Abstract P077: A blood-based lipid biomarker panel for personalized risk assessment of breast cancer

Abstract

Abstract Background: The metabolic syndrome characterized in part by obesity, hyperinsulinemia, and insulin resistance is associated with increased risk of breast cancer. However there remains a need to establish a circulating biomarker metabolic profile indicative of increased risk of breast cancer. In the current study, we performed a comprehensive metabolomics screen to identify biomarkers indicative of increased risk of breast cancer. Methods: Unbiased metabolomics profiling was conducted on an initial Development Set of plasmas collected from 353 newly diagnosed breast cancer cases and 141 controls. A deep learning neural network with 3 layers each containing 32 nodes based on 11 individual lipids corresponding to discrete lipid subclasses was built for risk prediction of breast cancer. The model was validated in an independent Test Set consisting of 79 breast cancer cases and 163 controls. Using a nested case: control matched design, we evaluated the performance of the model among body mass index (BMI) strata (≥ 30 or <30kg/m2). Results: An 11-marker lipid biomarker panel encompassing lipid subclasses with known pro-inflammatory and tumor promoting roles yielded an AUC of 0.75 (95% CI: 0.70-0.79) for distinguishing breast cancer cases from controls in the Development Set. Predictive performance of the lipid panel was comparable when stratifying cases into hormone-receptor (HR) positive, HER2-positive/HR negative, and triple-negative breast cancer subtypes. The biomarker panel had an AUC of 0.74 (95% CI: 0.68-0.81) in the independent Test Set. The predictive performance of the panel was most pronounced among obese subjects (BMI ≥ 30) with an AUC of 0.81 (95% CI: 0.71-0.91) in the Test Set. Conclusions: The lipid-based biomarker panel has utility for identifying women with ‘metabolic obesity’ who are at increased risk of breast cancer and would benefit from tailored screening. Citation Format: Johannes F. Fahrmann, Ehsan Irajizad, Jody Vykoukal, Angelica Gutierrez Barrera, Jennifer B. Dennison, Ranran Wu, Banu Arun, Abenaa Brewster, Samir Hanash. A blood-based lipid biomarker panel for personalized risk assessment of breast cancer. [abstract]. In: Proceedings of the AACR Special Conference: Precision Prevention, Early Detection, and Interception of Cancer; 2022 Nov 17-19; Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1 Suppl): Abstract nr P077.