PMID- 21486456
OWN - NLM
STAT- MEDLINE
DCOM- 20110922
LR  - 20211020
IS  - 1755-8794 (Electronic)
IS  - 1755-8794 (Linking)
VI  - 4
DP  - 2011 Apr 12
TI  - Virtual CGH: an integrative approach to predict genetic abnormalities from gene 
      expression microarray data applied in lymphoma.
PG  - 32
LID - 10.1186/1755-8794-4-32 [doi]
AB  - BACKGROUND: Comparative Genomic Hybridization (CGH) is a molecular approach for 
      detecting DNA Copy Number Alterations (CNAs) in tumor, which are among the key 
      causes of tumorigenesis. However in the post-genomic era, most studies in cancer 
      biology have been focusing on Gene Expression Profiling (GEP) but not CGH, and as 
      a result, an enormous amount of GEP data had been accumulated in public databases 
      for a wide variety of tumor types. We exploited this resource of GEP data to 
      define possible recurrent CNAs in tumor. In addition, the CNAs identified by GEP 
      would be more functionally relevant CNAs in the disease pathogenesis since the 
      functional effects of CNAs can be reflected by altered gene expression. METHODS: 
      We proposed a novel computational approach, coined virtual CGH (vCGH), which 
      employs hidden Markov models (HMMs) to predict DNA CNAs from their corresponding 
      GEP data. vCGH was first trained on the paired GEP and CGH data generated from a 
      sufficient number of tumor samples, and then applied to the GEP data of a new 
      tumor sample to predict its CNAs. RESULTS: Using cross-validation on 190 Diffuse 
      Large B-Cell Lymphomas (DLBCL), vCGH achieved 80% sensitivity, 90% specificity 
      and 90% accuracy for CNA prediction. The majority of the recurrent regions 
      defined by vCGH are concordant with the experimental CGH, including gains of 1q, 
      2p16-p14, 3q27-q29, 6p25-p21, 7, 11q, 12 and 18q21, and losses of 6q, 8p23-p21, 
      9p24-p21 and 17p13 in DLBCL. In addition, vCGH predicted some recurrent 
      functional abnormalities which were not observed in CGH, including gains of 1p, 
      2q and 6q and losses of 1q, 6p and 8q. Among those novel loci, 1q, 6q and 8q were 
      significantly associated with the clinical outcomes in the DLBCL patients (p < 
      0.05). CONCLUSIONS: We developed a novel computational approach, vCGH, to predict 
      genome-wide genetic abnormalities from GEP data in lymphomas. vCGH can be 
      generally applied to other types of tumors and may significantly enhance the 
      detection of functionally important genetic abnormalities in cancer research.
FAU - Geng, Huimin
AU  - Geng H
AD  - Department of Computer Science, University of Nebraska at Omaha, Omaha, NE 68182, 
      USA.
FAU - Iqbal, Javeed
AU  - Iqbal J
FAU - Chan, Wing C
AU  - Chan WC
FAU - Ali, Hesham H
AU  - Ali HH
LA  - eng
GR  - CA36727/CA/NCI NIH HHS/United States
GR  - CA84967/CA/NCI NIH HHS/United States
GR  - P20 RR16469/RR/NCRR NIH HHS/United States
GR  - U10 CA114778/CA/NCI NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, Non-U.S. Gov't
DEP - 20110412
PL  - England
TA  - BMC Med Genomics
JT  - BMC medical genomics
JID - 101319628
SB  - IM
MH  - Chromosomes, Human/genetics
MH  - Comparative Genomic Hybridization/*methods
MH  - *Gene Expression Profiling
MH  - Genes, Neoplasm/genetics
MH  - Humans
MH  - Lymphoma, Large B-Cell, Diffuse/*genetics
MH  - *Oligonucleotide Array Sequence Analysis
MH  - Reproducibility of Results
MH  - *Systems Integration
MH  - *User-Computer Interface
PMC - PMC3086850
EDAT- 2011/04/14 06:00
MHDA- 2011/09/23 06:00
PMCR- 2011/04/12
CRDT- 2011/04/14 06:00
PHST- 2010/06/23 00:00 [received]
PHST- 2011/04/12 00:00 [accepted]
PHST- 2011/04/14 06:00 [entrez]
PHST- 2011/04/14 06:00 [pubmed]
PHST- 2011/09/23 06:00 [medline]
PHST- 2011/04/12 00:00 [pmc-release]
AID - 1755-8794-4-32 [pii]
AID - 10.1186/1755-8794-4-32 [doi]
PST - epublish
SO  - BMC Med Genomics. 2011 Apr 12;4:32. doi: 10.1186/1755-8794-4-32.