dorsal/arxiv
View SchemaOutcome signature genes in breast cancer: is there a unique set?
| Authors | Liat Ein-Dor, Itai Kela, Gad Getz, David Givol, Eytan Domany |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0409016 |
| URL | https://arxiv.org/abs/q-bio/0409016 |
Abstract
Motivation: Predicting the metastatic potential of primary malignant tissues has direct bearing on choice of therapy. Several microarray studies yielded gene sets whose expression profiles successfully predicted survival (Ramaswamy et al 2003; Sorlie et al 2001; van't Veer et al 2003). Nevertheless, the overlap between these gene sets is almost zero. Such small overlaps were observed also in other complex diseases (Lossos et al 2003; Miklos and Maleszka 2004), and the variables that could account for the differences had evoked a wide interest. One of the main open questions in this context is whether the disparity can be attributed only to trivial reasons such as different technologies, different patients and different types of analysis. Results: To answer this question we concentrated on one single breast cancer dataset, and analyzed it by one single method, the one which was used by van't Veer et al to produce a set of outcome predictive genes. We showed that in fact the resulting set of genes is not unique; it is strongly influenced by the subset of patients used for gene selection. Many equally predictive lists could have been produced from the same analysis. Three main properties of the data explain this sensitivity: (a) many genes are correlated with survival; (b) the differences between these correlations are small; (c) the correlations fluctuate strongly when measured over different subsets of patients. A possible biological explanation for these properties is discussed.
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"abstract": "Motivation: Predicting the metastatic potential of primary malignant tissues\nhas direct bearing on choice of therapy. Several microarray studies yielded\ngene sets whose expression profiles successfully predicted survival (Ramaswamy\net al 2003; Sorlie et al 2001; van\u0027t Veer et al 2003). Nevertheless, the\noverlap between these gene sets is almost zero. Such small overlaps were\nobserved also in other complex diseases (Lossos et al 2003; Miklos and Maleszka\n2004), and the variables that could account for the differences had evoked a\nwide interest. One of the main open questions in this context is whether the\ndisparity can be attributed only to trivial reasons such as different\ntechnologies, different patients and different types of analysis. Results: To\nanswer this question we concentrated on one single breast cancer dataset, and\nanalyzed it by one single method, the one which was used by van\u0027t Veer et al to\nproduce a set of outcome predictive genes. We showed that in fact the resulting\nset of genes is not unique; it is strongly influenced by the subset of patients\nused for gene selection. Many equally predictive lists could have been produced\nfrom the same analysis. Three main properties of the data explain this\nsensitivity: (a) many genes are correlated with survival; (b) the differences\nbetween these correlations are small; (c) the correlations fluctuate strongly\nwhen measured over different subsets of patients. A possible biological\nexplanation for these properties is discussed.",
"arxiv_id": "q-bio/0409016",
"authors": [
"Liat Ein-Dor",
"Itai Kela",
"Gad Getz",
"David Givol",
"Eytan Domany"
],
"categories": [
"q-bio.QM",
"q-bio.GN"
],
"title": "Outcome signature genes in breast cancer: is there a unique set?",
"url": "https://arxiv.org/abs/q-bio/0409016"
},
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