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Related Concept Videos

Cancers Originate from Somatic Mutations in a Single Cell02:21

Cancers Originate from Somatic Mutations in a Single Cell

Cancer arises from mutations in the critical genes that allow healthy cells to escape cell cycle regulation and acquire the ability to proliferate indefinitely. Though originating from a single mutation event in one of the originator cells, cancer progresses when the mutant cell lines continue to gain more and more mutations, and finally, become malignant. For example, chronic myelogenous leukemia (CML) develops initially as a non-lethal increase in white blood cells, which progressively...
Cancers Originate from Somatic Mutations in a Single Cell02:21

Cancers Originate from Somatic Mutations in a Single Cell

Cancer arises from mutations in the critical genes that allow healthy cells to escape cell cycle regulation and acquire the ability to proliferate indefinitely. Though originating from a single mutation event in one of the originator cells, cancer progresses when the mutant cell lines continue to gain more and more mutations, and finally, become malignant. For example, chronic myelogenous leukemia (CML) develops initially as a non-lethal increase in white blood cells, which progressively...
Cancer-Critical Genes II: Tumor Suppressor Genes01:05

Cancer-Critical Genes II: Tumor Suppressor Genes

Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
When the function of certain critical genes, especially those involved in cell cycle regulation and cell growth signaling cascades, gets disrupted, it upsets the cell cycle progression. Such cells with unchecked cell cycles start proliferating uncontrollably and eventually develop into tumors.
Such genes that act...
Cancer-Critical Genes II: Tumor Suppressor Genes01:05

Cancer-Critical Genes II: Tumor Suppressor Genes

Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
When the function of certain critical genes, especially those involved in cell cycle regulation and cell growth signaling cascades, gets disrupted, it upsets the cell cycle progression. Such cells with unchecked cell cycles start proliferating uncontrollably and eventually develop into tumors.
Such genes that act...
Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...

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Learning oncogenic pathways from binary genomic instability data.

Pei Wang1, Dennis L Chao, Li Hsu

  • 1Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA.

Biometrics
|April 10, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces LogitNet, a new method to identify interactions between chromosomal aberrations in genomic instability data. The approach enhances understanding of disease development, particularly in cancer.

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Area of Science:

  • Genetics and genomics
  • Computational biology
  • Biostatistics

Background:

  • Genomic instability, characterized by chromosomal aberrations, is crucial in disease pathogenesis.
  • High-throughput genotyping generates high-dimensional binary data reflecting aberration status at multiple loci.
  • Understanding interactions between these aberrations offers insights into disease mechanisms.

Purpose of the Study:

  • To propose a novel statistical method, LogitNet, for inferring interactions among genomic aberration events.
  • To extend penalized logistic regression to incorporate spatial correlation in genomic instability data.

Main Methods:

  • LogitNet employs penalized logistic regression.
  • The method incorporates an extension to handle spatial correlation in genomic data.
  • Extensive simulation studies were conducted to evaluate performance.

Main Results:

  • LogitNet demonstrated strong performance in simulation studies.
  • The method effectively infers interactions among genomic aberration events.
  • The approach was illustrated using breast cancer genomic instability data.

Conclusions:

  • LogitNet provides a robust method for analyzing interactions in high-dimensional genomic instability data.
  • The findings contribute to a deeper understanding of disease development through aberration interactions.
  • The method has practical applications in analyzing complex genomic datasets, such as those from cancer research.