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Updated: Jul 14, 2026
Optimization of a Multiplex RNA-based Expression Assay Using Breast Cancer Archival Material
Published on: August 1, 2018
Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes
Joel S Parker1, Michael Mullins1, Maggie C U Cheang1
1From the Lineberger Comprehensive Cancer Center and Departments of Genetics, Pathology and Laboratory Medicine, and Department of Statistics and Operations Research, Carolina Center for Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC; Department of Pathology, University of Utah Health Sciences Center; ARUP Institute for Clinical and Experimental Pathology, Salt Lake City, UT; Genetic Pathology Evaluation Centre, Department of Pathology, Vancouver Coastal Health Research Institute; Departments of Pathology and Radiation Oncology, British Columbia Cancer Agency; Department of Pathology, University of British Columbia, Vancouver, British Columbia, Canada; Genome Sequencing Facility and Division of Oncology, Department of Medicine, Washington University School of Medicine, St Louis, MO; and Department of Pathology, Thomas Jefferson University, Philadelphia, PA.
Purpose:
To improve on current standards for breast cancer prognosis and prediction of chemotherapy benefit by developing a risk model that incorporates the gene expression-based "intrinsic" subtypes luminal A, luminal B, HER2-enriched, and basal-like.
Methods:
A 50-gene subtype predictor was developed using microarray and quantitative reverse transcriptase polymerase chain reaction data from 189 prototype samples. Test sets from 761 patients (no systemic therapy) were evaluated for prognosis, and 133 patients were evaluated for prediction of pathologic complete response (pCR) to a taxane and anthracycline regimen.
Results:
The intrinsic subtypes as discrete entities showed prognostic significance (P = 2.26E-12) and remained significant in multivariable analyses that incorporated standard parameters (estrogen receptor status, histologic grade, tumor size, and node status). A prognostic model for node-negative breast cancer was built using intrinsic subtype and clinical information. The C-index estimate for the combined model (subtype and tumor size) was a significant improvement on either the clinicopathologic model or subtype model alone. The intrinsic subtype model predicted neoadjuvant chemotherapy efficacy with a negative predictive value for pCR of 97%.
Conclusion:
Diagnosis by intrinsic subtype adds significant prognostic and predictive information to standard parameters for patients with breast cancer. The prognostic properties of the continuous risk score will be of value for the management of node-negative breast cancers. The subtypes and risk score can also be used to assess the likelihood of efficacy from neoadjuvant chemotherapy.
