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Common patterns of genetic evolution in human solid tumors

S E Shackney1, T V Shankey

  • 1Allegheny University of the Health Sciences, Department of Human Oncology, Allegheny Campus, Pittsburgh, Pennsylvania 15212, USA. shackney@pgh.AUHS.edu

Cytometry
|September 23, 1997
PubMed
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Human solid tumors exhibit genetic instability through two main pathways: microsatellite instability and p53/aneuploidy. The p53/aneuploidy pathway, common in many cancers, involves chromosomal changes and specific genetic alterations driving tumor progression.

Area of Science:

  • Oncology
  • Cancer Genetics
  • Tumor Evolution

Background:

  • Human solid tumors undergo genetic evolution with multiple abnormalities.
  • Genetic instability is a key feature, particularly in early colorectal cancer.
  • Two primary mechanisms generate genetic instability: microsatellite instability and p53/aneuploidy.

Purpose of the Study:

  • To elucidate the genetic evolutionary sequences in human solid tumors.
  • To identify distinct pathways of genetic instability and their associated alterations.
  • To provide a basis for improved cancer prognosis and therapy.

Main Methods:

  • Analysis of genetic abnormalities in human solid tumors.
  • Identification of microsatellite instability and p53/aneuploidy pathways.

Related Experiment Videos

  • Characterization of specific genetic changes, including gene overexpression and amplification.

    • Main Results:

    • Microsatellite instability affects <20% of colon cancers and involves near-diploid cells.
    • p53/aneuploidy affects most colon, breast, and other solid tumors, leading to chromosomal changes.
    • Specific evolutionary sequences (adeno/squamous, neuroendocrine) branch from p53/aneuploidy, involving genes like Her-2/neu, EGF receptor, ras, c-myc, Rb, and raf1.

    Conclusions:

    • Genetic evolutionary sequences provide a framework for understanding tumor progression.
    • Distinct pathways, particularly p53/aneuploidy, are crucial in common solid tumors.
    • Multiparameter cell-based methods can help define these sequences for tailored cancer treatment and prognosis.