Abstract
As molecular profiling data continues to accumulate, the design of integrative computational analyses that can provide insights into the dynamic aspects of cancer progression becomes feasible. Here, we present a novel computational method for the construction of cancer progression models based on the analysis of static tumor samples. We demonstrate the reliability of the method with simulated data, and describe the application to breast cancer data. Our findings support a linear, branching model for breast cancer progression. An interactive model facilitates the identification of key molecular events in the advance of disease to malignancy.
| Original language | English (US) |
|---|---|
| Number of pages | 1 |
| Journal | Genome Biology |
| Volume | 15 |
| Issue number | 8 |
| DOIs | |
| State | Published - Jan 1 2014 |
| Externally published | Yes |
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ASJC Scopus subject areas
- Ecology, Evolution, Behavior and Systematics
- Genetics
- Cell Biology
Cite this
Cancer progression modeling using static sample data. / Sun, Yijun; Yao, Jin; Nowak, Norma J.; Goodison, Steven.
In: Genome Biology, Vol. 15, No. 8, 01.01.2014.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Cancer progression modeling using static sample data
AU - Sun, Yijun
AU - Yao, Jin
AU - Nowak, Norma J.
AU - Goodison, Steven
PY - 2014/1/1
Y1 - 2014/1/1
N2 - As molecular profiling data continues to accumulate, the design of integrative computational analyses that can provide insights into the dynamic aspects of cancer progression becomes feasible. Here, we present a novel computational method for the construction of cancer progression models based on the analysis of static tumor samples. We demonstrate the reliability of the method with simulated data, and describe the application to breast cancer data. Our findings support a linear, branching model for breast cancer progression. An interactive model facilitates the identification of key molecular events in the advance of disease to malignancy.
AB - As molecular profiling data continues to accumulate, the design of integrative computational analyses that can provide insights into the dynamic aspects of cancer progression becomes feasible. Here, we present a novel computational method for the construction of cancer progression models based on the analysis of static tumor samples. We demonstrate the reliability of the method with simulated data, and describe the application to breast cancer data. Our findings support a linear, branching model for breast cancer progression. An interactive model facilitates the identification of key molecular events in the advance of disease to malignancy.
UR - http://www.scopus.com/inward/record.url?scp=84964315153&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84964315153&partnerID=8YFLogxK
U2 - 10.1186/s13059-014-0440-0
DO - 10.1186/s13059-014-0440-0
M3 - Article
C2 - 25155694
AN - SCOPUS:84964315153
VL - 15
JO - Genome Biology
JF - Genome Biology
SN - 1474-7596
IS - 8
ER -