Gene expression-based prognostic and predictive tools in breast cancer

Area of Science:

• Oncology
• Genomics
• Clinical Diagnostics

Background:

• Genomic assays measuring gene expression are increasingly used in clinical practice and recommended by international guidelines.
• These tests stratify patients into high- and low-risk cohorts, aiding prognostic and predictive decision support.
• Multigene tests offer potential cost reductions by identifying patients who do not require chemotherapy.

Purpose of the Study:

• To summarize commercially available genomic assays for breast cancer.
• To discuss the potential and limitations of current multigene tests.
• To explore emerging genomic approaches for improved cancer treatment prediction.

Main Methods:

• Review of commercially available RT-PCR and gene chip-based assays for breast cancer.
• Discussion of emerging techniques such as homologous recombination deficiency scoring, massive parallel sequencing, and miRNA expression signatures.
• Consideration of integrating genomic data with traditional diagnostics and clinical parameters.

Main Results:

• Current multigene tests can sub-divide patients into risk cohorts, but concordance in risk assessment is suboptimal.
• Existing tests have limitations in predicting therapy response.
• Emerging genomic techniques show promise for more accurate prognostication and prediction.

Conclusions:

• Combining multiple diagnostic analyses, including genomic signatures, offers maximal patient benefit.
• Genomic assays have the potential for significant cost reduction by optimizing systemic treatment.
• Further development and integration of genomic approaches are expected to drive the proliferation of these assays in cancer care.