Tumour markers are a very heterogeneous group of molecules that are generally found in very small concentrations in the plasma and serum of healthy individuals. In the process of neoplastic differentiation the cell can synthesize, release, or induce synthesis of other cells, thus increasing their concentration in plasma and serum. These substances may also increase their plasma concentration in patients without cancer due to processes that increase the release or reduce catabolism, and so give rise to false positives. An understanding of the main physiopathological processes that increase the concentrations of these substances could improve our interpretation of tumour markers and their clinical application. In this study we review the physiopathological processes that may increase the plasma concentrations of tumour markers. We performed a bibliography review in PubMed, searching for causes of false positives for the following tumour markers: α-Fetoprotein, CA 125, CA 15-3, CA 19-9, CA 72-4, carcinoembryonic antigen, CYFRA 21-1, squamous cell carcinoma, prostatic specific antigen, β(2)-microglobulin, choriogonadotropin (β chain), chromogranin A, neuron specific enolase, HER2-neu, progastrin releasing peptide, S-100, and thyroglobulin. The results favour the use of tests which can identify pathological processes that may increase tumour marker concentrations.

Treatment of solid-organ transplant (SOT) patients with ganciclovir (GCV)-valganciclovir (VGCV) according to the manufacturer's recommendations may result in over- or underexposure. Bayesian prediction based on a population pharmacokinetics model may optimize GCV-VGCV dosing, achieving the area under the curve (AUC) therapeutic target. We conducted a two-arm, randomized, open-label, 40% superiority trial in adult SOT patients receiving GCV-VGCV as prophylaxis or treatment of cytomegalovirus infection. Group A was treated according to the manufacturer's recommendations. For group B, the dosing was adjusted based on target exposures using a Bayesian prediction model (NONMEM). Fifty-three patients were recruited (27 in group A and 26 in group B). About 88.6% of patients in group B and 22.2% in group A reached target AUC, achieving the 40% superiority margin ( P < 0.001; 95% confidence interval [CI] difference, 47 to 86%). The time to reach target AUC was significantly longer in group A than in group B (55.9 ± 8.2 versus 15.8 ± 2.3 days, P < 0.001). A shorter time to viral clearance was observed in group B than in group A (12.5 versus 17.6 days; P = 0.125). The incidences of relapse (group A, 66.67%, and group B, 9.01%) and late-onset infection (group A, 36.7%, and group B, 7.7%) were higher in group A. Neutropenia and anemia were related to GCV overexposure. GCV-VCGV dose adjustment based on a population pharmacokinetics Bayesian prediction model optimizes GCV-VGCV exposure. (This study has been registered at ClinicalTrials.gov under registration no. NCT01446445.)