Evidence map›Paper›PMID 32817165›Full record

ArticleGenome research2020

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

Fan Gao, Xuedong Pan, Elissa B Dodd-Eaton, Carlos Vera Recio, Matthew D Montierth, Jasmina Bojadzieva, Phuong L Mai, Kristin Zelley, Valen E Johnson, Danielle Braun and 5 more

Open access · bronzeAbstract read
In one paragraph

Article in Genome research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 3 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors at 5 institutions in 1 country.

Fan Gao *Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.
Xuedong Pan *Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.
Elissa B Dodd-EatonDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.
Carlos Vera RecioDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.
Matthew D MontierthDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.
Jasmina BojadzievaDepartment of Genetics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.
Phuong L MaiClinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland 20892, USA.
Kristin ZelleyDivision of Oncology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania 19104, USA.
Valen E JohnsonDepartment of Statistics, Texas A&M University, College Station, Texas 77843, USA.
Danielle BraunDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA.
Kim E NicholsDivision of Oncology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania 19104, USA.
Judy E GarberDana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts 02215, USA.
Sharon A SavageClinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland 20892, USA.
Louise C StrongDepartment of Genetics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.
Wenyi WangDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.
The University of Texas MD Anderson Cancer Center · USChildren's Hospital of Philadelphia · USHarvard University · USNational Cancer Institute · USTexas A&M University · US

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Fabien Delerue · 1985 to 2026
$290.8M
TRANSCRIPTIONAL REGULATION OF WT-1P01CA034936 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI STRONG, LOUISE C · 1985 to 2010
$26.4M
TRAINING PROGRAM IN COMPUTATIONAL BIOLOGY AND MEDICINET15LM007093 · NLM · RICE UNIVERSITY · PI Lydia E. Kavraki · 1992 to 2026
$20.8M
Precision Assessment and Delivery of Cancer Risks in BRCA 1/2 Mutation CancersR01CA207365 · NCI · DANA-FARBER CANCER INST · PI CHEN, JINBO, REBBECK, TIMOTHY R · 2017 to 2021
$3.6M
Consistent variable selection in p>>n settingsR01CA158113 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI JOHNSON, VALEN EARL · 2011 to 2020
$2.8M
Innate Immune Mechanisms Controlling Flavivirus NeurovirulenceR01AI183793 · NIAID · UNIVERSITY OF MINNESOTA · PI Michael Gale, Andrew Atwell Oberst · 2024 to 2026
$2.4M
Statistical methods for genomic analysis of heterogeneous tumorsR01CA183793 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI WANG, WENYI · 2014 to 2018
$1.7M
Statistical methods and tools for cancer risk prediction in families with germline mutations in TP53R01CA239342 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI WANG, WENYI · 2019 to 2022
$1.5M
Experience-driven development of reliable stimulus representations in dynamic cortical networksK99EY034936 · NEI · MAX PLANCK FLORIDA CORPORATION · PI LEMPEL, AUGUSTO ABEL · 2024 to 2025
$224k
NCI NIH HHS N01 PC074400NCI NIH HHS P01 CA034936NCI NIH HHS P30 CA016672NCI NIH HHS R01 CA158113NCI NIH HHS R01 CA183793NCI NIH HHS R01 CA207365NCI NIH HHS R01 CA239342NEI NIH HHS K99 EY034936NIAID NIH HHS R01 AI183793NLM NIH HHS T15 LM007093
6 · The paper itself

Abstract

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

Indexed as

AdultBRCA1 ProteinBRCA2 ProteinBreast NeoplasmsFamilyFemaleGenetic Predisposition to DiseaseGerm-Line MutationHumansLi-Fraumeni SyndromeOvarian NeoplasmsPedigreeTumor Suppressor Protein p53Young AdultBRCA1 ProteinBRCA1 protein, humanBRCA2 ProteinBRCA2 protein, humanTP53 protein, humanTumor Suppressor Protein p53

Identifiers

PMID32817165
PMCPMC7462073
OpenAlexW3050921496

What OpenQuestion holds

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LicenceCC BY-NC
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.