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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.
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...
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Related Experiment Video

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Comparative Lesions Analysis Through a Targeted Sequencing Approach
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A novel multiscale framework for delineating cancer evolution from subclonal compositions.

Zhihao Yao1, Suoqin Jin2, Fuling Zhou3

  • 1School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, Hubei Province, China; Department of Microbiology, Oslo University Hospital and University of Oslo, Oslo, 0372, Oslo, Norway; Department of Clinical Molecular Biology (EpiGen), Akershus University Hospital and University of Oslo, Lørenskog, 1474, Viken, Norway.

Journal of Theoretical Biology
|February 2, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new multiscale framework to link cancer growth patterns with cellular evolution. The findings show initial subclonal composition accurately predicts diverse cancer growth dynamics.

Keywords:
Cancer evolutionCancer heterogeneityMachine learningMultiscale modelling

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Area of Science:

  • Oncology
  • Computational Biology
  • Systems Biology

Background:

  • Cancer evolution is heterogeneous, making it difficult to predict long-term outcomes from short-term data.
  • Understanding the relationship between population dynamics and cellular evolution is crucial for cancer research.

Purpose of the Study:

  • To develop a novel multiscale framework connecting cancer population dynamics (aggressive, bounded, indolent) with cellular-subclonal evolution.
  • To link ordinary differential equation (ODE)-based population models with cellular evolution models.

Main Methods:

  • Proposed a novel multiscale framework.
  • Employed the non-negative lasso (NN-LASSO) algorithm to integrate population and cellular evolution models.
  • Utilized a machine learning algorithm to analyze subclonal compositions.

Main Results:

  • Confirmed the significant impact of subclonal composition on cancer growth dynamics.
  • Identified two key subclones within heterogeneous growth patterns.
  • Demonstrated that initial subclonal compositions can accurately discriminate diverse cancer growth patterns.

Conclusions:

  • The multiscale framework effectively models cancer evolution, bridging long-term dynamics and short-term measurements.
  • This methodology offers a new approach for studying cancer evolution using both simulated and real-world data.