Project description:BackgroundThe clinical course of prostate cancer (PCa) is highly variable, demanding an individualized approach to therapy. Overtreatment of indolent PCa cases, which likely do not progress to aggressive stages, may be associated with severe side effects and considerable costs. These could be avoided by utilizing robust prognostic markers to guide treatment decisions.ResultsWe present a random forest-based classification model to predict aggressive behaviour of prostate cancer. DNA methylation changes between PCa cases with good or poor prognosis (discovery cohort with n = 70) were used as input. DNA was extracted from formalin-fixed tumour tissue, and genome-wide DNA methylation differences between both groups were assessed using Illumina HumanMethylation450 arrays. For the random forest-based modelling, the discovery cohort was randomly split into a training (80%) and a test set (20%). Our methylation-based classifier demonstrated excellent performance in discriminating prognosis subgroups in the test set (Kaplan-Meier survival analyses with log-rank p value < 0.0001). The area under the receiver operating characteristic curve (AUC) for the sensitivity analysis was 95%. Using the ICGC cohort of early- and late-onset prostate cancer (n = 222) and the TCGA PRAD cohort (n = 477) for external validation, AUCs for sensitivity analyses were 77.1% and 68.7%, respectively. Cancer progression-related DNA hypomethylation was frequently located in 'partially methylated domains' (PMDs)-large-scale genomic areas with progressive loss of DNA methylation linked to mitotic cell division. We selected several candidate genes with differential methylation in gene promoter regions for additional validation at the protein expression level by immunohistochemistry in > 12,000 tissue micro-arrayed PCa cases. Loss of ZIC2 protein expression was associated with poor prognosis and correlated with significantly shorter time to biochemical recurrence. The prognostic value of ZIC2 proved to be independent from established clinicopathological variables including Gleason grade, tumour stage, nodal stage and prostate-specific-antigen.ConclusionsOur results highlight the prognostic relevance of methylation loss in PMD regions, as well as of several candidate genes not previously associated with PCa progression. Our robust and externally validated PCa classification model either directly or via protein expression analyses of the identified top-ranked candidate genes will support the clinical management of prostate cancer.

Project description:Currently, there are no FDA approved screening tools for detecting early stage ovarian cancer in the general population. Development of a biomarker-based assay for early detection would significantly improve the survival of ovarian cancer patients.We used a multiplex approach to identify protein biomarkers for detecting early stage ovarian cancer. This new technology (Proseek® Multiplex Oncology Plates) can simultaneously measure the expression of 92 proteins in serum based on a proximity extension assay. We analyzed serum samples from 81 women representing healthy, benign pathology, early, and advanced stage serous ovarian cancer patients.Principle component analysis and unsupervised hierarchical clustering separated patients into cancer versus non-cancer subgroups. Data from the Proseek® plate for CA125 levels exhibited a strong correlation with current clinical assays for CA125 (correlation coefficient of 0.89, 95% CI 0.83, 0.93). CA125 and HE4 were present at very low levels in healthy controls and benign cases, while higher levels were found in early stage cases, with highest levels found in the advanced stage cases. Overall, significant trends were observed for 38 of the 92 proteins (p < 0.001), many of which are novel candidate serum biomarkers for ovarian cancer. The area under the ROC curve (AUC) for CA125 was 0.98 and the AUC for HE4 was 0.85 when comparing early stage ovarian cancer versus healthy controls. In total, 23 proteins had an estimated AUC of 0.7 or greater. Using a naïve Bayes classifier that combined 12 proteins, we improved the sensitivity corresponding to 95% specificity from 93 to 95% when compared to CA125 alone. Although small, a 2% increase would have a significant effect on the number of women correctly identified when screening a large population.These data demonstrate that the Proseek® technology can replicate the results established by conventional clinical assays for known biomarkers, identify new candidate biomarkers, and improve the sensitivity and specificity of CA125 alone. Additional studies using a larger cohort of patients will allow for validation of these biomarkers and lead to the development of a screening tool for detecting early stage ovarian cancer in the general population.

Project description:Zika, dengue, and chikungunya viruses are transmitted by mosquitoes, causing diseases with similar patient symptoms. However, they have different downstream patient-to-patient transmission potentials, and require very different patient treatments. Thus, recent Zika outbreaks make it urgent to develop tools that rapidly discriminate these viruses in patients and trapped mosquitoes, to select the correct patient treatment, and to understand and manage their epidemiology in real time. Unfortunately, current diagnostic tests, including those receiving 2016 emergency use authorizations and fast-track status, detect viral RNA by reverse transcription polymerase chain reaction (RT-PCR), which requires instrumentation, trained users, and considerable sample preparation. Thus, they must be sent to "approved" reference laboratories, requiring time. Indeed, in August 2016, the Center for Disease Control (CDC) was asking pregnant women who had been bitten by a mosquito and developed a Zika-indicating rash to wait an unacceptable 2 to 4 weeks before learning whether they were infected. We very much need tests that can be done on site, with few resources, and by trained but not necessarily licensed personnel. This video demonstrates an assay that meets these specifications, working with urine or serum (for patients) or crushed mosquito carcasses (for environmental surveillance), all without much sample preparation. Mosquito carcasses are captured on paper carrying quaternary ammonium groups (Q-paper) followed by ammonia treatment to manage biohazards. These are then directly, without RNA isolation, put into assay tubes containing freeze-dried reagents that need no chain of refrigeration. A modified form of reverse transcription loop-mediated isothermal amplification with target-specific fluorescently tagged displaceable probes produces readout, in 30 min, as a three-color fluorescence signal. This is visualized with a handheld, battery-powered device with an orange filter. Forward contamination is prevented with sealed tubes, and the use of thermolabile uracil DNA glycosylase (UDG) in the presence of dUTP in the amplification mixture.

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A perception-based nanosensor platform to detect cancer biomarkers.

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