Abstract LB409: OncodeAi™-Breast CED: A nanoparticle-enhanced IR molecular sensing platform for early breast cancer detection
Authors: Ali Khammanivong, Eng Hock Lee, Darlyna Khonkhammy, Caden Gunnarson, Ganesh D. Sockalingum, Saad Bedros, Robert Petcavich, Dan Que Pham
Journal: Cancer Research (2025)

Abstract Current methodologies for early breast cancer screening, such as mammography, often fail to detect tumors at their earliest stages, particularly in dense breast tissue. OncodeAi™-Breast Cancer Early Detection (CED) platform addresses this challenge by utilizing plasmonic nanoparticle- and chemical-enhanced infrared (IR) molecular sensing combined with advanced machine learning (ML) to identify distinct molecular fingerprints in patient blood samples. Objective: In this study, we aimed to evaluate the performance of OncodeAi™-Breast CED in detecting early-stage breast cancer using blinded blood samples prospectively collected from patients diagnosed with breast cancer and age-matched healthy controls. Methods: For model development, serum samples were collected from retrospective cohorts (N=4,120) consisting of healthy controls (n=2,263), breast cancer patients from stages 0 to IV (n=752), and a mixture of various cancer types (n=1,105) and used to train an ML model to identify molecular fingerprints indicative of breast cancer. Fourier-transform IR spectroscopy was employed to obtain the spectral molecular fingerprint for each sample, with the methodology enhanced by resonant surface-enhanced IR absorption (rSEIRA) facilitated by a proprietary nano-structured IR reflective sampling substrate. A deep learning-based ML algorithm was trained using the captured molecular fingerprint data based on an 80/20 training/validation split. For testing, an optimized model was deployed to identify the unknown samples from a blinded, prospectively collected serum samples from an independent multi-center cohort (N=185) that included breast cancer patients at various stages (n=60, Stages 0-III) and healthy controls (n=125). Blinded predictions were performed on the prospective samples across seven independent, twice-randomized technical repeats. Results: Our rSEIRA/ML-based OncodeAi™ platform achieved an average of 96.4% sensitivity (95% CI: 93.6%-99.3%) and 96.9% specificity (95% CI: 95.1%-98.7%) in detecting breast cancer of all stages across multiple technical repeats, with an average ROC AUC of 0.99. The average sensitivity for detecting early-stage disease (Stages 0-I; n=49) was 98.3% (95% CI: 96.0%-100%), and 10 out of 11 breast cancer patients with either stage II (n=6), III (n=3), or unknown stage (n=2) were correctly identified. Conclusion: OncodeAi™-Breast CED marks a transformation in breast cancer detection as an early disease screening tool. By integrating the precision of IR molecular fingerprinting with the advanced capabilities of nanotechnology and machine learning, OncodeAi™ delivers unparalleled accuracy in detecting early-stage cancer. Its non-invasive nature, low cost, high scalability, and superior sensitivity offer transformative potential in population-wide screening programs, especially for high-risk and underserved communities. This platform also holds potential for expansion to other malignancies, including lung and colorectal cancer. Citation Format: Ali Khammanivong, Eng Hock Lee, Darlyna Khonkhammy, Caden Gunnarson, Ganesh D. Sockalingum, Saad Bedros, Robert Petcavich, Dan Que Pham. OncodeAi™-Breast CED: A nanoparticle-enhanced IR molecular sensing platform for early breast cancer detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_2):Abstract nr LB409.