ArticleCancer research2020
Modeling Tumor Evolutionary Dynamics to Predict Clinical Outcomes for Patients with Metastatic Colorectal Cancer: A Retrospective Analysis.
Article in Cancer research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Model-Informed Dosing Strategies for Epcoritamab Monotherapy in Relapsed or Refractory Large B-Cell Lymphoma.Clinical pharmacology and therapeutics · 2026Article
- Evolutionary rescue model informs strategies for driving cancer cell populations to extinction.bioRxiv : the preprint server for biology · 2025Article
- Quantitative systems pharmacology modeling of HER2-positive metastatic breast cancer for translational efficacy evaluation and combination assessment across therapeutic modalities.Acta pharmacologica Sinica · 2024Article
- Realizing the promise of Project Optimus: Challenges and emerging opportunities for dose optimization in oncology drug development.CPT: pharmacometrics & systems pharmacology · 2024Review
- Development and validation of a nomogram for predicting in-hospital mortality in ICU patients with infective endocarditis.BMC medical informatics and decision making · 2024Article
- Support to early clinical decisions in drug development and personalised medicine with checkpoint inhibitors using dynamic biomarker-overall survival models.British journal of cancer · 2023Review
- Dissecting sources of variability in patient response to targeted therapy: anti-HER2 therapies as a case study.European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences · 2023Article
- Mapping lesion-specific response and progression dynamics and inter-organ variability in metastatic colorectal cancer.Nature communications · 2023Article
- Incorporating lesion-to-lesion heterogeneity into early oncology decision making.Frontiers in immunology · 2023Review
- Embracing Project Optimus: Can we Leverage Evolutionary Theory to Optimize Dosing in Oncology?Pharmaceutical research · 2022Article
- Small-molecule inhibitors, immune checkpoint inhibitors, and more: FDA-approved novel therapeutic drugs for solid tumors from 1991 to 2021.Journal of hematology & oncology · 2022Review
- Anti-cancer treatment schedule optimization based on tumor dynamics modelling incorporating evolving resistance.Scientific reports · 2022Article
- RGB-Marking to Identify Patterns of Selection and Neutral Evolution in Human Osteosarcoma Models.Cancers · 2021Article
- Modeling Pharmacokinetics and Pharmacodynamics of Therapeutic Antibodies: Progress, Challenges, and Future Directions.Pharmaceutics · 2021Review
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Authors and funding
5 authors.
Funding
Abstract
Over 50% of colorectal cancer patients develop resistance after a transient response to therapy. Understanding tumor resistance from an evolutionary perspective leads to better predictions of treatment outcomes. The objectives of this study were to develop a computational framework to analyze tumor longitudinal measurements and recapitulate the individual evolutionary dynamics in metastatic colorectal cancer (mCRC) patients. A stochastic modeling framework was developed to depict the whole spectrum of tumor evolution prior to diagnosis and during and after therapy. The evolutionary model was optimized using a nonlinear mixed effect (NLME) method based on the longitudinal measurements of liver metastatic lesions from 599 mCRC patients. The deterministic limits in the NLME model were applied to optimize the stochastic model for each patient. Cox proportional hazards models coupled with the least absolute shrinkage and selection operator (LASSO) algorithm were applied to predict patients' progression-free survival (PFS) and overall survival (OS). The stochastic evolutionary model well described the longitudinal profiles of tumor sizes. The evolutionary parameters optimized for each patient indicated substantial interpatient variability. The number of resistant subclones at diagnosis was found to be a significant predictor to survival, and the hazard ratios with 95% CI were 1.09 (0.79-1.49) and 1.54 (1.01-2.34) for patients with three or more resistant subclones. Coupled with several patient characteristics, evolutionary parameters strongly predict patients' PFS and OS. A stochastic computational framework was successfully developed to recapitulate individual patient evolutionary dynamics, which could predict clinical survival outcomes in mCRC patients. SIGNIFICANCE: A data analysis framework depicts the individual evolutionary dynamics of mCRC patients and can be generalized to project patient survival outcomes.
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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.