Algeciras‐Schimnich, Alicia

Growing evidence suggests that pretransplant alpha‐fetoprotein (AFP) predicts outcomes of hepatocellular carcinoma (HCC) patients treated with liver transplantation. We aimed to determine whether pretransplant AFP, Lens culinaris agglutinin‐reactive alpha‐fetoprotein (AFP‐L3), and des‐gamma‐carboxyprothrombin (DCP) predicted HCC recurrence after transplantation. A retrospective cohort study of 313 HCC patients undergoing transplantation between 2000 and 2008 was conducted, and 48 (15.3%) developed recurrence during a median follow‐up of 90.8 months. The 127 patients with available serum drawn before transplantation were included; they included 86 without recurrence and 41 with recurrence. Serum was tested for AFP, AFP‐L3%, and DCP in a blinded fashion with the μTASWako i30 immunoanalyzer. All biomarkers were significantly associated with HCC recurrence. The hazard ratios (HRs) were 3.5 [95% confidence interval (CI), 1.9‐6.7; P < 0.0001] for DCP ≥ 7.5 ng/mL and 2.8 (95% CI, 1.4‐5.4; P = 0.002) for AFP ≥ 250 ng/mL. The HR increased to 5.2 (95% CI, 2.3‐12.0; P < 0.0001) when AFP ≥ 250 ng/mL and DCP ≥7.5 ng/mL were considered together. When they were combined with the Milan criteria, the HR increased from 2.6 (95% CI, 1.4‐4.7; P = 0.003) for outside the Milan criteria to 8.6 (95% CI, 3.0‐24.6; P < 0.0001) for outside the Milan criteria and AFP ≥ 250 ng/mL and to 7.2 (95% CI, 2.8‐18.1; P < 0.0001) for outside the Milan criteria and DCP ≥7.5 ng/mL. Our findings suggest that biomarkers are useful for predicting the risk of HCC recurrence after transplantation. Using both biomarkers and the Milan criteria may be better than using the Milan criteria alone in optimizing the decision of liver transplantation eligibility. Liver Transpl 21:599–606, 2015. © 2015 AASLD.

BACKGROUND Maintaining consistency of results over time is a challenge in laboratory medicine. Lot-to-lot reagent changes are a major threat to consistency of results. METHODS For the period October 2007 through July 2012, we reviewed lot validation data for each new lot of insulin-like growth factor 1 (IGF-1) reagents (Siemens Healthcare Diagnostics) at Mayo Clinic, Rochester, MN, and the University of Virginia, Charlottesville, VA. Analyses of discarded patient samples were used for comparison of lots. For the same period, we determined the distributions of reported patient results for each lot of reagents at the 2 institutions. RESULTS Lot-to-lot validation studies identified no reagent lot as significantly different from the preceding lot. By contrast, significant lot-to-lot changes were seen in the means and medians of 105 668 reported patient IGF-I results during the period. The frequency of increased results increased nearly 2-fold to a high of 17%, without detectable changes in the underlying patient demographics. Retrospective statistical analysis indicated that lot-to-lot comparison protocols were underpowered and that validation studies for this assay required testing &gt;100 samples to achieve 90% power to detect reagent lots that would significantly alter the distributions of patient results. CONCLUSIONS The number of test samples required for adequate lot-to-lot validation protocols is high and may be prohibitively large, especially for low-volume or complex assays. Monitoring of the distributions of patient results has the potential to detect lot-to-lot inconsistencies relatively quickly. We recommend that manufacturers implement remote monitoring of patient results from analyzers in multiple institutions to allow rapid identification of between-lot result inconsistency.

BACKGROUND There is great interest in circulating microRNAs (miRNAs) as disease biomarkers. Translating promising miRNAs into validated clinical tests requires the characterization of many preanalytical and analytical parameters. METHODS miRNAs were extracted from serum and plasma samples of healthy volunteers, and miRNAs known to be present in serum and plasma (miR-15b, miR-16, miR-24, and miR-122) were amplified by reverse-transcription quantitative PCR. Stability and the effects of hemolysis were determined. Assay variation and its components, including the effect of adding control miRNA, were assessed by nested ANOVA. RESULTS miRNA concentrations were higher in plasma than in serum. Processing of plasma to remove subcellular/cellular components reduced miRNA concentrations to those of serum. The miRNAs analyzed were stable refrigerated or frozen for up to 72 h and were stable at room temperature for 24 h. Hemolysis increased the apparent concentration of 3 of the miRNAs. The total variability of replicate miRNA concentrations was &lt;2.0-fold, with most of the variability attributable to the extraction process and interassay imprecision. Normalizing results to those of spiked exogenous control miRNAs did not improve this variability. CONCLUSIONS Detailed validation of the preanalytical steps affecting miRNA detection and quantification is critical when considering the use of individual miRNAs as clinical biomarkers. Unless these causes of imprecision are considered and mitigated, only miRNAs that are extremely up- or downregulated will be suitable as clinical biomarkers.