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Abstract Mutations in theTP53tumor suppressor gene occur in >80% of the triple-negative or basal-like breast cancer. To test whether neomorphic functions of specificTP53missense mutations contribute to phenotypic heterogeneity, we characterized phenotypes of non-transformed MCF10A-derived cell lines expressing the ten most common missense mutant p53 proteins and observed a wide spectrum of phenotypic changes in cell survival, resistance to apoptosis and anoikis, cell migration, invasion and 3D mammosphere architecture. The p53 mutants R248W, R273C, R248Q, and Y220C are the most aggressive while G245S and Y234C are the least, which correlates with survival rates of basal-like breast cancer patients. Interestingly, a crucial amino acid difference at one position—R273C vs. R273H—has drastic changes on cellular phenotype. RNA-Seq and ChIP-Seq analyses show distinct DNA binding properties of different p53 mutants, yielding heterogeneous transcriptomics profiles, and MD simulation provided structural basis of differential DNA binding of different p53 mutants. Integrative statistical and machine-learning-based pathway analysis on gene expression profiles with phenotype vectors across the mutant cell lines identifies quantitative association of multiple pathways including the Hippo/YAP/TAZ pathway with phenotypic aggressiveness. Further, comparative analyses of large transcriptomics datasets on breast cancer cell lines and tumors suggest that dysregulation of the Hippo/YAP/TAZ pathway plays a key role in driving the cellular phenotypes towards basal-like in the presence of more aggressive p53 mutants. Overall, our study describes distinct gain-of-function impacts on protein functions, transcriptional profiles, and cellular behaviors of different p53 missense mutants, which contribute to clinical phenotypic heterogeneity of triple-negative breast tumors.
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This content will become publicly available on August 19, 2025
Predicting Neoantigens for Cancer Using Next-Generation IEDB & CEDAR Tools
Cancer is a group of diseases characterized by uncontrolled growth and spread of abnormal cells. The underlying cause of cancer relates to the cell cycle, during which DNA is replicated. Cancer cells accumulate DNA mutations that help them acquire cancerous features, such as evading cell death and indefinite growth [1]. If these DNA mutations are in coding regions, they are translated to mutated proteins. The epitopes that contain these mutations are called neoantigens. Neoantigens are highly tumor-specific and can be targeted with immunotherapies [2]. During cell division, tumor suppressor genes play a role in the case of DNA damage or replication errors. The p53 protein is a tumor suppressor gene product that prevents tumor formation by activating processes that block cell division when DNA damage has occurred [3]. Mutant p53 does not effectively bind DNA or activate the production of proteins necessary for the stop signal. This project explored a hypothesis that a set of distinct p53 protein mutations can be selected to serve as potential targets for cancer immunotherapy and vaccines by using immunoinformatics predictive analysis tools. By comparing these potential targets with experimental results, we can predict epitopes that may serve as neoantigen targets for immunotherapy. We identified candidate immunogenic epitopes using the NCI’s TP53 Database (NCI DB - tp53.isb-cgc.org), Cancer Epitope Database and Analysis Resource (CEDAR - cedar.iedb.org), and a powerful new bioinformatics tool (nextgen-tools.iedb.org/) [4] hosted by Immune Epitope Database (IEDB - iedb.org) and CEDAR. Comparing predicted epitopes to highly mutable regions of p53 in tumor variants from NCI DB revealed areas of overlap that may be priority candidate epitopes for immunotherapy. Experimental data from CEDAR tested the immunogenicity of normal and mutated protein versions to help avoid harmful cross-reactions. These results help predict cancer epitope amino acid sequences relevant to understanding the immune system's role in cancer progression, prevention, and treatment. These studies also set the stage for important subsequent undergraduate research projects to further characterize predicted cancer neoantigens.
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- Award ID(s):
- 2055036
- PAR ID:
- 10537891
- Publisher / Repository:
- Zenodo
- Date Published:
- Journal Name:
- Journal of advanced technological education
- ISSN:
- 2832-9627
- Subject(s) / Keyword(s):
- p53 mutations neoantigens cancer CEDAR bioinfomatics immunotherapeutic Open Access Resources
- Format(s):
- Medium: X
- Right(s):
- Creative Commons Attribution 4.0 International
- Sponsoring Org:
- National Science Foundation
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