TITLE:
Expressogram: A Visualization of Cytogenetic Landscape in Cancer Samples Using Gene Expression Microarrays
AUTHORS:
Peikai Chen, Y. S. Hung
KEYWORDS:
Microarrays; Cytogenetics; Cancer Landscape; Copy Number Aberrations
JOURNAL NAME:
Engineering,
Vol.5 No.10B,
December
26,
2013
ABSTRACT: In cancer genomes, there are frequent copy number
aberration (CNA) events, some of which are believed to be tumori-genic.
While copy numbers can be detected by a number of technologies, e.g., SNP
arrays, their relations with gene expressions are not well clarified. Here, we
describe an approach to visualize the global relations between copy numbers
and gene expressions using expression microarrays. We mapped the gene
expression signals detected by microar-ray probesets onto a reference human genome,
the RefSeq, based on their annotated physical positions, resulting in a
landscape that we called expressogram. To study the expressograms under various
conditions and their relations with cytogenetic events, such as CNAs, we
obtained three classes of array samples, namely samples of a cancer (e.g.,
liver cancer), normal samples in the same tissue, and normal samples of other
tissues. We developed a Bayesian based algorithm to estimate
a background signal from the latter two sources for the cancer samples. By
subtracting the estimated background from the raw signals of the cancer
samples, and subjecting the differences to a kernel-based smoothing scheme, we
produced an expressogram that shows strong consistency with the copy numbers.
This indicates that copy numbers are on average positively correlated with and
have strong impacts on gene expressions. To further explore the applicability
of these findings, we submit the expressograms to the significant CNA detection
algorithm GISTIC. The results strongly indicate that expressogram can also be
used to infer copy number events and significant regions of CNA affected
dysregulation.