ArticlePhysical biology2020
Integrating transcriptomics and bulk time course data into a mathematical framework to describe and predict therapeutic resistance in cancer.
Article in Physical biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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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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.
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Who cites it
21 citing papers in PubMed.
- Nicheformer: a foundation model for single-cell and spatial omics.Nature methods · 2025Article
- Deep learning identifies heterogeneous subpopulations in breast cancer cell lines.npj biomedical innovations · 2025Article
- Quantitative measurement of phenotype dynamics during cancer drug resistance evolution using genetic barcoding.Nature communications · 2025Article
- Understanding therapeutic tolerance through a mathematical model of drug-induced resistance.NPJ systems biology and applications · 2025Article
- Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A Review.IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society · 2025Article
- A mathematical model for predicting the spatiotemporal response of breast cancer cells treated with doxorubicin.Cancer biology & therapy · 2024Article
- Advancements in prospective single-cell lineage barcoding and their applications in research.Genome research · 2024Review
- Article
- TooManyCellsInteractive: A visualization tool for dynamic exploration of single-cell data.GigaScience · 2024Article
- Cellular barcoding tracks heterogeneous clones through selective pressures and phenotypic transitions.Trends in cancer · 2023Review
- A survey of open questions in adaptive therapy: Bridging mathematics and clinical translation.eLife · 2023Article
- Integration of quantitative methods and mathematical approaches for the modeling of cancer cell proliferation dynamics.American journal of physiology. Cell physiology · 2023Review
- A data assimilation framework to predict the response of glioma cells to radiation.Mathematical biosciences and engineering : MBE · 2023Article
- Model-informed experimental design recommendations for distinguishing intrinsic and acquired targeted therapeutic resistance in head and neck cancer.NPJ systems biology and applications · 2022Article
- Integrating single cell sequencing with a spatial quantitative systems pharmacology model spQSP for personalized prediction of triple-negative breast cancer immunotherapy response.Immunoinformatics (Amsterdam, Netherlands) · 2021Article
- Forecasting cancer: from precision to predictive medicine.Med (New York, N.Y.) · 2021Article
- Applications of high-resolution clone tracking technologies in cancer.Current opinion in biomedical engineering · 2021Article
- Forecasting cellular states: from descriptive to predictive biology via single-cell multiomics.Current opinion in systems biology · 2021Article
- Towards an Image-Informed Mathematical Model of In Vivo Response to Fractionated Radiation Therapy.Cancers · 2021Article
- Integrating Quantitative Assays with Biologically Based Mathematical Modeling for Predictive Oncology.iScience · 2020Review
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
A significant challenge in the field of biomedicine is the development of methods to integrate the multitude of dispersed data sets into comprehensive frameworks to be used to generate optimal clinical decisions. Recent technological advances in single cell analysis allow for high-dimensional molecular characterization of cells and populations, but to date, few mathematical models have attempted to integrate measurements from the single cell scale with other types of longitudinal data. Here, we present a framework that actionizes static outputs from a machine learning model and leverages these as measurements of state variables in a dynamic model of treatment response. We apply this framework to breast cancer cells to integrate single cell transcriptomic data with longitudinal bulk cell population (bulk time course) data. We demonstrate that the explicit inclusion of the phenotypic composition estimate, derived from single cell RNA-sequencing data (scRNA-seq), improves accuracy in the prediction of new treatments with a concordance correlation coefficient (CCC) of 0.92 compared to a prediction accuracy of CCC = 0.64 when fitting on longitudinal bulk cell population data alone. To our knowledge, this is the first work that explicitly integrates single cell clonally-resolved transcriptome datasets with bulk time-course data to jointly calibrate a mathematical model of drug resistance dynamics. We anticipate this approach to be a first step that demonstrates the feasibility of incorporating multiple data types into mathematical models to develop optimized treatment regimens from data.
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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.