Inferring intra-tumor heterogeneity from high-throughput DNA sequencing data

Layla Oesper, Ahmad Mahmoody, Benjamin J. Raphael

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Cancer is a disease driven in part by somatic mutations that accumulate during the lifetime of an individual. The clonal theory [1] posits that the cancerous cells in a tumor are descended from a single founder cell and that descendants of this cell acquired multiple mutations beneficial for tumor growth through rounds of selection and clonal expansion. A tumor is thus a heterogeneous population of cells, with different subpopulations of cells containing both clonal mutations from the founder cell or early rounds of clonal expansion, and subclonal mutations that occurred after the most recent clonal expansion. Most cancer sequencing projects sequence a mixture of cells from a tumor sample including admixture by normal (non-cancerous) cells and different subpopulations of cancerous cells. In addition most solid tumors exhibit extensive aneuploidy and copy number aberrations. Intra-tumor heterogeneity and aneuploidy conspire to complicate analysis of somatic mutations in sequenced tumor samples.

Original languageEnglish (US)
Title of host publicationResearch in Computational Molecular Biology - 17th Annual International Conference, RECOMB 2013, Proceedings
Pages171-172
Number of pages2
DOIs
StatePublished - Apr 3 2013
Externally publishedYes
Event17th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2013 - Beijing, China
Duration: Apr 7 2013Apr 10 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7821 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other17th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2013
CountryChina
CityBeijing
Period4/7/134/10/13

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Inferring intra-tumor heterogeneity from high-throughput DNA sequencing data'. Together they form a unique fingerprint.

  • Cite this

    Oesper, L., Mahmoody, A., & Raphael, B. J. (2013). Inferring intra-tumor heterogeneity from high-throughput DNA sequencing data. In Research in Computational Molecular Biology - 17th Annual International Conference, RECOMB 2013, Proceedings (pp. 171-172). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7821 LNBI). https://doi.org/10.1007/978-3-642-37195-0_14