Title |
A Differentiation-Based Phylogeny of Cancer Subtypes
|
---|---|
Published in |
PLoS Computational Biology, May 2010
|
DOI | 10.1371/journal.pcbi.1000777 |
Pubmed ID | |
Authors |
Markus Riester, Camille Stephan-Otto Attolini, Robert J. Downey, Samuel Singer, Franziska Michor |
Abstract |
Histopathological classification of human tumors relies in part on the degree of differentiation of the tumor sample. To date, there is no objective systematic method to categorize tumor subtypes by maturation. In this paper, we introduce a novel computational algorithm to rank tumor subtypes according to the dissimilarity of their gene expression from that of stem cells and fully differentiated tissue, and thereby construct a phylogenetic tree of cancer. We validate our methodology with expression data of leukemia, breast cancer and liposarcoma subtypes and then apply it to a broader group of sarcomas. This ranking of tumor subtypes resulting from the application of our methodology allows the identification of genes correlated with differentiation and may help to identify novel therapeutic targets. Our algorithm represents the first phylogeny-based tool to analyze the differentiation status of human tumors. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 3 | 2% |
United States | 3 | 2% |
France | 2 | 2% |
Spain | 2 | 2% |
Sweden | 1 | <1% |
Finland | 1 | <1% |
Canada | 1 | <1% |
Italy | 1 | <1% |
Switzerland | 1 | <1% |
Other | 1 | <1% |
Unknown | 106 | 87% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 34 | 28% |
Researcher | 30 | 25% |
Student > Master | 13 | 11% |
Professor > Associate Professor | 10 | 8% |
Student > Bachelor | 9 | 7% |
Other | 19 | 16% |
Unknown | 7 | 6% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 60 | 49% |
Biochemistry, Genetics and Molecular Biology | 16 | 13% |
Computer Science | 15 | 12% |
Medicine and Dentistry | 7 | 6% |
Immunology and Microbiology | 3 | 2% |
Other | 11 | 9% |
Unknown | 10 | 8% |