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Wavelet Transformations of Tumor Expression Profiles Reveals a Pervasive Genome-Wide Imprinting of Aneuploidy on the Cancer Transcriptome

¹ Cellular and Molecular Research, ² Division of Medical Science, National Cancer Centre, ³ Genome Institute of Singapore, ⁴ Department of Physiology, Faculty of Medicine, National University of Singapore, Singapore, Republic of Singapore

Requests for reprints: National Cancer Center/Genome Institute of Singapore, 11 Hospital Drive, Singapore 169610. Phone: 65-6-436-8385; Fax: 65-6-226-5694; E-mail: cmrtan{at}nccs.com.sg.

Aneuploidy is frequently observed in many human cancers, but its global effects on the cancer transcriptome are controversial. We did a systematic and unbiased genome-wide survey to determine the extent a tumor's abnormal karyotype (chromosomal amplifications and deletions) is detectably "imprinted" onto that tumor's gene expression profile. By using a novel methodology employing wavelet transform signal-processing algorithms to identify genomic regions of coordinated gene expression (wavelet variance scanning), we analyzed a series of gastric cancer cell lines and identified >100 genomic regions exhibiting distinct patterns of subtle but significant coordinated transcription, ranging from tens to hundreds of genes. A large majority (80%) of these regions could be specifically localized to a site of detectable genomic amplification or deletion; reciprocally, up to 47% of the total aneuploidy in each of the individual cell lines could be directly inferred from the gene expression data. Genome-wide portraits of tumor aneuploidy can thus be successfully reconstructed solely from gene expression data, implying that the effects of aneuploidy must be pervasively and globally imprinted within the cancer transcriptome. Aneuploidy may contribute to tumor behavior not just by affecting the expression of a few key oncogenes and tumor suppressor genes but also by subtly altering the expression levels of hundreds of genes in the oncogenome.

Key Words: Wavelet Transforms • Cancer genome anatomy: comparative expression patterns • Computational methods (CAAD, CAMM)

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