A platform for interrogating cancer-associated p53 alleles.

Area of Science:

• Molecular Biology
• Genetics
• Cancer Research

Background:

• The p53 gene is frequently mutated in human cancers, contributing to tumor development through loss of tumor suppression and acquisition of oncogenic functions.
• Differentiating between disease-causing p53 mutations and natural genetic variations is crucial for clinical applications.
• Understanding the functional impact of specific p53 variants is essential for cancer therapy development.

Purpose of the Study:

• To establish an in vivo model system for interrogating the functional activity of human p53 variants using Drosophila.
• To investigate the effects of common cancer-associated p53 mutations on p53-mediated cellular responses.
• To explore the relationship between p53 stabilization, transcriptional activity, and nuclear localization.

Main Methods:

• Engineered Drosophila melanogaster strains to express human p53 alleles, creating 'humanized' p53 variants.
• Assessed the ability of human p53 variants to transcriptionally activate a biosensor and induce apoptosis in response to DNA damage.
• Examined the formation of nuclear foci by human p53 variants in germline cells and their interaction with endogenous fly p53.

Main Results:

• Human p53 variants expressed in Drosophila successfully activated biosensors and induced apoptosis upon DNA damage, similar to endogenous fly p53.
• Common cancer-associated human p53 alleles failed to complement these essential functions in the 'humanized' fly model.
• Stimulus-dependent activation of human p53 occurred independently of protein stabilization, and cancer-associated variants disrupted nuclear foci formation and biosensor activity.

Conclusions:

• The developed Drosophila platform provides a functional in vivo system for evaluating human p53 variants.
• Cancer-associated p53 mutations impair critical p53 functions, including transcriptional activation and proper nuclear localization.
• Simple phenotypic assays in this model can potentially stratify disease-relevant p53 alleles.