Evidence map›Paper›PMID 42709923›Full record

ArticlePLoS computational biology2026

Clusters, fingers, and singles: A mechanical landscape of tumor invasion.

Sheriff Akeeb, Adam I Marcus, Yi Jiang

Abstract read
In one paragraph

Article in PLoS computational biology, 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
–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

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

3 authors.

Sheriff AkeebDepartment of Mathematics and Statistics, Georgia State University, Atlanta, Georgia, United States of America.
Adam I MarcusDepartment of Hematology and Medical Oncology, Winship Cancer Institute, Emory School of Medicine, Atlanta, Georgia, United States of America.
Yi JiangDepartment of Mathematics and Statistics, Georgia State University, Atlanta, Georgia, United States of America.ORCID 0000-0002-7519-6365

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Collective invasion is a key mechanism by which tumors disseminate and metastasize, involving coordinated migration of heterogeneous cell populations. Experimental studies in spheroid-based assays have identified specialized leader and follower cells that work together during this process, but the biophysical rules governing their interaction remain unclear. We present a mechanistic, cell-based computational model using the Cellular Potts framework to investigate how heterotypic adhesion, leader motility, and follower proliferation jointly shape invasion. Leader-follower tumors were simulated across 13 310 parameter sets, and invasion was quantified by invasive and infiltrative areas, finger-like protrusions, solitary defectors, and detached clusters. From these simulations, we identified four distinct invasion phenotypes: non-invasive, bulk collective, single-cell, and multimodal. Multimodal invasion-the coexistence of cohesive strands, solitary cells, and small clusters-emerged as the most prevalent phenotype, particularly under moderate adhesion and high motility. Proliferation increased tumor bulk rather than determining invasion mode, which was governed primarily by adhesion and leader motility. Mapping outcomes across the parameter space revealed sharp transitions between invasion modes, underscoring trade-offs between adhesion and motility in shaping invasion complexity. Our results show that hybrid invasion behaviors, previously considered rare, arise robustly from simple mechanical rules and are favored in a broad region of the parameter space. This framework reconciles binary models of invasion with experimental observations of heterogeneity, providing predictive insights into how modulating adhesion and motility may modify invasive behavior.

Indexed as

Models, BiologicalNeoplasm InvasivenessNeoplasmsAnimalsCell AdhesionCell MovementCell ProliferationComputational BiologyComputer SimulationHumans

Identifiers

PMID42709923
PMCPMC13568528

What Socratic holds

Textmetadata
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Registered trials

None linked

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 Socratic graph.