ArticleProceedings of the National Academy of Sciences of the United States of America2026
Quantitative calibration of a spatial QSP model identifies fibroblast impact on HCC immunotherapy.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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
2 citing papers in PubMed.
- Structured Schemas for Provenance-Rich, LLM-Assisted QSP Model Calibration.CPT: pharmacometrics & systems pharmacology · 2026Article
- Structured Schemas for Provenance-Rich, LLM-Assisted QSP Model Calibration.bioRxiv : the preprint server for biology · 2026Article
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Authors and funding
12 authors.
Funding
Abstract
Computational models are increasingly used to predict treatment response and optimize cancer therapeutic strategies. Quantitative systems pharmacology (QSP) models mechanistically simulate tumor progression and pharmacological interventions, enabling virtual clinical trials, model-informed drug development, and biomarker discovery, but they lack spatial resolution to represent tumor microenvironment (TME) architecture. Coupling QSP with agent-based modeling creates spatial QSP (spQSP) frameworks capable of resolving tissue-level organization at single-cell resolution; however, parameterizing these models with human tumor data remains challenging. Here, we extend an existing spQSP model of liver cancer by mechanistically incorporating a fibroblast module and develop an Approximate Bayesian Computation-Sequential Monte Carlo calibration pipeline that integrates spatial molecular data. This calibration framework matches tumor architectures between spQSP simulations and spatial molecular data by fitting statistical summaries of cellular neighborhoods. The calibrated model reproduces fibroblast-mediated exclusion of lymphocyte infiltration observed in spatial transcriptomics and predicts posttreatment spatial tumor states in an independent cohort receiving immune checkpoint inhibitor and tyrosine kinase inhibitor combination therapy. Finally, we identify spatial and nonspatial pretreatment biomarkers associated with therapeutic response. Together, this study demonstrates how integrating spatial omics with mechanistic modeling enables quantitative calibration, reveals the spatial role of fibroblasts in shaping immunosuppressive TMEs, and supports in silico biomarker discovery toward personalized cancer therapy.
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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.