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BioMark Expands Its Liquid Biopsy Platform Receptor Status for Breast Cancer
Vancouver, British Columbia--(Newsfile Corp. - December 10, 2024) - BioMark Diagnostics Inc. (CSE: BUX) (FSE: 20B) (OTC Pink: BMKDF) ("BioMark"), a leader in liquid biopsy tests for early cancer detection, announced a significant advancement in its breast cancer diagnostic capabilities. A groundbreaking study published in the International Journal of Molecular Sciences (IJMS) Special Edition demonstrates the potential of BioMark's blood-based assay to accurately predict estrogen receptor (ER) status in breast cancer patients. This non-invasive test, leveraging metabolomics and machine learning, holds promise to revolutionize breast cancer diagnosis and treatment.
Researchers analyzed blood samples from breast cancer patients and healthy controls, identifying unique metabolic signatures associated with ER-positive and ER-negative tumors. Utilizing advanced machine learning, researchers achieved an impressive 93% accuracy in predicting ER status. BioMark's CEO and President, Rashid Bux, accentuates the company platform's growing robustness: "Prior investments in AI and machine learning tools are proving valuable, yielding crucial insights from data and empowering healthcare professionals to make informed decisions for improved patient care. With our most recent assay in breast cancer, we can now detect cancer stages, subtypes, and receptor status, offering a more comprehensive analysis and paving the way for earlier diagnoses and personalized treatment options. This breakthrough has the potential to significantly impact breast cancer diagnosis and treatment, leading to improved patient outcomes and potentially saving lives."
Building upon this progress in breast cancer diagnosis, BioMark continues its commitment to innovation by presenting new findings at the upcoming 47th San Antonio Breast Cancer Symposium (SABCS). On December 11th, 2024, during a poster session, Dr. Jean-François Haince, General Manager and CSO, will share data highlighting how plasma metabolomics can be applied to lobular breast cancer biomarker discovery. These insights emphasize the power of machine learning techniques in analyzing complex metabolomic data and identifying significant biomarkers that can differentiate between different subtypes of breast cancer, specifically lobular breast cancer and ductal carcinoma.
Mr. Bux further adds, "The company remains dedicated to developing accurate and accessible tools for early cancer diagnosis, extending beyond lung and breast cancer to explore its platform's potential for other cancer types and improved overall patient outcomes."