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.

Factors impacting adolescent and young adult cancer patients' decision to pursue genetic counseling and testing.

PURPOSE: Adolescent and young adult (AYA) cancer patients face challenges when navigating cancer treatment and survivorship. Many are at risk for cancer predisposition syndromes; however, factors influencing pursuit of genetic counseling and testing have not been reported. We describe AYA cancer patients' decision-making process, including motivational factors and barriers, as it relates to utilization of genetic services. METHODS: Thirty AYAs diagnosed with cancer previously referred for cancer predisposition genetic counseling completed semi-structured interviews via audio-only Zoom calls. Thematic analysis was used to perform qualitative analysis and identify major themes. RESULTS: The sample comprised 21 AYAs who had genetic counseling and nine who did not. Motivational factors identified included learning genetic counseling is an available service, concern about the impact of a hereditary syndrome on family members and family planning, learning about the need for cancer screening or prevention, affordability of genetic testing, and easing worry about additional cancer risks. For those who did not pursue genetic counseling, barriers included scheduling or other priorities, worry, and cost. However, the majority expressed they would reconsider genetic counseling in the future. CONCLUSION: AYA cancer patients have similar motivational factors to pursue genetic counseling compared to other patients; however, their younger age of diagnosis may alter how these factors affect decision-making. While there are barriers limiting access to genetic services, they did not decrease interest in future genetic counseling for most patients. Genetic counseling and testing should be discussed with patients who previously declined genetic services.

Impact of a Genetic Evaluation Initiative to Increase Access to Genetic Services for Adolescent and Young Adults at a Tertiary Cancer Hospital.

Purpose: Adolescents and young adults (AYAs) with cancer are at increased risk for inherited cancer predisposition syndromes. Genetic counseling (GC) is important for accurate risk assessment, diagnosis, and management of inherited cancers. Numerous barriers prevent AYA access to genetic services. This study describes outcomes of a genetic evaluation initiative (GEI) regarding utilization of genetic services among AYAs. Methods: To improve AYA access to GC, the AYA program at UT MD Anderson Cancer Center implemented GEI, a process for identifying and referring eligible patients for GC. We collected retrospective electronic medical record data between July 12, 2018 and July 12, 2019 to capture AYA's clinical characteristics, genetic referral, scheduled appointments, counseling, testing, and results. Results: In total, 516 AYAs were referred to the AYA clinic during the study period with a median age of first cancer diagnosis of 17 years. One hundred sixty-six AYAs were identified who would benefit from genetic evaluation, 57 (34.3%) of whom had previously undergone counseling. One hundred nine patients were recommended for referral to GC, and 64.2% (70/109) were referred by the AYA team. To date, 58.6% (41/70) met with a genetic counselor and 75.6% (31/41) completed genetic testing, which yielded 1 pathogenic, 2 uncertain, and 29 benign results. Conclusion: The GEI resulted in a 72.0% relative increase in the rate of GC utilization and represents a novel approach to increasing AYA patient access to cancer genetic services in this population.

Multigene panel testing results in patients with multiple breast cancer primaries.

Currently, the NCCN guidelines recommend testing of BRCA1 and BRCA2 for females with multiple breast primaries, if her first diagnosis was ≤50 years old. With the increase in uptake of multigene panels, testing for genes outside of BRCA1 and BRCA2 has become more prevalent. This study looked at a single institution's cohort of women with multiple primary breast cancers that underwent panel testing to determine the rates of pathogenic mutations in non-BRCA genes. The genetic testing results for each individual were reviewed, along with patient characteristics. Descriptive analysis and two-tailed Z tests were used to analyze the data. Out of 85 eligible women, 33 (38.8%) tested positive for a pathogenic mutation in a cancer predisposition gene: 9 BRCA1, 5 BRCA2, 5 ATM, 1 BARD1, 4 CHEK2, 1 MSH2, 1 MSH6, 2 PALB2, 1 PMS2, 1 PTEN and 3 TP53. Overall, 17.6% tested positive for a non-BRCA breast cancer predisposition gene. There was no difference in the age of first or second breast cancer diagnosis in comparison with genetic testing outcomes. This study found a high positive rate for all individuals with multiple breast cancers, regardless of age, for both BRCA1 and BRCA2 and non-BRCA genes. Future studies should investigate whether individuals with multiple breast cancer primaries that do not meet BRCA1 and BRCA2 testing criteria should undergo genetic testing, regardless of the age of diagnosis.