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.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117 J+Mol+Cell+Biol
2015 ; 7
(3
): 203-13
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An information theoretic method to identify combinations of genomic alterations
that promote glioblastoma
#MMPMID25941339
Melamed RD
; Wang J
; Iavarone A
; Rabadan R
J Mol Cell Biol
2015[Jun]; 7
(3
): 203-13
PMID25941339
show ga
Tumors are the result of accumulated genomic alterations that cooperate
synergistically to produce uncontrollable cell growth. Although identifying
recurrent alterations among large collections of tumors provides a way to
pinpoint genes that endow a selective advantage in oncogenesis and progression,
it fails to address the genetic interactions behind this selection process. A
non-random pattern of co-mutated genes is evidence for selective forces acting on
tumor cells that harbor combinations of these genetic alterations. Although
existing methods have successfully identified mutually exclusive gene sets, no
current method can systematically discover more general genetic relationships. We
develop Genomic Alteration Modules using Total Correlation (GAMToC), an
information theoretic framework that integrates copy number and mutation data to
identify gene modules with any non-random pattern of joint alteration.
Additionally, we present the Seed-GAMToC procedure, which uncovers the mutational
context of any putative cancer gene. The software is publicly available. Applied
to glioblastoma multiforme samples, GAMToC results show distinct subsets of
co-occurring mutations, suggesting distinct mutational routes to cancer and
providing new insight into mutations associated with proneural, proneural/G-CIMP,
and classical types of the disease. The results recapitulate known relationships
such as mutual exclusive mutations, place these alterations in the context of
other mutations, and find more complex relationships such as conditional mutual
exclusivity.