ArticleNPJ systems biology and applications2022
Model-informed experimental design recommendations for distinguishing intrinsic and acquired targeted therapeutic resistance in head and neck cancer.
Article in NPJ systems biology and applications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 16 citations in OpenAlex.
- Employing nullclines to balance treatment efficacy and neurotoxicity for sustained tumor control.Mathematical biosciences · 2025Article
- Personalized cancer treatment strategies incorporating irreversible and reversible drug resistance mechanisms.NPJ systems biology and applications · 2025Article
- Tumor microenvironment governs the prognostic landscape of immunotherapy for head and neck squamous cell carcinoma: A computational model-guided analysis.PLoS computational biology · 2025Article
- Understanding therapeutic tolerance through a mathematical model of drug-induced resistance.NPJ systems biology and applications · 2025Article
- Guidelines for mechanistic modeling and analysis in cardiovascular research.American journal of physiology. Heart and circulatory physiology · 2024Review
- Minimally sufficient experimental design using identifiability analysis.NPJ systems biology and applications · 2024Article
- Decoding the research landscape of drug resistance and therapeutic approaches in head and neck cancer: a bibliometric analysis from 2000 to 2023.Frontiers in pharmacology · 2024Article
- Investigating tumor-host response dynamics in preclinical immunotherapy experiments using a stepwise mathematical modeling strategy.Mathematical biosciences · 2023Article
- Predicting response to combination evofosfamide and immunotherapy under hypoxic conditions in murine models of colon cancer.Mathematical biosciences and engineering : MBE · 2023Article
- A Continuation Technique for Maximum Likelihood Estimators in Biological Models.Bulletin of mathematical biology · 2023Article
- A practical guide for the generation of model-based virtual clinical trials.Frontiers in systems biology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 4 institutions in 1 country.
Funding
Abstract
The promise of precision medicine has been limited by the pervasive resistance to many targeted therapies for cancer. Inferring the timing (i.e., pre-existing or acquired) and mechanism (i.e., drug-induced) of such resistance is crucial for designing effective new therapeutics. This paper studies cetuximab resistance in head and neck squamous cell carcinoma (HNSCC) using tumor volume data obtained from patient-derived tumor xenografts. We ask if resistance mechanisms can be determined from this data alone, and if not, what data would be needed to deduce the underlying mode(s) of resistance. To answer these questions, we propose a family of mathematical models, with each member of the family assuming a different timing and mechanism of resistance. We present a method for fitting these models to individual volumetric data, and utilize model selection and parameter sensitivity analyses to ask: which member(s) of the family of models best describes HNSCC response to cetuximab, and what does that tell us about the timing and mechanisms driving resistance? We find that along with time-course volumetric data to a single dose of cetuximab, the initial resistance fraction and, in some instances, dose escalation volumetric data are required to distinguish among the family of models and thereby infer the mechanisms of resistance. These findings can inform future experimental design so that we can best leverage the synergy of wet laboratory experimentation and mathematical modeling in the study of novel targeted cancer therapeutics.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.