Genetic background may contribute to PAM50 gene expression breast cancer subtype assignments.
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ABSTRACT:
SUBMITTER: Hu Y
PROVIDER: S-EPMC3756056 | biostudies-literature | 2013
REPOSITORIES: biostudies-literature
Publications
Genetic background may contribute to PAM50 gene expression breast cancer subtype assignments.
Hu Ying Y Bai Ling L Geiger Thomas T Goldberger Natalie N Walker Renard C RC Green Jeffery E JE Wakefield Lalage M LM Hunter Kent W KW
PloS one 20130828 8
Recent advances in genome wide transcriptional analysis have provided greater insights into the etiology and heterogeneity of breast cancer. Molecular signatures have been developed that stratify the conventional estrogen receptor positive or negative categories into subtypes that are associated with differing clinical outcomes. It is thought that the expression patterns of the molecular subtypes primarily reflect cell-of-origin or tumor driver mutations. In this study however, using a genetical ...[more]
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Project description:Motivation and backgroundThe patient's immune system plays an important role in cancer pathogenesis, prognosis and susceptibility to treatment. Recent work introduced an immune related breast cancer. This subtyping is based on the expression profiles of the tumor samples. Specifically, one study showed that analyzing 658 genes can lead to a signature for subtyping tumors. Furthermore, this classification is independent of other known molecular and clinical breast cancer subtyping. Finally, that study shows that the suggested subtyping has significant prognostic implications.ResultsIn this work we develop an efficient signature associated with survival in breast cancer. We begin by developing a more efficient signature for the above-mentioned breast cancer immune-based subtyping. This signature represents better performance with a set of 579 genes that obtains an improved Area Under Curve (AUC). We then determine a set of 193 genes and an associated classification rule that yield subtypes with a much stronger statistically significant (log rank p-value < 2 × 10-4 in a test cohort) difference in survival. To obtain these improved results we develop a feature selection process that matches the high-dimensionality character of the data and the dual performance objectives, driven by survival and anchored by the literature subtyping.
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