1
|
Nguyen NH, Dodd-Eaton EB, Corredor JL, Woodman-Ross J, Green S, Gutierrez AM, Arun BK, Wang W. Validating Risk Prediction Models for Multiple Primaries and Competing Cancer Outcomes in Families With Li-Fraumeni Syndrome Using Clinically Ascertained Data. J Clin Oncol 2024:JCO2301926. [PMID: 38569124 DOI: 10.1200/jco.23.01926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 12/02/2023] [Accepted: 02/07/2024] [Indexed: 04/05/2024] Open
Abstract
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.
Collapse
Affiliation(s)
- Nam H Nguyen
- The University of Texas MD Anderson Cancer Center, Department of Bioinformatics and Computation Biology, Houston, TX
- Rice University, Department of Statistics, Houston, TX
| | - Elissa B Dodd-Eaton
- The University of Texas MD Anderson Cancer Center, Department of Bioinformatics and Computation Biology, Houston, TX
| | - Jessica L Corredor
- The University of Texas MD Anderson Cancer Center, Department of Clinical Cancer Genetics, Houston, TX
| | - Jacynda Woodman-Ross
- The University of Texas MD Anderson Cancer Center, Department of Clinical Cancer Genetics, Houston, TX
| | - Sierra Green
- The University of Texas MD Anderson Cancer Center, Department of Clinical Cancer Genetics, Houston, TX
| | - Angelica M Gutierrez
- The University of Texas MD Anderson Cancer Center, Department of Breast Medical Oncology, Houston, TX
| | - Banu K Arun
- The University of Texas MD Anderson Cancer Center, Department of Clinical Cancer Genetics, Houston, TX
- The University of Texas MD Anderson Cancer Center, Department of Breast Medical Oncology, Houston, TX
| | - Wenyi Wang
- The University of Texas MD Anderson Cancer Center, Department of Bioinformatics and Computation Biology, Houston, TX
| |
Collapse
|
2
|
Nguyen NH, Dodd-Eaton EB, Peng G, Corredor JL, Jiao W, Woodman-Ross J, Arun BK, Wang W. LFSPROShiny: An Interactive R/Shiny App for Prediction and Visualization of Cancer Risks in Families With Deleterious Germline TP53 Mutations. JCO Clin Cancer Inform 2024; 8:e2300167. [PMID: 38346271 PMCID: PMC10871774 DOI: 10.1200/cci.23.00167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 11/13/2023] [Accepted: 12/19/2023] [Indexed: 02/15/2024] Open
Abstract
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.
Collapse
Affiliation(s)
- Nam H Nguyen
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX
- Department of Statistics, Rice University, Houston, TX
| | - Elissa B Dodd-Eaton
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Gang Peng
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN
| | - Jessica L Corredor
- Department of Clinical Cancer Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Wenwei Jiao
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX
- Department of Statistics, North Caroline State University, Raleigh, NC
| | - Jacynda Woodman-Ross
- Department of Clinical Cancer Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Banu K Arun
- Department of Clinical Cancer Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Wenyi Wang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX
| |
Collapse
|
3
|
Nguyen NH, Dodd-Eaton EB, Corredor JL, Woodman-Ross J, Green S, Hernandez ND, Gutierrez Barrera AM, Arun BK, Wang W. 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. medRxiv 2023:2023.08.31.23294849. [PMID: 37693464 PMCID: PMC10491358 DOI: 10.1101/2023.08.31.23294849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
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.
Collapse
Affiliation(s)
- Nam H. Nguyen
- The University of Texas MD Anderson Cancer Center, Department of Bioinformatics and Computation Biology, Houston, TX
- Rice University, Department of Statistics, Houston, TX
| | - Elissa B. Dodd-Eaton
- The University of Texas MD Anderson Cancer Center, Department of Bioinformatics and Computation Biology, Houston, TX
| | - Jessica L. Corredor
- The University of Texas MD Anderson Cancer Center, Department of Clinical Cancer Genetics, Houston, TX
| | - Jacynda Woodman-Ross
- The University of Texas MD Anderson Cancer Center, Department of Clinical Cancer Genetics, Houston, TX
| | - Sierra Green
- The University of Texas MD Anderson Cancer Center, Department of Clinical Cancer Genetics, Houston, TX
| | - Nathaniel D. Hernandez
- The University of Texas MD Anderson Cancer Center, Department of Clinical Cancer Genetics, Houston, TX
| | | | - Banu K. Arun
- The University of Texas MD Anderson Cancer Center, Department of Breast Medical Oncology, Houston, TX
| | - Wenyi Wang
- The University of Texas MD Anderson Cancer Center, Department of Bioinformatics and Computation Biology, Houston, TX
| |
Collapse
|
4
|
Nguyen NH, Dodd-Eaton EB, Peng G, Corredor JL, Jiao W, Woodman-Ross J, Arun BK, Wang W. LFSPROShiny: an interactive R/Shiny app for prediction and visualization of cancer risks in families with deleterious germline TP53 mutations. medRxiv 2023:2023.08.11.23293956. [PMID: 37645796 PMCID: PMC10462184 DOI: 10.1101/2023.08.11.23293956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
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.
Collapse
Affiliation(s)
- Nam H Nguyen
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX
- Department of Statistics, Rice University, Houston, TX
| | - Elissa B Dodd-Eaton
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Gang Peng
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN
| | - Jessica L. Corredor
- Department of Clinical Cancer Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Wenwei Jiao
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX
- Department of Statistics, North Caroline State University, Raleigh, NC
| | - Jacynda Woodman-Ross
- Department of Clinical Cancer Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Banu K. Arun
- Department of Clinical Cancer Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Wenyi Wang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX
| |
Collapse
|