Concordance among gene-expression-based predictors for breast cancer.

By C. Fan, D. Oh, L. Wessels, B. Weigelt, D. Nuyten, Andrew B. Nobel, L. V. van’t Veer, Charles M. Perou. New England Journal of Medicine, 2006

1,393 citations recorded by the source corpus.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b3fb1a0f3b6a3e8ae521512385d385c074963c47

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Citations in the corpus

Improved classification of breast cancer by analysis of genetic alterations and gene expression profiling (2011, methodology): “Many of these signatures have also been shown to be prognostic factors and identify patients at increased risk of developing distant metastases [45, 46], including a 70gene prognosis signature [1, 44], a 76 gene prognosis signature [47] and a 21-gene classifier for paraffin-embedded tissues (Oncotyp”
Annual Report to the Nation on the Status of Cancer, 1975–2011, Featuring Incidence of Breast Cancer Subtypes by Race/Ethnicity, Poverty, and State (2015, background): “The molecular subtypes can be approximated by HR/HER2 status; ie, Luminal A (HR+/HER2-), Luminal B (HR+/HER2+), HER2-enriched (HR-/HER2+), and triple-negative (HR-/HER2-) (19,21,22,25,26).”
Genetic determinants of cancer metastasis (2007, background): “And yet, largely non-overlapping signatures can identify the same patients in a given cohort, as recently demonstrated in a comparative analysis of the intrinsic-subtype, poor-prognosis, wound-response, and recurrence-score signature”
Epidemiology of basal-like breast cancer (2008, result): “The “intrinsic” classification system showed significant agreement in predicting clinical outcomes when compared with three other gene-expression based classification schemes, suggesting that these profiling methods identify distinct, stable biologic properties of breast tumors [9].”
Finding large average submatrices in high dimensional data (2009, methodology): “Breast cancer subtypes were initially identified using hierarchical clustering of gene expression data, and have subsequently been validated in several datasets [cf. Fan et al. (2006)] and across platforms [cf. Sorlie et al. (2003)].”
Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome (2011, result): “Contrasting with this diversity, meta-analyses of several outcome signatures have shown that they have essentially equivalent prognostic performances [35,36], and are highly correlated with proliferation [7–8,37], a predictor of breast cancer outcome that has been used for decades [38–40].”
Triple negative tumours: a critical review (2007, background): “Basal-like cancers, as defined by microarrays or by immunohistochemical surrogates, have been shown to have a more aggressive clinical behaviour.(10,42,43) In fact, some studies have demonstrated that expression of basal keratins is a prognostic factor independent of tumour size, grade and lymph nod”
Biological determinants of endocrine resistance in breast cancer (2009, result): “Because of their shared biological features, many of the signatures selected on the basis of correlations with outcome are largely concordant , both with each other and with more generally derived prognostic signatures that have not been tested for association with response to tamoxifen, for exampl”
The Influence of Feature Selection Methods on Accuracy, Stability and Interpretability of Molecular Signatures (2011, background): “bust manner. In the case of breast cancer prognostic signatures, for example, several recent studies have shown that functional analysis of the signatures can highlight coherent biological processes (Fan et al., 2006; Reyal et al., 2008; Shen et al., 2008; Abraham et al., 2010; Shi et al., 2010). Ju”