Evolutionary Double-Bind Treatment Using Radiation Therapy and Natural Killer Cell-Based Immunotherapy in Prostate Cancer
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».