Increasing participation in cervical screening by targeting long-term nonattenders: Randomized health services study.

High screening participation in the population is essential for optimal prevention of cervical cancer. Offering a high-risk human papillomavirus (HPV) self-test has previously been shown to increase participation. In this randomized health services study, we evaluated four strategies with regard to participation. Women who had not attended organized cervical screening in 10 years were eligible for inclusion. This group comprised 16,437 out of 413,487 resident women ages 33-60 (<4% of the screening target group). Among these 16,437 long-term nonattenders, 8,000 women were randomized to either (i) a HPV self-sampling kit sent directly; (ii) an invitation to order a HPV self-sampling kit using a new open source eHealth web application; (iii) an invitation to call a coordinating midwife with questions and concerns; or (iv) the standard annual renewed invitation letter with prebooked appointment time (routine practice). Overall participation, by arm, was (i) 18.7%; (ii) 10.7%; (iii) 1.9%; and (iv) 1.7%. The relative risk of participation in Arm 1 was 11.0 (95% CI 7.8-15.5), 6.3 (95% CI 4.4-8.9) in Arm 2 and 1.1 (95% CI 0.7-1.7) in Arm 3, compared to Arm 4. High-risk HPV prevalence among women who returned kits in study Arms 1 and 2 was 12.2%. In total, 63 women were directly referred to colposcopy from Arms 1 and 2; of which, 43 (68.3%) attended and 17 had a high-grade cervical lesion (CIN2+) in histology (39.5%). Targeting long-term nonattending women with sending or offering the opportunity to order self-sampling kits further increased the participation in an organized screening program.

ViraPipe: scalable parallel pipeline for viral metagenome analysis from next generation sequencing reads.

Motivation: Next Generation Sequencing (NGS) technology enables identification of microbial genomes from massive amount of human microbiomes more rapidly and cheaper than ever before. However, the traditional sequential genome analysis algorithms, tools, and platforms are inefficient for performing large-scale metagenomic studies on ever-growing sample data volumes. Currently, there is an urgent need for scalable analysis pipelines that enable harnessing all the power of parallel computation in computing clusters and in cloud computing environments. We propose ViraPipe, a scalable metagenome analysis pipeline that is able to analyze thousands of human microbiomes in parallel in tolerable time. The pipeline is tuned for analyzing viral metagenomes and the software is applicable for other metagenomic analyses as well. ViraPipe integrates parallel BWA-MEM read aligner, MegaHit De novo assembler, and BLAST and HMMER3 sequence search tools. We show the scalability of ViraPipe by running experiments on mining virus related genomes from NGS datasets in a distributed Spark computing cluster. Results: ViraPipe analyses 768 human samples in 210 minutes on a Spark computing cluster comprising 23 nodes and 1288 cores in total. The speedup of ViraPipe executed on 23 nodes was 11x compared to the sequential analysis pipeline executed on a single node. The whole process includes parallel decompression, read interleaving, BWA-MEM read alignment, filtering and normalizing of non-human reads, De novo contigs assembling, and searching of sequences with BLAST and HMMER3 tools. Contact: ilari.maarala@aalto.fi. Availability and implementation: https://github.com/NGSeq/ViraPipe.

NordScreen - an interactive tool for presenting cervical cancer screening indicators in the Nordic countries.

Introduction: Quality assurance and improvement of cancer screening programs require up-to-date monitoring systems and evidence-based indicators. National quality reports exist but the definition and calculation of indicators vary making comparisons between countries difficult. The aim is to stimulate collaborative research and quality improvements in screening through freely available, comparable and regularly updated quality indicators. The project currently includes data on cervical cancer screening but population-based screening programs for breast cancer and colorectal cancer may be included in the future. Material and methods: Through a network of Nordic and Baltic screening managers, population-based individual screening data from each country were converted to standard format in each collaborative center, aggregated by the same R program script and then uploaded to the NordScreen online platform. Registry data included all cervical tests except for Finland where only tests based on invitation are included. Results: The NordScreen collaboration has so far collated standardized indicators based on 32.8 million screening tests from four Nordic countries and Estonia. Interactive comparison of test coverage and distribution of women by number of tests are currently possible online. In 2016, the test coverage within a time interval of 5.5 years in the age group 30-64 year-olds was between 78 and 84% in Iceland, Norway and Sweden whereas 70% in Finland. The application allows users to choose indicator specifications interactively. Conclusions: NordScreen is a pilot model for comparable, reliable and accessible cross-country comparisons of cancer screening. Comparability between countries is enhanced by a uniform data structure and standardized calculations. The comparison of coverage rates to national figures shows that the methods used nationally and in the NordScreen project produce similar results.

Machine Learning for detection of viral sequences in human metagenomic datasets.

BACKGROUND: Detection of highly divergent or yet unknown viruses from metagenomics sequencing datasets is a major bioinformatics challenge. When human samples are sequenced, a large proportion of assembled contigs are classified as "unknown", as conventional methods find no similarity to known sequences. We wished to explore whether machine learning algorithms using Relative Synonymous Codon Usage frequency (RSCU) could improve the detection of viral sequences in metagenomic sequencing data. RESULTS: We trained Random Forest and Artificial Neural Network using metagenomic sequences taxonomically classified into virus and non-virus classes. The algorithms achieved accuracies well beyond chance level, with area under ROC curve 0.79. Two codons (TCG and CGC) were found to have a particularly strong discriminative capacity. CONCLUSION: RSCU-based machine learning techniques applied to metagenomic sequencing data can help identify a large number of putative viral sequences and provide an addition to conventional methods for taxonomic classification.

Viruses in cancers among the immunosuppressed.

Most cancer forms known to be caused by viruses are increased among the immunosuppressed, but several cancer forms without established viral etiology are also increased, notably nonmelanoma skin carcinoma (NMSC). We followed all 13,429 solid organ transplantation patients in Sweden for cancer occurrence after transplantation. We requested these tumor specimens and sequenced the first 89 specimens received (62 NMSCs, 27 other cancers). The sequences were analyzed for viruses based on two bioinformatics algorithms (paracel-blast (sensitive for detection of known viruses) and hidden Markov model (HMM; sensitive for distantly related viruses)). Among the 62 NMSCs, the virus family detected in the largest proportion of specimens was Mimiviridae (9/62 NMSCs). The majority of the virus-related reads belonged to Papillomaviridae. The HMM analysis identified 86 additional previously not described viral contigs related to 11 virus families, with reads related to Mimiviridae being the most common (detected in 28/62 NMSCs) with the most prevalent contig (Mimivirus SE906, 1937 bp) detected in 17/62 NMSCs. Among the 27 other cancers, viral sequences were detected in only 5 specimens by blast analysis, compared to in all 27 specimens by HMM (Mimiviridae, Poxviridae, Phycodnaviridae and virus-related sequences yet unclassified to any family). 99% of the virus reads belonged to a single previously not described sequence (Mimivirus SE996, 911 bp). A multitude of viruses is readily detectable in specimens with cancers occurring among the immunosuppressed, with sequences related to Mimiviridae being the most prevalent. Further research would be needed to elucidate the biological significance of the viruses.

Transcription of human papillomavirus oncogenes in head and neck squamous cell carcinomas.

Some head and neck cancers are caused by human papillomavirus (HPV). As HPV vaccination can prevent infection, an estimation of which HPV types have an active viral oncogene transcription in what proportion of tumors might allow estimation of the proportion of head & neck cancers preventable by HPV vaccination. We used all RNA sequencing data from primary tumors of head and neck squamous cell carcinomas from the Cancer Genome Atlas (n = 500 patients). We analysed 3.7 terabyte of sequencing data with the bioinformatics pipeline ViraPipe. Paired end reads were quality filtered using the original code and aligned to known HPV sequences. HPV transcripts were found in 113/500 specimens, with transcription of both the E6 and E7 viral oncogenes in 90 specimens. HPV16 had E6/E7 transcription in 67 cases, HPV33 in 14 cases, HPV18 in 6 cases and HPV35 in 5 cases. HPV oncogene transcription was most common in tumors from tonsils (34/40, 85%), followed by palate (4/5, 80%), base of tongue (10/20, 50%), oropharynx (4/10, 40%), and gum (4/11, 36%). Comparison to the cancer incidence statistics in the USA indicates that vaccine-preventable HPV16/18/33 oncogene transcription would be found in about 8.3% female and 20.2% male patients of head and neck cancers in the USA. Transcription of the HPV oncogenes is present in a large proportion of head and neck cancers in the TCGA database. If these cancers are caused by HPV, prevention of HPV16/18/33 infections would prevent ~49 300 annual head and neck cancer cases in the USA alone.

ViraMiner: Deep learning on raw DNA sequences for identifying viral genomes in human samples.

Despite its clinical importance, detection of highly divergent or yet unknown viruses is a major challenge. When human samples are sequenced, conventional alignments classify many assembled contigs as "unknown" since many of the sequences are not similar to known genomes. In this work, we developed ViraMiner, a deep learning-based method to identify viruses in various human biospecimens. ViraMiner contains two branches of Convolutional Neural Networks designed to detect both patterns and pattern-frequencies on raw metagenomics contigs. The training dataset included sequences obtained from 19 metagenomic experiments which were analyzed and labeled by BLAST. The model achieves significantly improved accuracy compared to other machine learning methods for viral genome classification. Using 300 bp contigs ViraMiner achieves 0.923 area under the ROC curve. To our knowledge, this is the first machine learning methodology that can detect the presence of viral sequences among raw metagenomic contigs from diverse human samples. We suggest that the proposed model captures different types of information of genome composition, and can be used as a recommendation system to further investigate sequences labeled as "unknown" by conventional alignment methods. Exploring these highly-divergent viruses, in turn, can enhance our knowledge of infectious causes of diseases.

Extension of the viral ecology in humans using viral profile hidden Markov models.

When human samples are sequenced, many assembled contigs are "unknown", as conventional alignments find no similarity to known sequences. Hidden Markov models (HMM) exploit the positions of specific nucleotides in protein-encoding codons in various microbes. The algorithm HMMER3 implements HMM using a reference set of sequences encoding viral proteins, "vFam". We used HMMER3 analysis of "unknown" human sample-derived sequences and identified 510 contigs distantly related to viruses (Anelloviridae (n = 1), Baculoviridae (n = 34), Circoviridae (n = 35), Caulimoviridae (n = 3), Closteroviridae (n = 5), Geminiviridae (n = 21), Herpesviridae (n = 10), Iridoviridae (n = 12), Marseillevirus (n = 26), Mimiviridae (n = 80), Phycodnaviridae (n = 165), Poxviridae (n = 23), Retroviridae (n = 6) and 89 contigs related to described viruses not yet assigned to any taxonomic family). In summary, we find that analysis using the HMMER3 algorithm and the "vFam" database greatly extended the detection of viruses in biospecimens from humans.

Viremia preceding multiple sclerosis: Two nested case-control studies.

Infections have been suggested to be involved in Multiple Sclerosis (MS). We used metagenomic sequencing to detect both known and yet unknown microorganisms in 2 nested case control studies of MS. Two different cohorts were followed for MS using registry linkages. Serum samples taken before diagnosis as well as samples from matched control subjects were selected. In cohort1 with 75 cases and 75 controls, most viral reads were Anelloviridae-related and >95% detected among the cases. Among samples taken up to 2 years before MS diagnosis, Anellovirus species TTMV1, TTMV6 and TTV27 were significantly more common among cases. In cohort2, 93 cases and 93 controls were tested under the pre-specified hypothesis that the same association would be found. Although most viral reads were again related to Anelloviridae, no significant case-control differences were seen. We conclude that the Anelloviridae-MS association may be due to multiple hypothesis testing, but other explanations are possible.

Viruses in case series of tumors: Consistent presence in different cancers in the same subject.

Studies investigating presence of viruses in cancer often analyze case series of cancers, resulting in detection of many viruses that are not etiologically linked to the tumors where they are found. The incidence of virus-associated cancers is greatly increased in immunocompromised individuals. Non-melanoma skin cancer (NMSC) is also greatly increased and a variety of viruses have been detected in NMSC. As immunosuppressed patients often develop multiple independent NMSCs, we reasoned that viruses consistently present in independent tumors might be more likely to be involved in tumorigenesis. We sequenced 8 different NMSCs from 1 patient in comparison to 8 different NMSCs from 8 different patients. Among the latter, 12 different virus sequences were detected, but none in more than 1 tumor each. In contrast, the patient with multiple NMSCs had human papillomavirus type 15 and type 38 present in 6 out of 8 NMSCs.