PMID- 27362268
OWN - NLM
STAT- MEDLINE
DCOM- 20170724
LR  - 20240327
IS  - 1932-6203 (Electronic)
IS  - 1932-6203 (Linking)
VI  - 11
IP  - 6
DP  - 2016
TI  - FISHtrees 3.0: Tumor Phylogenetics Using a Ploidy Probe.
PG  - e0158569
LID - 10.1371/journal.pone.0158569 [doi]
LID - e0158569
AB  - Advances in fluorescence in situ hybridization (FISH) make it feasible to detect 
      multiple copy-number changes in hundreds of cells of solid tumors. Studies using 
      FISH, sequencing, and other technologies have revealed substantial intra-tumor 
      heterogeneity. The evolution of subclones in tumors may be modeled by 
      phylogenies. Tumors often harbor aneuploid or polyploid cell populations. Using a 
      FISH probe to estimate changes in ploidy can guide the creation of trees that 
      model changes in ploidy and individual gene copy-number variations. We present 
      FISHtrees 3.0, which implements a ploidy-based tree building method based on 
      mixed integer linear programming (MILP). The ploidy-based modeling in FISHtrees 
      includes a new formulation of the problem of merging trees for changes of a 
      single gene into trees modeling changes in multiple genes and the ploidy. When 
      multiple samples are collected from each patient, varying over time or tumor 
      regions, it is useful to evaluate similarities in tumor progression among the 
      samples. Therefore, we further implemented in FISHtrees 3.0 a new method to build 
      consensus graphs for multiple samples. We validate FISHtrees 3.0 on a simulated 
      data and on FISH data from paired cases of cervical primary and metastatic tumors 
      and on paired breast ductal carcinoma in situ (DCIS) and invasive ductal 
      carcinoma (IDC). Tests on simulated data show improved accuracy of the 
      ploidy-based approach relative to prior ploidyless methods. Tests on real data 
      further demonstrate novel insights these methods offer into tumor progression 
      processes. Trees for DCIS samples are significantly less complex than trees for 
      paired IDC samples. Consensus graphs show substantial divergence among most 
      paired samples from both sets. Low consensus between DCIS and IDC trees may help 
      explain the difficulty in finding biomarkers that predict which DCIS cases are at 
      most risk to progress to IDC. The FISHtrees software is available at 
      ftp://ftp.ncbi.nih.gov/pub/FISHtrees.
FAU - Gertz, E Michael
AU  - Gertz EM
AUID- ORCID: 0000-0001-8390-4387
AD  - Computational Biology Branch, National Center for Biotechnology Information, U.S. 
      National Institutes of Health, Bethesda, MD, United States of America.
FAU - Chowdhury, Salim Akhter
AU  - Chowdhury SA
AD  - Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, 
      United States of America.
AD  - Carnegie Mellon/University of Pittsburgh Joint Ph.D. Program in Computational 
      Biology, Pittsburgh, PA, United States of America.
FAU - Lee, Woei-Jyh
AU  - Lee WJ
AD  - Computational Biology Branch, National Center for Biotechnology Information, U.S. 
      National Institutes of Health, Bethesda, MD, United States of America.
FAU - Wangsa, Darawalee
AU  - Wangsa D
AD  - Section of Cancer Genomics, Genetics Branch, Center for Cancer Research, National 
      Cancer Institute, U.S. National Institutes of Health, Bethesda, MD, United States 
      of America.
FAU - Heselmeyer-Haddad, Kerstin
AU  - Heselmeyer-Haddad K
AD  - Section of Cancer Genomics, Genetics Branch, Center for Cancer Research, National 
      Cancer Institute, U.S. National Institutes of Health, Bethesda, MD, United States 
      of America.
FAU - Ried, Thomas
AU  - Ried T
AD  - Section of Cancer Genomics, Genetics Branch, Center for Cancer Research, National 
      Cancer Institute, U.S. National Institutes of Health, Bethesda, MD, United States 
      of America.
FAU - Schwartz, Russell
AU  - Schwartz R
AD  - Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, 
      United States of America.
AD  - Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, 
      United States of America.
FAU - Schaffer, Alejandro A
AU  - Schaffer AA
AD  - Computational Biology Branch, National Center for Biotechnology Information, U.S. 
      National Institutes of Health, Bethesda, MD, United States of America.
LA  - eng
GR  - R01 CA140214/CA/NCI NIH HHS/United States
PT  - Journal Article
DEP - 20160630
PL  - United States
TA  - PLoS One
JT  - PloS one
JID - 101285081
RN  - 0 (Biomarkers, Tumor)
SB  - IM
MH  - Biomarkers, Tumor/genetics
MH  - Breast Neoplasms/*genetics/pathology
MH  - Carcinoma, Ductal, Breast/*genetics/pathology
MH  - Carcinoma, Intraductal, Noninfiltrating/*genetics/pathology
MH  - *Databases, Genetic
MH  - Female
MH  - Humans
MH  - In Situ Hybridization, Fluorescence/*methods
MH  - Ploidies
MH  - Uterine Cervical Neoplasms/*genetics/pathology
PMC - PMC4928784
COIS- Competing Interests: The authors have declared that no competing interests exist.
EDAT- 2016/07/01 06:00
MHDA- 2017/07/25 06:00
PMCR- 2016/06/30
CRDT- 2016/07/01 06:00
PHST- 2016/01/29 00:00 [received]
PHST- 2016/06/19 00:00 [accepted]
PHST- 2016/07/01 06:00 [entrez]
PHST- 2016/07/01 06:00 [pubmed]
PHST- 2017/07/25 06:00 [medline]
PHST- 2016/06/30 00:00 [pmc-release]
AID - PONE-D-16-04236 [pii]
AID - 10.1371/journal.pone.0158569 [doi]
PST - epublish
SO  - PLoS One. 2016 Jun 30;11(6):e0158569. doi: 10.1371/journal.pone.0158569. 
      eCollection 2016.