads to more cost-effective management compared to CA125.…”

Cystatin C provides a better estimate of the effect of glomerular filtration rate on serum human epididymis protein 4 concentrations

“…Accordingly, a revision of current guidelines for OC management by including this novel biomarker is expected. This should require an optimization of its interpretation [2,3]. In particular, to preserve the higher diagnostic specificity of HE4 vs. carbohydrate antigen 125 (CA125), investigations sought to identify factors influencing HE4 results [4,5].…”

A new robust statistical model for interpretation of differences in serial test results from an individual

“…The RCV concept was first introduced by Harris and Yasaka to identify the change needed between two serial results from the same individual to be statistically significantly different [7]. It has been increasingly popular in laboratory medicine and currently there is a general agreement in considering RCV as the best tool for monitoring, but also for making diagnosis when the RI approach is inadequate [8]. RCV has also been applied to set objective criteria for use in Δ-checking quality control techniques and in autoverification and auto-validation strategies [9].…”