Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes

# Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes > OpenAlex Metadata Hub · https://openalex.org/W2132619562 ## Bibliographic - **DOI:** 10.1200/jco.2008.18.1370 - **Year:** 2009 - **Citations:** 4902 - **Open Access:** Yes (hybrid) - **License:** public-domain - **Source:** https://ascopubs.org/doi/pdfdirect/10.1200/JCO.2008.18.1370?role=tab ## Authors - Joel S. Parker - Michael E. Mullins - Maggie C.U. Cheang - Samuel Leung - David Voduc - Tammi L. Vickery - Sherri R. Davies - Christiane Fauron - Xiaping He - Zhiyuan Hu - John F. Quackenbush - Inge J. Stijleman - Juan Palazzo - J. S. Marron - Andrew B. Nobel - Elaine R. Mardis - Torsten O. Nielsen - Matthew J. Ellis - Charles M. Perou - Philip S. Bernard ## Abstract UNLABELLED: 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. ## Keywords Medicine, Breast cancer, Oncology, Cancer, Internal medicine ## Concepts - Medicine - Breast cancer - Oncology - Cancer - Internal medicine --- *Metadata only — full text not imported unless Open Access license permits.*

Bài “Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes” được TradingBase chuyển thành Knowledge Product cho trader — không phải trang đọc abstract OpenAlex. Tóm lược học thuật (đã diễn giải): UNLABELLED: 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 pr… Phần Trading Insights bên dưới nối nghiên cứu với Forex, vàng, USD, lãi suất và risk regime — để bạn đưa vào journal và playbook. Metadata DOI/OA chỉ là rail tham chiếu; nội dung chính là summary, takeaways và ứng dụng thị trường do Content Factory sinh.