OBJECTIVE: To describe a genetic progression pathway in breast cancer by a maximum likelihood-based tree model representing the dependencies between chromosomal imbalances. STUDY DESIGN: One hundred six cases were studied by comparative genomic hybridization, followed by maximum likelihood estimation of an oncogenetic tree model. RESULTS: The tree model identified 3 clusters with correlated chromosomal imbalances. The first cluster included losses at 4q, 5q, 6q, 9p, 13q and a gain at 17q; the second cluster included gains at 1q, 8q, 16p and 20q; the third cluster included losses at 8p, 11q, 16q and 18q. The imbalances nearest the root of the tree were the loss at 13q (cluster 1), the gain at 1q (cluster 2) and the loss at 18q (cluster 3), reflecting an early change in breast cancer evolution. Cox regression analysis revealed the tumor stage and the grade as relevant for overall survival (p = 0.001) and the tumor stage, the grade and the loss at 16q as relevant for disease-free survival (p = 0.001). CONCLUSION: Methods like oncogenetic tree analysis provide insights into the genetic progression of breast cancer and may extract relevant markers detected by screening methods like comparative genomic hybridization for further studies.
OBJECTIVE: To describe a genetic progression pathway in breast cancer by a maximum likelihood-based tree model representing the dependencies between chromosomal imbalances. STUDY DESIGN: One hundred six cases were studied by comparative genomic hybridization, followed by maximum likelihood estimation of an oncogenetic tree model. RESULTS: The tree model identified 3 clusters with correlated chromosomal imbalances. The first cluster included losses at 4q, 5q, 6q, 9p, 13q and a gain at 17q; the second cluster included gains at 1q, 8q, 16p and 20q; the third cluster included losses at 8p, 11q, 16q and 18q. The imbalances nearest the root of the tree were the loss at 13q (cluster 1), the gain at 1q (cluster 2) and the loss at 18q (cluster 3), reflecting an early change in breast cancer evolution. Cox regression analysis revealed the tumor stage and the grade as relevant for overall survival (p = 0.001) and the tumor stage, the grade and the loss at 16q as relevant for disease-free survival (p = 0.001). CONCLUSION: Methods like oncogenetic tree analysis provide insights into the genetic progression of breast cancer and may extract relevant markers detected by screening methods like comparative genomic hybridization for further studies.
Authors: Hesed M Padilla-Nash; Karen Hathcock; Nicole E McNeil; David Mack; Daniel Hoeppner; Rea Ravin; Turid Knutsen; Raluca Yonescu; Danny Wangsa; Kathleen Dorritie; Linda Barenboim; Yue Hu; Thomas Ried Journal: Genes Chromosomes Cancer Date: 2011-12-08 Impact factor: 5.006
Authors: A M Brewster; P Thompson; A A Sahin; K Do; M Edgerton; J L Murray; S Tsavachidis; R Zhou; Y Liu; L Zhang; G Mills; M Bondy Journal: Cancer Prev Res (Phila) Date: 2011-07-27
Authors: Yu-Kang Cheng; Rameen Beroukhim; Ross L Levine; Ingo K Mellinghoff; Eric C Holland; Franziska Michor Journal: PLoS Comput Biol Date: 2012-01-05 Impact factor: 4.475
Authors: Brigitte L Thériault; Paulina Cybulska; Patricia A Shaw; Brenda L Gallie; Marcus Q Bernardini Journal: J Ovarian Res Date: 2014-12-21 Impact factor: 4.234