Development of a multivariable risk model integrating urinary cell DNA methylation and cell-free RNA data for the detection of significant prostate cancer.

BACKGROUND: Prostate cancer exhibits severe clinical heterogeneity and there is a critical need for clinically implementable tools able to precisely and noninvasively identify patients that can either be safely removed from treatment pathways or those requiring further follow up. Our objectives were to develop a multivariable risk prediction model through the integration of clinical, urine-derived cell-free messenger RNA (cf-RNA) and urine cell DNA methylation data capable of noninvasively detecting significant prostate cancer in biopsy naïve patients. METHODS: Post-digital rectal examination urine samples previously analyzed separately for both cellular methylation and cf-RNA expression within the Movember GAP1 urine biomarker cohort were selected for a fully integrated analysis (n = 207). A robust feature selection framework, based on bootstrap resampling and permutation, was utilized to find the optimal combination of clinical and urinary markers in a random forest model, deemed ExoMeth. Out-of-bag predictions from ExoMeth were used for diagnostic evaluation in men with a clinical suspicion of prostate cancer (PSA ≥ 4 ng/mL, adverse digital rectal examination, age, or lower urinary tract symptoms). RESULTS: As ExoMeth risk score (range, 0-1) increased, the likelihood of high-grade disease being detected on biopsy was significantly greater (odds ratio = 2.04 per 0.1 ExoMeth increase, 95% confidence interval [CI]: 1.78-2.35). On an initial TRUS biopsy, ExoMeth accurately predicted the presence of Gleason score ≥3 + 4, area under the receiver-operator characteristic curve (AUC) = 0.89 (95% CI: 0.84-0.93) and was additionally capable of detecting any cancer on biopsy, AUC = 0.91 (95% CI: 0.87-0.95). Application of ExoMeth provided a net benefit over current standards of care and has the potential to reduce unnecessary biopsies by 66% when a risk threshold of 0.25 is accepted. CONCLUSION: Integration of urinary biomarkers across multiple assay methods has greater diagnostic ability than either method in isolation, providing superior predictive ability of biopsy outcomes. ExoMeth represents a more holistic view of urinary biomarkers and has the potential to result in substantial changes to how patients suspected of harboring prostate cancer are diagnosed.

A four-group urine risk classifier for predicting outcomes in patients with prostate cancer.

OBJECTIVES: To develop a risk classifier using urine-derived extracellular vesicle (EV)-RNA capable of providing diagnostic information on disease status prior to biopsy, and prognostic information for men on active surveillance (AS). PATIENTS AND METHODS: Post-digital rectal examination urine-derived EV-RNA expression profiles (n = 535, multiple centres) were interrogated with a curated NanoString panel. A LASSO-based continuation ratio model was built to generate four prostate urine risk (PUR) signatures for predicting the probability of normal tissue (PUR-1), D'Amico low-risk (PUR-2), intermediate-risk (PUR-3), and high-risk (PUR-4) prostate cancer. This model was applied to a test cohort (n = 177) for diagnostic evaluation, and to an AS sub-cohort (n = 87) for prognostic evaluation. RESULTS: Each PUR signature was significantly associated with its corresponding clinical category (P < 0.001). PUR-4 status predicted the presence of clinically significant intermediate- or high-risk disease (area under the curve = 0.77, 95% confidence interval [CI] 0.70-0.84). Application of PUR provided a net benefit over current clinical practice. In an AS sub-cohort (n = 87), groups defined by PUR status and proportion of PUR-4 had a significant association with time to progression (interquartile range hazard ratio [HR] 2.86, 95% CI 1.83-4.47; P < 0.001). PUR-4, when used continuously, dichotomized patient groups with differential progression rates of 10% and 60% 5 years after urine collection (HR 8.23, 95% CI 3.26-20.81; P < 0.001). CONCLUSION: Urine-derived EV-RNA can provide diagnostic information on aggressive prostate cancer prior to biopsy, and prognostic information for men on AS. PUR represents a new and versatile biomarker that could result in substantial alterations to current treatment of patients with prostate cancer.

Gene-Transcript Expression in Urine Supernatant and Urine Cell-Sediment Are Different but Equally Useful for Detecting Prostate Cancer.

There is considerable interest in urine as a non-invasive liquid biopsy to detect prostate cancer (PCa). PCa-specific transcripts such as the TMPRSS2:ERG fusion gene can be found in both urine extracellular vesicles (EVs) and urine cell-sediment (Cell) but the relative usefulness of these and other genes in each fraction in PCa detection has not been fully elucidated. Urine samples from 76 men (PCa n = 40, non-cancer n = 36) were analysed by NanoString for 154 PCa-associated genes-probes, 11 tissue-specific, and six housekeeping. Comparison to qRT-PCR data for four genes (PCA3, OR51E2, FOLH1, and RPLP2) was strong (r = 0.51-0.95, Spearman p < 0.00001). Comparing EV to Cells, differential gene expression analysis found 57 gene-probes significantly more highly expressed in 100 ng of amplified cDNA products from the EV fraction, and 26 in Cells (p < 0.05; edgeR). Expression levels of prostate-specific genes (KLK2, KLK3) measured were ~20× higher in EVs, while PTPRC (white-blood Cells) was ~1000× higher in Cells. Boruta analysis identified 11 gene-probes as useful in detecting PCa: two were useful in both fractions (PCA3, HOXC6), five in EVs alone (GJB1, RPS10, TMPRSS2:ERG, ERG_Exons_4-5, HPN) and four from Cell (ERG_Exons_6-7, OR51E2, SPINK1, IMPDH2), suggesting that it is beneficial to fractionate whole urine prior to analysis. The five housekeeping genes were not significantly differentially expressed between PCa and non-cancer samples. Expression signatures from Cell, EV and combined data did not show evidence for one fraction providing superior information over the other.

Impact of gender, ethnicity and social deprivation on access to surgical or transcatheter aortic valve replacement in aortic stenosis: a retrospective database study in England.

OBJECTIVE: To assess gender, ethnicity, and deprivation-based differences in provision of aortic valve replacement (AVR) in England for adults with aortic stenosis (AS). METHODS: We retrospectively identified adults with AS from the English Hospital Episode Statistics (HES) between April 2016 and March 2019 and those who subsequently had an AVR. We separately used HES-linked Clinical Practice Research Datalink (CPRD) to identify people with AVR and evaluate the timeliness of their procedure (CPRD-AVR cohort). ORs for AVR in people with an AS diagnosis were estimated using multivariable logistic regression adjusted for age, region and comorbidity. AVR was considered timely if performed electively and without evidence of cardiac decompensation before AVR. RESULTS: 183 591 adults with AS were identified in HES; of these, 31 436 underwent AVR. The CPRD-AVR cohort comprised 10 069 adults. Women had lower odds of receiving AVR compared with men (OR 0.65; 95% CI 0.63 to 0.66); as did people of black (OR 0.70; 95% CI 0.60 to 0.82) or South Asian (OR 0.75; 95% CI 0.69 to 0.82) compared with people of white ethnicities. People in the most deprived areas were less likely to receive AVR than the least deprived areas (OR 0.8; 95% CI 0.75 to 0.86). Timely AVR occurred in 65% of those of white ethnicities compared with 55% of both those of black and South Asian ethnicities. 77% of the least deprived had a timely procedure compared with 58% of the most deprived; there was no gender difference. CONCLUSIONS: In this large, national dataset, female gender, black or South Asian ethnicities and high deprivation were associated with significantly reduced odds of receiving AVR in England. A lower proportion of people of minority ethnicities or high deprivation had a timely procedure. Public health initiatives may be required to increase clinician and public awareness of unconscious biases towards minority and vulnerable populations to ensure timely AVR for everyone.

A Model to Detect Significant Prostate Cancer Integrating Urinary Peptide and Extracellular Vesicle RNA Data.

There is a clinical need to improve assessment of biopsy-naïve patients for the presence of clinically significant prostate cancer (PCa). In this study, we investigated whether the robust integration of expression data from urinary extracellular vesicle RNA (EV-RNA) with urine proteomic metabolites can accurately predict PCa biopsy outcome. Urine samples collected within the Movember GAP1 Urine Biomarker study (n = 192) were analysed by both mass spectrometry-based urine-proteomics and NanoString gene-expression analysis (167 gene-probes). Cross-validated LASSO penalised regression and Random Forests identified a combination of clinical and urinary biomarkers for predictive modelling of significant disease (Gleason Score (Gs) ≥ 3 + 4). Four predictive models were developed: 'MassSpec' (CE-MS proteomics), 'EV-RNA', and 'SoC' (standard of care) clinical data models, alongside a fully integrated omics-model, deemed 'ExoSpec'. ExoSpec (incorporating four gene transcripts, six peptides, and two clinical variables) is the best model for predicting Gs ≥ 3 + 4 at initial biopsy (AUC = 0.83, 95% CI: 0.77−0.88) and is superior to a standard of care (SoC) model utilising clinical data alone (AUC = 0.71, p < 0.001, 1000 resamples). As the ExoSpec Risk Score increases, the likelihood of higher-grade PCa on biopsy is significantly greater (OR = 2.8, 95% CI: 2.1−3.7). The decision curve analyses reveals that ExoSpec provides a net benefit over SoC and could reduce unnecessary biopsies by 30%.

Integration of Urinary EN2 Protein & Cell-Free RNA Data in the Development of a Multivariable Risk Model for the Detection of Prostate Cancer Prior to Biopsy.

The objective is to develop a multivariable risk model for the non-invasive detection of prostate cancer prior to biopsy by integrating information from clinically available parameters, Engrailed-2 (EN2) whole-urine protein levels and data from urinary cell-free RNA. Post-digital-rectal examination urine samples collected as part of the Movember Global Action Plan 1 study which has been analysed for both cell-free-RNA and EN2 protein levels were chosen to be integrated with clinical parameters (n = 207). A previously described robust feature selection framework incorporating bootstrap resampling and permutation was applied to the data to generate an optimal feature set for use in Random Forest models for prediction. The fully integrated model was named ExoGrail, and the out-of-bag predictions were used to evaluate the diagnostic potential of the risk model. ExoGrail risk (range 0-1) was able to determine the outcome of an initial trans-rectal ultrasound guided (TRUS) biopsy more accurately than clinical standards of care, predicting the presence of any cancer with an area under the receiver operator curve (AUC) = 0.89 (95% confidence interval(CI): 0.85-0.94), and discriminating more aggressive Gleason ≥ 3 + 4 disease returning an AUC = 0.84 (95% CI: 0.78-0.89). The likelihood of more aggressive disease being detected significantly increased as ExoGrail risk score increased (Odds Ratio (OR) = 2.21 per 0.1 ExoGrail increase, 95% CI: 1.91-2.59). Decision curve analysis of the net benefit of ExoGrail showed the potential to reduce the numbers of unnecessary biopsies by 35% when compared to current standards of care. Integration of information from multiple, non-invasive biomarker sources has the potential to greatly improve how patients with a clinical suspicion of prostate cancer are risk-assessed prior to an invasive biopsy.

Convergence of Prognostic Gene Signatures Suggests Underlying Mechanisms of Human Prostate Cancer Progression.

The highly heterogeneous clinical course of human prostate cancer has prompted the development of multiple RNA biomarkers and diagnostic tools to predict outcome for individual patients. Biomarker discovery is often unstable with, for example, small changes in discovery dataset configuration resulting in large alterations in biomarker composition. Our hypothesis, which forms the basis of this current study, is that highly significant overlaps occurring between gene signatures obtained using entirely different approaches indicate genes fundamental for controlling cancer progression. For prostate cancer, we found two sets of signatures that had significant overlaps suggesting important genes (p < 10-34 for paired overlaps, hypergeometrical test). These overlapping signatures defined a core set of genes linking hormone signalling (HES6-AR), cell cycle progression (Prolaris) and a molecular subgroup of patients (PCS1) derived by Non Negative Matrix Factorization (NNMF) of control pathways, together designated as SIG-HES6. The second set (designated SIG-DESNT) consisted of the DESNT diagnostic signature and a second NNMF signature PCS3. Stratifications using SIG-HES6 (HES6, PCS1, Prolaris) and SIG-DESNT (DESNT) classifiers frequently detected the same individual high-risk cancers, indicating that the underlying mechanisms associated with SIG-HES6 and SIG-DESNT may act together to promote aggressive cancer development. We show that the use of combinations of a SIG-HES6 signature together with DESNT substantially increases the ability to predict poor outcome, and we propose a model for prostate cancer development involving co-operation between the SIG-HES6 and SIG-DESNT pathways that has implication for therapeutic design.

Methodology for the at-home collection of urine samples for prostate cancer detection.

Urine from patients with prostate cancer (PCa) contains gene transcripts that have been used for PCa diagnosis and prognosis. Historically, patient urine samples have been collected after a digital rectal examination of the prostate, which was thought necessary to boost the levels of prostatic secretions in the urine. We herein describe methodology that allows urine to be collected by patients at home and then posted to a laboratory for analysis. RNA yields and quality were comparable to those for post digital rectal examination urine, and there was improved sensitivity for the detection of TMPRSS2:ERG transcripts by RT-PCR. The At-Home collection protocol has opened up the potential to perform large-scale PCa studies without the inconvenience, cost, discomfort and expense of patients having to visit the clinic.

The effect of zirconium doping of cerium dioxide nanoparticles on pulmonary and cardiovascular toxicity and biodistribution in mice after inhalation.

Development and manufacture of nanomaterials is growing at an exponential rate, despite an incomplete understanding of how their physicochemical characteristics affect their potential toxicity. Redox activity has been suggested to be an important physicochemical property of nanomaterials to predict their biological activity. This study assessed the influence of redox activity by modification of cerium dioxide nanoparticles (CeO2 NPs) via zirconium (Zr) doping on the biodistribution, pulmonary and cardiovascular effects in mice following inhalation. Healthy mice (C57BL/6 J), mice prone to cardiovascular disease (ApoE-/-, western-diet fed) and a mouse model of neurological disease (5 × FAD) were exposed via nose-only inhalation to CeO2 NPs with varying amounts of Zr-doping (0%, 27% or 78% Zr), or clean air, over a four-week period (4 mg/m3 for 3 h/day, 5 days/week). Effects were assessed four weeks post-exposure. In all three mouse models CeO2 NP exposure had no major toxicological effects apart from some modest inflammatory histopathology in the lung, which was not related to the amount of Zr-doping. In ApoE-/- mice CeO2 did not change the size of atherosclerotic plaques, but there was a trend towards increased inflammatory cell content in relation to the Zr content of the CeO2 NPs. These findings show that subacute inhalation of CeO2 NPs causes minimal pulmonary and cardiovascular effect four weeks post-exposure and that Zr-doping of CeO2 NPs has limited effect on these responses. Further studies with nanomaterials with a higher inherent toxicity or a broader range of redox activities are needed to fully assess the influence of redox activity on the toxicity of nanomaterials.

Inhaled Nanoparticles Accumulate at Sites of Vascular Disease.

The development of engineered nanomaterials is growing exponentially, despite concerns over their potential similarities to environmental nanoparticles that are associated with significant cardiorespiratory morbidity and mortality. The mechanisms through which inhalation of nanoparticles could trigger acute cardiovascular events are emerging, but a fundamental unanswered question remains: Do inhaled nanoparticles translocate from the lung in man and directly contribute to the pathogenesis of cardiovascular disease? In complementary clinical and experimental studies, we used gold nanoparticles to evaluate particle translocation, permitting detection by high-resolution inductively coupled mass spectrometry and Raman microscopy. Healthy volunteers were exposed to nanoparticles by acute inhalation, followed by repeated sampling of blood and urine. Gold was detected in the blood and urine within 15 min to 24 h after exposure, and was still present 3 months after exposure. Levels were greater following inhalation of 5 nm (primary diameter) particles compared to 30 nm particles. Studies in mice demonstrated the accumulation in the blood and liver following pulmonary exposure to a broader size range of gold nanoparticles (2-200 nm primary diameter), with translocation markedly greater for particles <10 nm diameter. Gold nanoparticles preferentially accumulated in inflammation-rich vascular lesions of fat-fed apolipoproteinE-deficient mice. Furthermore, following inhalation, gold particles could be detected in surgical specimens of carotid artery disease from patients at risk of stroke. Translocation of inhaled nanoparticles into the systemic circulation and accumulation at sites of vascular inflammation provides a direct mechanism that can explain the link between environmental nanoparticles and cardiovascular disease and has major implications for risk management in the use of engineered nanomaterials.

Protein corona formation in bronchoalveolar fluid enhances diesel exhaust nanoparticle uptake and pro-inflammatory responses in macrophages.

In biological fluids nanoparticles bind a range of molecules, particularly proteins, on their surface. The resulting protein corona influences biological activity and fate of nanoparticle in vivo. Corona composition is often determined by the biological milieu encountered at the entry portal into the body, and, can therefore, depend on the route of exposure to the nanoparticle. For environmental nanoparticles where exposure is by inhalation, this will be lung lining fluid. This study examined plasma and bronchoalveolar fluid (BALF) protein binding to engineered and environmental nanoparticles. We hypothesized that protein corona on nanoparticles would influence nanoparticle uptake and subsequent pro-inflammatory biological response in macrophages. All nanoparticles bound plasma and BALF proteins, but the profile of bound proteins varied between nanoparticles. Focusing on diesel exhaust nanoparticles (DENP), we identified proteins bound from plasma to include fibrinogen, and those bound from BALF to include albumin and surfactant proteins A and D. The presence on DENP of a plasma-derived corona or one of purified fibrinogen failed to evoke an inflammatory response in macrophages. However, coronae formed in BALF increased DENP uptake into macrophages two fold, and increased nanoparticulate carbon black (NanoCB) uptake fivefold. Furthermore, a BALF-derived corona increased IL-8 release from macrophages in response to DENP from 1720 ± 850 pg/mL to 5560 ± 1380 pg/mL (p = 0.014). These results demonstrate that the unique protein corona formed on nanoparticles plays an important role in determining biological reactivity and fate of nanoparticle in vivo. Importantly, these findings have implications for the mechanism of detrimental properties of environmental nanoparticles since the principle route of exposure to such particles is via the lung.

Cardiovascular health effects of oral and pulmonary exposure to multi-walled carbon nanotubes in ApoE-deficient mice.

Exposure to high aspect ratio nanomaterials, such as multi-walled carbon nanotubes (MWCNTs) may be associated with increased risk of atherosclerosis, pulmonary disease, and cancer. In the present study, we investigated the cardiovascular and pulmonary health effects of 10 weeks of repeated oral or pulmonary exposures to MWCNTs (4 or 40µg each week) in Apolipoprotein E-deficient (ApoE-/-) mice fed a Western-type diet. Intratracheal instillation of MWCNTs was associated with oxidative damage to DNA in lung tissue and elevated levels of lipid peroxidation products in plasma, whereas the exposure only caused a modest pulmonary inflammation in terms of increased numbers of lymphocytes in bronchoalveolar lavage fluid. Ultrasound imaging in live animals revealed an increase in the inner and outer wall thickness of the aortic arch at 10 weeks after pulmonary exposure to MWCNTs, which may suggest artery remodelling. However, we did not find accelerated plaque progression in the aorta or the brachiocephalic artery by histopathology. Furthermore, repeated oral exposure to MWCNTs did not cause changes in the composition of gut microbiota of exposed mice. Collectively, this study indicates that repeated pulmonary exposure to MWCNTs was associated with oxidative stress, whereas cardiovascular effects encompassed remodelling of the aorta wall.