Development and validation of a short-term breast health measure as a supplement to screening mammography

• Published in: Biomarker research Vol. 10; no. 1; pp. 1 - 9
• Main Authors: Daily, Anna, Ravishankar, Prashanth, Wang, Wanyi, Krone, Ryan, Harms, Steve, Klimberg, V. Suzanne
• Format: Journal Article
• Language: English
• Published: London BioMed Central 25.10.2022; BioMed Central Ltd; BMC
• Subjects: Biomarker validation; Biomarkers; Biomedical and Life Sciences; Biomedicine; Breast; Breast cancer; Cancer biomarkers; Cancer Research; ELISA; Enzyme-linked immunosorbent assay; Mammography; Medical laboratories; Mortality; Proteins; Proteomics; Receiver operator characteristic curves; Regression analysis; Tear fluids; Womens health; United States > US; Cancer biomarkers; Tear fluids; Breast cancer; Biomarker validation; ELISA; Receiver operator characteristic curves
• ISSN: 2050-7771; 2050-7771
• DOI: 10.1186/s40364-022-00420-1

Background There is a growing body of evidence to support tears as a non-traditional biological fluid in clinical laboratory testing. In addition to the simplicity of tear fluid processing, the ability to access key cancer biomarkers in high concentrations quickly and inexpensively is significantly enhanced. Tear fluid is a dynamic environment rich in both proteomic and genomic information, making it an ideal medium for exploring the potential for biological testing modalities. Methods All protocols involving human subjects were reviewed and approved by the University of Arkansas IRB committee (13-11-289) prior to sample collection. Study enrollment was open to women ages 18 and over from October 30, 2017-June 19, 2019 at The Breast Center, Fayetteville, AR and Bentonville, AR. Convenience sampling was used and samples were age/sex matched, with enrollment open to individuals at any point of the breast health continuum of care. Tear samples were collected using the Schirmer strip method from 847 women. Concentration of selected tear proteins were evaluated using standard sandwich ELISA techniques and the resulting data, combined with demographic and clinical covariates, was analyzed using logistic regression analysis to build a model for classification of samples. Results Logistic regression analysis produced three models, which were then evaluated on cases and controls at two diagnostic thresholds and resulted in sensitivity ranging from 52 to 90% and specificity from 31 to 79%. Sensitivity and specificity variation is dependent on the model being evaluated as well as the selected diagnostic threshold providing avenues for assay optimization. Conclusions and relevance The work presented here builds on previous studies focused on biomarker identification in tear samples. Here we show successful early classification of samples using two proteins and minimal clinical covariates.