RESUMEN
Invasive cervical cancers (ICC), caused by HPV infections, have a heterogeneous molecular landscape. We investigate the detection, timing, and HPV type specificity of somatic mutations in 3929 HPV-positive exfoliated cervical cell samples from individuals undergoing cervical screening in the U.S. using deep targeted sequencing in ICC cases, precancers, and HPV-positive controls. We discover a subset of hotspot mutations rare in controls (2.6%) but significantly more prevalent in precancers, particularly glandular precancer lesions (10.2%), and cancers (25.7%), supporting their involvement in ICC carcinogenesis. Hotspot mutations differ by HPV type, and HPV18/45-positive ICC are more likely to have multiple hotspot mutations compared to HPV16-positive ICC. The proportion of cells containing hotspot mutations is higher (i.e., higher variant allele fraction) in ICC and mutations are detectable up to 6 years prior to cancer diagnosis. Our findings demonstrate the feasibility of using exfoliated cervical cells for detection of somatic mutations as potential diagnostic biomarkers.
Asunto(s)
Cuello del Útero , Mutación , Infecciones por Papillomavirus , Neoplasias del Cuello Uterino , Humanos , Femenino , Neoplasias del Cuello Uterino/virología , Neoplasias del Cuello Uterino/genética , Neoplasias del Cuello Uterino/patología , Infecciones por Papillomavirus/virología , Infecciones por Papillomavirus/genética , Cuello del Útero/virología , Cuello del Útero/patología , Adulto , Persona de Mediana Edad , Papillomaviridae/genética , Papillomavirus Humano 16/genética , Papillomavirus Humano 16/aislamiento & purificación , Secuenciación de Nucleótidos de Alto Rendimiento , Displasia del Cuello del Útero/virología , Displasia del Cuello del Útero/genética , Displasia del Cuello del Útero/patologíaRESUMEN
Many datasets are being produced by consortia that seek to characterize healthy and disease tissues at single-cell resolution. While biospecimen and experimental information is often captured, detailed metadata standards related to data matrices and analysis workflows are currently lacking. To address this, we develop the matrix and analysis metadata standards (MAMS) to serve as a resource for data centers, repositories, and tool developers. We define metadata fields for matrices and parameters commonly utilized in analytical workflows and developed the rmams package to extract MAMS from single-cell objects. Overall, MAMS promotes the harmonization, integration, and reproducibility of single-cell data across platforms.