Evidence map›Paper›PMID 40399443›Full record

ArticleCommunications biology2025

Parallel and convergent dynamics in the evolution of primary breast and lung adenocarcinomas.

Robert A Gatenby, Jamie K Teer, Kenneth Y Tsai, Joel S Brown

Abstract read
In one paragraph

Article in Communications biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

4 authors.

Robert A GatenbyCancer Biology and Evolution Program, Tampa, FL, USA. Robert.Gatenby@moffitt.org.ORCID http://orcid.org/0000-0002-1621-1510
Jamie K TeerBiostatistics and Bioinformatics Department, Tampa, FL, USA.ORCID http://orcid.org/0000-0003-4513-0282
Kenneth Y TsaiCancer Biology and Evolution Program, Tampa, FL, USA.ORCID http://orcid.org/0000-0001-5325-212X
Joel S BrownCancer Biology and Evolution Program, Tampa, FL, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer development requires an evolutionary transformation from mammalian cells fully regulated by and integrated into multicellular tissue to cancer cells  that, as single cell protists, are individually subject to Darwinian selection. Through genetic and epigenetic mechanisms of inheritance, the evolving cancer phenotype must acquire independence from host controls, downregulate differentiated functions that benefit the host but not individual cells, and generate phenotypic traits that increase fitness in the context of the selection forces within the local microenvironment. Here, we investigate this  evolutionary transition in breast (BRCA) and lung (LUAD, without EGFR, KRAS or BRAF driver mutations) adenocarcinomas using bulk mutation and expression data from the TCGA database. We define evolution selection for genes and molecular pathways based on 1) changes in gene expression compared to normal tissue, and 2) significantly larger or smaller observed mutation rates compared to those expected based on the gene size. We find BRCA and LUAD disable different genes and gene pathways associated with tissue-specific signaling and differentiated functions but promote common molecular pathways associated with cell cycle, cell-cell interactions, cytoskeleton, voltage gated ion channels, and microenvironmental niche construction. Thus, tissue-specific parallel evolution in early cancer development is followed by convergence to a common cancer phenotype.

Indexed as

Adenocarcinoma of LungBreast NeoplasmsEvolution, MolecularLung NeoplasmsFemaleGene Expression Regulation, NeoplasticHumansMutation

Identifiers

PMID40399443
PMCPMC12095661

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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.