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Related Concept Videos

Tumor Progression02:07

Tumor Progression

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Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
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Mutagenicity and carcinogenicity refer to the ability of drugs to cause genetic defects and induce cancer, respectively. The International Agency for Research on Cancer (IARC) classifies agents into four groups based on their carcinogenic potential. Group 1 agents are known human carcinogens; group 2A agents are probably carcinogenic to humans; group 3 agents lack data to support their role in carcinogenesis; and group 4 includes agents for which data support that they are not likely to be...
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Generation of Microtumors Using 3D Human Biogel Culture System and Patient-derived Glioblastoma Cells for Kinomic Profiling and Drug Response Testing
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Revisiting the tumorigenesis timeline with a data-driven generative model.

Kamel Lahouel1, Laurent Younes2, Ludmila Danilova1

  • 1Division of Biostatistics and Bioinformatics, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205.

Proceedings of the National Academy of Sciences of the United States of America
|December 29, 2019
PubMed
Summary
This summary is machine-generated.

This study presents a mathematical model for tumor evolution, revealing that cancer often begins early with slow subsequent mutations. It explains why driver gene effects vary across different cancer types.

Keywords:
cancerdriver genesfitnessmutationstumorigenesis

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Area of Science:

  • Cancer Biology
  • Evolutionary Dynamics
  • Mathematical Modeling

Background:

  • Cancer arises from accumulated genetic and epigenetic changes in key genes.
  • The precise timing of these genetic events and their tissue-specific variability remains poorly understood.
  • Genome-wide sequencing studies highlight the need for better models of tumor evolution.

Purpose of the Study:

  • To develop a mathematical model explaining the full process of tumor evolution.
  • To investigate the role of driver gene fitness advantages and carrying capacity in cancer development.
  • To elucidate the reasons behind the tissue-dependent timing of driver gene mutations.

Main Methods:

  • Developed a mathematical model incorporating fitness advantages of driver genes and population dynamics.
  • Focused on mutational events and their impact on tumor progression.
  • Applied the model to analyze cancer incidence data for colorectal, pancreatic, and leukemia, as well as inherited conditions like Lynch syndrome and familial adenomatous polyposis.

Main Results:

  • The model successfully recapitulates observed cancer incidence across multiple cancer types and inherited conditions by adjusting only two tissue-specific parameters: stem cell number and cell division frequency.
  • Suggests a generalized early onset of tumorigenesis followed by slow mutational waves, challenging previous models.
  • Provides estimates for fitness increases induced by driver mutations, finding them often larger and highly tissue-dependent.

Conclusions:

  • The developed mathematical model offers insights into the evolutionary dynamics of cancer.
  • The findings provide a mechanistic explanation for the tissue-dependent selective fitness advantage of specific driver genes.
  • This work advances our understanding of cancer initiation and progression, highlighting the importance of tissue-specific factors.