Sorting cancer karyotypes by elementary operations

Michal Ozery-Flato, Ron Shamir

Research output: Contribution to journalArticlepeer-review


Since the discovery of the "Philadelphia chromosome" in chronic myelogenous leukemia in 1960, there has been ongoing intensive research of chromosomal aberrations in cancer. These aberrations, which result in abnormally structured genomes, became a hallmark of cancer. Many studies provide evidence for the connection between chromosomal alterations and aberrant genes involved in the carcinogenesis process. An important problem in the analysis of cancer genomes is inferring the history of events leading to the observed aberrations. Cancer genomes are usually described in the form of karyotypes, which present the global changes in the genomes' structure. In this study, we propose a mathematical framework for analyzing chromosomal aberrations in cancer karyotypes. We introduce the problem of sorting karyotypes by elementary operations, which seeks a shortest sequence of elementary chromosomal events transforming a normal karyotype into a given (abnormal) cancerous karyotype. Under certain assumptions, we prove a lower bound for the elementary distance, and present a polynomial-time 3-approximation algorithm for the problem. We applied our algorithm to karyotypes from the Mitelman database, which records cancer karyotypes reported in the scientific literature. Approximately 94% of the karyotypes in the database, totaling 58,464 karyotypes, supported our assumptions, and each of them was subjected to our algorithm. Remarkably, even though the algorithm is only guaranteed to generate a 3-approximation, it produced a sequence whose length matched the lower bound (and hence optimal) in 99.9% of the tested karyotypes.

Original languageEnglish
Pages (from-to)1445-1460
Number of pages16
JournalJournal of Computational Biology
Issue number10
StatePublished - 1 Oct 2009


  • Combinatorics
  • Computational molecular biology
  • Gene expression
  • Gene networks
  • Genetic variation
  • Sequence analysis


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