Evidence map›Paper›PMID 39121170›Full record

ArticlePLoS computational biology2024

Evolution of phenotypic plasticity leads to tumor heterogeneity with implications for therapy.

Simon Syga, Harish P Jain, Marcus Krellner, Haralampos Hatzikirou, Andreas Deutsch

Abstract read
In one paragraph

Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. AScience signaling · 2026
    Article
  3. Article
  4. Go-or-grow models in biology: a monster on a leash.Journal of mathematical biology · 2025
    Review
  5. Article
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

5 authors.

Simon SygaCenter for Interdisciplinary Digital Sciences, Department Information Services and High Performance Computing, TUD Dresden University of Technology, Dresden, Germany.ORCID 0000-0001-9955-9012
Harish P JainNjord Centre, Department of Physics, University of Oslo, Oslo, Norway.
Marcus KrellnerSchool of Mathematics and Statistics, University of St Andrews, St Andrews, United Kingdom.
Haralampos HatzikirouCenter for Interdisciplinary Digital Sciences, Department Information Services and High Performance Computing, TUD Dresden University of Technology, Dresden, Germany.ORCID 0000-0002-1270-7885
Andreas DeutschCenter for Interdisciplinary Digital Sciences, Department Information Services and High Performance Computing, TUD Dresden University of Technology, Dresden, Germany.

Funding

Bundesministerium für Bildung und Forschung 031L0237CEuropean Union 101054921Worldwide Cancer Research 23-0177
6 · The paper itself

Abstract

Cancer is a significant global health issue, with treatment challenges arising from intratumor heterogeneity. This heterogeneity stems mainly from somatic evolution, causing genetic diversity within the tumor, and phenotypic plasticity of tumor cells leading to reversible phenotypic changes. However, the interplay of both factors has not been rigorously investigated. Here, we examine the complex relationship between somatic evolution and phenotypic plasticity, explicitly focusing on the interplay between cell migration and proliferation. This type of phenotypic plasticity is essential in glioblastoma, the most aggressive form of brain tumor. We propose that somatic evolution alters the regulation of phenotypic plasticity in tumor cells, specifically the reaction to changes in the microenvironment. We study this hypothesis using a novel, spatially explicit model that tracks individual cells' phenotypic and genetic states. We assume cells change between migratory and proliferative states controlled by inherited and mutation-driven genotypes and the cells' microenvironment. We observe that cells at the tumor edge evolve to favor migration over proliferation and vice versa in the tumor bulk. Notably, different genetic configurations can result in this pattern of phenotypic heterogeneity. We analytically predict the outcome of the evolutionary process, showing that it depends on the tumor microenvironment. Synthetic tumors display varying levels of genetic and phenotypic heterogeneity, which we show are predictors of tumor recurrence time after treatment. Interestingly, higher phenotypic heterogeneity predicts poor treatment outcomes, unlike genetic heterogeneity. Our research offers a novel explanation for heterogeneous patterns of tumor recurrence in glioblastoma patients.

Indexed as

Brain NeoplasmsCell MovementCell ProliferationGlioblastomaPhenotypeTumor MicroenvironmentComputational BiologyGenetic HeterogeneityHumansModels, BiologicalMutationNeoplasms

Identifiers

PMID39121170
PMCPMC11338451

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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.