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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 use...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10491358/ https://www.ncbi.nlm.nih.gov/pubmed/37693464 http://dx.doi.org/10.1101/2023.08.31.23294849 |
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author | Nguyen, Nam H. Dodd-Eaton, Elissa B. Corredor, Jessica L. Woodman-Ross, Jacynda Green, Sierra Hernandez, Nathaniel D. Gutierrez Barrera, Angelica M. Arun, Banu K. Wang, Wenyi |
author_facet | Nguyen, Nam H. Dodd-Eaton, Elissa B. Corredor, Jessica L. Woodman-Ross, Jacynda Green, Sierra Hernandez, Nathaniel D. Gutierrez Barrera, Angelica M. Arun, Banu K. Wang, Wenyi |
author_sort | Nguyen, Nam H. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10491358 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-104913582023-09-09 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 Nguyen, Nam H. Dodd-Eaton, Elissa B. Corredor, Jessica L. Woodman-Ross, Jacynda Green, Sierra Hernandez, Nathaniel D. Gutierrez Barrera, Angelica M. Arun, Banu K. Wang, Wenyi medRxiv Article 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. Cold Spring Harbor Laboratory 2023-09-02 /pmc/articles/PMC10491358/ /pubmed/37693464 http://dx.doi.org/10.1101/2023.08.31.23294849 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator. |
spellingShingle | Article Nguyen, Nam H. Dodd-Eaton, Elissa B. Corredor, Jessica L. Woodman-Ross, Jacynda Green, Sierra Hernandez, Nathaniel D. Gutierrez Barrera, Angelica M. Arun, Banu K. Wang, Wenyi 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 |
title | 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 |
title_full | 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 |
title_fullStr | 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 |
title_full_unstemmed | 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 |
title_short | 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 |
title_sort | 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 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10491358/ https://www.ncbi.nlm.nih.gov/pubmed/37693464 http://dx.doi.org/10.1101/2023.08.31.23294849 |
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