TP53: the unluckiest of genes?

We employ a computationally intensive framework that integrates mathematical programming and graph neural networks to elucidate functional phenotypic heterogeneity in disease by classifying entire pathways of interest under various conditions. Our approach combines two distinct yet seamlessly integrated mathematical modelling schemes: (i) we first leverage Prior-Knowledge gene regulatory Networks (PKNs) derived from comprehensive and established databases to reconstruct and optimize their topology using genomic and transcriptomic data and mathematical programming optimization, and (ii) we apply tailored Graph Convolutional Networks (GCNs) to classify each network as a single data point atgraph-level, using Mode of Regulation (MoR) and gene activity profiles as node embeddings. These networks may vary in their biological or molecular annotations, which serves as a labelling scheme for their supervised classification. As a case study, we investigate the DNA damage and repair pathway using theTP53regulon in a pancancer study, classifying gene networks across differentTP53mutation types. Our findings pave the way for a systematic, causality-driven approach to classify stimuli or stress conditions and their functional implications, thereby advancing drug discovery and precision medicine.

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Selective metabolic regulations by p53 mutant variants in pancreatic cancer

Caporali,

Butera,

Ruzza

et al. 2024

J Exp Clin Cancer Res

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Background Approximately half of all human cancers harbour mutations in the p53 gene, leading to the generation of neomorphic p53 mutant proteins. These mutants can exert gain-of-function (GOF) effects, potentially promoting tumour progression. However, the clinical significance of p53 GOF mutations, as well as the selectivity of individual variants, remains controversial and unclear. Methods To elucidate the metabolic regulations and molecular underpinnings associated with the specific p53R270H and p53R172H mutant variants (the mouse equivalents of human p53R273H and p53R175H, respectively), we employed a comprehensive approach. This included integrating global metabolomic analysis with epigenomic and transcriptomic profiling in mouse pancreatic cancer cells. Additionally, we assessed metabolic parameters such as oxygen consumption rate and conducted analyses of proliferation and cell–cell competition to validate the biological impact of metabolic changes on pancreatic ductal adenocarcinoma (PDAC) phenotype. Our findings were further corroborated through analysis of clinical datasets from human cancer cohorts. Results Our investigation revealed that the p53R270H variant, but not p53R172H, sustains mitochondrial function and energy production while also influencing cellular antioxidant capacity. Conversely, p53R172H, while not affecting mitochondrial metabolism, attenuates the activation of pro-tumorigenic metabolic pathways such as the urea cycle. Thus, the two variants selectively control different metabolic pathways in pancreatic cancer cells. Mechanistically, p53R270H induces alterations in the expression of genes associated with oxidative stress and reduction in mitochondrial respiration. In contrast, p53R172H specifically impacts the expression levels of enzymes involved in the urea metabolism. However, our analysis of cell proliferation and cell competition suggested that the expression of either p53R270H or p53R172H does not influence confer any selective advantage to this cellular model in vitro. Furthermore, assessment of mitochondrial priming indicated that the p53R270H-driven mitochondrial effect does not alter cytochrome c release or the apoptotic propensity of pancreatic cancer cells. Conclusions Our study elucidates the mutant-specific impact of p53R270H and p53R172H on metabolism of PDAC cancer cells, highlighting the need to shift from viewing p53 mutant variants as a homogeneous group of entities to a systematic assessment of each specific p53 mutant protein. Moreover, our finding underscores the importance of further exploring the significance of p53 mutant proteins using models that more accurately reflect tumor ecology.

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TP53: the unluckiest of genes?

Cited by 3 publications

References 73 publications

Vaccines targeting p53 mutants elicit anti-tumor immunity

Vaccines targeting p53 mutants elicit anti-tumor immunity

Mathematical Programming and Graph Neural Networks illuminate functional heterogeneity of pathways in disease

Selective metabolic regulations by p53 mutant variants in pancreatic cancer

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