The changing microRNA landscape by color and cloudiness:a cautionary tale for nipple aspirate fluid biomarker analysis

Purpose: Investigation of nipple aspirate fluid (NAF)-based microRNAs (miRNAs) as a potential screening tool for women at increased risk of developing breast cancer is the scope of our research. While aiming to identify discriminating NAF-miRNAs between women with different mammographic densities, we were confronted with an unexpected confounder: NAF sample appearance. Here we report and alert for the impact of NAF color and cloudiness on miRNA assessment. Methods: Seven classes of NAF colors coupled with cloudiness appearance were established. Using 173 NAF samples from 154 healthy women (19 samples were bilaterally collected), the expression of 14 target and 2 candidate endogenous control (EC) miRNAs was investigated using Taqman Advanced miRNA assays to identify significant differential expression patterns between color-cloudiness classes. Inter- and intra-individual variation of miRNA expression was analyzed using the coefficient of variation (CV). Results: We found that between the seven NAF classes, fold change miRNA expression differences ranged between 2.4 and 19.6 depending on the interrogated miRNA. Clear NAF samples exhibited higher miRNA expression levels compared to cloudy NAF samples with fold change differences ranging between 1.1 and 6.2. Inter-individual and intra-individual miRNA expression was fairly stable (CV &lt; 15 %), but nevertheless impacted by NAF sample appearance. Within NAF classes, inter-individual variation was largest for green samples (CV 6-15 %) and smallest for bloody samples (CV 2-6 %). Conclusions: Our data indicate that NAF color and cloudiness influence miRNA expression and should, therefore, be systematically registered using an objective color classification system. Given that sample appearance is an inherent feature of NAF, these variables should be statistically controlled for in multivariate data analyses. This cautionary note and recommendations could be of value beyond the field of NAF-miRNAs, given that variability in sample color and cloudiness is likewise observed in liquid biopsies such as urine, cerebrospinal fluid and sputum, and could thereby influence the levels of miRNAs and other biomarkers.</p

Computer-Aided Diagnosis in Multiparametric Magnetic Resonance Imaging Screening of Women With Extremely Dense Breasts to Reduce False-Positive Diagnoses

OBJECTIVES: To reduce the number of false-positive diagnoses in the screening of women with extremely dense breasts using magnetic resonance imaging (MRI), we aimed to predict which BI-RADS 3 and BI-RADS 4 lesions are benign. For this purpose, we use computer-aided diagnosis (CAD) based on multiparametric assessment. MATERIALS AND METHODS: Consecutive data were used from the first screening round of the DENSE (Dense Tissue and Early Breast Neoplasm Screening) trial. In this trial, asymptomatic women with a negative screening mammography and extremely dense breasts were screened using multiparametric MRI. In total, 4783 women, aged 50 to 75 years, enrolled and were screened in 8 participating hospitals between December 2011 and January 2016. In total, 525 lesions in 454 women were given a BI-RADS 3 (n = 202), 4 (n = 304), or 5 score (n = 19). Of these lesions, 444 were benign and 81 were malignant on histologic examination.The MRI protocol consisted of 5 different MRI sequences: T1-weighted imaging without fat suppression, diffusion-weighted imaging, T1-weighted contrast-enhanced images at high spatial resolution, T1-weighted contrast-enhanced images at high temporal resolution, and T2-weighted imaging. A machine-learning method was developed to predict, without deterioration of sensitivity, which of the BI-RADS 3- and BI-RADS 4-scored lesions are actually benign and could be prevented from being recalled. BI-RADS 5 lesions were only used for training, because the gain in preventing false-positive diagnoses is expected to be low in this group. The CAD consists of 2 stages: feature extraction and lesion classification. Two groups of features were extracted: the first based on all multiparametric sequences, the second based only on sequences that are typically used in abbreviated MRI protocols. In the first group, 49 features were used as candidate predictors: 46 were automatically calculated from the MRI scans, supplemented with 3 clinical features (age, body mass index, and BI-RADS score). In the second group, 36 image features and the same 3 clinical features were used. Each group was considered separately in a machine-learning model to differentiate between benign and malignant lesions. We developed a Ridge regression model using 10-fold cross validation. Performance of the models was analyzed using an accuracy measure curve and receiver-operating characteristic analysis. RESULTS: Of the total number of BI-RADS 3 and BI-RADS 4 lesions referred to additional MRI or biopsy, 425/487 (87.3%) were false-positive. The full multiparametric model classified 176 (41.5%) and the abbreviated-protocol model classified 111 (26.2%) of the 425 false-positive BI-RADS 3- and BI-RADS 4-scored lesions as benign without missing a malignant lesion.If the full multiparametric CAD had been used to aid in referral, recall for biopsy or repeat MRI could have been reduced from 425/487 (87.3%) to 311/487 (63.9%) lesions. For the abbreviated protocol, it could have been 376/487 (77.2%). CONCLUSIONS: Dedicated multiparametric CAD of breast MRI for BI-RADS 3 and 4 lesions in screening of women with extremely dense breasts has the potential to reduce false-positive diagnoses and consequently to reduce the number of biopsies without missing cancers

Automated rating of background parenchymal enhancement in MRI of extremely dense breasts without compromising the association with breast cancer in the DENSE trial

Objectives: Background parenchymal enhancement (BPE) on dynamic contrast-enhanced MRI (DCE-MRI) as rated by radiologists is subject to inter- and intrareader variability. We aim to automate BPE category from DCE-MRI. Methods: This study represents a secondary analysis of the Dense Tissue and Early Breast Neoplasm Screening trial. 4553 women with extremely dense breasts who received supplemental breast MRI screening in eight hospitals were included. Minimal, mild, moderate and marked BPE rated by radiologists were used as reference. Fifteen quantitative MRI features of the fibroglandular tissue were extracted to predict BPE using Random Forest, Naïve Bayes, and KNN classifiers. Majority voting was used to combine the predictions. Internal-external validation was used for training and validation. The inverse-variance weighted mean accuracy was used to express mean performance across the eight hospitals. Cox regression was used to verify non inferiority of the association between automated rating and breast cancer occurrence compared to the association for manual rating. Results: The accuracy of majority voting ranged between 0.56 and 0.84 across the eight hospitals. The weighted mean prediction accuracy for the four BPE categories was 0.76. The hazard ratio (HR) of BPE for breast cancer occurrence was comparable between automated rating and manual rating (HR = 2.12 versus HR = 1.97, P = 0.65 for mild/moderate/marked BPE relative to minimal BPE). Conclusion: It is feasible to rate BPE automatically in DCE-MRI of women with extremely dense breasts without compromising the underlying association between BPE and breast cancer occurrence. The accuracy for minimal BPE is superior to that for other BPE categories

The Physiological MicroRNA Landscape in Nipple Aspirate Fluid: Differences and Similarities with Breast Tissue, Breast Milk, Plasma and Serum

BACKGROUND: MicroRNAs (miRNAs) target 60% of human messenger RNAs and can be detected in tissues and biofluids without loss of stability during sample processing, making them highly appraised upcoming biomarkers for evaluation of disease. However, reporting of the abundantly expressed miRNAs in healthy samples is often surpassed. Here, we characterized for the first time the physiological miRNA landscape in a biofluid of the healthy breast: nipple aspirate fluid (NAF), and compared NAF miRNA expression patterns with publically available miRNA expression profiles of healthy breast tissue, breast milk, plasma and serum. METHODS: MiRNA RT-qPCR profiling of NAF (n = 41) and serum (n = 23) samples from two healthy female cohorts was performed using the TaqMan OpenArray Human Advanced MicroRNA 754-Panel. MiRNA quantification data based on non-targeted or multi-targeted profiling techniques for breast tissue, breast milk, plasma and serum were retrieved from the literature by means of a systematic search. MiRNAs from each individual study were orderly ranked between 1 and 50, combined into an overall ranking per sample type and compared. RESULTS: NAF expressed 11 unique miRNAs and shared 21/50 miRNAs with breast tissue. Seven miRNAs were shared between the five sample types. Overlap between sample types varied between 42% and 62%. Highly ranked NAF miRNAs have established roles in breast carcinogenesis. CONCLUSION: This is the first study to characterize and compare the unique physiological NAF-derived miRNA landscape with the physiological expression pattern in breast tissue, breast milk, plasma and serum. Breast-specific sources did not mutually overlap more than with systemic sources. Given their established role in carcinogenesis, NAF miRNA assessment could be a valuable tool in breast tumor diagnostics

The changing microRNA landscape by color and cloudiness: a cautionary tale for nipple aspirate fluid biomarker analysis

Purpose: Investigation of nipple aspirate fluid (NAF)-based microRNAs (miRNAs) as a potential screening tool for women at increased risk of developing breast cancer is the scope of our research. While aiming to identify discriminating NAF-miRNAs between women with different mammographic densities, we were confronted with an unexpected confounder: NAF sample appearance. Here we report and alert for the impact of NAF color and cloudiness on miRNA assessment. Methods: Seven classes of NAF colors coupled with cloudiness appearance were established. Using 173 NAF samples from 154 healthy women (19 samples were bilaterally collected), the expression of 14 target and 2 candidate endogenous control (EC) miRNAs was investigated using Taqman Advanced miRNA assays to identify significant differential expression patterns between color-cloudiness classes. Inter- and intra-individual variation of miRNA expression was analyzed using the coefficient of variation (CV). Results: We found that between the seven NAF classes, fold change miRNA expression differences ranged between 2.4 and 19.6 depending on the interrogated miRNA. Clear NAF samples exhibited higher miRNA expression levels compared to cloudy NAF samples with fold change differences ranging between 1.1 and 6.2. Inter-individual and intra-individual miRNA expression was fairly stable (CV < 15 %), but nevertheless impacted by NAF sample appearance. Within NAF classes, inter-individual variation was largest for green samples (CV 6-15 %) and smallest for bloody samples (CV 2-6 %). Conclusions: Our data indicate that NAF color and cloudiness influence miRNA expression and should, therefore, be systematically registered using an objective color classification system. Given that sample appearance is an inherent feature of NAF, these variables should be statistically controlled for in multivariate data analyses. This cautionary note and recommendations could be of value beyond the field of NAF-miRNAs, given that variability in sample color and cloudiness is likewise observed in liquid biopsies such as urine, cerebrospinal fluid and sputum, and could thereby influence the levels of miRNAs and other biomarkers

Природный и антропогенный факторы формирования и развития культурного ландшафта Форосского парка

Цель данной статьи: на примере небольшой территории Южного берега Крыма – парка в пгт. Форос и прилегающей к нему местности – показать роль и место культурного ландшафта в формировании человеком исторического геокультурного пространства

A treelet transform analysis to relate nutrient patterns to the risk of hormonal receptor-defined breast cancer in the European Prospective Investigation into Cancer and Nutrition (EPIC)

Objective: Pattern analysis has emerged as a tool to depict the role of multiple nutrients/foods in relation to health outcomes. The present study aimed at extracting nutrient patterns with respect to breast cancer (BC) aetiology. Design: Nutrient patterns were derived with treelet transform (TT) and related to BC risk. TT was applied to twenty-three log-transformed nutrient densities from dietary questionnaires. Hazard ratios (HR) and 95 % confidence intervals computed using Cox proportional hazards models quantified the association between quintiles of nutrient pattern scores and risk of overall BC, and by hormonal receptor and menopausal status. Principal component analysis was applied for comparison. Setting: The European Prospective Investigation into Cancer and Nutrition (EPIC). Subjects: Women (n 334 850) from the EPIC study. Results: The first TT component (TC1) highlighted a pattern rich in nutrients found in animal foods loading on cholesterol, protein, retinol, vitamins B12 and D, while the second TT component (TC2) reflected a diet rich in β-carotene, riboflavin, thiamin, vitamins C and B6, fibre, Fe, Ca, K, Mg, P and folate. While TC1 was not associated with BC risk, TC2 was inversely associated with BC risk overall (HRQ5 v. Q1=0·89, 95 % CI 0·83, 0·95, Ptrend<0·01) and showed a significantly lower risk in oestrogen receptor-positive (HRQ5 v. Q1=0·89, 95 % CI 0·81, 0·98, Ptrend=0·02) and progesterone receptor-positive tumours (HRQ5 v. Q1=0·87, 95 % CI 0·77, 0·98, Ptrend<0·01). Conclusions: TT produces readily interpretable sparse components explaining similar amounts of variation as principal component analysis. Our results suggest that participants with a nutrient pattern high in micronutrients found in vegetables, fruits and cereals had a lower risk of BC

Nutrient-wide association study of 92 foods and nutrients and breast cancer risk

Background: Several dietary factors have been reported to be associated with risk of breast cancer, but to date, unequivocal evidence only exists for alcohol consumption. We sought to systematically assess the association between intake of 92 foods and nutrients and breast cancer risk using a nutrient-wide association study. Methods: Using data from 272,098 women participating in the European Prospective Investigation into Cancer and Nutrition (EPIC) study, we assessed dietary intake of 92 foods and nutrients estimated by dietary questionnaires. Cox regression was used to quantify the association between each food/nutrient and risk of breast cancer. A false discovery rate (FDR) of 0.05 was used to select the set of foods and nutrients to be replicated in the independent Netherlands Cohort Study (NLCS). Results: Six foods and nutrients were identified as associated with risk of breast cancer in the EPIC study (10,979 cases). Higher intake of alcohol overall was associated with a higher risk of breast cancer (hazard ratio (HR) for a 1 SD increment in intake = 1.05, 95% CI 1.03–1.07), as was beer/cider intake and wine intake (HRs per 1 SD increment = 1.05, 95% CI 1.03–1.06 and 1.04, 95% CI 1.02–1.06, respectively), whereas higher intakes of fibre, apple/ pear, and carbohydrates were associated with a lower risk of breast cancer (HRs per 1 SD increment = 0.96, 95% CI 0.94–0.98; 0.96, 95% CI 0.94–0.99; and 0.96, 95% CI 0.95–0.98, respectively). When evaluated in the NLCS (2368 cases), estimates for each of these foods and nutrients were similar in magnitude and direction, with the exception of beer/cider intake, which was not associated with risk in the NLCS. Conclusions: Our findings confirm a positive association of alcohol consumption and suggest an inverse association of dietary fibre and possibly fruit intake with breast cancer risk

Psychosocial factors and cancer incidence (PSY-CA):Protocol for individual participant data meta-analyses

OBJECTIVES: Psychosocial factors have been hypothesized to increase the risk of cancer. This study aims (1) to test whether psychosocial factors (depression, anxiety, recent loss events, subjective social support, relationship status, general distress, and neuroticism) are associated with the incidence of any cancer (any, breast, lung, prostate, colorectal, smoking-related, and alcohol-related); (2) to test the interaction between psychosocial factors and factors related to cancer risk (smoking, alcohol use, weight, physical activity, sedentary behavior, sleep, age, sex, education, hormone replacement therapy, and menopausal status) with regard to the incidence of cancer; and (3) to test the mediating role of health behaviors (smoking, alcohol use, weight, physical activity, sedentary behavior, and sleep) in the relationship between psychosocial factors and the incidence of cancer.METHODS: The psychosocial factors and cancer incidence (PSY-CA) consortium was established involving experts in the field of (psycho-)oncology, methodology, and epidemiology. Using data collected in 18 cohorts (N = 617,355), a preplanned two-stage individual participant data (IPD) meta-analysis is proposed. Standardized analyses will be conducted on harmonized datasets for each cohort (stage 1), and meta-analyses will be performed on the risk estimates (stage 2).CONCLUSION: PSY-CA aims to elucidate the relationship between psychosocial factors and cancer risk by addressing several shortcomings of prior meta-analyses.</p