Distribution of HPV genotypes in cervical intraepithelial lesions and cervical cancer in Tanzanian women.

BACKGROUND: Infection with human papillomavirus (HPV) is associated with uterine cervical intraepithelial neoplasia (CIN) and invasive cancers (ICC). Approximately 80% of ICC cases are diagnosed in under-developed countries. Vaccine development relies on knowledge of HPV genotypes characteristic of LSIL, HSIL and cancer; however, these genotypes remain poorly characterized in many African countries. To contribute to the characterization of HPV genotypes in Northeastern Tanzania, we recruited 215 women from the Reproductive Health Clinic at Kilimanjaro Christian Medical Centre. Cervical scrapes and biopsies were obtained for cytology and HPV DNA detection. RESULTS: 79 out of 215 (36.7%) enrolled participants tested positive for HPV DNA, with a large proportion being multiple infections (74%). The prevalence of HPV infection increased with lesion grade (14% in controls, 67% in CIN1 cases and 88% in CIN2-3). Among ICC cases, 89% had detectable HPV. Overall, 31 HPV genotypes were detected; the three most common HPV genotypes among ICC were HPV16, 35 and 45. In addition to these genotypes, co-infection with HPV18, 31, 33, 52, 58, 68 and 82 was found in 91% of ICC. Among women with CIN2-3, HPV53, 58 and 84/83 were the most common. HPV35, 45, 53/58/59 were the most common among CIN1 cases. CONCLUSIONS: In women with no evidence of cytological abnormalities, the most prevalent genotypes were HPV58 with HPV16, 35, 52, 66 and 73 occurring equally. Although numerical constraints limit inference, findings that 91% of ICC harbor only a small number of HPV genotypes suggests that prevention efforts including vaccine development or adjuvant screening should focus on these genotypes.

Detection of amino-terminal extracellular domain of somatostatin receptor 2 by specific monoclonal antibodies and quantification of receptor density in medulloblastoma.

Somatostatin receptor 2 (SSTR2) is expressed by most medulloblastomas (MEDs). We isolated monoclonal antibodies (MAbs) to the 12-mer (33)QTEPYYDLTSNA(44), which resides in the extracellular domain of the SSTR2 amino terminus, screened the peptide-bound MAbs by fluorescence microassay on D341 and D283 MED cells, and demonstrated homogeneous cell-surface binding, indicating that all cells expressed cell surface-detectable epitopes. Five radiolabeled MAbs were tested for immunoreactive fraction (IRF), affinity (KA) (Scatchard analysis vs. D341 MED cells), and internalization by MED cells. One IgG(3) MAb exhibited a 50-100% IRF, but low KA. Four IgG(2a) MAbs had 46-94% IRFs and modest KAs versus intact cells (0.21-1.2 x 10(8) M(-1)). Following binding of radiolabeled MAbs to D341 MED at 4 degrees C, no significant internalization was observed, which is consistent with results obtained in the absence of ligand. However, all MAbs exhibited long-term association with the cells; binding at 37 degrees C after 2 h was 65-66%, and after 24 h, 52-64%. In tests with MAbs C10 and H5, the number of cell surface receptors per cell, estimated by Scatchard and quantitative FACS analyses, was 3.9 x 10(4) for the "glial" phenotype DAOY MED cell line and 0.6-8.8 x 10(5) for four neuronal phenotype MED cell lines. Our results indicate a potential immunotherapeutic application for these MAbs.

Modeling the evolution of regulatory elements by simultaneous detection and alignment with phylogenetic pair HMMs.

The computational detection of regulatory elements in DNA is a difficult but important problem impacting our progress in understanding the complex nature of eukaryotic gene regulation. Attempts to utilize cross-species conservation for this task have been hampered both by evolutionary changes of functional sites and poor performance of general-purpose alignment programs when applied to non-coding sequence. We describe a new and flexible framework for modeling binding site evolution in multiple related genomes, based on phylogenetic pair hidden Markov models which explicitly model the gain and loss of binding sites along a phylogeny. We demonstrate the value of this framework for both the alignment of regulatory regions and the inference of precise binding-site locations within those regions. As the underlying formalism is a stochastic, generative model, it can also be used to simulate the evolution of regulatory elements. Our implementation is scalable in terms of numbers of species and sequence lengths and can produce alignments and binding-site predictions with accuracy rivaling or exceeding current systems that specialize in only alignment or only binding-site prediction. We demonstrate the validity and power of various model components on extensive simulations of realistic sequence data and apply a specific model to study Drosophila enhancers in as many as ten related genomes and in the presence of gain and loss of binding sites. Different models and modeling assumptions can be easily specified, thus providing an invaluable tool for the exploration of biological hypotheses that can drive improvements in our understanding of the mechanisms and evolution of gene regulation.

Comparison of pattern detection methods in microarray time series of the segmentation clock.

While genome-wide gene expression data are generated at an increasing rate, the repertoire of approaches for pattern discovery in these data is still limited. Identifying subtle patterns of interest in large amounts of data (tens of thousands of profiles) associated with a certain level of noise remains a challenge. A microarray time series was recently generated to study the transcriptional program of the mouse segmentation clock, a biological oscillator associated with the periodic formation of the segments of the body axis. A method related to Fourier analysis, the Lomb-Scargle periodogram, was used to detect periodic profiles in the dataset, leading to the identification of a novel set of cyclic genes associated with the segmentation clock. Here, we applied to the same microarray time series dataset four distinct mathematical methods to identify significant patterns in gene expression profiles. These methods are called: Phase consistency, Address reduction, Cyclohedron test and Stable persistence, and are based on different conceptual frameworks that are either hypothesis- or data-driven. Some of the methods, unlike Fourier transforms, are not dependent on the assumption of periodicity of the pattern of interest. Remarkably, these methods identified blindly the expression profiles of known cyclic genes as the most significant patterns in the dataset. Many candidate genes predicted by more than one approach appeared to be true positive cyclic genes and will be of particular interest for future research. In addition, these methods predicted novel candidate cyclic genes that were consistent with previous biological knowledge and experimental validation in mouse embryos. Our results demonstrate the utility of these novel pattern detection strategies, notably for detection of periodic profiles, and suggest that combining several distinct mathematical approaches to analyze microarray datasets is a valuable strategy for identifying genes that exhibit novel, interesting transcriptional patterns.

Explicit DNase sequence bias modeling enables high-resolution transcription factor footprint detection.

DNaseI footprinting is an established assay for identifying transcription factor (TF)-DNA interactions with single base pair resolution. High-throughput DNase-seq assays have recently been used to detect in vivo DNase footprints across the genome. Multiple computational approaches have been developed to identify DNase-seq footprints as predictors of TF binding. However, recent studies have pointed to a substantial cleavage bias of DNase and its negative impact on predictive performance of footprinting. To assess the potential for using DNase-seq to identify individual binding sites, we performed DNase-seq on deproteinized genomic DNA and determined sequence cleavage bias. This allowed us to build bias corrected and TF-specific footprint models. The predictive performance of these models demonstrated that predicted footprints corresponded to high-confidence TF-DNA interactions. DNase-seq footprints were absent under a fraction of ChIP-seq peaks, which we show to be indicative of weaker binding, indirect TF-DNA interactions or possible ChIP artifacts. The modeling approach was also able to detect variation in the consensus motifs that TFs bind to. Finally, cell type specific footprints were detected within DNase hypersensitive sites that are present in multiple cell types, further supporting that footprints can identify changes in TF binding that are not detectable using other strategies.

Development of a Novel c-MET-Based CTC Detection Platform.

UNLABELLED: Amplification of the MET oncogene is associated with poor prognosis, metastatic dissemination, and drug resistance in many malignancies. We developed a method to capture and characterize circulating tumor cells (CTC) expressing c-MET using a ferromagnetic antibody. Immunofluorescence was used to characterize cells for c-MET, DAPI, and pan-CK, excluding CD45(+) leukocytes. The assay was validated using appropriate cell line controls spiked into peripheral blood collected from healthy volunteers (HV). In addition, peripheral blood was analyzed from patients with metastatic gastric, pancreatic, colorectal, bladder, renal, or prostate cancers. CTCs captured by c-MET were enumerated, and DNA FISH for MET amplification was performed. The approach was highly sensitive (80%) for MET-amplified cells, sensitive (40%-80%) for c-MET-overexpressed cells, and specific (100%) for both c-MET-negative cells and in 20 HVs. Of 52 patients with metastatic carcinomas tested, c-MET CTCs were captured in replicate samples from 3 patients [gastric, colorectal, and renal cell carcinoma (RCC)] with 6% prevalence. CTC FISH demonstrated that MET amplification in both gastric and colorectal cancer patients and trisomy 7 with gain of MET gene copies in the RCC patient. The c-MET CTC assay is a rapid, noninvasive, sensitive, and specific method for detecting MET-amplified tumor cells. CTCs with MET amplification can be detected in patients with gastric, colorectal, and renal cancers. IMPLICATIONS: This study developed a novel c-MET CTC assay for detecting c-MET CTCs in patients with MET amplification and warrants further investigation to determine its clinical applicability. Mol Cancer Res; 14(6); 539-47. ©2016 AACR.