Supervised risk predictor of breast cancer based on intrinsic subtypes.

By J. Parker, Michael Mullins, M. Cheang, S. Leung, D. Voduc, T. Vickery, S. Davies, C. Fauron, Xiaping He, Zhiyuan Hu and 10 more. 2009

4,443 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Cancer Research, Genetics

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Citations in the corpus

Opportunities and obstacles for deep learning in biology and medicine (2017, background): “Substantial heterogeneity still remains within these four subtypes [22,23].”
Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013 (2013, methodology): “Intrinsic subtypes Identification of intrinsic subtypes is most precise using molecular technologies [22].”
Human Tumor-Associated Macrophage and Monocyte Transcriptional Landscapes Reveal Cancer-Specific Reprogramming, Biomarkers, and Therapeutic Targets (2019, methodology): “Samples were assigned to breast cancer subtypes based on hierarchical clustering of PAM50 genes (Parker et al., 2009).”
Data-Driven Tree Transforms and Metrics (2017, methodology): “Below the organized data we provide clinical details for each of the samples: two types of breast cancer subtype labels, the refined labels introduced in [42] and the standard PAM50 subtypes [39], hormone receptor status (ER, PR) and HER2 status.”
The clonal and mutational evolution spectrum of primary triple-negative breast cancers (2012, background): “16) analysis on RNA-seq expression profiles) and 43% of non-basal TNBC cases harbouring a validated somatic mutation.”
Comprehensive molecular portraits of human breast tumors (2012, positive): “Supervised clustering of mRNA expression data has reproducibly established that breast cancers encompass several distinct disease entities, often referred to astheintrinsicsubtypesofbreast cancer 13,14 .”
Comprehensive Genomic Analysis Identifies Novel Subtypes and Targets of Triple-negative Breast Cancer (2014, methodology): “Intrinsic subtypes were established with the PAM50 Breast Cancer Intrinsic Classifier (19) and compared with 67 non-TNBC randomly sampled tumors representing 80% of the assigned sample (confirmed by Pearson correlation).”
Triple-negative breast cancer--current status and future directions. (2009, background): “This group has recently been indicated to represent an artifact of having a high percentage of contamination of normal breast tissue in the specimen rather than a distinct BC subtype [10].”
Genome Remodeling in a Basal-like Breast Cancer Metastasis and Xenograft (2010, background): “One validated translocation t(4;9)(188855443;139022258), assembled in all three tumours, involved a long terminal repeat (LTR) from the ERVL-MaLR family on chromosome 4 and ABCA2 on chromosome 9.”
Clinical implications of the intrinsic molecular subtypes of breast cancer. (2015, neutral): “defined bymRNA expression only [7].”