Performance of LFSPRO germline carrier risk predictions compared to standard genetic counseling practice on prospectively collected probands.

Purpose: Current clinical guidelines for genetic testing for Li-Fraumeni Syndrome (LFS) have many limitations, primarily the criteria don't consider detailed personal and family history information and may miss many individuals with LFS. A personalized risk assessment tool, LFSPRO, was created to estimate a proband's risk for LFS based on personal and family history information. The purpose of this study is to compare LFSPRO to existing clinical criteria to determine if LFSPRO can outperform these tools.

LFSPROShiny: An Interactive R/Shiny App for Prediction and Visualization of Cancer Risks in Families With Deleterious Germline Mutations.

Purpose: LFSPRO is an R library that implements risk prediction models for Li-Fraumeni syndrome (LFS), a genetic disorder characterized by deleterious germline mutations in the gene. To facilitate the use of these models in clinics, we developed LFSPROShiny, an interactive R/Shiny interface of LFSPRO that allows genetic counselors (GCs) to perform risk predictions without any programming components and further visualize the risk profiles of their patients to aid the decision-making process.

Methods: LFSPROShiny implements two models that have been validated on multiple LFS patient cohorts: a competing risk model that predicts cancer-specific risks for the first primary and a recurrent-event model that predicts the risk of a second primary tumor.

Validating risk prediction models for multiple primaries and competing cancer outcomes in families with Li-Fraumeni syndrome using clinically ascertained data at a single institute.

Purpose: There exists a barrier between developing and disseminating risk prediction models in clinical settings. We hypothesize this barrier may be lifted by demonstrating the utility of these models using incomplete data that are collected in real clinical sessions, as compared to the commonly used research cohorts that are meticulously collected.

Patients And Methods: Genetic counselors (GCs) collect family history when patients (i.

LFSPROShiny: an interactive R/Shiny app for prediction and visualization of cancer risks in families with deleterious germline mutations.

Purpose: LFSPRO is an R library that implements risk prediction models for Li-Fraumeni syndrome (LFS), a genetic disorder characterized by deleterious germline mutations in the gene. To facilitate the use of these models in clinics, we developed LFSPROShiny, an interactive R/Shiny interface of LFSPRO that allows genetic counselors (GCs) to perform risk predictions without any programming components, and further visualize the risk profiles of their patients to aid the decision-making process.

Methods: LFSPROShiny implements two models that have been validated on multiple LFS patient cohorts: a competing-risk model that predicts cancer-specific risks for the first primary, and a recurrent-event model that predicts the risk of a second primary tumor.

Personalized Risk Prediction for Cancer Survivors: A Bayesian Semi-parametric Recurrent Event Model with Competing Outcomes.

Multiple primary cancers are increasingly more frequent due to improved survival of cancer patients. Characteristics of the first primary cancer largely impact the risk of developing subsequent primary cancers. Hence, model-based risk characterization of cancer survivors that captures patient-specific variables is needed for healthcare policy making.

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

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.

Penetrance of Different Cancer Types in Families with Li-Fraumeni Syndrome: A Validation Study Using Multicenter Cohorts.

Li-Fraumeni syndrome (LFS) is a rare hereditary cancer syndrome associated with an autosomal-dominant mutation inheritance in the tumor suppressor gene and a wide spectrum of cancer diagnoses. The previously developed R package, LFSPRO, is capable of estimating the risk of an individual being a mutation carrier. However, an accurate estimation of the penetrance of different cancer types in LFS is crucial to improve the clinical characterization and management of high-risk individuals.

Penetrance Estimates Over Time to First and Second Primary Cancer Diagnosis in Families with Li-Fraumeni Syndrome: A Single Institution Perspective.

Li-Fraumeni syndrome (LFS) is a rare autosomal dominant disorder associated with germline mutations and an increased lifetime risk of multiple primary cancers (MPC). Penetrance estimation of time to first and second primary cancer within LFS remains challenging because of limited data and the difficulty of characterizing the effects of a primary cancer on the penetrance of a second primary cancer. Using a recurrent events survival modeling approach that incorporates a family-wise likelihood to efficiently integrate the pedigree structure, we estimated the penetrance for both first and second primary cancer diagnosis from a pediatric sarcoma cohort at MD Anderson Cancer Center [MDACC, Houston, TX; number of families = 189; single primary cancer (SPC) = 771; and MPC = 87].