Inferring tumor progression from genomic heterogeneity

Genome Res. 2010 Jan;20(1):68-80. doi: 10.1101/gr.099622.109. Epub 2009 Nov 10.

Abstract

Cancer progression in humans is difficult to infer because we do not routinely sample patients at multiple stages of their disease. However, heterogeneous breast tumors provide a unique opportunity to study human tumor progression because they still contain evidence of early and intermediate subpopulations in the form of the phylogenetic relationships. We have developed a method we call Sector-Ploidy-Profiling (SPP) to study the clonal composition of breast tumors. SPP involves macro-dissecting tumors, flow-sorting genomic subpopulations by DNA content, and profiling genomes using comparative genomic hybridization (CGH). Breast carcinomas display two classes of genomic structural variation: (1) monogenomic and (2) polygenomic. Monogenomic tumors appear to contain a single major clonal subpopulation with a highly stable chromosome structure. Polygenomic tumors contain multiple clonal tumor subpopulations, which may occupy the same sectors, or separate anatomic locations. In polygenomic tumors, we show that heterogeneity can be ascribed to a few clonal subpopulations, rather than a series of gradual intermediates. By comparing multiple subpopulations from different anatomic locations, we have inferred pathways of cancer progression and the organization of tumor growth.

Publication types

  • Evaluation Study
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Breast Neoplasms* / genetics
  • Breast Neoplasms* / pathology
  • Carcinoma, Ductal, Breast* / genetics
  • Carcinoma, Ductal, Breast* / pathology
  • Chromosome Breakpoints
  • Comparative Genomic Hybridization / methods*
  • Disease Progression*
  • Female
  • Flow Cytometry / methods*
  • Gene Dosage
  • Genetic Heterogeneity*
  • Humans
  • In Situ Hybridization, Fluorescence
  • Informatics
  • Molecular Sequence Data
  • Oligonucleotide Array Sequence Analysis
  • Ploidies
  • Sequence Analysis, DNA

Associated data

  • GEO/GSE16672