Evidence map›Paper›PMID 41735583›Full record

ArticleBritish journal of cancer2026

Gompertz growth with a shared carrying capacity optimally simulates primary and metastatic tumor growth dynamics.

Pirmin Schlicke, Preethi Korangath, Xiaoxi Pan, Caner Ercan, Kathleen Gabrielson, Lyndsey Werhane, Yinyin Yuan, Sébastien Benzekry, Robert Ivkov, Heiko Enderling

Abstract read
In one paragraph

Article in British journal of cancer, 2026. 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
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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

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Pirmin SchlickeDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. pirmin.schlicke@plus.ac.at.ORCID http://orcid.org/0000-0001-9619-728X
Preethi KorangathDepartment of Radiation Oncology and Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, ML, USA.
Xiaoxi PanInstitute for Data Science in Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Caner ErcanDepartment of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.ORCID http://orcid.org/0000-0002-5611-2699
Kathleen GabrielsonDepartment of Molecular and Comparative Pathobiology, Johns Hopkins University School of Medicine, Baltimore, ML, USA.
Lyndsey WerhaneDepartment of Radiation Oncology and Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, ML, USA.
Yinyin YuanInstitute for Data Science in Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.ORCID http://orcid.org/0000-0002-8556-4707
Sébastien BenzekryCOMPutational Pharmacology and Clinical Oncology Department, Aix-Marseille University, Marseille, France.ORCID http://orcid.org/0000-0002-3749-8637
Robert IvkovDepartment of Radiation Oncology and Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, ML, USA.
Heiko EnderlingDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. henderling@mdanderson.org.ORCID http://orcid.org/0000-0002-9696-6410

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCancer is a systemic disease with most deaths attributed to metastatic burden. Primary and metastatic tumors, albeit at different anatomic locations, are interconnected through multiple biological processes. Pre-clinical and clinical observations of growth acceleration of metastases after surgery, or abscopal effects outside the radiation field are widely reported, yet reliably triggering favorable and avoiding unfavorable systemic responses remains an unmet clinical need. Understanding local and systemic tumor interaction dynamics will help guide future treatments.

methodsWe analyze the data of multiple in vivo tumor models. We formalize the systemic interplay of tumors as mathematical differential equation and calibrate parameters for each cell line and mouse type. Using model selection metrics, we identify classic tumor growth models with a novel shared carrying capacity parsimoniously describe the pan-cancer experimental data.

resultsShared systemic carrying capacity, metastatic spread potential, and metastatic growth rates differ across tested cell lines and mouse strains. Bi-directional concomitant systemic interconnectivity explains the observed metastatic explosion after primary tumor surgery. DISCUSSION: Future investigations should reproduce this analysis in clinical settings and evaluate whether this shared carrying capacity model could help stratify patients at risk of metastatic disease below clinical detectability and inform strategies to control oligometastatic cancer.

Indexed as

Models, BiologicalNeoplasmsAnimalsCell Line, TumorHumansMiceNeoplasm Metastasis

Identifiers

PMID41735583
PMCPMC13035863

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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.