{
  "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. Author summary: When cancer cells leave a primary tumor, they do not just invade as lone cells but also as cohesive fingers and small cell clusters, depending on how strongly cells stick together, how hard some cells pull, and how fast the population grows. We built a simple, physics-based computational model in which a small number of “leader” cells pull on the more proliferative “followers.” By systematically varying three key properties, cell-cell adhesion, leader motility, and follower proliferation, we generated a large set of simulations of invasion patterns and grouped them into four invasion modes. The resulting phase diagram shows where tumors grow as compact masses (no invasion), or invade as protruding “fingers,” as single cells, or in a mixed multimodal fashion with fingers, clusters, and single cells. Surprisingly, this mixed state was the most common outcome, arising when adhesion is moderate and leader motility is strong. This map provides intuition and practical rules for thinking about how changes in adhesion or motility might reorganize invasion, for example, by shifting cluster-forming fronts toward more compact, less dissemination-prone configurations.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Mathematics and Statistics, Georgia State University, Atlanta, Georgia, United States of America",
    "Department of Hematology and Medical Oncology, Winship Cancer Institute, Emory School of Medicine, Atlanta, Georgia, United States of America"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Sheriff Akeeb"
    },
    {
      "affs": [
        1
      ],
      "name": "Adam I. Marcus"
    },
    {
      "name": "Yi Jiang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1p1m80v5ejp3m.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pcbi.1014747",
  "fulltext_url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014747",
  "id": "1p1m80v5ejp3m",
  "issns": [
    "1553-7358"
  ],
  "landing_url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014747",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2611|Mathematical Biology Tumor Growth"
    ],
    [
      "plos",
      "/Biology and life sciences/Biophysics"
    ],
    [
      "plos",
      "/Biology and life sciences/Biophysics/Biophysical simulations"
    ],
    [
      "plos",
      "/Biology and life sciences/Cell biology/Cell motility"
    ],
    [
      "plos",
      "/Biology and life sciences/Cell biology/Cell motility/Cell migration"
    ],
    [
      "plos",
      "/Biology and life sciences/Computational biology/Biophysical simulations"
    ],
    [
      "plos",
      "/Biology and life sciences/Developmental biology/Cell migration"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Cancers and neoplasms"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Cancers and neoplasms/Invasive tumors"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Cancers and neoplasms/Lung and intrathoracic tumors/Non-small cell lung cancer"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Cancers and neoplasms/Malignant tumors"
    ],
    [
      "plos",
      "/Physical sciences/Physics/Biophysics"
    ],
    [
      "plos",
      "/Physical sciences/Physics/Biophysics/Biophysical simulations"
    ]
  ],
  "openalex": "W4416512222",
  "pdf_url": "https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1014747&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-08",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/1p1m80v5ejp3m.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:56Z",
      "native_id": "10.1371/journal.pcbi.1014747",
      "source": "plos",
      "url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014747"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "plos:biophysics",
      "subfield": "biophysics"
    },
    {
      "field": "biology",
      "origin": "plos:cell biology",
      "subfield": "cell-biology"
    },
    {
      "field": "medicine",
      "origin": "plos:cancers and neoplasms",
      "subfield": "oncology"
    }
  ],
  "title": "Clusters, fingers, and singles: A mechanical landscape of tumor invasion",
  "title_text": "Clusters, fingers, and singles: A mechanical landscape of tumor invasion",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:56Z",
  "url": "https://research.draconitic.com/paper/1p1m80v5ejp3m",
  "venue": "PLOS Computational Biology",
  "versions": []
}