and other omics data can be assisted to recognize the sensitive
mutation by removing the passenger mutations.
1.The direct mapping of mutation information on pathway/net-
work knowledge. A network-based method has been used to
integrate copy number alteration data with human protein-
protein interaction networks and pathway databases to identify
pathways that are commonly disrupted in many different types
of cancer, which are likely essential for tumor formation in the
majority of the cancers [79].
2.The combination of mutation and transcriptome. As a typical
quantitative association approach, the eQTL (expression quan-
titative trait locus)-based analyses have been proposed to inves-
tigate the germ line determinants of gene expression in tumors
by using the multilevel information from The Cancer Genome
Atlas (TCGA) [80]. And in an investigation of the aggressive
lung tumor subtype with poor prognosis, an integrated ana-
lyses have been conducted to identify pathogenetically relevant
mutated genes by a generalizable framework for the identifica-
tion of biologically relevant genes in the context of high muta-
tional background [81].
3.The further consideration of epigenetic influence. A genome-
scale analysis of 276 samples has been analyzed to characterize
the somatic alterations in colorectal carcinoma, including
exome sequence, DNA copy number, promoter methylation,
and messenger RNA and microRNA expression [82–85]. Simi-
larly, 178 lung squamous cell carcinomas have been deeply
profiled to provide a comprehensive landscape of genomic
and epigenomic alterations in squamous cell lung cancers and
develop molecularly targeted agents for target treatment
[86–88].
4.The additional integration with protein expression. The direct
study of the functional proteome has the potential to provide a
wealth of information that complements and extends genomic,
epigenomic, and transcriptomic analysis. The resultant proteo-
mic data in TCGA can be integrated with genomic and tran-
scriptomic analyses of the same samples to identify
commonalities, differences, emergent pathways, and network
biology within and across tumor lineages [89]. By integrating
information across platforms including reverse phase protein
arrays, a hypothesis is held that much of the clinically observ-
able plasticity and heterogeneity occurs within, and not across,
the major biological subtypes of breast cancer [90, 91]. Besides,
the Tied Diffusion through Interacting Events (TieDIE) is
developed to integrate differentially expressed master tran-
scriptional regulators, functionally mutated genes, and differ-
entially activated kinases to synthesize a robust signalingIntegrative Analysis of Omics Big Data 119