Treatment of evolving cancers will require dynamic decision support.
cancer ecology and evolution
mathematical modeling
treatment scheduling
Journal
Annals of oncology : official journal of the European Society for Medical Oncology
ISSN: 1569-8041
Titre abrégé: Ann Oncol
Pays: England
ID NLM: 9007735
Informations de publication
Date de publication:
Oct 2023
Oct 2023
Historique:
received:
10
02
2023
revised:
01
08
2023
accepted:
21
08
2023
medline:
4
12
2023
pubmed:
1
10
2023
entrez:
30
9
2023
Statut:
ppublish
Résumé
Cancer research has traditionally focused on developing new agents, but an underexplored question is that of the dose and frequency of existing drugs. Based on the modus operandi established in the early days of chemotherapies, most drugs are administered according to predetermined schedules that seek to deliver the maximum tolerated dose and are only adjusted for toxicity. However, we believe that the complex, evolving nature of cancer requires a more dynamic and personalized approach. Chronicling the milestones of the field, we show that the impact of schedule choice crucially depends on processes driving treatment response and failure. As such, cancer heterogeneity and evolution dictate that a one-size-fits-all solution is unlikely-instead, each patient should be mapped to the strategy that best matches their current disease characteristics and treatment objectives (i.e. their 'tumorscape'). To achieve this level of personalization, we need mathematical modeling. In this perspective, we propose a five-step 'Adaptive Dosing Adjusted for Personalized Tumorscapes (ADAPT)' paradigm to integrate data and understanding across scales and derive dynamic and personalized schedules. We conclude with promising examples of model-guided schedule personalization and a call to action to address key outstanding challenges surrounding data collection, model development, and integration.
Identifiants
pubmed: 37777307
pii: S0923-7534(23)00806-2
doi: 10.1016/j.annonc.2023.08.008
pmc: PMC10688269
mid: NIHMS1935438
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
867-884Subventions
Organisme : NCI NIH HHS
ID : U01 CA232382
Pays : United States
Organisme : NCI NIH HHS
ID : U54 CA193489
Pays : United States
Informations de copyright
Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.
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