Mock Samples That Mimic Human Cervicovaginal Samples to Accelerate the Development and Evaluation of Assays for High‐Risk HPV for Cervical Cancer Screening

Nearly all cervical cancer cases are caused by high-risk human papillomavirus (hrHPV) infections. The World Health Organization recommends screening for hrHPV using nucleic acid amplification tests (NAATs) that detect hrHPV DNA or mRNA. Lack of access to affordable, point-of-care screening tests in resource-limited settings leads to women presenting with advanced-stage cervical cancer, with many dying of the disease. There is a significant need to develop point-of-care NAATs to improve screening and early detection. Because access to real clinical samples is limited, initial evaluation of NAATs is often performed using contrived samples created using some combination of extracted hrHPV DNA and/or cultured hrHPV-positive cells. When mock samples do not adequately recapitulate the contents of clinical cervicovaginal samples, it can delay clinical translation of potentially promising assays. To improve the value of contrived samples, we characterized the composition of 32 hrHPV DNA-positive cervicovaginal clinical samples. We also describe a simple method to generate contrived samples that mimic the diversity of clinical cervicovaginal samples, and test them using the methods used to characterize the clinical samples. Results show that the hrHPV DNA content of cervicovaginal samples varies by approximately eight orders of magnitude and spans from 100% linear integrated DNA to 100% circular non-integrated DNA, that the concentration of hrHPV mRNA also varies by nearly nine orders of magnitude between patient samples, and that the concentration of potential inhibitors such as hemoglobin varies by more than three orders of magnitude. hrHPV DNA and mRNA extracted from contrived samples exhibited expected patterns of DNA quantity, DNA conformation, and mRNA quantity. Altogether, the protocols described here to generate mock samples can help NAAT developers optimize test performance prior to clinical evaluation, potentially improving test performance and reducing time to deployment.