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A novel network regularized matrix decomposition method to detect mutated cancer
genes in tumour samples with inter-patient heterogeneity
#MMPMID28588243
Xi J
; Li A
; Wang M
Sci Rep
2017[Jun]; 7
(1
): 2855
PMID28588243
show ga
Inter-patient heterogeneity is a major challenge for mutated cancer genes
detection which is crucial to advance cancer diagnostics and therapeutics. To
detect mutated cancer genes in heterogeneous tumour samples, a prominent strategy
is to determine whether the genes are recurrently mutated in their interaction
network context. However, recent studies show that some cancer genes in different
perturbed pathways are mutated in different subsets of samples. Subsequently,
these genes may not display significant mutational recurrence and thus remain
undiscovered even in consideration of network information. We develop a novel
method called mCGfinder to efficiently detect mutated cancer genes in tumour
samples with inter-patient heterogeneity. Based on matrix decomposition framework
incorporated with gene interaction network information, mCGfinder can
successfully measure the significance of mutational recurrence of genes in a
subset of samples. When applying mCGfinder on TCGA somatic mutation datasets of
five types of cancers, we find that the genes detected by mCGfinder are
significantly enriched for known cancer genes, and yield substantially smaller
p-values than other existing methods. All the results demonstrate that mCGfinder
is an efficient method in detecting mutated cancer genes.
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