Cellular heterogeneity mediates inherent sensitivity–specificity tradeoff in cancer targeting by synthetic circuits

Significance The recent advance in the use of viral vectors for gene delivery, combined with the design of synthetic gene circuits to diagnose and target cells, brings opportunities for effective treatment of cancer. So far, gene circuits have been considered logical devices capable of discriminating normal from malignant cells as discrete states, ignoring cellular heterogeneity in cancer expression markers. We addressed the inherent limitations heterogeneity imposes on the precision of targeting circuits. Using molecular parameters to control circuit gain amplification and threshold, we show an inherent tradeoff emerges between specificity and sensitivity. In light of this tradeoff, the molecular optimization of targeting circuits will be an important step for effective implementation of personalized gene therapy. Synthetic gene circuits are emerging as a versatile means to target cancer with enhanced specificity by combinatorial integration of multiple expression markers. Such circuits must also be tuned to be highly sensitive because escape of even a few cells might be detrimental. However, the error rates of decision-making circuits in light of cellular variability in gene expression have so far remained unexplored. Here, we measure the single-cell response function of a tunable logic AND gate acting on two promoters in heterogeneous cell populations. Our analysis reveals an inherent tradeoff between specificity and sensitivity that is controlled by the AND gate amplification gain and activation threshold. We implement a tumor-mimicking cell-culture model of cancer cells emerging in a background of normal ones, and show that molecular parameters of the synthetic circuits control specificity and sensitivity in a killing assay. This suggests that, beyond the inherent tradeoff, synthetic circuits operating in a heterogeneous environment could be optimized to efficiently target malignant state with minimal loss of specificity.

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