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Cancer-Critical Genes I: Proto-oncogenes

Cancer-Critical Genes I: Proto-oncogenes

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 I: Proto-oncogenes

Cancer-Critical Genes I: Proto-oncogenes

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 Cells

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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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Combinatorial Gene Control

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Cancer-Critical Genes II: Tumor Suppressor Genes

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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.
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Related Experiment Video

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Genome-wide RNAi Screening to Identify Host Factors That Modulate Oncolytic Virus Therapy

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Optimization of oncogene expression through intra-population competition.

Joshua P Ferreira¹, Clifford L Wang

¹Department of Chemical Engineering, Stanford University, CA, USA.

Biotechnology Journal

|July 12, 2013

Summary

This study reveals an optimal H-Ras oncogene level for maximal cell proliferation. Exceeding this level decreases proliferation, suggesting tumor evolution optimizes gene expression for growth.

Keywords:

Cell line engineering Gene expression optimizations Inter-species competition Population dynamic Ras oncogene

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Engineering Oncogenic Heterozygous Gain-of-Function Mutations in Human Hematopoietic Stem and Progenitor Cells

Engineering Oncogenic Heterozygous Gain-of-Function Mutations in Human Hematopoietic Stem and Progenitor Cells

Published on: March 10, 2023

Area of Science:

Molecular Biology
Cancer Research
Genetics

Background:

While many gene functions are known, the quantitative link between gene expression and cellular outcomes, particularly in cancer, remains unclear.
Understanding oncogene dosage is crucial for deciphering cancer development and progression.

Purpose of the Study:

To investigate the quantitative relationship between H-Ras oncogene expression levels and net cell proliferation rates.
To explore the potential of intra-population competition assays for studying gene dosage effects on proliferation.

Main Methods:

Engineered cell populations to express varying levels of the H-Ras oncogene.
Utilized intra-population competition where engineered cells competed for population share.
Employed flow cytometry to monitor population dynamics and determine proliferation rates over time.

Main Results:

Identified an optimal H-Ras G12V expression level, approximately 1.2-fold that of wild-type Ras, for maximal net proliferation under suppressed wild-type Ras activation.
Observed a decrease in proliferation rates as H-Ras G12V expression levels exceeded this optimal threshold.
Demonstrated the utility of intra-population competition for quantifying proliferation rates linked to gene expression levels.

Conclusions:

Tumor evolution may involve optimizing oncogene expression levels to achieve maximal cell proliferation.
Engineered intra-population competitions offer a versatile method to study gene dosage effects on proliferation and cell dynamics.
This approach can be applied to diverse biological questions, including tumor evolution and cell line optimization.