ArticleScientific reports2021
A mathematical model of tumor regression and recurrence after therapeutic oncogene inactivation.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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.
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Who cites it
6 citing papers in PubMed, 12 citations in OpenAlex.
- Approximate Bayesian inference in a model for self-generated gradient collective cell movement.Computational statistics · 2025Article
- Simulating Interclonal Interactions in Diffuse Large B-Cell Lymphoma.Bioengineering (Basel, Switzerland) · 2023Article
- The MYC oncogene - the grand orchestrator of cancer growth and immune evasion.Nature reviews. Clinical oncology · 2022Review
- Mathematical characterization of population dynamics in breast cancer cells treated with doxorubicin.Frontiers in molecular biosciences · 2022Article
- A novel interpretable machine learning algorithm to identify optimal parameter space for cancer growth.Frontiers in big data · 2022Article
- Kinetics of Nanomedicine in Tumor Spheroid as anFrontiers in bioengineering and biotechnology · 2021Review
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
Abstract
The targeted inactivation of individual oncogenes can elicit regression of cancers through a phenomenon called oncogene addiction. Oncogene addiction is mediated by cell-autonomous and immune-dependent mechanisms. Therapeutic resistance to oncogene inactivation leads to recurrence but can be counteracted by immune surveillance. Predicting the timing of resistance will provide valuable insights in developing effective cancer treatments. To provide a quantitative understanding of cancer response to oncogene inactivation, we developed a new 3-compartment mathematical model of oncogene-driven tumor growth, regression and recurrence, and validated the model using a MYC-driven transgenic mouse model of T-cell acute lymphoblastic leukemia. Our mathematical model uses imaging-based measurements of tumor burden to predict the relative number of drug-sensitive and drug-resistant cancer cells in MYC-dependent states. We show natural killer (NK) cell adoptive therapy can delay cancer recurrence by reducing the net-growth rate of drug-resistant cells. Our studies provide a novel way to evaluate combination therapy for personalized cancer treatment.
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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 OpenQuestion graph.