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Open Access Hypothesis

Tumor heterogeneity in neoplasms of breast, colon, and skin

Jian Li1, Kai Wang12, Thomas Dyrsø Jensen1, Shengting Li12, Lars Bolund13 and Carsten Wiuf2*

  • * Corresponding author: Carsten Wiuf wiuf@birc.au.dk

  • † Equal contributors

Author Affiliations

1 Institute of Human Genetics, Aarhus University, DK-8000 Aarhus, Denmark

2 BiRC - Bioinformatics Research Centre, Aarhus University, DK-8000 Aarhus, Denmark

3 BGI-Shenzhen, Shenzhen 518083, China

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BMC Research Notes 2010, 3:321  doi:10.1186/1756-0500-3-321

Published: 25 November 2010

Abstract

Background

Different cell subpopulations in a single tumor may show diverse capacities for growth, differentiation, metastasis formation, and sensitivity to treatments. Thus, heterogeneity is an important feature of tumors. However, due to limitations in experimental and analytical techniques, tumor heterogeneity has rarely been studied in detail.

Presentation of the hypothesis

Different tumor types have different heterogeneity patterns, thus heterogeneity could be a characteristic feature of a particular tumor type.

Testing the hypothesis

We applied our previously published mathematical heterogeneity model to decipher tumor heterogeneity through the analysis of genetic copy number aberrations revealed by array CGH data for tumors of three different tissues: breast, colon, and skin. The model estimates the number of subpopulations present in each tumor. The analysis confirms that different tumor types have different heterogeneity patterns. Computationally derived genomic copy number profiles from each subpopulation have also been analyzed and discussed with reference to the multiple hypothetical relationships between subpopulations in origin-related samples.

Implications of the hypothesis

Our observations imply that tumor heterogeneity could be seen as an independent parameter for determining the characteristics of tumors. In the context of more comprehensive usage of array CGH or genome sequencing in a clinical setting our study provides a new way to realize the full potential of tumor genetic analysis.