Dataset Information

Deep sequencing of HPV16 genomes: A new high-throughput tool for exploring the carcinogenicity and natural history of HPV16 infection.

ABSTRACT: For unknown reasons, there is huge variability in risk conferred by different HPV types and, remarkably, strong differences even between closely related variant lineages within each type. HPV16 is a uniquely powerful carcinogenic type, causing approximately half of cervical cancer and most other HPV-related cancers. To permit the large-scale study of HPV genome variability and precancer/cancer, starting with HPV16 and cervical cancer, we developed a high-throughput next-generation sequencing (NGS) whole-genome method. We designed a custom HPV16 AmpliSeq™ panel that generated 47 overlapping amplicons covering 99% of the genome sequenced on the Ion Torrent Proton platform. After validating with Sanger, the current "gold standard" of sequencing, in 89 specimens with concordance of 99.9%, we used our NGS method and custom annotation pipeline to sequence 796 HPV16-positive exfoliated cervical cell specimens. The median completion rate per sample was 98.0%. Our method enabled us to discover novel SNPs, large contiguous deletions suggestive of viral integration (OR of 27.3, 95% CI 3.3-222, P=0.002), and the sensitive detection of variant lineage coinfections. This method represents an innovative high-throughput, ultra-deep coverage technique for HPV genomic sequencing, which, in turn, enables the investigation of the role of genetic variation in HPV epidemiology and carcinogenesis.

SUBMITTER: Cullen M

PROVIDER: S-EPMC4669577 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

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Deep sequencing of HPV16 genomes: A new high-throughput tool for exploring the carcinogenicity and natural history of HPV16 infection.

Cullen Michael M Boland Joseph F JF Schiffman Mark M Zhang Xijun X Wentzensen Nicolas N Yang Qi Q Chen Zigui Z Yu Kai K Mitchell Jason J Roberson David D Bass Sara S Burdette Laurie L Machado Moara M Ravichandran Sarangan S Luke Brian B Machiela Mitchell J MJ Andersen Mark M Osentoski Matt M Laptewicz Michael M Wacholder Sholom S Feldman Ashlie A Raine-Bennett Tina T Lorey Thomas T Castle Philip E PE Yeager Meredith M Burk Robert D RD Mirabello Lisa L

Papillomavirus research (Amsterdam, Netherlands) 20151201

For unknown reasons, there is huge variability in risk conferred by different HPV types and, remarkably, strong differences even between closely related variant lineages within each type. HPV16 is a uniquely powerful carcinogenic type, causing approximately half of cervical cancer and most other HPV-related cancers. To permit the large-scale study of HPV genome variability and precancer/cancer, starting with HPV16 and cervical cancer, we developed a high-throughput next-generation sequencing (NG ...[more]

PMID: 26645052

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Project description:The genetic diversity of human papillomavirus (HPV) 16 within cervical cells and tissue is usually associated with persistent virus infection and precancerous lesions. To explore the HPV16 mutation patterns contributing to the cervical cancer (CC) progression, a total of 199 DNA samples from HPV16-positive cervical specimens were collected and divided into high-grade squamous intraepithelial lesion (HSIL) and the non-HSIL(NHSIL) groups. The HPV16 E6 region (nt 7125-7566) was sequenced using next-generation sequencing. Based on HPV16 E6 amino acid mutation features selected by Lasso algorithm, four machine learning approaches were used to establish HSIL prediction models. The receiver operating characteristic was used to evaluate the model performance in both training and validation cohorts. Western blot was used to detect the degradation of p53 by the E6 variants. Based on the 13 significant mutation features, the logistic regression (LR) model demonstrated the best predictive performance in the training cohort (AUC = 0.944, 95% CI: 0.913-0.976), and also achieved a high discriminative ability in the independent validation cohort (AUC = 0.802, 95% CI: 0.601-1.000). Among these features, the E6 D32E and H85Y variants have higher ability to degrade p53 compared to the E6 wildtype (P < 0.05). In conclusion, our study provides evidence for the first time that HPV16 E6 sequences contain vital mutation features in predicting HSIL. Moreover, the D32E and H85Y variants of E6 exhibited a significantly higher ability to degrade p53, which may play a vital role in the development of CC. IMPORTANCE The study provides evidence for the first time that HPV16 E6 sequences contain vital mutation features in predicting the high-grade squamous intraepithelial lesion and can reduce even more unneeded colposcopies without a loss of sensitivity to detect cervical cancer. Moreover, the D32E and H85Y variants of E6 exhibited a significantly higher ability to degrade p53, which may play a vital role in the development of cervical cancer.

Project description:Next-generation sequencing provides a high-resolution insight into human genetic information. However, the focus of previous studies has primarily been on low-coverage data due to the high cost of sequencing. Although the 1000 Genomes Project and the Haplotype Reference Consortium have both provided powerful reference panels for imputation, low-frequency and novel variants remain difficult to discover and call with accuracy on the basis of low-coverage data. Deep sequencing provides an optimal solution for the problem of these low-frequency and novel variants. Although whole-exome sequencing is also a viable choice for exome regions, it cannot account for noncoding regions, sometimes resulting in the absence of important, causal variants. For Han Chinese populations, the majority of variants have been discovered based upon low-coverage data from the 1000 Genomes Project. However, high-coverage, whole-genome sequencing data are limited for any population, and a large amount of low-frequency, population-specific variants remain uncharacterized. We have performed whole-genome sequencing at a high depth (∼×80) of 90 unrelated individuals of Chinese ancestry, collected from the 1000 Genomes Project samples, including 45 Northern Han Chinese and 45 Southern Han Chinese samples. Eighty-three of these 90 have been sequenced by the 1000 Genomes Project. We have identified 12 568 804 single nucleotide polymorphisms, 2 074 210 short InDels, and 26 142 structural variations from these 90 samples. Compared to the Han Chinese data from the 1000 Genomes Project, we have found 7 000 629 novel variants with low frequency (defined as minor allele frequency < 5%), including 5 813 503 single nucleotide polymorphisms, 1 169 199 InDels, and 17 927 structural variants. Using deep sequencing data, we have built a greatly expanded spectrum of genetic variation for the Han Chinese genome. Compared to the 1000 Genomes Project, these Han Chinese deep sequencing data enhance the characterization of a large number of low-frequency, novel variants. This will be a valuable resource for promoting Chinese genetics research and medical development. Additionally, it will provide a valuable supplement to the 1000 Genomes Project, as well as to other human genome projects.

Dataset Information

Deep sequencing of HPV16 genomes: A new high-throughput tool for exploring the carcinogenicity and natural history of HPV16 infection.

Publications

Deep sequencing of HPV16 genomes: A new high-throughput tool for exploring the carcinogenicity and natural history of HPV16 infection.

Similar Datasets

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