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.

Abstract 5761: Bayesian estimation of a semi-parametric recurrent event model with competing outcomes for personalized risk prediction among cancer survivors

Multiple primary cancer (MPC) is becoming more common in the general population, accounting for approximately 20% of incidence cases in the United States. Epidemiological studies indicate a strong dependence of the subsequent primary on the characteristics of the first primary. For example, there is evidence that individuals with a first primary breast cancer is more likely to develop a second primary lung cancer. These observations highlight the need to develop a statistical model that characterizes age-to-onset of cancers beyond the first primary, while accounting for the complex relationships between the cancer occurrences. Such a modeling framework was previously lacking. We hereby propose a Bayesian semiparametric framework, where the occurrences of each cancer type follow a non-homogeneous Poisson process. The time-varying intensity of this process is conditioned on genetic and demographic covariates, such as status of genetic mutation and sex, as well as on a patient’s cancer history, such as type and timing of the first primary, thus allowing our model to capture the heterogeneity in cancer risks across individuals. Since our model requires a dataset that is enriched with MPC cases, we utilize data collected from families affected with Li-Fraumeni Syndrome (LFS), which is a genetic disorder characterized by germline mutations in the tumor-suppressor gene TP53. People with LFS are at higher risks of certain cancer types, and many cancer survivors develop additional primary malignancy. We train and cross-validate our model on a patient cohort selected according to clinical LFS criteria at MD Anderson Cancer Center from year 2000 to 2015. The cohort consists of 11,186 individuals across 429 families, out of which 2,286 were diagnosed with at least one primary cancer and 335 were tested positive for germline mutations in TP53. Upon model training, we construct cancer-specific penetrance curves for the second primary cancer, which vary considerably among patients with different covariates and cancer history, thus highlighting the utility of our model for personalized risk prediction. Our penetrance estimates display good performance when being used to make cancer-specific predictions of the second primary among cancer survivors, achieving AUCs (Areas under the Receiver Operating Characteristic curves) of 0.91, 0.76 and 0.68 respectively for sarcoma, breast cancer, and all other cancers combined. While we apply our model to an LFS dataset, the statistical framework is general, and can be tailored to analyze any time-to-event datasets with suitably selected sets of covariates. Future applications include population-based characterization of specific cancer type combinations over time to impact public health policy making.

Citation Format: Nam H. Nguyen, Elissa B. Dodd-Eaton, Seung Jun Shin, Jing Ning, Wenyi Wang. Bayesian estimation of a semi-parametric recurrent event model with competing outcomes for personalized risk prediction among cancer survivors. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5761.

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. We propose a Bayesian semi-parametric framework, where the occurrence processes of the competing cancer types follow independent non-homogeneous Poisson processes and adjust for covariates including the type and age at diagnosis of the first primary. Applying this framework to a historically collected cohort with families presenting a highly enriched history of multiple primary tumors and diverse cancer types, we have derived a suite of age-to-onset penetrance curves for cancer survivors. This includes penetrance estimates for second primary lung cancer, potentially impactful to ongoing cancer screening decisions. Using Receiver Operating Characteristic (ROC) curves, we have validated the good predictive performance of our models in predicting second primary lung cancer, sarcoma, breast cancer, and all other cancers combined, with areas under the curves (AUCs) at 0.89, 0.91, 0.76 and 0.68, respectively. In conclusion, our framework provides covariate-adjusted quantitative risk assessment for cancer survivors, hence moving a step closer to personalized health management for this unique population.

Determining concordance of LFSPRO TP53 germline carrier risk predictions to standard genetic counseling practice

10593

Background: Li-Fraumeni Syndrome (LFS) is a hereditary cancer syndrome associated with a germline mutation in the TP53 tumor suppressor gene and an increased risk of developing a spectrum of cancers throughout a carrier’s lifetime. Early cancer detection in patients with TP53 mutations can dramatically improve cancer survival rates. However, it can be difficult to identify patients at risk of LFS due to the overlap of the multiple cancer types with other inherited cancer syndromes. Risk prediction modeling has been widely accepted by the clinical community, however, currently there is not an available tool to assist in risk prediction of LFS during genetic counseling sessions. LFSPRO is a statistical model that has previously been validated on large research cohorts in predicting the likelihood of a proband having LFS based off detailed patient and family history information. To improve the clinical utility of LFSPRO, a user-friendly interface is still needed. Additionally, we aim to evaluate concordance between LFSPRO’s abilities in predicting the likelihood of a proband having LFS and currently established clinical testing criteria. Methods: We developed a Shiny App to create a user-friendly interface for genetic counselors (GCs) to run LFSPRO. Determining concordance of LFSPRO to standard germline testing criteria and the clinical utility of the LFSPRO Shiny App is underway by GCs within the Department of Clinical Cancer Genetics at MD Anderson Cancer Center through prospective data collection and completion of user surveys. Following a standard genetic counseling session, individuals identified as concerning for a potential germline TP53 mutation are evaluated with LFSPRO to compare performance of the model to established TP53 testing guidelines. Concordance is then evaluated in prediction of TP53 mutation carrier status from LFSPRO, current clinical criteria and clinical judgment. Data collection began 12/21/2021 and is currently ongoing. Results: Close collaboration with GCs and clinicians on the development of the LFSPRO Shiny App has achieved automated de-identified input data, clear pedigree drawing, important demographic data, interpretable risk results and risk visualizations to be used at the GC's discretion. To date, 29 individual’s family data have been run through LFSPRO. Of these, 11 have not currently completed TP53 testing and concordance cannot be determined. Of the 18 remaining, 14 individuals’ LFSPRO results were concordant with current clinical criteria. Further data collection is needed to appropriately analyze concordance. Conclusions: The LFSPRO Shiny App aims to provide an additional tool for GCs and clinical providers to assess patient risk for LFS. This ongoing study will help establish the clinical utility of LFSPRO in a single institution’s genetic counseling practice with the potential to be applied to other patient care settings in the future.

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. 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.

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 TP53 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 TP53 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. Here, we developed a competing risk-based statistical model that incorporates the pedigree structure efficiently into the penetrance estimation and corrects for ascertainment bias while also increasing the effective sample size of this rare population. This enabled successful estimation of TP53 penetrance for three LFS cancer types: breast (BR), sarcoma (SA), and others (OT), from 186 pediatric sarcoma families collected at MD Anderson Cancer Center (Houston, TX). Penetrance validation was performed on a combined dataset of two clinically ascertained family cohorts with cancer to overcome internal bias in each (total number of families = 668). The age-dependent onset probability distributions of specific cancer types were different. For breast cancer, the TP53 penetrance went up at an earlier age than the reported BRCA1/2 penetrance. The prediction performance of the penetrance estimates was validated by the combined independent cohorts (BR = 85, SA = 540, and OT = 158). Area under the ROC curves (AUC) were 0.92 (BR), 0.75 (SA), and 0.81 (OT). The new penetrance estimates have been incorporated into the current LFSPRO R package to provide risk estimates for the diagnosis of breast cancer, sarcoma, or other cancers. SIGNIFICANCE: These findings provide specific penetrance estimates for LFS-associated cancers, which will likely impact the management of families at high risk of LFS.See related article by Shin et al., p. 347.

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 TP53 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]. Validation of the risk prediction performance was performed using an independent MDACC clinical cohort of TP53 tested individuals (SPC = 102 and MPC = 58). These findings showed that an individual diagnosed at a later age was more likely to be diagnosed with a second primary cancer. In addition, TP53 mutation carriers had a HR of 1.65 (95% confidence interval, 1.1-2.5) for developing a second primary cancer versus SPC. The area under the ROC (AUC) curve for predicting individual outcomes of MPC versus SPC was 0.77. In summary, we provide the first set of penetrance estimates for first and second primary cancer for TP53 germline mutation carriers and demonstrate its accuracy for cancer risk assessment. SIGNIFICANCE: These findings present an open-source R package LFSPRO that could be used for genetic counseling and health management of individuals with LFS as it estimates the risk of both first and second primary cancer diagnosis.See related article by Shin et al., p. 354.