| Title |
Significance Analysis of Prognostic Signatures
|
|---|---|
| Published in |
PLoS Computational Biology, January 2013
|
| DOI | 10.1371/journal.pcbi.1002875 |
| Pubmed ID | |
| Authors |
Andrew H. Beck, Nicholas W. Knoblauch, Marco M. Hefti, Jennifer Kaplan, Stuart J. Schnitt, Aedin C. Culhane, Markus S. Schroeder, Thomas Risch, John Quackenbush, Benjamin Haibe-Kains |
| Abstract |
A major goal in translational cancer research is to identify biological signatures driving cancer progression and metastasis. A common technique applied in genomics research is to cluster patients using gene expression data from a candidate prognostic gene set, and if the resulting clusters show statistically significant outcome stratification, to associate the gene set with prognosis, suggesting its biological and clinical importance. Recent work has questioned the validity of this approach by showing in several breast cancer data sets that "random" gene sets tend to cluster patients into prognostically variable subgroups. This work suggests that new rigorous statistical methods are needed to identify biologically informative prognostic gene sets. To address this problem, we developed Significance Analysis of Prognostic Signatures (SAPS) which integrates standard prognostic tests with a new prognostic significance test based on stratifying patients into prognostic subtypes with random gene sets. SAPS ensures that a significant gene set is not only able to stratify patients into prognostically variable groups, but is also enriched for genes showing strong univariate associations with patient prognosis, and performs significantly better than random gene sets. We use SAPS to perform a large meta-analysis (the largest completed to date) of prognostic pathways in breast and ovarian cancer and their molecular subtypes. Our analyses show that only a small subset of the gene sets found statistically significant using standard measures achieve significance by SAPS. We identify new prognostic signatures in breast and ovarian cancer and their corresponding molecular subtypes, and we show that prognostic signatures in ER negative breast cancer are more similar to prognostic signatures in ovarian cancer than to prognostic signatures in ER positive breast cancer. SAPS is a powerful new method for deriving robust prognostic biological signatures from clinically annotated genomic datasets. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 50% |
| Brazil | 1 | 17% |
| Unknown | 2 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 50% |
| Scientists | 3 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 111 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 2 | 2% |
| United States | 2 | 2% |
| Netherlands | 1 | <1% |
| Ukraine | 1 | <1% |
| United Kingdom | 1 | <1% |
| Denmark | 1 | <1% |
| Belgium | 1 | <1% |
| Unknown | 102 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 41 | 37% |
| Student > Ph. D. Student | 16 | 14% |
| Student > Master | 8 | 7% |
| Other | 7 | 6% |
| Student > Bachelor | 5 | 5% |
| Other | 17 | 15% |
| Unknown | 17 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 35 | 32% |
| Medicine and Dentistry | 21 | 19% |
| Biochemistry, Genetics and Molecular Biology | 17 | 15% |
| Computer Science | 11 | 10% |
| Mathematics | 4 | 4% |
| Other | 6 | 5% |
| Unknown | 17 | 15% |
Attention Score in Context
This research output has an Altmetric Attention Score of 10. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 March 2022.
All research outputs
#3,561,374
of 25,374,647 outputs
Outputs from PLoS Computational Biology
#3,093
of 8,960 outputs
Outputs of similar age
#35,179
of 288,068 outputs
Outputs of similar age from PLoS Computational Biology
#32
of 137 outputs
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