Olaris’ metabolite profiling platform and machine learning algorithms have the potential to accurately indicate whether HER2-positive metastatic breast cancer patients will respond to trastuzumab therapy
Olaris’ metabolite profiling platform and machine learning algorithms have the potential to accurately indicate whether HER2-positive metastatic breast cancer patients will respond to trastuzumab therapy
WALTHAM, MASS. (PRWEB) MARCH 02, 2020
Olaris, a precision medicine company that is working to fundamentally change how diseases are treated, today announced the findings from its exploratory analysis of “biomarkers of response” (BoR) that predict whether a HER2-positive (Her2+) metastatic breast cancer patient will respond to Herceptin® (trastuzumab) therapy.
Anti-Her2 therapies, such as Herceptin (trastuzumab), have dramatically improved prognosis for Her2+ breast cancer patients. However, due to patient heterogeneity the level of response varies for each patient. Using a proprietary metabolomics platform and customized machine learning algorithms, Olaris is focused on identifying the biomarker signatures that can indicate whether an individual HER2-positive breast cancer patient is likely to respond to trastuzumab as part of first line therapy.
“When someone receives the diagnosis of HER2-positive metastatic breast cancer, there are several targeted therapies available for this subtype of cancer,” said Dr. Elizabeth O’Day, founder and CEO of Olaris. “By measuring the complete set of metabolites in an individual we aim to identify which one of those specific treatments will be most effective for each individual patient.”
As a first step, Olaris completed an exploratory analysis focused on retrospectively evaluating the pretreatment serum metabolome for association with PFS (progression free survival) in a cohort of 26 trastuzumab-treated metastatic breast cancer patients from a single institution. Using machine learning, Olaris constructed a BoR model that showed significant discriminatory ability (receiver-operating curve (ROC) analysis area under the curve (AUC) of 0.964). The results were presented at the annual San Antonio Breast Cancer Symposium.
Dr. Allan Lipton, oncologist and professor from Pennsylvania State University College of Medicine and collaborator on the project, said, “These results show promise that the Olaris BoR platform is capable of identifying a biomarker signature that predicts PFS. Upon validation in larger patient populations, this signature could help guide future treatment.”
O’Day added, “That’s why we are here. Olaris is committed to completing larger multi-center patient studies to validate these signatures, so we can create precision medicine tools that empower patients and physicians to optimize treatment choices.”
About Olaris
Olaris is working to fundamentally change how diseases are treated. The company’s metabolomics platform and machine learning algorithms produce Biomarkers of Response (BoR), which removes the guesswork when treating an individual patient’s disease. To learn more, visit http://www.olarisbor.com.
