ArticleRoyal Society open science2019
Article in Royal Society open science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 1 of them a synthesis that pooled it.
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
43 citing papers in PubMed, 1 synthesis or guideline pooled it, 61 citations in OpenAlex.
- Recent applications of quantitative systems pharmacology and machine learning models across diseases.Journal of pharmacokinetics and pharmacodynamics · 2022Pooled it
- Virtual clinical trials of anti-PD-1 and anti-CTLA-4 immunotherapy in advanced hepatocellular carcinoma using a quantitative systems pharmacology model.Journal for immunotherapy of cancer · 2022Trial
- Quantitative Systems Pharmacology Model Predicts Enhanced Antitumor Efficacy of Combined Cytotoxic T‑Lymphocyte-Associated Protein 4 (CTLA-4) and Programmed Cell Death Protein 1 (PD-1) Blockade in a Syngeneic Mouse Model of Breast Cancer.ACS pharmacology & translational science · 2026Article
- Biomarkers of mRNA vaccine efficacy derived from mechanistic modeling of tumor-immune interactions.PLoS computational biology · 2025Article
- Quantitative Systems Pharmacology Modeling in Immuno-Oncology: Hypothesis Testing, Dose Optimization, and Efficacy Prediction.Handbook of experimental pharmacology · 2025Review
- An industry perspective on current QSP trends in drug development.Journal of pharmacokinetics and pharmacodynamics · 2024Article
- Quantitative systems pharmacology modeling of macrophage-targeted therapy combined with PD-L1 inhibition in advanced NSCLC.Clinical and translational science · 2024Article
- Endogenous pAKT activity is associated with response to AKT inhibition alone and in combination with immune checkpoint inhibition in murine models of TNBC.Cancer letters · 2024Article
- Development and Evaluation of a Quantitative Systems Pharmacology Model for Mechanism Interpretation and Efficacy Prediction of Atezolizumab in Combination with Carboplatin and Nab-Paclitaxel in Patients with Non-Small-Cell Lung Cancer.Pharmaceuticals (Basel, Switzerland) · 2024Article
- A review of mechanistic learning in mathematical oncology.Frontiers in immunology · 2024Review
- Transport Barriers Influence the Activation of Anti-Tumor Immunity: A Systems Biology Analysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Investigating tumor-host response dynamics in preclinical immunotherapy experiments using a stepwise mathematical modeling strategy.Mathematical biosciences · 2023Article
- Treatment of evolving cancers will require dynamic decision support.Annals of oncology : official journal of the European Society for Medical Oncology · 2023Review
- A transcriptome-informed QSP model of metastatic triple-negative breast cancer identifies predictive biomarkers for PD-1 inhibition.Science advances · 2023Article
- Generating immunogenomic data-guided virtual patients using a QSP model to predict response of advanced NSCLC to PD-L1 inhibition.NPJ precision oncology · 2023Article
- Mathematical modeling identifies LAG3 and HAVCR2 as biomarkers of T cell exhaustion in melanoma.iScience · 2023Article
- Generating immunogenomic data-guided virtual patients using a QSP model to predict response of advanced NSCLC to PD-L1 inhibition.bioRxiv : the preprint server for biology · 2023Article
- In silico cancer immunotherapy trials uncover the consequences of therapy-specific response patterns for clinical trial design and outcome.Nature communications · 2023Article
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Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
Abstract
The low response rate of immune checkpoint blockade in breast cancer has highlighted the need for predictive biomarkers to identify responders. While a number of clinical trials are ongoing, testing all possible combinations is not feasible. In this study, a quantitative systems pharmacology model is built to integrate immune-cancer cell interactions in patients with breast cancer, including central, peripheral, tumour-draining lymph node (TDLN) and tumour compartments. The model can describe the immune suppression and evasion in both TDLN and the tumour microenvironment due to checkpoint expression, and mimic the tumour response to checkpoint blockade therapy. We investigate the relationship between the tumour response to checkpoint blockade therapy and composite tumour burden, PD-L1 expression and antigen intensity, including their individual and combined effects on the immune system, using model-based simulations. The proposed model demonstrates the potential to make predictions of tumour response of individual patients given sufficient clinical measurements, and provides a platform that can be further adapted to other types of immunotherapy and their combination with molecular-targeted therapies. The patient predictions demonstrate how this systems pharmacology model can be used to individualize immunotherapy treatments. When appropriately validated, these approaches may contribute to optimization of breast cancer treatment.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.