High frequency of de novo mutations in Li-Fraumeni syndrome

BackgroundDevelopment of tumours such as adrenocortical carcinomas (ACC), choroid plexus tumours (CPT) or female breast cancers before age 31 or multiple primary cancers belonging to the Li-Fraumeni (LFS) spectrum is, independently of the familial history, highly suggestive of a germline TP53 mutation. The aim of this study was to determine the contribution of de novo and mosaic mutations to LFS.Methods and resultsAmong 328 unrelated patients harbouring a germline TP53 mutation identified by Sanger sequencing and/or QMPSF, we could show that the mutations had occurred de novo in 40 cases, without detectable parental age effect. Sanger sequencing revealed two mosaic mutations in a child with ACC and in an unaffected father of a child with medulloblastoma. Re-analysis of blood DNA by next-generation sequencing, performed at a depth above 500X, from 108 patients suggestive of LFS without detectable TP53 mutations, allowed us to identify 6 additional cases of mosaic TP53 mutations, in 2/49 children with ACC, 2/21 children with CPT, in 1/31 women with breast cancer before age 31 and in a patient who developed an osteosarcoma at age 12, a breast carcinoma and a breast sarcoma at age 35.ConclusionsThis study performed on a large series of TP53 mutation carriers allows estimating the contribution to LFS of de novo mutations to at least 14% (48/336) and suggests that approximately one-fifth of these de novo mutations occur during embryonic development. Considering the medical impact of TP53 mutation identification, medical laboratories in charge of TP53 testing should ensure the detection of mosaic mutations.

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Abstract 2406: A general probabilistic algorithm to predict de novo mutations in familial diseases as demonstrated in Li-Fraumeni Syndrome

10.1158/1538-7445.sabcs18-2406 ◽

2019 ◽

Author(s):

Xuedong Pan ◽

Fan Gao ◽

Elissa B. Dodd ◽

Jasmina Bojadzieva ◽

Phuong L. Mai ◽

...

Keyword(s):

De Novo ◽

De Novo Mutations ◽

Probabilistic Algorithm ◽

Li Fraumeni Syndrome ◽

Li Fraumeni

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Abstract 2406: A general probabilistic algorithm to predict de novo mutations in familial diseases as demonstrated in Li-Fraumeni Syndrome

10.1158/1538-7445.am2019-2406 ◽

2019 ◽

Author(s):

Xuedong Pan ◽

Fan Gao ◽

Elissa B. Dodd ◽

Jasmina Bojadzieva ◽

Phuong L. Mai ◽

...

Keyword(s):

De Novo ◽

De Novo Mutations ◽

Probabilistic Algorithm ◽

Li Fraumeni Syndrome ◽

Li Fraumeni

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A pedigree-based prediction model identifies carriers of deleterious de novo mutations in families with Li-Fraumeni syndrome

Genome Research ◽

10.1101/gr.249599.119 ◽

2020 ◽

Vol 30 (8) ◽

pp. 1170-1180

Author(s):

Fan Gao ◽

Xuedong Pan ◽

Elissa B. Dodd-Eaton ◽

Carlos Vera Recio ◽

Matthew D. Montierth ◽

...

Keyword(s):

Prediction Model ◽

De Novo ◽

De Novo Mutations ◽

Li Fraumeni Syndrome ◽

Li Fraumeni

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A pedigree-based prediction model identifies carriers of deleterious de novo mutations in families with Li-Fraumeni syndrome

10.1101/2020.02.10.942409 ◽

2020 ◽

Author(s):

Fan Gao ◽

Xuedong Pan ◽

Elissa B. Dodd-Eaton ◽

Carlos Vera Recio ◽

Matthew D. Montierth ◽

...

Keyword(s):

Molecular Mechanisms ◽

De Novo ◽

Cancer Genetics ◽

De Novo Mutations ◽

Cancer Syndrome ◽

Li Fraumeni Syndrome ◽

Inherited Cancer ◽

Li Fraumeni ◽

Validation Set ◽

Hotspot Mutation

ABSTRACTDe novo mutations (DNMs) are increasingly recognized as rare disease causal factors. Identifying DNM carriers will allow researchers to study the likely distinct molecular mechanisms of DNMs. We developed Famdenovo to predict DNM status (DNM or familial mutation (FM)) of deleterious autosomal dominant germline mutations for any syndrome. We introduce Famdenovo.TP53 for Li-Fraumeni syndrome (LFS) and analyze 324 LFS family pedigrees from four US cohorts: a validation set of 186 pedigrees and a discovery set of 138 pedigrees. The concordance index for Famdenovo.TP53 prediction was 0.95 (95% CI: [0.92, 0.98]). Forty individuals (95% CI: [30, 50]) were predicted as DNM carriers, increasing the total number from 42 to 82. We compared clinical and biological features of FM versus DNM carriers: 1) cancer and mutation spectra along with parental ages were similarly distributed; 2) ascertainment criteria like early-onset breast cancer (age 20 to 35 years) provides a condition for an unbiased estimate of the DNM rate: 48% (23 DNMs versus 25 FMs); 3) hotspot mutation R248W was not observed in DNMs, although it was as prevalent as hotspot mutation R248Q in FMs. Furthermore, we introduce Famdenovo.BRCA for hereditary breast and ovarian cancer syndrome, and apply it to a small set of family data from the Cancer Genetics Network. In summary, we introduce a novel statistical approach to systematically evaluate deleterious DNMs in inherited cancer syndromes. Our approach may serve as a foundation for future studies evaluating how new deleterious mutations can be established in the germline, such as those in TP53.

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Identification and characterization of de novo TP53 mutation carriers in Li-Fraumeni syndrome families: A single institution experience.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.10538 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. 10538-10538

Author(s):

Carlos Christian Vera Recio ◽

Jessica Corredor ◽

Elissa Dodd-Eaton ◽

Angelica M. Gutierrez-Barrera ◽

Najat C. Daw ◽

...

Keyword(s):

Tp53 Mutation ◽

De Novo ◽

De Novo Mutation ◽

Brier Score ◽

Li Fraumeni Syndrome ◽

Mutation Carriers ◽

Cancer Types ◽

Li Fraumeni ◽

Identification And Characterization

10538 Background: Li-Fraumeni syndrome (LFS) is an inherited cancer syndrome mainly caused by a deleterious mutation in TP53. An estimated 48% of LFS patients present due to a deleterious de novo mutation (DNM) in TP53. The knowledge of DNM status, DNM or familial mutation (FM), of an LFS patient requires genetic testing of both parents which is often inaccessible, making de novo LFS patients an understudied population. Famdenovo.TP53 is a Mendelian Risk prediction model used to predict DNM status of TP53 mutation carriers based on the cancer-family history and several input genetic parameters, including disease-gene penetrance. The good predictive performance of Famdenovo.TP53 was demonstrated using data collected from four historical US cohorts. We hypothesize that by incorporating penetrance estimates that are specific for different types of cancers diagnosed in family members, we can develop a model with further improved calibration, accuracy and prediction. Methods: We present Famdenovo.CS, which uses cancer-specific penetrance estimates that were derived previously using a Bayesian semi-parametric competing risk model, to calculate the DNM probability. We use our model to analyze 101 families recently collected from the Clinical Cancer Genetic program at MD Anderson Cancer Center (CCG-TP53) that includes 20 families with known DNM status and 81 families with unknown DNM status. We used the concordance index (AUC), observed:expected ratios (OE) and Brier score (BS) to measure our model’s discrimination, calibration and accuracy, respectively. We estimate the proportion of probands that present a DNM and compare DNM to FM carriers in several areas including: cancer types diagnosed, age at diagnosis, number of primary cancers diagnosed, sex, amino acid change caused by mutation in TP53. Results: Famdenovo.CS showed equally good discrimination and calibration performance to Famdenovo.TP53, while improving the overall accuracy, demonstrated by a decrease in the Brier score of -0.09 (95% CI: [-0.02, -0.19]). Of the 101 probands in the CCG-TP53 cohort, we predict 39 to be DNMs and 62 to be FMs. The cancer types and ages of diagnosis observed in FMs and DNMs are similarly distributed. Conclusions: Famdenovo.CS shows improved model accuracy in the CCG cohort. DNMs in TP53 are a prevalent cause of LFS and we did not find differences in the clinical characteristics of DNM and FM carriers. Our model allows for a systematic identification and characterization of TP53 DNM carriers.

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Pre- and Post-Zygotic TP53 De Novo Mutations in SHH-Medulloblastoma

Cancers ◽

10.3390/cancers12092503 ◽

2020 ◽

Vol 12 (9) ◽

pp. 2503

Author(s):

Jacopo Azzollini ◽

Elisabetta Schiavello ◽

Francesca Romana Buttarelli ◽

Carlo Alfredo Clerici ◽

Laura Tizzoni ◽

...

Keyword(s):

Family History ◽

De Novo ◽

Wide Spectrum ◽

De Novo Mutations ◽

Autosomal Dominant Disorder ◽

P53 Overexpression ◽

Tp53 Mutations ◽

Li Fraumeni Syndrome ◽

First Case ◽

Germline Testing

Li-Fraumeni syndrome (LFS) is an autosomal dominant disorder caused by mutations in the TP53 gene, predisposing to a wide spectrum of early-onset cancers, including brain tumors. In medulloblastoma patients, the role of TP53 has been extensively investigated, though the prevalence of de novo mutations has not been addressed. We characterized TP53 mutations in a monocentric cohort of consecutive Sonic Hedgehog (SHH)-activated medulloblastoma patients. Germline testing was offered based on tumor p53 immunostaining positivity. Among 24 patients, three (12.5%) showed tumor p53 overexpression, of whom two consented to undergo germline testing and resulted as carriers of TP53 mutations. In the first case, family history was uneventful and the mutation was not found in either of the parents. The second patient, with a family history suggestive of LFS, unexpectedly resulted as a carrier of the mosaic mutation c.742=/C>T p.(Arg248=/Trp). The allele frequency was 26% in normal tissues and 42–77% in tumor specimens. Loss of heterozygosity (LOH) in the tumor was also confirmed. Notably, the mosaic case has been in complete remission for more than one year, while the first patient, as most TP53-mutated medulloblastoma cases from other cohorts, showed a severe and rapidly progressive disease. Our study reported the first TP53 mosaic mutation in medulloblastoma patients and confirmed the importance of germline testing in p53 overexpressed SHH-medulloblastoma, regardless of family history.

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Evolutionary dynamics and structural consequences of de novo beneficial mutations and mutant lineages arising in a constant environment

BMC Biology ◽

10.1186/s12915-021-00954-0 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Margie Kinnersley ◽

Katja Schwartz ◽

Dong-Dong Yang ◽

Gavin Sherlock ◽

Frank Rosenzweig

Keyword(s):

High Frequency ◽

High Throughput Sequencing ◽

Evolutionary Dynamics ◽

De Novo ◽

Allelic Diversity ◽

Microbial Evolution ◽

De Novo Mutations ◽

Beneficial Mutations ◽

Adaptive Mutations ◽

The Impact

Abstract Background Microbial evolution experiments can be used to study the tempo and dynamics of evolutionary change in asexual populations, founded from single clones and growing into large populations with multiple clonal lineages. High-throughput sequencing can be used to catalog de novo mutations as potential targets of selection, determine in which lineages they arise, and track the fates of those lineages. Here, we describe a long-term experimental evolution study to identify targets of selection and to determine when, where, and how often those targets are hit. Results We experimentally evolved replicate Escherichia coli populations that originated from a mutator/nonsense suppressor ancestor under glucose limitation for between 300 and 500 generations. Whole-genome, whole-population sequencing enabled us to catalog 3346 de novo mutations that reached > 1% frequency. We sequenced the genomes of 96 clones from each population when allelic diversity was greatest in order to establish whether mutations were in the same or different lineages and to depict lineage dynamics. Operon-specific mutations that enhance glucose uptake were the first to rise to high frequency, followed by global regulatory mutations. Mutations related to energy conservation, membrane biogenesis, and mitigating the impact of nonsense mutations, both ancestral and derived, arose later. New alleles were confined to relatively few loci, with many instances of identical mutations arising independently in multiple lineages, among and within replicate populations. However, most never exceeded 10% in frequency and were at a lower frequency at the end of the experiment than at their maxima, indicating clonal interference. Many alleles mapped to key structures within the proteins that they mutated, providing insight into their functional consequences. Conclusions Overall, we find that when mutational input is increased by an ancestral defect in DNA repair, the spectrum of high-frequency beneficial mutations in a simple, constant resource-limited environment is narrow, resulting in extreme parallelism where many adaptive mutations arise but few ever go to fixation.

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Genetic and stochastic influences upon tumor formation and tumor types in Li-Fraumeni mouse models

Life Science Alliance ◽

10.26508/lsa.202000952 ◽

2020 ◽

Vol 4 (3) ◽

pp. e202000952

Author(s):

Chang S Chan ◽

Yvonne Sun ◽

Hua Ke ◽

Yuhan Zhao ◽

Merzu Belete ◽

...

Keyword(s):

High Frequency ◽

Tumor Tissue ◽

Tumor Formation ◽

Tissue Type ◽

Genetic Modifiers ◽

Stochastic Effects ◽

Li Fraumeni Syndrome ◽

Increased Risk ◽

Li Fraumeni ◽

Tumor Types

p53 is the most frequently mutated gene in human cancers. Li-Fraumeni syndrome patients inheriting heterozygous p53 mutations often have a much-increased risk to develop cancer(s) at early ages. Recent studies suggest that some individuals inherited p53 mutations do not have the early onset or high frequency of cancers. These observations suggest that other genetic, environmental, immunological, epigenetic, or stochastic factors modify the penetrance of the cancerous mutant Tp53 phenotype. To test this possibility, this study explored dominant genetic modifiers of Tp53 mutations in heterozygous mice with different genetic backgrounds. Both genetic and stochastic effects upon tumor formation were observed in these mice. The genetic background of mice carrying Tp53 mutations has a strong influence upon the tissue type of the tumor produced and the number of tumors formed in a single mouse. The onset age of a tumor is correlated with the tissue type of that tumor, although identical tumor tissue types can occur at very different ages. These observations help to explain the great diversity of cancers in different Li-Fraumeni patients over lifetimes.

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