Evidence map›Paper›PMID 41279804›Full record

ArticlebioRxiv : the preprint server for biology2025

Quantitative Calibration of a Spatial QSP Model Identifies Fibroblast Impact on HCC Immunotherapy.

Shuming Zhang, Hanwen Wang, Yeonju Cho, Wendy Wong, Mark Yarchoan, Elizabeth M Jaffee, Won Jin Ho, Luciane T Kagohara, Elana J Fertig, Aleksander S Popel and 1 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Shuming ZhangDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Hanwen WangDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Yeonju ChoDepartment of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Wendy WongDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Mark YarchoanBloomberg-Kimmel Immunotherapy Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Elizabeth M JaffeeBloomberg-Kimmel Immunotherapy Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Won Jin HoDepartment of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA.ORCID 0000-0003-2644-5086
Luciane T KagoharaBloomberg-Kimmel Immunotherapy Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Elana J FertigInstitute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA.
Aleksander S PopelDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Atul DeshpandeBloomberg-Kimmel Immunotherapy Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Funding

Integrating bioinformatics into multiscale models for hepatocellular carcinomaU01CA212007 · NCI · JOHNS HOPKINS UNIVERSITY · PI EWALD, ANDREW JOSEF, FERTIG, ELANA · 2018 to 2022
$3.2M
Informing mechanistic rules of agent-based models with single-cell multi-omicsU24CA284156 · NCI · TRUSTEES OF INDIANA UNIVERSITY · PI Elana Fertig, Paul T Macklin · 2024 to 2026
$2.3M
Single-cell and imaging data integration software to spatially resolve the tumor microenvironmentU01CA253403 · NCI · JOHNS HOPKINS UNIVERSITY · PI FERTIG, ELANA · 2020 to 2022
$1.2M
NCI NIH HHS U01 CA212007NCI NIH HHS U01 CA253403NCI NIH HHS U24 CA284156
6 · The paper itself

Abstract

Computational models are increasingly used to predict treatment response and optimize cancer therapy strategies. Among these, quantitative systems pharmacology (QSP) models mechanistically simulate tumor progression and pharmacological interventions, enabling virtual clinical trials, model-informed drug development, and biomarker identification. Coupling QSP with an agent-based model yields a spatial QSP (spQSP) platform that captures tissue-level spatial organization of the tumor microenvironment (TME). However, parameterizing such models to represent tumor biology remains an open problem. In this study, we developed a calibration framework using the Approximate Bayesian Computation - Sequential Monte Carlo (ABC-SMC) approach to calibrate the spQSP model with a combination of clinical and spatial molecular data, reflecting the TME characteristics of human tumors. This calibration framework matches tumor architectures between spQSP model predictions and patient spatial molecular data by fitting statistical summaries of cellular neighborhoods. We demonstrate that model calibration using CODEX data from untreated HCC patients enables prediction of TME spatial molecular states in an independent cohort receiving immune-checkpoint inhibitor (ICI) and tyrosine kinase inhibitor (TKI) combination therapy. Finally, we identify spatial and non-spatial pretreatment biomarkers and assess their predictive power for therapeutic response. This workflow demonstrates how integrating spatial-omics with multiscale mechanistic models enables quantitative calibration, biological insight, and in silico biomarker discovery, providing a framework for personalized cancer therapy across tumor types.

Identifiers

PMID41279804
PMCPMC12637429

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Registered trials

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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.