Understanding tumor suppression through the p53 target gene network
Abstract/Contents
- Abstract
- TP53 encodes the potent tumor suppressor, p53. TP53 is mutated in nearly half of sporadic human cancers, and inheritance of a mutant TP53 allele in the germline causes Li-Fraumeni Syndrome, which highly predisposes those affected to developing cancer early and often in their lives. Cementing the importance of p53 in tumor suppression are the data from p53 null mice, which succumb within months to mostly thymic lymphomas with 100% penetrance. Despite its unequivocal importance in tumor suppression, the precise mechanisms of p53-mediated tumor suppression remain incompletely understood. p53 is a stress-inducible transcription factor. In response to various cellular stresses, p53 accumulates in the cell, binds to DNA in a sequence-specific manner, and activates expression of target genes. Various pieces of evidence support this transcriptional activation p53 as being critical for tumor suppression. Notably, genetically engineered mouse models of cancer demonstrate that inactivation of both p53 transactivation domains (TAD) renders p53TAD1,2 comparable to p53 null alleles in tumor suppression. The p53 target gene network encompasses hundreds of genes that regulate diverse cellular processes. Our understanding of which p53 target genes and which biological processes are key to p53-mediated tumor suppression remains incomplete. Factors that complicate answering these questions are that study of p53 target genes has largely focused on in vitro studies using acute p53-activating stresses and that p53 might suppress tumorigenesis in a context-specific manner. To address this question of which p53 target genes are most important for tumor suppression, we have developed a sensitive, in vivo, combinatorial screening platform. Using a comprehensive sgRNA library targeting 272 target genes of p53, we identified Zmat3 and Cdkn1a as potent and cooperative effectors of p53-mediated tumor suppression. Using meta-analyses of published p53-dependent gene expression studies in human cell lines as well as CRISPR/Cas9 screening data compiled in the Cancer Dependency Map, we demonstrated that ZMAT3 and CDKN1A are near-universal, evolutionarily conserved target genes of p53 that serve as brakes to enhanced cellular fitness in pooled sgRNA enrichment screens. Finally, using RNA-seq, differential splicing analysis, and shotgun proteomics, we link Zmat3 and Cdkn1a loss with dysregulation of signal transduction and cell division respectively, suggesting that, through these two target genes, p53 engages parallel cellular processes in tumor suppression. These studies illuminate important modules of p53-mediated tumor suppression, an understanding that could contribute to the improved diagnosis and treatment of p53-deficient tumors in the clinic.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2023; ©2023 |
| Publication date | 2023; 2023 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Boutelle, Anthony Michael |
|---|---|
| Degree supervisor | Attardi, Laura |
| Thesis advisor | Attardi, Laura |
| Thesis advisor | Dixon, Scott James, 1977- |
| Thesis advisor | Jackson, Peter K. (Peter Kent) |
| Thesis advisor | Lu, Sydney |
| Degree committee member | Dixon, Scott James, 1977- |
| Degree committee member | Jackson, Peter K. (Peter Kent) |
| Degree committee member | Lu, Sydney |
| Associated with | Stanford University, School of Medicine |
| Associated with | Stanford University, Cancer Biology Program |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Anthony M. Boutelle. |
|---|---|
| Note | Submitted to the Cancer Biology Program. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/tc255zk5333 |
Access conditions
- Copyright
- © 2023 by Anthony Michael Boutelle
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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