Cargando…

Development and Validation of a Simulation Model–Based Clinical Decision Tool: Identifying Patients Where 21-Gene Recurrence Score Testing May Change Decisions

There is a need for industry-independent decision tools that integrate clinicopathologic features, comorbidities, and genomic information for women with node-negative, invasive, hormone receptor–positive, human epidermal growth factor receptor-2–negative (early-stage) breast cancer. METHODS: We adap...

Descripción completa

Detalles Bibliográficos
Autores principales: Jayasekera, Jinani, Sparano, Joseph A., O'Neill, Suzanne, Chandler, Young, Isaacs, Claudine, Kurian, Allison W., Kushi, Lawrence, Schechter, Clyde B., Mandelblatt, Jeanne
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wolters Kluwer Health 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8425835/
https://www.ncbi.nlm.nih.gov/pubmed/34251881
http://dx.doi.org/10.1200/JCO.21.00651
_version_ 1783749920066371584
author Jayasekera, Jinani
Sparano, Joseph A.
O'Neill, Suzanne
Chandler, Young
Isaacs, Claudine
Kurian, Allison W.
Kushi, Lawrence
Schechter, Clyde B.
Mandelblatt, Jeanne
author_facet Jayasekera, Jinani
Sparano, Joseph A.
O'Neill, Suzanne
Chandler, Young
Isaacs, Claudine
Kurian, Allison W.
Kushi, Lawrence
Schechter, Clyde B.
Mandelblatt, Jeanne
author_sort Jayasekera, Jinani
collection PubMed
description There is a need for industry-independent decision tools that integrate clinicopathologic features, comorbidities, and genomic information for women with node-negative, invasive, hormone receptor–positive, human epidermal growth factor receptor-2–negative (early-stage) breast cancer. METHODS: We adapted an extant Cancer Intervention and Surveillance Modeling Network simulation model to estimate the 10-year risk of distant recurrence, breast cancer–specific mortality, other-cause mortality, and life-years gained with chemoendocrine versus endocrine therapy. We simulated outcomes for 1,512 unique patient subgroups based on all possible combinations of age, tumor size, grade, and comorbidity level; simulations were performed with and without 21-gene recurrence scores (RSs). Model inputs were derived from clinical trials, large US cohort studies, registry, and claims data. External validation was performed by comparing results to observed rates in two independent sources. We highlight results for one scenario where treatment choice may be uncertain. RESULTS: Chemoendocrine versus endocrine therapy in a 65-69-year-old woman with a small (≤ 2 cm), intermediate-grade tumor, and mild comorbidities provides a 1.3% absolute reduction in 10-year distant recurrence risk, with 0.23 life-years gained. With these tumor features, a woman like this will have a 28% probability of having an RS 16-20, 18% RS 21-25, and 11% RS 26+. If testing is done, and her RS is 16-20, chemoendocrine therapy reduces 10-year distant recurrence risk to 1%, with 0.20 life-years gained, a similar result as without testing. The absolute benefits would increase to 4.8%-5.5% if the RS was 26+. The model closely reproduced observed rates in both independent data sets. CONCLUSION: Our validated clinical decision tool is flexible, readily adaptable to include new therapies, and can support discussions about genomic testing and early breast cancer treatment.
format Online
Article
Text
id pubmed-8425835
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Wolters Kluwer Health
record_format MEDLINE/PubMed
spelling pubmed-84258352022-09-10 Development and Validation of a Simulation Model–Based Clinical Decision Tool: Identifying Patients Where 21-Gene Recurrence Score Testing May Change Decisions Jayasekera, Jinani Sparano, Joseph A. O'Neill, Suzanne Chandler, Young Isaacs, Claudine Kurian, Allison W. Kushi, Lawrence Schechter, Clyde B. Mandelblatt, Jeanne J Clin Oncol ORIGINAL REPORTS There is a need for industry-independent decision tools that integrate clinicopathologic features, comorbidities, and genomic information for women with node-negative, invasive, hormone receptor–positive, human epidermal growth factor receptor-2–negative (early-stage) breast cancer. METHODS: We adapted an extant Cancer Intervention and Surveillance Modeling Network simulation model to estimate the 10-year risk of distant recurrence, breast cancer–specific mortality, other-cause mortality, and life-years gained with chemoendocrine versus endocrine therapy. We simulated outcomes for 1,512 unique patient subgroups based on all possible combinations of age, tumor size, grade, and comorbidity level; simulations were performed with and without 21-gene recurrence scores (RSs). Model inputs were derived from clinical trials, large US cohort studies, registry, and claims data. External validation was performed by comparing results to observed rates in two independent sources. We highlight results for one scenario where treatment choice may be uncertain. RESULTS: Chemoendocrine versus endocrine therapy in a 65-69-year-old woman with a small (≤ 2 cm), intermediate-grade tumor, and mild comorbidities provides a 1.3% absolute reduction in 10-year distant recurrence risk, with 0.23 life-years gained. With these tumor features, a woman like this will have a 28% probability of having an RS 16-20, 18% RS 21-25, and 11% RS 26+. If testing is done, and her RS is 16-20, chemoendocrine therapy reduces 10-year distant recurrence risk to 1%, with 0.20 life-years gained, a similar result as without testing. The absolute benefits would increase to 4.8%-5.5% if the RS was 26+. The model closely reproduced observed rates in both independent data sets. CONCLUSION: Our validated clinical decision tool is flexible, readily adaptable to include new therapies, and can support discussions about genomic testing and early breast cancer treatment. Wolters Kluwer Health 2021-09-10 2021-07-12 /pmc/articles/PMC8425835/ /pubmed/34251881 http://dx.doi.org/10.1200/JCO.21.00651 Text en © 2021 by American Society of Clinical Oncology https://creativecommons.org/licenses/by-nc-nd/4.0/Creative Commons Attribution Non-Commercial No Derivatives 4.0 License: https://creativecommons.org/licenses/by-nc-nd/4.0/
spellingShingle ORIGINAL REPORTS
Jayasekera, Jinani
Sparano, Joseph A.
O'Neill, Suzanne
Chandler, Young
Isaacs, Claudine
Kurian, Allison W.
Kushi, Lawrence
Schechter, Clyde B.
Mandelblatt, Jeanne
Development and Validation of a Simulation Model–Based Clinical Decision Tool: Identifying Patients Where 21-Gene Recurrence Score Testing May Change Decisions
title Development and Validation of a Simulation Model–Based Clinical Decision Tool: Identifying Patients Where 21-Gene Recurrence Score Testing May Change Decisions
title_full Development and Validation of a Simulation Model–Based Clinical Decision Tool: Identifying Patients Where 21-Gene Recurrence Score Testing May Change Decisions
title_fullStr Development and Validation of a Simulation Model–Based Clinical Decision Tool: Identifying Patients Where 21-Gene Recurrence Score Testing May Change Decisions
title_full_unstemmed Development and Validation of a Simulation Model–Based Clinical Decision Tool: Identifying Patients Where 21-Gene Recurrence Score Testing May Change Decisions
title_short Development and Validation of a Simulation Model–Based Clinical Decision Tool: Identifying Patients Where 21-Gene Recurrence Score Testing May Change Decisions
title_sort development and validation of a simulation model–based clinical decision tool: identifying patients where 21-gene recurrence score testing may change decisions
topic ORIGINAL REPORTS
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8425835/
https://www.ncbi.nlm.nih.gov/pubmed/34251881
http://dx.doi.org/10.1200/JCO.21.00651
work_keys_str_mv AT jayasekerajinani developmentandvalidationofasimulationmodelbasedclinicaldecisiontoolidentifyingpatientswhere21generecurrencescoretestingmaychangedecisions
AT sparanojosepha developmentandvalidationofasimulationmodelbasedclinicaldecisiontoolidentifyingpatientswhere21generecurrencescoretestingmaychangedecisions
AT oneillsuzanne developmentandvalidationofasimulationmodelbasedclinicaldecisiontoolidentifyingpatientswhere21generecurrencescoretestingmaychangedecisions
AT chandleryoung developmentandvalidationofasimulationmodelbasedclinicaldecisiontoolidentifyingpatientswhere21generecurrencescoretestingmaychangedecisions
AT isaacsclaudine developmentandvalidationofasimulationmodelbasedclinicaldecisiontoolidentifyingpatientswhere21generecurrencescoretestingmaychangedecisions
AT kurianallisonw developmentandvalidationofasimulationmodelbasedclinicaldecisiontoolidentifyingpatientswhere21generecurrencescoretestingmaychangedecisions
AT kushilawrence developmentandvalidationofasimulationmodelbasedclinicaldecisiontoolidentifyingpatientswhere21generecurrencescoretestingmaychangedecisions
AT schechterclydeb developmentandvalidationofasimulationmodelbasedclinicaldecisiontoolidentifyingpatientswhere21generecurrencescoretestingmaychangedecisions
AT mandelblattjeanne developmentandvalidationofasimulationmodelbasedclinicaldecisiontoolidentifyingpatientswhere21generecurrencescoretestingmaychangedecisions