Evolutionary Double-Bind Treatment Using Radiation Therapy and Natural Killer Cell-Based Immunotherapy in Prostate Cancer
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".