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Breast Cancer Diagnostic Panel

A Diagnostic Panel Focused on Increasing Survival

BPGbio and the U.S. Department of Defense (DoD) have merged forces in establishing a CRADA to engage their vast retrospective and prospective biobank with BPGbio’s multiomics and Bayesian Artificial Intelligence capabilities to deconvolute the molecular drivers of breast cancer for the purpose of target discovery and novel diagnostics.

Current Breast Cancer Diagnostic Landscape

Earlier Detection is Critical to Improving Survivability

Breast cancer represents the most predominantly diagnosed cancer (~276,480 new cases in U.S. annually) and the 2nd most predominant cause of death in women from cancer.

  • 70-80% of newly diagnosed breast cancers are classified as ER+ subtype
  • ER+ breast cancers historically have demonstrated a 20-30% resistance to hormonal therapies, thus leading to the critical need to identify patients earlier that would respond to more targeted therapies (CDK4/6, ADC, chemo, CAR)

Addressing The Need For Earlier Detection

There is a critical unmet need to develop novel therapeutics and prescriptive diagnostics to support patients that are refractory to 1st and 2nd line therapies in addition to guide therapeutic selection as well as inform likelihood of progression based on cancer subtype.

BPGBio has developed a 34 gene panel test with the DoD to “upstage” breast cancer patients so women are treated most effectively at time of diagnoses, thereby potentially leading to better disease outcomes, and saving more lives. As imaging, pathology, and AI enters the mainstream for cancer diagnostics, BPGbio seeks to add molecular precision to how patients are treated as early as possible following diagnoses and is completing clinical validation of the gene panel.

The BPGbio Breast Cancer Diagnostic Panel

Utilizing a proteogenomic appropriate on extensively phenotyped and characterized breast cancers, we were able to develop an adaptive biomarker panel for identifying new molecular subtypes in breast cancer.

Integrating clinical outcome data, we were able to narrow down to a 34 protein/gene combination that stratified molecular subtypes and impacted survival.

This 34 gene panel is unique since it identified a biological signature of the microenvironment focus on metabolism and immune system.

The microenvironment and transitional signature also demonstrated utility in additional cancer types, suggesting a broader application for this 34 gene panel for theranostics and clinical outcome metrics.