Abstract P6-03-07: Development of an at-home breast health assessment test, to increase compliance with screening mammography using proteins from tears

Abstract

Abstract Anna E. Daily1,*, Prashanth Ravishankar1, Wanyi Wang3, Ryan Krone3, Steve Harms1,2, and V Suzanne Klimberg1,4,5 1Namida Lab Inc, Fayetteville, Arkansas; 2The Breast Center-Medical Associates of Northwest Arkansas, Fayetteville, Arkansas; 3Elite Research LLC, Irving, Texas; 4Department of Surgery, University of Texas Medical Branch, Galveston, Texas; 5Breast Surgical Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas. *anna@namidalab.com 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 make them an attractive biofluid source. Here we report our biomarker discovery study on tears to identify and validate candidate biomarkers for breast cancer and develop a model that is significantly associated with a positive breast cancer diagnosis. Methods: Participants were recruited from individuals having a yearly screening mammogram, biopsy, and/or recently diagnosed with breast cancer. Imaging results were obtained from clinical sites and samples were then classified as: control (normal imaging no biopsy) or diagnosed breast cancer pre-treatment (diagnosed by biopsy). Biomarker discovery was conducted using 102 individual tear samples collected using the Schirmer strip collection method. Liquid chromatography/tandem mass spectrometry (LC-MS/MS) was performed to identify protein biomarker candidates with altered expression levels in breast cancer patients. ELISA assay to confirm LC-MS/MS trends for biomarkers of interest was conducted using 171 tear samples. An additional round of validation utilizing 848 samples was performed which included protein concentrations determined by ELISA and collection of demographic and clinical covariates. The resulting concentration data, combined with the demographic and clinical covariates, was analyzed using logistic regression analysis to build a model for classification of samples as positive or negative. Results: A total of 301 proteins were identified by LC-MS/MS and narrowed to a list of 14 proteins (p-value < 0.05) with potential significance in breast cancer patients. Three biomarkers, S100A8 (p-value = 0.0069), S100A9 (p-value = 0.0048), and Galectin-3 binding protein (p-value = 0.01) with an increased expression in breast cancer patients were selected for validation using ELISA. Logistic regression analysis produced three models, which were then evaluated on breast cancer cases and controls at two diagnostic thresholds and resulted in sensitivity ranging from 52% - 90% and specificity from 31% - 79%. Conclusions: Our results demonstrate clinical feasibility for tear proteins to detect breast cancer and includes the most extensive published data set of individually analyzed tear samples. This analysis suggests that models developed using tear fluid have clinical validity and could be used in further development of a biological assay. We envision positioning this assay as a tool for activation around breast health screening for low to average risk patients who may be screening avoidant or adverse to encourage participation in screening mammography. Citation Format: Anna Daily, Prashanth Ravishankar, Victoria S. Klimberg, Steve Harms. Development of an at-home breast health assessment test, to increase compliance with screening mammography using proteins from tears [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-03-07.