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A pedigree-based prediction model identifies carriers of deleterious de novo mutations in families with Li-Fraumeni syndrome.
Gao, Fan; Pan, Xuedong; Dodd-Eaton, Elissa B; Recio, Carlos Vera; Montierth, Matthew D; Bojadzieva, Jasmina; Mai, Phuong L; Zelley, Kristin; Johnson, Valen E; Braun, Danielle; Nichols, Kim E; Garber, Judy E; Savage, Sharon A; Strong, Louise C; Wang, Wenyi.
Afiliación
  • Gao F; Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.
  • Pan X; Department of Statistics, Rice University, Houston, Texas 77005, USA.
  • Dodd-Eaton EB; Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.
  • Recio CV; Department of Statistics, Texas A&M University, College Station, Texas 77843, USA.
  • Montierth MD; Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.
  • Bojadzieva J; Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.
  • Mai PL; Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.
  • Zelley K; Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, Texas 77030, USA.
  • Johnson VE; Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.
  • Braun D; Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland 20892, USA.
  • Nichols KE; Division of Oncology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania 19104, USA.
  • Garber JE; Department of Statistics, Texas A&M University, College Station, Texas 77843, USA.
  • Savage SA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA.
  • Strong LC; Department of Data Sciences, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA.
  • Wang W; Division of Oncology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania 19104, USA.
Genome Res ; 30(8): 1170-1180, 2020 08.
Article en En | MEDLINE | ID: mdl-32817165
De 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 20-35 yr) provides a condition for an unbiased estimate of the DNM rate: 48% (23 DNMs vs. 25 FMs); and (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.
Asunto(s)

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Neoplasias Ováricas / Neoplasias de la Mama / Síndrome de Li-Fraumeni / Mutación de Línea Germinal / Predisposición Genética a la Enfermedad Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Límite: Adult / Female / Humans Idioma: En Revista: Genome Res Asunto de la revista: BIOLOGIA MOLECULAR / GENETICA Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Neoplasias Ováricas / Neoplasias de la Mama / Síndrome de Li-Fraumeni / Mutación de Línea Germinal / Predisposición Genética a la Enfermedad Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Límite: Adult / Female / Humans Idioma: En Revista: Genome Res Asunto de la revista: BIOLOGIA MOLECULAR / GENETICA Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos