Scientists say they have developed a new computational method to reduce variability in common research biomarker tests. They see their techniques as a promising step in improving the ability of biomedical researchers and basic scientists to reproduce data and facilitate more consistent results across laboratories and long-term projects. Researchers from Boston Medical Center (BMC) and Boston University School of Medicine (BUSM) named their novel software program ELISAtools, which provides a platform to compare data from research-use-only assay kits and minimize variability over months or even years. The team’s work (“A computational solution to improve biomarker reproducibility during long-term projects”) appears online in PLOS One.

“Biomarkers are fundamental to basic and clinical research outcomes by reporting host responses and providing insight into disease pathophysiology. Measuring biomarkers with research-use ELISA kits is universal, yet lack of kit standardization and unexpected lot-to-lot variability presents analytic challenges for long-term projects. During an ongoing two-year project measuring plasma biomarkers in cancer patients, control concentrations for one biomarker (PF) decreased significantly after changes in ELISA kit lots. A comprehensive operations review pointed to standard curve shifts with the new kits, an analytic variable that jeopardized data already collected on hundreds of patient samples. After excluding other reasonable contributors to data variability, a computational solution was developed to provide a uniform platform for data analysis across multiple ELISA kit lots,” the investigators wrote.

“The solution (ELISAtools) was developed within open-access R software in which variability between kits is treated as a batch effect. A defined best-fit Reference standard curve is modeled, a unique Shift factor “S” is calculated for every standard curve, and data adjusted accordingly. The averaged S factors for PF ELISA kit lots #1–5 ranged from -0.086 to 0.735, and reduced control inter-assay variability from 62.4% to <9%, within quality control limits. S factors calculated for four other biomarkers provided a quantitative metric to monitor ELISAs over the 10 month study period for quality control purposes. Reproducible biomarker measurements are essential, particularly for long-term projects with valuable patient samples. Use of research-use ELISA kits is ubiquitous and judicious use of this computational solution maximizes biomarker reproducibility.”

Enzyme-Linked Immunosorbent Assay (ELISA) tests are used globally across clinical, biomedical, and basic research fields to measure biomarkers in a range of mediums, including blood, plasma, and urine. Clinical ELISA test kits used in the hospital setting are regulated to ensure tight quality control boundaries for accuracy and consistency. However, the hundreds of commercially available research-use-only ELISA test kits are not regulated, which often leads to noticeable variability in results over time, between testing kits, and across different laboratories, according to the scientists.

The BMC-BUSM research team unexpectedly encountered high variability from one ELISA test kit during a project for the National Cancer Institute measuring thrombosis and inflammation biomarkers in the plasma of cancer subjects and healthy donors. After the first year of the project, they realized the data was changing significantly as they received different shipments of the kit from the manufacturer.

The researchers determined that differences in the ELISA kit were causing the issue. They had data from over 400 patient samples that could not be compared due to these differences in the ELISA kits. To solve this problem, the team created the ELISAtools software program to reduce future variability in test results.

“After implementing this software, the variability in test results dropped from over 60%, to under 9%, well within our quality control limits,” said Deborah J. Stearns-Kurosawa, PhD, senior author of the study and associate professor of pathology and laboratory medicine at BUSM. “We work on studies that go on for years, and this tool creates a constant, level playing field that we believe will improve accuracy and clinical utility of research.”

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