Evidence map›Paper›PMID 41767406›Full record

ArticleVirus evolution2026

Clonal interference and changing selective pressures shape the escape of SARS-CoV-2 from hundreds of antibodies.

Hugh K Haddox, Omar Abdel Aziz, Jared G Galloway, Javen Kent, Cameron R Cooper, Chris Jennings-Shaffer, Will Dumm, Seth D Temple, Jesse D Bloom, Frederick A Matsen

Erratum issuedAbstract read
In one paragraph

Article in Virus evolution, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. 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

5 · Who and what money

Authors and funding

10 authors.

Hugh K HaddoxComputational Biology Program, Fred Hutchinson Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109, United States.
Omar Abdel AzizComputational Biology Program, Fred Hutchinson Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109, United States.
Jared G GallowayComputational Biology Program, Fred Hutchinson Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109, United States.
Javen KentComputational Biology Program, Fred Hutchinson Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109, United States.
Cameron R CooperComputational Biology Program, Fred Hutchinson Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109, United States.
Chris Jennings-ShafferComputational Biology Program, Fred Hutchinson Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109, United States.
Will DummComputational Biology Program, Fred Hutchinson Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109, United States.
Seth D TempleComputational Biology Program, Fred Hutchinson Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109, United States.
Jesse D BloomComputational Biology Program, Fred Hutchinson Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109, United States.
Frederick A MatsenComputational Biology Program, Fred Hutchinson Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109, United States.

Funding

Blending deep learning with probabilistic mechanistic models to predict and understand the evolution and function of adaptive immune receptorsR01AI146028 · NIAID · FRED HUTCHINSON CANCER RESEARCH CENTER · PI MATSEN, FREDERICK ALBERT · 2019 to 2024
$3.4M
High-Performance Compute Cluster for Comprehensive Cancer and Infectious Diseases ResearchS10OD028685 · OD · FRED HUTCHINSON CANCER RESEARCH CENTER · PI BRADLEY, PHILIP · 2020 to 2020
$2.0M
NIAID NIH HHS R01 AI146028NIH HHS S10 OD028685
6 · The paper itself

Abstract

SARS-CoV-2 has evolved increased resistance to human polyclonal antibody responses. But, how it escaped individual monoclonal antibodies from these responses has not been thoroughly explored. Cao et al. used deep mutational scanning to identify mutations that allow SARS-CoV-2 to escape individual antibodies, doing so for hundreds of different antibodies. Here, we use these data to reconstruct how the virus escaped each antibody in nature. For each antibody, we predict how levels of escape changed in the global SARS-CoV-2 population over time. For many antibodies, these levels dramatically fluctuated due to escape mutations being displaced by clade-turnover events. We validate predicted patterns using pseudovirus neutralization data. Fitness effects estimated from natural sequences suggest that mutations are displaced due to clonal interference between clades and that the order in which mutations arose is shaped by changing selective pressures. Overall, this work suggests that SARS-CoV-2 evaded polyclonal responses via complex evolutionary dynamics.

Indexed as

clonal interferencedeep mutational scanningescapeevolutionSARS-CoV-2

Identifiers

PMID41767406
PMCPMC12936870

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 OpenQuestion graph.