Evidence map›Paper›PMID 41990740›Full record

ArticleAmerican journal of human genetics2026

Performance of LFSPRO prediction in TP53 mutation status for prospectively collected probands.

Jessica L Corredor, Ruonan Li, Elissa B Dodd-Eaton, Jacynda Casey, Ashley H Woodson, Nam H Nguyen, Gang Peng, Angelica M Gutierrez, Banu K Arun, Wenyi Wang

Abstract read
In one paragraph

Article in American journal of human genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Jessica L CorredorDepartment of Clinical Cancer Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Ruonan LiDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Elissa B Dodd-EatonDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Jacynda CaseyDepartment of Clinical Cancer Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Ashley H WoodsonDepartment of Clinical Cancer Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Nam H NguyenDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Department of Statistics, Rice University, Houston, TX, USA.
Gang PengDepartment of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA.
Angelica M GutierrezDepartment of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Banu K ArunDepartment of Clinical Cancer Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. Electronic address: barun@mdanderson.org.
Wenyi WangDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. Electronic address: wwang7@mdanderson.org.

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI DIANE BODURKA · 1985 to 2026
$290.8M
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
NCI NIH HHS P30 CA016672NCI NIH HHS R01 CA239342
6 · The paper itself

Abstract

Genetic counseling and testing for germline mutations are essential for identifying individuals at increased risk for cancer. Pathogenic/likely pathogenic (P/LP) variants in TP53 are diagnostic of Li-Fraumeni syndrome (LFS), a highly penetrant disorder with diverse, early-onset tumors. Current clinical guidelines, such as Chompret and classic criteria, provide frameworks for identifying individuals at risk for P/LP TP53 variants; however, genetic counselors often encounter people with features concerning for LFS that do not clearly meet established criteria, creating challenges for risk assessment and testing decisions. We evaluated whether LFSPRO, a Mendelian, family-history-based model that estimates the individual's probability of harboring a deleterious TP53 variant, improves identification of individuals with LFS relative to guideline criteria. In a prospectively collected cohort of 178 probands who underwent clinical genetic counseling and germline TP53 testing, LFSPRO showed superior discrimination compared with Chompret criteria, with higher sensitivity (81% vs. 33%) and specificity (88% vs. 65%) and improved positive predictive values (PPVs: 0.53 vs. 0.14; negative predictive values [NPVs]: 0.96 vs. 0.85). Receiver operating characteristic analysis confirmed strong discriminatory performance (area under the curve [AUC] = 0.88). Calibration analysis using observed-to-expected ratios indicated good agreement between predicted and observed P/LP variant frequencies (observed/expected = 1.07). These findings demonstrate that LFSPRO outperforms traditional guideline-based criteria for identifying individuals with TP53 mutations in real-world clinical settings. By providing quantitative, well-calibrated TP53 P/LP variant probabilities rather than binary classifications, LFSPRO can enhance genetic counseling and support testing decisions, particularly for individuals who do not clearly meet existing criteria.

Indexed as

Germ-Line MutationLi-Fraumeni SyndromeMutationTumor Suppressor Protein p53FemaleGenetic CounselingGenetic TestingHumansMaleProspective StudiesROC CurveTP53 protein, humanTumor Suppressor Protein p53Bayes calculationCancer prevention and early detectionclinic-based individualsfamily historyMendelian modelsrisk prediction

Identifiers

PMID41990740
PMCPMC13277690

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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.