Programmed evolution: Using asexual gene drives to sculpt tumor populations and combat genetic diversity

Justin Pritchard, Scott Leighow, Joshua Reynolds et al.,  Research Square,  2024.

Resistance evolution is responsible for the failure of most targeted anticancer therapies. Tumor heterogeneity is so profound that pre-existing resistance is thought to be guaranteed at the time that advanced disease is detected. The practice of waiting for treatment failure and then responding to the refractory tumor with next-generation targeted therapies locks clinicians in an evolutionary arms race until no further treatment options are available. Here, we posit that disease evolution can be reproducibly reprogrammed to design more readily treated tumors, regardless of the exact ensemble of pre-existing genetic heterogeneity. To this end, we conceive of a modular genetic platform that couples an inducible fitness advantage with a shared fitness cost. Using stochastic models of evolutionary dynamics, we identify the design criteria of these “selection gene drives.” We then build prototypes that leverage the selective pressure of various approved tyrosine kinase inhibitors and employ therapeutic mechanisms as diverse as prodrug catalysis and immune activity induction. By using saturating mutagenesis across a drug target and genome-scale loss-of-function libraries, we show that our selection gene drives can eradicate extremely diverse forms of genetic resistance. Finally, using theory to guide treatment scheduling, we demonstrate that model-informed switch engagement can create dramatic in vivo efficacy. These results establish selection gene drives as a powerful new paradigm for evolution-guided anticancer therapy.

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