Evidence map›Paper›PMID 38965430›Full record

ArticleNature biotechnology2025

Programming tumor evolution with selection gene drives to proactively combat drug resistance.

Scott M Leighow, Joshua A Reynolds, Ivan Sokirniy, Shun Yao, Zeyu Yang, Haider Inam, Dominik Wodarz, Marco Archetti, Justin R Pritchard

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Pre-empting drug resistance.Nature reviews. Cancer · 2024
    Article
  4. Review
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

9 authors.

Scott M LeighowDepartment of Biomedical Engineering, The Pennsylvania State University, University Park, PA, USA.
Joshua A ReynoldsDepartment of Biomedical Engineering, The Pennsylvania State University, University Park, PA, USA.ORCID http://orcid.org/0000-0001-6680-5993
Ivan SokirniyDepartment of Biomedical Engineering, The Pennsylvania State University, University Park, PA, USA.ORCID http://orcid.org/0000-0002-1846-5469
Shun YaoHuck Institute For The Life Sciences, The Pennsylvania State University, University Park, PA, USA.
Zeyu YangDepartment of Biomedical Engineering, The Pennsylvania State University, University Park, PA, USA.
Haider InamDepartment of Biomedical Engineering, The Pennsylvania State University, University Park, PA, USA.
Dominik WodarzDepartment of Biology, University of California San Diego, San Diego, CA, USA.
Marco ArchettiHuck Institute For The Life Sciences, The Pennsylvania State University, University Park, PA, USA.
Justin R PritchardDepartment of Biomedical Engineering, The Pennsylvania State University, University Park, PA, USA. jrp94@psu.edu.ORCID http://orcid.org/0000-0001-7749-4473

Funding

Personalization and Failure Testing of Dual Switch Gene Drives in Lung CancerU01CA265709 · NCI · PENNSYLVANIA STATE UNIVERSITY, THE · PI PRITCHARD, JUSTIN · 2021 to 2025
$2.6M
Model Driven Construction of Dual-switch Selection Gene Drives to Combat Drug ResistanceR21EB026617 · NIBIB · PENNSYLVANIA STATE UNIVERSITY, THE · PI PRITCHARD, JUSTIN · 2019 to 2021
$628k
NCI NIH HHS U01 CA265709NIBIB NIH HHS R21 EB026617U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI) 1U01CA265709
6 · The paper itself

Abstract

Most targeted anticancer therapies fail due to drug resistance evolution. Here we show that tumor evolution can be reproducibly redirected to engineer therapeutic opportunity, regardless of the exact ensemble of pre-existing genetic heterogeneity. We develop a selection gene drive system that is stably introduced into cancer cells and is composed of two genes, or switches, that couple an inducible fitness advantage with a shared fitness cost. Using stochastic models of evolutionary dynamics, we identify the design criteria for selection gene drives. We then build prototypes that harness the selective pressure of multiple approved tyrosine kinase inhibitors and employ therapeutic mechanisms as diverse as prodrug catalysis and immune activity induction. We show that selection gene drives can eradicate diverse forms of genetic resistance in vitro. Finally, we demonstrate that model-informed switch engagement effectively targets pre-existing resistance in mouse models of solid tumors. These results establish selection gene drives as a powerful framework for evolution-guided anticancer therapy.

Indexed as

Drug Resistance, NeoplasmGene Drive TechnologyNeoplasmsAnimalsAntineoplastic AgentsCell Line, TumorHumansMiceSelection, GeneticAntineoplastic Agents

Identifiers

PMID38965430
PMCPMC12285669

What OpenQuestion holds

Textmetadata
LicenceTDM
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.