Immunological Biomarker Predicts Breast Cancer Relapse

Scientists at Virginia Commonwealth University Massey Cancer Center, (Richmond, VA, USA), investigated whether a distinct network of immune function genes at the tumor site can predict a low risk versus high risk of distant relapse in breast cancer patients. This prediction would be regardless of the status of estrogen receptor (ER), progesterone receptor (PR), or Human Epidermal growth factor Receptor 2 (HER-2/neu) in the tumors. They conducted retrospective studies in a diverse cohort of breast cancer patients with a 1-5 year tumor relapse versus those with up to seven years relapse-free survival. They extracted ribonucleic acid (RNA) from the frozen tumor specimens at the time of diagnosis and subjected to microarray analysis and real-time RT-PCR. Paraffin-embedded tissues were also subjected to immunohistochemistry staining.

The investigators found that a specific five-gene signature related to tumor infiltrating immune cells could accurately predict relapse-free survival. Of the 17 patients, there were eight that relapsed within five years and nine that have remained cancer-free up to seven years. The five-gene signature was found to predict relapse in these patients with over 85% accuracy. Real-time RT-PCR confirmed the 5-gene prognostic signature was distinct from the 70-gene signature of MammaPrint panel, (Agendia; Irvine, CA, USA) and from the Oncotype DX, recurrence score assay panel (Genomic Health; Redwood City, CA, USA).

Masoud Manjili, DVM, PhD, the lead author of the study, said, "We know that the body initiates an immune response when it detects cancer, and immune system cells are usually present at the site of the tumor. Our test differs from currently-used tests by looking for a biological response to the presence of cancer, and not relying on genes expressed by the actual cancer cells." The study was published on April 11, 2011, in the journal Breast Cancer Research and Treatment.

Related Links:
Virginia Commonwealth University
Agendia
Genomic Health