ArticleCancers2021
Standing Variations Modeling Captures Inter-Individual Heterogeneity in a Deterministic Model of Prostate Cancer Response to Combination Therapy.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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
8 citing papers in PubMed, 14 citations in OpenAlex.
- Simultaneous Population PKPD Analysis of Belantamab Mafodotin, Soluble BCMA, and Serum M-Protein in Relapsed/Refractory Multiple Myeloma.CPT: pharmacometrics & systems pharmacology · 2026Article
- Practical parameter identifiability and handling of censored data with Bayesian inference in mathematical tumour models.NPJ systems biology and applications · 2024Article
- Building consensus on the application of organoid-based drug sensitivity testing in cancer precision medicine and drug development.Theranostics · 2024Review
- Article
- Practical Understanding of Cancer Model Identifiability in Clinical Applications.Life (Basel, Switzerland) · 2023Article
- Advances in cancer vaccines for immunotherapy of prostate cancer.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2023Article
- High Accuracy Indicators of Androgen Suppression Therapy Failure for Prostate Cancer-A Modeling Study.Cancers · 2022Article
- Exploring the link between chronobiology and drug delivery: effects on cancer therapy.Journal of molecular medicine (Berlin, Germany) · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sipuleucel-T (Provenge) is the first live cell vaccine approved for advanced, hormonally refractive prostate cancer. However, survival benefit is modest and the optimal combination or schedule of sipuleucel-T with androgen depletion remains unknown. We employ a nonlinear dynamical systems approach to modeling the response of hormonally refractive prostate cancer to sipuleucel-T. Our mechanistic model incorporates the immune response to the cancer elicited by vaccination, and the effect of androgen depletion therapy. Because only a fraction of patients benefit from sipuleucel-T treatment, inter-individual heterogeneity is clearly crucial. Therefore, we introduce our novel approach, Standing Variations Modeling, which exploits inestimability of model parameters to capture heterogeneity in a deterministic model. We use data from mouse xenograft experiments to infer distributions on parameters critical to tumor growth and to the resultant immune response. Sampling model parameters from these distributions allows us to represent heterogeneity, both at the level of the tumor cells and the individual (mouse) being treated. Our model simulations explain the limited success of sipuleucel-T observed in practice, and predict an optimal combination regime that maximizes predicted efficacy. This approach will generalize to a range of emerging cancer immunotherapies.
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.