Journal of Clinical Oncology · 2023 · 160 citations · 0 references
MedicineBioinformaticsOncologyBreast OncologyMolecular OncologyBreast Cancer OncologyBreast Cancer Risk AssessmentCancer GenomicsCancer RiskMolecular Tumor ProfilingRisk PredictionBreast Cancer Molecular SubtypesBreast Cancer PrognosisBreast Cancer Risk ModelsBreast Risk AssessmentCancer Risk Prediction ModelsClinical Risk Prediction ToolsMolecular Subtype PredictionSubtype Prognosis Modeling
The study aims to enhance breast cancer prognosis and chemotherapy benefit prediction by developing a risk model that integrates gene‑expression–based intrinsic subtypes (luminal A, luminal B, HER2‑enriched, basal‑like). A 50‑gene subtype predictor was built from microarray and qRT‑PCR data of 189 samples, then applied to 761 untreated patients for prognosis and to 133 patients for predicting pathologic complete response to taxane‑anthracycline therapy; a prognostic model for node‑negative breast cancer was subsequently constructed using intrinsic subtype and clinical variables. Intrinsic subtypes were independently prognostic and, when combined with tumor size, improved the C‑index over clinicopathologic or subtype models alone; the subtype model also predicted neoadjuvant chemotherapy efficacy with a 97 % negative predictive value for pathologic complete response, demonstrating that intrinsic subtype and the derived risk score provide significant prognostic and predictive information for breast cancer management.
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.