Drugging p53 in cancer: one protein, many targets

Hassin, Ori; Oren, Moshe

doi:10.1038/s41573-022-00571-8

Cited by 276 publications

(250 citation statements)

References 190 publications

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“…The p53 tumor suppressor plays a key role in multiple signaling pathways that control cell cycle and is responsible for the stability of the human genome. Transcription factor p53 dysfunction occurs in most human malignancies [ 1 ]. Two main causes are responsible for this dysfunction: mutations in the TP53 gene and suppression of wild-type p53 mediated by its negative regulators MDM2/MDM4.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Thermal Stability of Mutant p53 Proteins via Differential Scanning Fluorimetry

Khadiullina

Mirgayazova

Davletshin

et al. 2022

Life

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The p53 protein is a transcription factor that preserves the integrity of the genome. The TP53 gene has inactivating mutations in about 50% of all human cancers. Some missense mutations lead to decreased thermal stability in the p53 protein, its unfolding and aggregation under physiological conditions. A general understanding of the impact of point mutations on the stability and conformation of mutant p53 is essential for the design and development of small molecules that target specific p53 mutations. In this work, we determined the thermostability properties of some of the most common mutant forms of the p53 protein—p53(R273H), p53(R248Q), p53(R248W) and p53(Y220C)—that are often considered as attractive therapeutic targets. The results showed that these missense mutations lead to destabilization of the p53 protein and a decrease in its melting temperature.

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Section: Introductionmentioning

confidence: 99%

Assessment of Thermal Stability of Mutant p53 Proteins via Differential Scanning Fluorimetry

Khadiullina

Mirgayazova

Davletshin

et al. 2022

Life

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“…Small molecules are being developed that can either protect p53 from its negative regulators or restore the function of mutant p53 proteins. In addition, there is a focus on drugs tailored to specific p53 mutations that are more prevalent in the population (Hassin and Oren 2022). A similar therapeutic approach of conformational change/stabilization by allosteric modulators can be applied toward either inhibition or activation of splicing factors (Fig.…”

Section: Small Moleculesmentioning

confidence: 99%

Targeting splicing factors for cancer therapy

Bashari¹,

Siegfried²,

Karni³

2023

RNA

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Alternative splicing (AS) of mRNAs is an essential regulatory mechanism in eukaryotic gene expression. AS misregulation, caused by either dysregulation or mutation of splicing factors, has been shown to be involved in cancer development and progression, making splicing factors suitable targets for cancer therapy. In recent years, various types of pharmacological modulators, such as small molecules and oligonucleotides, targeting distinct components of the splicing machinery, have been under development to treat multiple disorders. Although these approaches have promise, targeting the core spliceosome components disrupts the early stages of spliceosome assembly and can lead to non-specific and toxic effects. New research directions have been focused on targeting specific splicing factors for a more precise effect. In this perspective, we will highlight several approaches for targeting splicing factors and their functions and suggest ways to improve their specificity.

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“…This is likely because no p53-based treatments have been licensed yet. The notion that there is no treatment for p53 is increasingly being disproved, according to a high-impact review that was just released [ 5 ]. Based on the centrality of p53 in human cancer, it has the potential to revolutionize cancer treatment if successful.…”

Section: Introductionmentioning

confidence: 99%

Development and Assessment of a Novel Core Biopsy-Based Prediction Model for Pathological Complete Response to Neoadjuvant Chemotherapy in Women with Breast Cancer

Lan

Chen

et al. 2023

IJERPH

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Purpose: Pathological complete response (pCR), the goal of NAC, is considered a surrogate for favorable outcomes in breast cancer (BC) patients administrated neoadjuvant chemotherapy (NAC). This study aimed to develop and assess a novel nomogram model for predicting the probability of pCR based on the core biopsy. Methods: This was a retrospective study involving 920 BC patients administered NAC between January 2012 and December 2018. The patients were divided into a primary cohort (769 patients from January 2012 to December 2017) and a validation cohort (151 patients from January 2017 to December 2018). After converting continuous variables to categorical variables, variables entering the model were sequentially identified via univariate analysis, a multicollinearity test, and binary logistic regression analysis, and then, a nomogram model was developed. The performance of the model was assessed concerning its discrimination, accuracy, and clinical utility. Results: The optimal predictive threshold for estrogen receptor (ER), Ki67, and p53 were 22.5%, 32.5%, and 37.5%, respectively (all p < 0.001). Five variables were selected to develop the model: clinical T staging (cT), clinical nodal (cN) status, ER status, Ki67 status, and p53 status (all p ≤ 0.001). The nomogram showed good discrimination with the area under the curve (AUC) of 0.804 and 0.774 for the primary and validation cohorts, respectively, and good calibration. Decision curve analysis (DCA) showed that the model had practical clinical value. Conclusions: This study constructed a novel nomogram model based on cT, cN, ER status, Ki67 status, and p53 status, which could be applied to personalize the prediction of pCR in BC patients treated with NAC.

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Drugging p53 in cancer: one protein, many targets

Cited by 276 publications

References 190 publications

Assessment of Thermal Stability of Mutant p53 Proteins via Differential Scanning Fluorimetry

Assessment of Thermal Stability of Mutant p53 Proteins via Differential Scanning Fluorimetry

Targeting splicing factors for cancer therapy

Development and Assessment of a Novel Core Biopsy-Based Prediction Model for Pathological Complete Response to Neoadjuvant Chemotherapy in Women with Breast Cancer

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