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Chromosome Replicating Timing Combined with Fluorescent In situ Hybridization
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Methods and challenges in timing chromosomal abnormalities within cancer samples.

Elizabeth Purdom1, Christine Ho, Catherine S Grasso

  • 1Department of Statistics, University of California, Berkeley, 367 Evans Hall Berkeley, CA 94720-3860, USA, Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR 97239, USA and Department of Dermatology, University of California, San Francisco, CA 94115, USA.

Bioinformatics (Oxford, England)
|September 26, 2013
PubMed
Summary

This study introduces improved computational methods for ordering chromosomal amplifications in tumors, enhancing our understanding of cancer development and potentially aiding diagnostics. The new methods offer more accurate timing of genetic events in individual tumors.

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Area of Science:

  • Genomics
  • Cancer Biology
  • Computational Biology

Background:

  • Tumors frequently acquire chromosomal amplifications, which can be crucial for tumor growth and early diagnostic markers.
  • Existing methods infer the order of genetic alterations from large patient cohorts.
  • Recent methods attempt to order amplifications within a single tumor, but require further evaluation.

Purpose of the Study:

  • To evaluate and improve computational methods for ordering chromosomal amplifications in individual tumors.
  • To develop more accurate and robust statistical procedures for analyzing tumor evolution.
  • To assess the utility of ordering amplifications for understanding tumor etiology.

Main Methods:

  • Developed a maximum-likelihood estimation procedure accounting for sequencing variability.
  • Proposed a Bayesian estimation procedure to stabilize timing estimates.
  • Implemented the methods in the R package 'cancerTiming'.

Main Results:

  • The model for timing chromosomal amplifications has limitations, especially for highly amplified regions.
  • Estimating the order of early tumor progression events can be sensitive to biases.
  • The proposed maximum-likelihood method outperforms previous partial maximum-likelihood approaches.
  • Bayesian methods provide stable estimates in specific scenarios.
  • Analysis of ovarian tumors suggests variations in amplification acquisition.

Conclusions:

  • The developed computational methods provide more accurate and reliable ordering of chromosomal amplifications.
  • These advancements offer new possibilities for studying tumor etiology and identifying diagnostic markers.
  • The 'cancerTiming' R package facilitates the application of these methods in cancer research.