BCI Index Helps Clinicians Individualize Patient Treatment

Samantha DiGrande

During the San Antonio Breast Cancer Symposium held in San Antonio, Texas from December 4-8, 2018, Biotheranostics presented new data evaluating the impact of the Breast Cancer Index (BCI) in patients with early stage hormone receptor positive (HR+) breast cancer who are classified as an immediate risk for distant recurrence by previous genomic testing.

The BCI, which holds guidelines designation from the American Joint Committee on Cancer, is a molecular, gene expression-based test designed to provide information to help physicians individualize treatment decisions for patients with HR+ breast cancer.

Researchers analyzed patients with lymph node negative HR+ breast cancer and found that according to BCI, 51.5% of patients across the spectrum were classified with intermediate genomic risk scores. The test also identified patient age as high risk for late (greater than 5 years) recurrence, which indicates a need to continue anti-estrogen treatment up to 10 years.

Irrespective of age, 20% of patients were found to have a greater than 10% risk of disease recurrence before 10 years. BCI was also able to identify 38% of patients at an intermediate genomic-risk as having a high likelihood of benefitting from extended endocrine treatment. These results strongly assist oncologists in the individualization of patient care.

“Breast Cancer Index has consistently demonstrated that it is the strongest assay employing gene expression alone for prognostication of late distance recurrence in lymph node negative patients. Breast Cancer Index can help patients and clinicians approach the difficult decision related to extension of endocrine therapy with confidence by integrating the prognostic and predictive information provided by BCI,” said Catherine Schnabel, PhD, chief scientific officer of Biotheranostics.

The authors concluded that these data “highlight the importance of measuring different features of tumor biology for risk classification and prediction of therapy response.”
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