Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures

By P. Wirapati, C. Sotiriou, Susanne Kunkel, P. Farmer, S. Pradervand, Benjamin Haibe-Kains, C. Desmedt, M. Ignatiadis, T. Sengstag, F. Schütz and 3 more. Breast Cancer Research, 2008

862 citations recorded by the source corpus.

IntroductionBreast cancer subtyping and prognosis have been studied extensively by gene expression profiling, resulting in disparate signatures with little overlap in their constituent genes. Although a previous study demonstrated a prognostic concordance among gene expression signatures, it was limited to only one dataset and did not fully elucidate how the different genes were related to one another nor did it examine the contribution of well-known biological processes of breast cancer tumorigenesis to their prognostic performance.MethodTo address the above issues and to further validate these initial findings, we performed the largest meta-analysis of publicly available breast cancer gene expression and clinical data, which are comprised of 2,833 breast tumors. Gene coexpression modules of three key biological processes in breast cancer (namely, proliferation, estrogen receptor [ER], and HER2 signaling) were used to dissect the role of constituent genes of nine prognostic signatures.ResultsUsing a meta-analytical approach, we consolidated the signatures associated with ER signaling, ERBB2 amplification, and proliferation. Previously published expression-based nomenclature of breast cancer 'intrinsic' subtypes can be mapped to the three modules, namely, the ER-/HER2- (basal-like), the HER2+ (HER2-like), and the low- and high-proliferation ER+/HER2- subtypes (luminal A and B). We showed that all nine prognostic signatures exhibited a similar prognostic performance in the entire dataset. Their prognostic abilities are due mostly to the detection of proliferation activity. Although ER- status (basal-like) and ERBB2+ expression status correspond to bad outcome, they seem to act through elevated expression of proliferation genes and thus contain only indirect information about prognosis. Clinical variables measuring the extent of tumor progression, such as tumor size and nodal status, still add independent prognostic information to proliferation genes.ConclusionThis meta-analysis unifies various results of previous gene expression studies in breast cancer. It reveals connections between traditional prognostic factors, expression-based subtyping, and prognostic signatures, highlighting the important role of proliferation in breast cancer prognosis.

Subfields: Cancer Research, Immunology, Oncology

View Paper: https://www.semanticscholar.org/paper/02f1b4707cc52cdcbcc31dcd7b0f0b47577db8d4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

In Immunology

In Oncology

Citations in the corpus

Biological determinants of endocrine resistance in breast cancer (2009, background): “In a recent meta-analysis that compared nine prognostic signatures, the subset of genes related to proliferation within each signature was at least as good a predictor as the whole signatur”
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, background): “Many different multi-gene assays provide prognostic information, primarily derived from their sampling of proliferation genes [97], which emphasizes the need for some measure of proliferation in any surrogate classification.”
Comprehensive literature review and statistical considerations for microarray meta-analysis (2012, background): “Researchers usually apply arbitrary criteria, such as number of samples or array platforms (e.g. (112,133,134) and many others), to make the decision.”
Breast cancer intrinsic subtype classification, clinical use and future trends. (2015, background): “However, the prognostic value provided by MapQuant Dx is only applicable to ER positive tumors [53].”
Multi-omics Data Integration, Interpretation, and Its Application (2020, background): “Wirapati et al 66 showed the application of PARADIGM to derive novel insights into breast cancer using copy number and gene expression data (Table 2).”
Tailoring therapies—improving the management of early breast cancer: St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2015 (2015, background): “The various multiparameter molecular marker assays all include genes re ﬂ ecting proliferative activity: indeed, it has been suggested that the majority of the prognostic information in these tests comes from the proliferative genes included [23].”
The Influence of Feature Selection Methods on Accuracy, Stability and Interpretability of Molecular Signatures (2011, background): “On the positive side, some authors noticed that the biological functions captured by different signatures are similar, in spite of the little overlap between them at the gene level (Shen et al., 2008; Reyal et al., 2008; Wirapati et al., 2008).”