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Record W4414563664 · doi:10.1016/j.ijrobp.2025.09.034

Evolutionary Double-Bind Treatment Using Radiation Therapy and Natural Killer Cell-Based Immunotherapy in Prostate Cancer

2025· article· en· W4414563664 on OpenAlexaff
Kimberly A. Luddy, Jeffrey West, Mark Robertson‐Tessi, Bina Desai, Hannah Newman, Verónica Estrella, Taylor M. Bursell, Sarah Barrett, Jacintha O’Sullivan, Laure Marignol, Robert A. Gatenby, Joel S. Brown, Alexander R.A. Anderson, Cliona O’Farrelly

Bibliographic record

VenueInternational Journal of Radiation Oncology*Biology*Physics · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsTrinity College
FundersScience Foundation IrelandNational Cancer InstituteHealth Research Board
KeywordsProstate cancerImmunotherapyRadiation therapyCancerDrug resistanceCancer treatmentProstate

Abstract

fetched live from OpenAlex

PURPOSE: Evolution-informed therapies exploit evolutionary consequences of drug resistance to inhibit treatment resistance and prolong time to progression. One strategy, termed an evolutionary double-bind, uses an initial therapy to elicit a specific adaptive response by cancer cells, which is then selectively targeted by a follow-on therapy. Although the concept of an evolutionary double-bind has long been hypothesized in cancer, it has not been measured. Here, to our knowledge, we present the first example of a quantifiable double-bind: radiation therapy (RT) with natural killer (NK) cells. RT induces lethal double-strand DNA breaks, but cancer cells adapt. Although this increases resistance to DNA-damaging agents, it also enhances expression of NK cell ligands creating an obvious choice for a double-bind strategy. METHODS AND MATERIALS: We investigated this potential evolutionary double-bind through in vitro studies and evolution-based mathematical models. Using multiple prostate cancer cell lines, we evaluated surface and soluble NK ligand expression following RT. In vitro competition experiments were performed with an isogenic radiation-resistant cell line model. We introduced a two-population Lotka-Volterra competition model, consisting of radiation-sensitive and radiation-resistant populations modeling intrinsic growth rates with fixed carrying capacity and inter-specific competition terms. RESULTS: Alterations in NK cell ligands resulted in a twofold increase in sensitivity to NK cell-mediated killing with selective targeting of RT-resistant cells. These dynamics were framed mathematically to quantify the double bind. RT alone slowed overall growth but strongly selected for RT-resistant cells. NK cell therapy alone suppressed the RT-resistant population, but with a surviving population of radiation-sensitive cells. Model simulation predicted that optimal tumor control would be achieved through initial RT followed by NK cells. Subsequent experiments confirmed the model prediction. CONCLUSIONS: We conclude that RT and NK cell-based immunotherapy produce an evolutionary double-bind. This multidimensional approach addresses the immediate challenge of treatment resistance and lays the groundwork for the development of personalized treatment regimens tailored to the evolving dynamics of individual tumors. SIGNIFICANCE: Clinical experience demonstrates that prostate cancer has a remarkable capacity to evolve resistance to all currently available treatments resulting in progression and, ultimately, patient death. Resistance mechanisms often come at a fitness cost placing cells in a bind when competing with surrounding cells. A carefully chosen secondary drug can introduce a double-bind targeting the adaptive resistance mechanism. This manuscript provides the first direct experimental evidence quantifying an "evolutionary double-bind' in prostate cancer supporting the combination of DNA-damaging agents and NK cell-based immunotherapy in evolutionarily guided treatment designs. Our work is mathematically novel in that it extends Evolutionary Game Theory models and establishes an experimental-mathematical framework to quantify genuine evolutionary double binds applicable across cancer types and treatment modalities.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.314
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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