The TP53 tumor suppressor is the most frequently altered gene in human cancers, and has been a major focus of oncology research. The p53 protein is a transcription factor that can activate the expression of multiple target genes and plays critical roles in regulating cell cycle, apoptosis, and genomic stability, and is widely regarded as the "guardian of the genome". Accumulating evidence has shown that p53 also regulates cell metabolism, ferroptosis, tumor microenvironment, autophagy and so on, all of which contribute to tumor suppression. Mutations in TP53 not only impair its tumor suppressor function, but also confer oncogenic properties to p53 mutants. Since p53 is mutated and inactivated in most malignant tumors, it has been a very attractive target for developing new anti-cancer drugs. However, until recently, p53 was considered an "undruggable" target and little progress has been made with p53-targeted therapies. Here, we provide a systematic review of the diverse molecular mechanisms of the p53 signaling pathway and how TP53 mutations impact tumor progression. We also discuss key structural features of the p53 protein and its inactivation by oncogenic mutations. In addition, we review the efforts that have been made in p53-targeted therapies, and discuss the challenges that have been encountered in clinical development. 

/pdf/targeting-p53-pathways-mechanisms-structures-and-advances-in-4lz29aui.pdf

Targeting p53 pathways: mechanisms, structures, and advances in therapy

Health care is transitioning from genetics to genomics, in which single-gene testing for diagnosis is being replaced by multi-gene panels, genome-wide sequencing, and other multi-genic tests for disease diagnosis, prediction, prognosis, and treatment. This health care transition is spurring a new set of increased or novel liability risks for health care providers and test laboratories. This article describes this transition in both medical care and liability, and addresses 11 areas of potential increased or novel liability risk, offering recommendations to both health care and legal actors to address and manage those liability risks.

https://journals.sagepub.com/doi/pdf/10.1177/1073110520916994

From Genetics to Genomics: Facing the Liability Implications in Clinical Care.

Objective To determine whether the sensitivity and specificity of SNP chips are adequate for detecting rare pathogenic variants in a clinically unselected population. Design Retrospective, population based diagnostic evaluation. Participants 49 908 people recruited to the UK Biobank with SNP chip and next generation sequencing data, and an additional 21 people who purchased consumer genetic tests and shared their data online via the Personal Genome Project. Main outcome measures Genotyping (that is, identification of the correct DNA base at a specific genomic location) using SNP chips versus sequencing, with results split by frequency of that genotype in the population. Rare pathogenic variants in the BRCA1 and BRCA2 genes were selected as an exemplar for detailed analysis of clinically actionable variants in the UK Biobank, and BRCA related cancers (breast, ovarian, prostate, and pancreatic) were assessed in participants through use of cancer registry data. Results Overall, genotyping using SNP chips performed well compared with sequencing; sensitivity, specificity, positive predictive value, and negative predictive value were all above 99% for 108 574 common variants directly genotyped on the SNP chips and sequenced in the UK Biobank. However, the likelihood of a true positive result decreased dramatically with decreasing variant frequency; for variants that are very rare in the population, with a frequency below 0.001% in UK Biobank, the positive predictive value was very low and only 16% of 4757 heterozygous genotypes from the SNP chips were confirmed with sequencing data. Results were similar for SNP chip data from the Personal Genome Project, and 20/21 individuals analysed had at least one false positive rare pathogenic variant that had been incorrectly genotyped. For pathogenic variants in the BRCA1 and BRCA2 genes, which are individually very rare, the overall performance metrics for the SNP chips versus sequencing in the UK Biobank were: sensitivity 34.6%, specificity 98.3%, positive predictive value 4.2%, and negative predictive value 99.9%. Rates of BRCA related cancers in UK Biobank participants with a positive SNP chip result were similar to those for age matched controls (odds ratio 1.31, 95% confidence interval 0.99 to 1.71) because the vast majority of variants were false positives, whereas sequence positive participants had a significantly increased risk (odds ratio 4.05, 2.72 to 6.03). Conclusions SNP chips are extremely unreliable for genotyping very rare pathogenic variants and should not be used to guide health decisions without validation.

https://www.bmj.com/content/bmj/372/bmj.n214.full.pdf

Use of SNP chips to detect rare pathogenic variants: retrospective, population based diagnostic evaluation

Objective To determine the diagnostic accuracy of SNP-chips frequently used by direct-to-consumer genetic testing companies for genotyping rare genetic variants. Methods We assessed the diagnostic accuracy of genotypes from SNP-chips (index test) with next generation sequencing data (reference test) in 49,908 individuals recruited to UK Biobank. We compared the genotyping accuracy of SNP-chip variants covered by the next generation sequencing data by allele frequency. We further used the ClinVar database to select rare pathogenic variants in the BRCA1 and BRCA2 genes as an exemplar for detailed analysis. Cancer registry data was gathered for BRCA-related cancers (breast, ovarian, prostate and pancreatic) across all participants. Results SNP-chip genotype accuracy is high overall, but the likelihood of a true positive result reduces substantially with decreasing allele frequency. The sensitivity, specificity, positive predictive (PPV) and negative predictive value (NPV) for heterozygous genotypes are all >99% for 108,574 single nucleotide variants directly genotyped by the UK Biobank SNP-chips. However, for variants with a frequency Discussion SNP-chips are extremely unreliable for genotyping very rare pathogenic variants and should not be used to guide health decisions without validation. SUMMARY BOX Section 1: What is already known on this topic SNP-chips are an accurate and affordable method for genotyping common genetic variants across the genome. They are often used by direct-to-consumer genetic testing companies and research studies, but there several reports suggesting they perform poorly for genotyping rare genetic variants. Section 2: What this study adds Our study confirms that SNP-chips are highly inaccurate for genotyping rare and clinically-actionable variants. Using large-scale SNP-chip and sequencing data from UK Biobank, we show that the SNP-chip false discovery rate is >84% for very rare variants (

/pdf/very-rare-pathogenic-genetic-variants-detected-by-snp-chips-4bs8bymzii.pdf

Very rare pathogenic genetic variants detected by SNP-chips are usually false positives: implications for direct-to-consumer genetic testing

Objectives To determine the analytical validity of SNP-chips for genotyping very rare genetic variants. Design Retrospective study using data from two publicly available resources, the UK Biobank and the Personal Genome Project. Setting Research biobanks and direct-to-consumer genetic testing in the UK and USA. Participants 49,908 individuals recruited to UK Biobank, and 21 individuals who purchased consumer genetic tests and shared their data online via the Personal Genomes Project. Main outcome measures We assessed the analytical validity of genotypes from SNP-chips (index test) with sequencing data (reference standard). We evaluated the genotyping accuracy of the SNP-chips and split the results by variant frequency. We went on to select rare pathogenic variants in the BRCA1 and BRCA2 genes as an exemplar for detailed analysis of clinically-actionable variants in UK Biobank, and assessed BRCA-related cancers (breast, ovarian, prostate and pancreatic) in participants using cancer registry data. Results SNP-chip genotype accuracy is high overall; sensitivity, specificity and precision are all >99% for 108,574 common variants directly genotyped by the UK Biobank SNP-chips. However, the likelihood of a true positive result reduces dramatically with decreasing variant frequency; for variants with a frequency Conclusion SNP-chips are extremely unreliable for genotyping very rare pathogenic variants and should not be used to guide health decisions without validation. SUMMARY BOX Section 1: What is already known on this topic SNP-chips are an accurate and affordable method for genotyping common genetic variants across the genome. They are often used by direct-to-consumer (DTC) genetic testing companies and research studies, but there several case reports suggesting they perform poorly for genotyping rare genetic variants when compared with sequencing. Section 2: What this study adds Our study confirms that SNP-chips are highly inaccurate for genotyping rare, clinically-actionable variants. Using large-scale SNP-chip and sequencing data from UK Biobank, we show that SNP-chips have a very low precision of

/pdf/assessing-the-analytical-validity-of-snp-chips-for-detecting-3whxen8dnv.pdf

Assessing the analytical validity of SNP-chips for detecting very rare pathogenic variants: implications for direct-to-consumer genetic testing

First, do no harm: direct-to-consumer genetic testing.

Inherited TP53 Variants and Risk of Prostate Cancer

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First, do no harm: direct-to-consumer genetic testing.

Inherited TP53 Variants and Risk of Prostate Cancer