Performance of LFSPRO TP53 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. Additionally, we gauged genetic counselors' (GCs) experience using LFSPRO for their patients. Methods: Between December 2021 and March 2024, GCs identified patients concerning for LFS based on the patients' personal and family history information. This information was entered into LFSPRO to predict the risk to have a pathogenic/pathogenic (LP/P) germline TP53 variant. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) was compared between LFSPRO and Chompret criteria. Select GCs were asked to fill out surveys regarding their experience using LFSPRO following their genetic counseling appointments. Results: LFSPRO's sensitivity and specificity were 0.529 and 0.781 compared to Chompret's respective 0.235 and 0.677. Additionally, LFSPRO had a positive predictive value (PPV) of 0.30 compared to Chompret's 0.114. LFSPRO's risk prediction was concordant with genetic testing results in 75% of probands. Eighty-one percent of GC surveys reported LFSPRO being concordant with the GC's expectations and 75% would feel comfortable sharing the results with patients. Conclusion: LFSPRO showed improved sensitivity and specificity compared to Chompret criteria and GCs report a positive experience with LFSPRO. LFSPRO can be used to increase access to genetic testing for patients at risk for LFS and could help healthcare providers give more direct risk assessments regarding LFS testing and management for patients.

Validating Risk Prediction Models for Multiple Primaries and Competing Cancer Outcomes in Families With Li-Fraumeni Syndrome Using Clinically Ascertained Data.

PURPOSE: There exists a barrier between developing and disseminating risk prediction models in clinical settings. We hypothesize that this barrier may be lifted by demonstrating the utility of these models using incomplete data that are collected in real clinical sessions, as compared with the commonly used research cohorts that are meticulously collected. MATERIALS AND METHODS: Genetic counselors (GCs) collect family history when patients (ie, probands) come to MD Anderson Cancer Center for risk assessment of Li-Fraumeni syndrome, a genetic disorder characterized by deleterious germline mutations in the TP53 gene. Our clinical counseling-based (CCB) cohort consists of 3,297 individuals across 124 families (522 cases of single primary cancer and 125 cases of multiple primary cancers). We applied our software suite LFSPRO to make risk predictions and assessed performance in discrimination using AUC and in calibration using observed/expected (O/E) ratio. RESULTS: For prediction of deleterious TP53 mutations, we achieved an AUC of 0.78 (95% CI, 0.71 to 0.85) and an O/E ratio of 1.66 (95% CI, 1.53 to 1.80). Using the LFSPRO.MPC model to predict the onset of the second cancer, we obtained an AUC of 0.70 (95% CI, 0.58 to 0.82). Using the LFSPRO.CS model to predict the onset of different cancer types as the first primary, we achieved AUCs between 0.70 and 0.83 for sarcoma, breast cancer, or other cancers combined. CONCLUSION: We describe a study that fills in the critical gap in knowledge for the utility of risk prediction models. Using a CCB cohort, our previously validated models have demonstrated good performance and outperformed the standard clinical criteria. Our study suggests that better risk counseling may be achieved by GCs using these already-developed mathematical models.

LFSPROShiny: An Interactive R/Shiny App for Prediction and Visualization of Cancer Risks in Families With Deleterious Germline TP53 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 TP53 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. Starting with a visualization template, we keep regular contact with GCs, who ran LFSPROShiny in their counseling sessions, to collect feedback and discuss potential improvement. On receiving the family history as input, LFSPROShiny renders the family into a pedigree and displays the risk estimates of the family members in a tabular format. The software offers interactive overlaid side-by-side bar charts for visualization of the patients' cancer risks relative to the general population. RESULTS: We walk through a detailed example to illustrate how GCs can run LFSPROShiny in clinics from data preparation to downstream analyses and interpretation of results with an emphasis on the utilities that LFSPROShiny provides to aid decision making. CONCLUSION: Since December 2021, we have applied LFSPROShiny to over 100 families from counseling sessions at the MD Anderson Cancer Center. Our study suggests that software tools with easy-to-use interfaces are crucial for the dissemination of risk prediction models in clinical settings, hence serving as a guideline for future development of similar models.

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.e., probands) come to MD Anderson Cancer Center for risk assessment of Li-Fraumeni syndrome, a genetic disorder characterized by deleterious germline mutations in the TP53 gene. Our clinical counseling-based (CCB) cohort consists of 3,297 individuals across 124 families (522 cases of single primary cancer and 125 cases of multiple primary cancers). We applied our software suite LFSPRO to make risk predictions and assessed performance in discrimination using area under the curve (AUC), and in calibration using observed/expected (O/E) ratio. Results: For prediction of deleterious TP53 mutations, we achieved an AUC of 0.81 (95% CI, 0.70 - 0.91) and an O/E ratio of 0.96 (95% CI, 0.70 - 1.21). Using the LFSPRO.MPC model to predict the onset of the second cancer, we obtained an AUC of 0.70 (95% CI, 0.58 - 0.82). Using the LFSPRO.CS model to predict the onset of different cancer types as the first primary, we achieved AUCs between 0.70 and 0.83 for sarcoma, breast cancer, or other cancers combined. Conclusion: We describe a study that fills in the critical gap in knowledge for the utility of risk prediction models. Using a CCB cohort, our previously validated models have demonstrated good performance and outperformed the standard clinical criteria. Our study suggests better risk counseling may be achieved by GCs using these already-developed mathematical models.

LFSPROShiny: an interactive R/Shiny app for prediction and visualization of cancer risks in families with deleterious germline TP53 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 TP53 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. Starting with a visualization template, we keep regular contact with GCs, who ran LFSPROShiny in their counseling sessions, to collect feedback and discuss potential improvement. Upon receiving the family history as input, LFSPROShiny renders the family into a pedigree, and displays the risk estimates of the family members in a tabular format. The software offers interactive overlaid side-by-side bar charts for visualization of the patients' cancer risks relative to the general population. Results: We walk through a detailed example to illustrate how GCs can run LFSPROShiny in clinics, from data preparation to downstream analyses and interpretation of results with an emphasis on the utilities that LFSPROShiny provides to aid decision making. Conclusion: Since Dec 2021, we have applied LFSPROShiny to over 100 families from counseling sessions at MD Anderson Cancer Center. Our study suggests that software tools with easy-to-use interfaces are crucial for the dissemination of risk prediction models in clinical settings, hence serving as a guideline for future development of similar models.