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Analysis of Human T Cell Activity in an Allogeneic Co-Culture Setting of Pre-Treated Tumor Cells

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Published on: March 7, 2025

An evolutionary perspective on anti-tumor immunity.

David J Klinke¹

¹Department of Chemical Engineering, West Virginia University Morgantown, WV, USA ; Mary Babb Randolph Cancer Center, West Virginia University Morgantown, WV, USA ; Department of Microbiology, Immunology, and Cell Biology, West Virginia University Morgantown, WV, USA.

Frontiers in Oncology

|January 22, 2013

Summary

Cancer evolution is key to understanding treatment resistance. New technologies help quantify cellular diversity and model how cancer cells change their environment to survive and grow.

Keywords:

Bayesian inference next generation sequencing proteomics simulation

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Area of Science:

Evolutionary biology
Cancer biology
Genomics

Background:

Molecular-targeted therapies face challenges in achieving durable cancer responses.
Viewing cancer through an evolutionary lens highlights heterogeneity, dynamics, and selective fitness landscapes.
Somatic mutations drive cellular changes, perturbing tissue homeostasis and resource competition.

Purpose of the Study:

To review technological advances enhancing the understanding of cancer's evolutionary attributes.
To focus on how new technologies illuminate cellular heterogeneity, control structures, and fitness landscape alterations.

Main Methods:

Review of recent technological advancements in genome sequencing, computational power, and proteomics.
Analysis of how these technologies address cancer's evolutionary dynamics.
Focus on quantifying heterogeneity, testing control structures via simulation, and identifying crosstalk mechanisms.

Main Results:

Genome sequencing advances enable quantification of cellular heterogeneity in cancer.
Computational power allows simulation-based testing of intra- and intercellular control structures.
Proteomics identifies novel cancer cell crosstalk mechanisms altering the fitness landscape.

Conclusions:

Technological progress is crucial for understanding cancer as an evolutionary process.
Advances in sequencing, computation, and proteomics provide new insights into cancer dynamics and treatment resistance.
Further research integrating these technologies can unravel complex cancer evolutionary strategies.