Evidence map›Paper›PMID 42282841›Full record

ArticlebioRxiv : the preprint server for biology2026

Convergent Evolution in Tumor Genomes Targets Functional Domains.

Hai Chen, Li Liu

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for 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

2 authors.

Hai ChenCollege of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA.
Li LiuCollege of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA.ORCID 0000-0003-4002-7497

Funding

Discover and Analyze Germline-Somatic Interactions in CancerR01LM013438 · NLM · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI LIU, LI, YANG, PING · 2021 to 2023
$1.0M
NLM NIH HHS R01 LM013438
6 · The paper itself

Abstract

Tumor evolution is shaped by selective pressures that repeatedly favor similar functional outcomes across genetically distinct cancers. While convergent evolution in cancer has been studied at the gene level, this work investigates selection on smaller functional units, namely protein domains. Using >9,500 primary tumor exomes from The Cancer Genome Atlas, we quantified selection strengths acting on missense and truncating mutations aggregated by protein domain. This analysis identified 818 domains under significant positive selection across tumor types. Notably, approximately half of these domains belonged to genes that would be difficult to implicate using conventional gene-centric approaches due to low mutational recurrence or mutations outside functionally critical regions. We classified positively selected domains by evolutionary antiquity. The most ancient domains trace back to pre-eukaryotes and are involved in core cellular processes (e.g., DNA mismatch repair and metabolism) and tend to accumulate the highest numbers of mutations. The majority of positively selected domains originated in early eukaryotes and are enriched for regulatory control and cellular organization, whereas metazoan-specific domains are primarily associated with signaling and cell-cell communication. These results suggest that cancer preferentially exploits deeply conserved biology, with regulatory complexity driving tumor adaptation, while recent evolutionary innovations are relatively fragile and dispensable. Collectively, these findings establish a domain-centered framework for understanding disease mechanisms and developing therapeutic strategies. By focusing on shared functional domains, this framework enables the identification of functionally convergent therapeutic targets and provides a new perspective for interpreting drug resistance, tumor recurrence, and relapse.

Identifiers

PMID42282841
PMCPMC13251958

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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.