Competitive Amplification Networks enable molecular pattern recognition with PCR

Goertz, J. P.; Sedgwick, R.; Smith, F.; Kaforou, M. P.; Wright, V. J.; Herberg, J. A.; Kote-Jarai, Z.; Eeles, R.; Levin, M.; Misener, R.; van der Wilk, M.; Stevens, M. M.

doi:10.1101/2023.06.29.546934

bioRxiv · 10.1101/2023.06.29.546934

Competitive Amplification Networks enable molecular pattern recognition with PCR

Goertz, J. P.·Sedgwick, R.·Smith, F.·Kaforou, M. P.·Wright, V. J.·Herberg, J. A.·Kote-Jarai, Z.·Eeles, R.·Levin, M.·Misener, R.·van der Wilk, M.·Stevens, M. M.

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Abstract

Gene expression has great potential to be used as a clinical diagnostic tool. However, despite the progress in identifying these gene expression signatures, clinical translation has been hampered by a lack of purpose-built. readily deployable testing platforms. We have developed Competitive Amplification Networks. CANs to enable analysis of an entire gene expression signature in a single PCR reaction. CANs consist of natural and synthetic amplicons that compete for shared primers during amplification, forming a reaction network that leverages the molecular machinery of PCR. These reaction components are tuned such that the final fluorescent signal from the assay is exactly calibrated to the conclusion of a statistical model. In essence, the reaction acts as a biological computer, simultaneously detecting the RNA targets, interpreting their level in the context of the gene expression signature, and aggregating their contributions to the final diagnosis. We illustrate the clinical validity of this technique, demonstrating perfect diagnostic agreement with the gold-standard approach of measuring each gene independently. Crucially, CAN assays are compatible with existing qPCR instruments and workflows. CANs hold the potential to enable rapid deployment and massive scalability of gene expression analysis to clinical laboratories around the world, in highly developed and low-resource J settings alike. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=162 SRC="FIGDIR/small/546934v3_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@79d1c5org.highwire.dtl.DTLVardef@1bafb87org.highwire.dtl.DTLVardef@d78e31org.highwire.dtl.DTLVardef@1b85125_HPS_FORMAT_FIGEXP M_FIG C_FIG

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Goertz, J. P., Sedgwick, R., Smith, F., Kaforou, M. P., Wright, V. J., Herberg, J. A., Kote-Jarai, Z., Eeles, R., Levin, M., Misener, R., van der Wilk, M., Stevens, M. M.. 2023-07-01. Competitive Amplification Networks enable molecular pattern recognition with PCR. https://doi.org/10.1101/2023.06.29.546934

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