High-risk human papillomavirus (HPV) genotypes—most notably HPV16 and HPV18—account for the majority of HPV-attributable cervical cancers and are central drivers of cervical carcinogenesis through sustained expression of the viral oncogenes E6 and E7. In contrast, HPV11 is considered a low-risk genotype, most commonly associated with benign anogenital warts and recurrent respiratory papillomatosis rather than cervical carcinoma; it is sometimes included in assay panels as a clinically relevant comparator or specificity control.
As cervical disease progresses from transient infection to persistent infection and, in some cases, high-grade lesions and invasive cancer, the measurable HPV DNA burden can change substantially. In this context, detecting true biological shifts can be challenging with conventional quantitative real-time PCR (qPCR) when target abundance is low or near the assay’s limit of quantification.
A TaqMan Gene Expression Assay was used to optimize and quantify HPV11, HPV16, and HPV18 targets in human biological matrices (whole blood or plasma), where sample volume is limited and target abundance can span a wide dynamic range. To maximize sensitivity and reproducibility, we first performed a rigorous optimization on the quantitative real-time PCR (qPCR) platform before transferring the assay to digital PCR (dPCR) for final quantification.
Method optimization began with qPCR experiments using serial dilutions of quantified HPV plasmid DNA to generate standard curves. These data were used to confirm primer/probe performance, assess amplification efficiency, and establish the limit of detection (LOD)—critical prerequisites for reliable qPCR-based quantification, particularly when copy number is low and copy number variation may influence apparent signal.
To address the constraints of limited DNA input and improve precision at low target levels, we then leveraged dPCR (QuantStudio Absolute Q Digital PCR System). In dPCR, each sample is partitioned into thousands of individual PCR microreactions, such that some partitions contain the target (positive) and others do not (negative). After endpoint amplification, the fraction of negative partitions is used to calculate the absolute target concentration (copies per input volume) using Poisson statistics, enabling direct reporting in copy number units.
This workflow provides a smooth transition from qPCR to dPCR: qPCR establishes robust assay performance (efficiency, sensitivity, specificity), while dPCR delivers absolute quantification without a standard curve, improved tolerance to variability near the lower range, and clearer resolution for gene expression profiling, copy number analysis, and copy number variation (CNV) assessment in complex human matrices.
To address these limitations, we developed a sensitive and robust digital PCR (dPCR) assay capable of simultaneously detecting and accurately quantifying multiple HPV genotypes. Performance was benchmarked against real-time qPCR using serially diluted HPV11, HPV16, and HPV18 plasmid DNA standards, with water/no-template controls included throughout to monitor background and contamination.
Across the same input DNA samples, dPCR consistently delivered superior precision and sensitivity, with excellent specificity—particularly in the low-abundance range where qPCR measurements can become variable and less dependable.