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Tumor Progression02:07

Tumor Progression

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.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
Tumor Progression02:07

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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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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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The mammalian target of rapamycin or mTOR protein was discovered in 1994 due to its direct interaction with rapamycin. The protein gets its name from a yeast homolog called TOR. The mTOR protein complex in mammalian cells plays a major role in balancing anabolic processes such as the synthesis of proteins, lipids, and nucleotides and catabolic processes, such as autophagy in response to environmental cues, such as availability of nutrients and growth factors.
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Related Experiment Video

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An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Quantifying cancer progression with conjunctive Bayesian networks.

Moritz Gerstung1, Michael Baudis, Holger Moch

  • 1Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058 Basel, Switzerland. moritz.gerstung@bsse.ethz.ch

Bioinformatics (Oxford, England)
|August 21, 2009
PubMed
Summary
This summary is machine-generated.

Understanding cancer progression requires mapping genetic mutation order. This study introduces a Bayesian network model to reveal complex oncogenetic pathways and improve survival predictions for better cancer diagnostics and prognosis.

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Area of Science:

  • Genomics
  • Computational Biology
  • Cancer Research

Background:

  • Cancer evolves through accumulating genetic mutations, but the precise timing and order of these alterations are not well understood.
  • Tumor progression is driven by genetic changes, yet the underlying oncogenetic pathways remain largely enigmatic.
  • Accurate modeling of cancer progression is crucial for improved diagnostics and prognosis.

Purpose of the Study:

  • To develop a probabilistic graphical model for understanding mutation accumulation and interdependencies during cancer progression.
  • To model cancer progression as a temporal process, separating unobservable mutation accumulation from observable, error-prone detection.
  • To infer complex oncogenetic pathways and their dynamics for improved genetics-based survival predictions.

Main Methods:

  • A Bayesian network was employed to model the temporal accumulation of mutations.
  • Model parameters were estimated using the Expectation-Maximization algorithm.
  • A simulated annealing procedure was used to obtain the underlying interaction graph representing oncogenetic pathways.

Main Results:

  • The study identified multiple complex oncogenetic pathways across various cancer types, significantly deviating from linear or tree-like models.
  • The developed model successfully separates the unobservable mutation accumulation process from observable mutation detection.
  • Inferred progression dynamics were shown to enhance the accuracy of genetics-based survival predictions.

Conclusions:

  • The probabilistic graphical model provides novel insights into the complex, non-linear nature of cancer progression.
  • The inferred oncogenetic pathways can significantly improve cancer diagnostics and prognosis through enhanced survival predictions.
  • The ct-cbn software package is available for researchers to apply these methods to cytogenetic data.