Whole genome and exome sequencing is a standard tool for the diagnosis of patients suffering from rare and other genetic disorders. The interpretation of the tens of thousands of variants returned from such tests remains a major challenge. Here we focus on the problem of prioritising variants with respect to the observed disease phenotype. We hypothesise that linking patterns of gene expression across multiple tissues to the phenotypes will aid in discovering disease causing variants. To test this, we construct classifiers that learn associations between tissue-specific gene expression and disease phenotypes. We find that using Genotype-Tissue Expression project (GTEx) expression data in conjunction with disease agnostic variant prioritisation methods (CADD or MetaSVM) results in consistent improvements in classification accuracy. Our method represents a previously overlooked avenue of utilising existing expression data for clinical diagnostics, and also opens the door to use of other functional genomic data sets in the same manner. Genome sequencing is being widely adopted for diagnosis of genetic diseases, but identifying the causal variants remains challenging. Here, the authors introduce a tool that incorporates tissue-specific gene expression data into predicting variant pathogenicity, improving accuracy. Despite considerable efforts in this area, the majority of patients remain undiagnosed3. Whole genome sequencing (WGS) has many benefits over whole exome sequencing (WES) due to coverage of the entire genome versus the protein-coding genome. Entire genome coverage allows reliable detection of copy number variation and of variants in non-coding regions, both of which have been associated with disease4. In addition, WGS does not rely on capture technologies and hence offers better coverage in protein coding and GC rich regions compared with WES5. These advantages have seen WGS adopted as the default choice for genetic diagnoses in the United Kingdom6, but regardless of the technology used the problem of discovering the causative variants remains the same.Clinical genome and exome sequencing has become widely adopted for diagnosis of genetic diseases since becoming commercially available in 20117. The vast majority of these variants are benign and unrelated to the patient’s disease. The challenge is therefore to discover the few variants which are likely to cause the disease for further investigations. This prioritisation task is achieved through a series of filtering steps. Common steps include, filtering by allele frequency in large sequencing cohorts and by the predicted impact of the variant on the protein sequence8. After filtering, the remaining variants are ranked by consulting disease-variant (e.g. ClinVar9) and disease-gene databases (e.g. OMIM10) to determine whether any variants have been previously reported to be associated with the disease under study or a similar disease. Further refinements to the list of candidate variants can be achieved through use of in-silico variant prioritisation tools12.When calling variants from WES, 60,000–100,000 variants are found on average13. This result suggests that developing classifiers for variants in specific disease groups can improve prediction accuracy.To predict the pathogenicity or deleteriousness of variants, variant prioritisation tools use a number of features. They can be grouped into three main categories: (1) tools based on genome conservation, (2) tools predicting the effect of variants on protein function and (3) meta prediction tools combining multiple predictions. Currently, it is common practice to train and assess these methods based on the complete set of known disease causing variants. However, we recently demonstrated that the performance of these tools, including the performance of the best methods, varies considerably when applied to variants causing different diseases15. Furthermore, we anticipate that projects including the Human Cell Atlas16 will continue to provide data, further increasing our understanding of cell-type-specific gene expression and regulation.Variants that affect a cell’s core set of specific genes can disrupt normal protein function, leading to pathogenic cell types and ultimately disease. Hence, if we are trying to identify causal variants for a patient with a neurological phenotype we should prioritise variants in neuronal genes higher than other variants. While we currently do not have a complete compendium of cell-type-specific gene expression across all conditions , recent large scale consortia projects have amassed a considerable amount of data that could be used in this contextFigure 17. The classifiers include variant prioritisation scores in conjunction with tissue or cell-specific gene expression from the Genotype-Tissue Expression project (GTEx)15 or the Functional ANnoTation Of the Mammalian genome consortium (FANTOM)18. We assess whether inclusion of gene expression leads to improvements in variant classification compared with using established variant prioritisation scores on their own. We call our method VARiant Prioritisation by Phenotype (VARPP) and test 1879 disease phenotypes.Here we construct random forest classifiers for phenotypic abnormality terms found in the Human Phenotype Ontology (HPO)We assessed the performance of VARPP using auPRC and PP100 (as described in “Methods”) against use of the variant prioritisation tools alone , brain (other), heart , heart (left ventricle), kidney, liver, muscle, pituitary, salivary gland, skin, spleen, vagina and whole blood  and MetaSVM (1308/2046 = 64%) alone when using rankings from VARPP  would dominate biological signals. Initiatives like the Human Cell Atlas are bringing us closer to understanding cellular function and how this function is affected by disease. Development of novel in silico approaches work hand in hand with these large data sets and will continue to be essential for understanding disease. These same approaches show translational promise in helping to address critical clinical analysis needs for genetic and rare diseases.13. Therefore, we only briefly describe these aforementioned components  and focus on the new components.We previously described in detail each component of an automated pipeline to integrate phenotypes with annotated variants23 within R 3.4.324 to read in the obo file downloaded from https://hpo.jax.org/app/download/ontology on 23 June 2017.We retrieved 12,461 HPO Phenotypic Abnormality terms and used package ontologyIndex25. We used the command line version (https://github.com/WGLab/phenolyzer), with options specified to generate the same result as the Phenolyzer web server with default settings . We only considered genes with a known disease association (seed genes in Phenolyzer). We did not apply a threshold to genes selected for each HPO term based on the Phenolyzer confidence score.Retrieval of disease genes associated with the HPO terms, was performed using Phenolyzer27. We selected ClinVar9 pathogenic variants and used the converted rank scores for CADD12 and MetaSVM11. We removed ClinVar pathogenic variants with minor allele frequencies greater than or equal to 0.01 in the following cohorts: (1) 1000 Genomes28, (2) UK10K_COHORT_TWINSUK29, (3) UK10K_COHORT_ALSPAC29, (4) NHLBI GO Exome Sequencing Project30, and (5) Exome Aggregation Consortium31 as they are unlikely to be causal variants. After selecting pathogenic variants within the seed genes for each HPO term, we excluded terms having fewer than 25 genes. In total we produced 1879 data sets, one for each HPO term, with a median of 1073 pathogenic variants (Supplementary Data Variants in the seed genes returned by Phenolyzer were annotated using dbNSFP version 3.4a (release 12 March 2017)org.Hs.eg.db32 to retrieve all gene symbols across the human genome. Variants within these genes were annotated using dbNSFP version 3.4a (release 12 March 2017). We defined benign variants as those not present in ClinVar and common in at least one population (MAF ≥ 0.01 in the same populations as above). We excluded benign variants present in the 2916 genes containing pathogenic variants. After filtering, there were 55,523 benign variants across 12,313 genes . Gene expression data (read counts of robust phase 1 CAGE peaks for human samples with annotation [hg19]) was downloaded using the FANTOM5 table extraction tool (TET). CAGE peaks with gene annotation (for a single gene) were retained and we summed peak counts within the same gene to obtain an overall measure of gene expression. We annotated the cell types and subtypes using the FANTOM5 ontology terms list (fantom.gsc.riken.jp/5/sstar/FF_Ontology_terms_list). The plotCMDS function from the yarn package was used to calculate multidimensional scaling (MDS) coordinates for the samples. Similarly to Paulson et al.33, we iteratively examined the MDS plots to determine whether particular cell subtypes could be grouped together, resulting in a final set of 81 cell groups . Then, nonparametric specificity percentile scores were calculated by ranking the nonparametric specificity score for each tissue or cell type and dividing these ranks by the number of genes. Low scoring genes (close to zero) are specifically expressed, whereas high scoring genes (close to one) are either ubiquitously expressed or not expressed. The final expression table for GTEx contains 18,442 genes and 41 tissues, and for FANTOM5 there are 16,096 genes and 157 cell types  in combination with variant prioritisation scores (CADD or MetaSVM) to predict pathogenicity. We chose CADD because it is widely used for variant filtering, and MetaSVM because we previously found it to be a top performer for variant prioritisation13. We also considered specificity of gene expression, because disease genes usually display tissue-specific expression37. Data and scripts required to run VAARP are available in the following GitHub repository https://github.com/deniando/VARPP along with instructions for use.VARPP uses random forests38. The in-bag samples are used to build the tree, and the out-of-bag samples are used to internally validate the tree. This internal validation will be biased if the independence assumption is violated. In our case, sampling variants would result in overly optimistic performance of the classifier because variants in the same gene could be present in both in-bag and out-of-bag samples of a single tree. To mitigate this bias, we implemented a two-stage bootstrap approach for the random forests to account for clustering of variants within genes39. This involves bootstrap sampling with replacement at the gene level (rather than at the variant level), followed by selection of a single variant within each gene. Sampling at the gene level ensures that variants in the same gene can never be present in both in-bag and out-of-bag samples. Furthermore, we selected a single variant within each gene, rather than all variants, as this sampling method was shown to improve the performance of random forest40.An overview of VARPP is shown in Supplementary Fig. CADD + GTEx expressionMetaSVM + GTEx expressionCADD + FANTOM5 expressionMetaSVM + FANTOM5 expressionCADD + GTEx specificity percentileMetaSVM + GTEx specificity percentileCADD + FANTOM5 specificity percentileMetaSVM + FANTOM5 specificity percentileEight different classifiers were considered that differed in the predictor variables used, as follows:41 to grow 2000 trees and to calculate unscaled permutation importance measures for each predictor variable. The permutation importance measure is calculated when predicting from each out-of-bag tree. Each predictor variable is randomly permuted in turn and change in accuracy of the predictions is calculated. Important variables will induce large changes in accuracy. Ranger does not handle missing values in predictor variables, hence we remove variants that do not have complete data across all predictor variables prior to growing each tree.We wrote a custom R function to implement the above, using the ranger package42 was used to calculate the area under the precision-recall curve (auPRC) based on the interpolation method of Davis & Goadrich43. We chose to focus on the auPRC rather than the area under the receiver operating characteristic curve (auROC), due to the inherently imbalanced ratio of pathogenic to benign variants. In addition to the auPRC, we also calculated the proportion of true pathogenic variants (i.e. ClinVar pathogenic variants) in the top 100 predictions of pathogenicity (referred to as PP100 from this point on). This measure focuses on the top ranked variants as these are more likely to be considered for follow-up. In contrast, auPRC is an overall measure of the ranking of all variants, including the benign variants. We used a paired t-test to compare these two performance measures across all 1879 HPO terms, for VARPP versus the variant prioritisation tools.We compared performance of VARPP versus using either CADD or MetaSVM alone to identify pathogenic variants. The R precrec package44 was used to remove variants with a minor allele frequency greater than 0.01. BCFtools (www.samtools.github.io/bcftools/) was used to extract each of the 2504 individuals from the multi-sample VCF files, whilst simultaneously removing rows that did not contain variants . The vcf-concat Perl script from VCFtools was used to concatenate the VCF files for each chromosome into one file per individual. We annotated variants in each VCF file using dbNSFP version 2.9.3 (release March 12 2017) and based on this annotation we removed variants with minor allele frequencies greater than 0.01 in any of the following cohorts: (1) 1000 Genomes, (2) NHLBI GO Exome Sequencing Project, (3) Atherosclerosis Risk in Communities Study45, and (4) Exome Aggregation Consortium.To assess how well VARPP ranks a single causal variant within an individual exome we randomly spiked ClinVar pathogenic variants into exomes of healthy individuals obtained by the 1000 Genomes Project (1000 G). Variant call format (VCF) files for each chromosome were downloaded from http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/. VCFtoolsWe downloaded all ClinVar variants from ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/tab_delimited/ on 7 August 2018. Single nucleotide variants were filtered to those reported on build hg19 as this matches the build of the 1000 G VCF files. We selected variants assigned Clinvar pathogenic clinical significance, and also required variants to be annotated with one or more HPO terms. After filtering, 2365 ClinVar pathogenic variants remained as our spike-in variant set. The spike-in variant set contains 567 unique genes and 589 unique HPO annotations. We annotated our spike-in variant set with dbNSFP and annotation was returned for 2237 variants. We removed a further 8 variants with minor allele frequencies greater than 0.01 in any of the aforementioned cohorts  associated with each spike-in variant, and the corresponding 1000 G sample was retained for further analysis with VARPP if at least 25 seed genes were returned. Pathogenic variants within the seed genes were selected and VARPP was fitted as described above and tested back on each 1000 G sample. When testing back on each sample, the test set consisted of variants in genes that were not seen by VARPP. We wrote a custom R function to implement these steps. The whole process was carried out three times, so we could check for consistency across results (Supplementary Data Further information on research design is available in the Supplementary InformationPeer Review FileDescription of additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Reporting Summary


The recent developments in anti-angiogenic and immunomodulatory drugs show that the tumour micro-environment (TME) becomes increasingly important in cancer research. Here we investigated the correlation between the Gleason score (GS) and the TME by comparing tissue expression profiles of steroid hormone receptors, cancer activated fibroblast (CAF) markers and vessel densities between different GS groups. Therefore, matched patient cohorts were composed for different GS (6-7-8). Tissue micro-arrays with 6 samples/patient were processed for immunohistochemistry. Stained slides were digitised, stroma and epithelium were selectively annotated, and all selected areas were quantitatively analysed for marker expression. The most striking findings were decreased stromal expression levels of several steroid hormone receptors, increased CAF-phenotypes and increased vessel densities in high GS prostate cancer compared to low GS prostate cancer and paired prostate non-tumour tissue. The present data reveal a complex correlation between prostate cancer differentiation and TME components and suggest that different GS can be associated with different possible actionable targets in the TME. The use of standardised digital image analysis tools generated robust and reproducible quantitative data, which is novel and more informative compared to the classic semi-quantitative and observer-dependent visual scoring of immunohistochemistry. The ISUP Grade Group system recognizes five distinct grade groups based on the classic GS system and has the advantage to offer a simplified and more straightforward classification1. When added to clinical stage and serum PSA level, the Gleason grading remains a powerful prognostic marker to guide therapy decision for PCa3.The Gleason score (GS) system and the Grade Group system recently introduced by the international society for uropathology (ISUP) are still the mainstay of prostate cancer (PCa) grading5. This TME consists of an interconnected network of stromal fibroblasts, immune cells, blood vessels, mesenchymal stem cells (MSCs), pericytes, fat cells, neural cells and secreted soluble and insoluble factors such as chemokines, cytokines and extracellular matrices6. Interactions between neoplastic cells and the TME are complex and change progressively during the multistep transformation of normal cells into high-grade malignancies and the subsequent cancer dissemination process6.The key element in the GS is the evaluation of the morphology of tumour glands. It is intriguing that this longstanding methodology remains such a powerful prognostic tool. During the past years our knowledge of the molecular features of PCa and of the role of the tumour micro-environment (TME) in PCa progression has gradually expanded10, cancer activated fibroblast (CAF) markers13 and vascular markers14. However, most of these studies focus on individual markers and/or pathways, and therefore transversal studies crossing the relation between GS and the different key elements of the TME are lacking. In the present study we investigated the relation between GS and established TME markers by comparing tissue expression profiles of steroid hormone receptors (SHR: androgen receptor (AR), progesterone receptor (PR) and estrogen receptor alpha (ERα)), CAF markers  and alpha smooth muscle actin (αSMA)) and the vascular marker CD31 in paired PCa and prostate non-tumour (PNT) tissue. Immunohistochemistry (IHC) is an important part of the methodology to study the TME. We aimed to generate robust quantitative IHC data using calibrated image acquisition and validated image analysis algorithms, as reported previously15.Many studies revealed relations between GS and the TME, including GS-dependent changes in expression of steroid hormone receptors (SHR)In all the results below protein expression is quantified in terms of the labelling index (LI) which is representative of the percentage of positive cells.While PR is only expressed in stromal cells, AR and ER can be expressed in both epithelial and stromal cells Figs –3. TheseTo refine these observations, we computed the epithelium/stroma LI ratios for AR and ER and checked whether they significantly differ between PNT and PCa samples. As expected from Fig. In PCa samples we observed no significant differences in stromal expression of AR, PR and ER nor for the epithelial expression of AR and ER across the different GS groups. However, considering the expression difference computed between the PCa and PNT samples for each patient, Fig. We refined the previous analysis by computing the tumour/non-tumour LI ratios for each  SHR expression. The sole significant variation evidenced across the three GS groups was the tumour/non-tumour ratio of stromal AR expression, which decreases in high GSs  (n = 14) and ISUP GG3 (GS 4 + 3 = 7) (n = 18) did not show any significant difference in the SHR expression profiles between them.No significant differences were found in heterogeneity of SHR expression between the GS groups. When considering all patients together (n = 90), there is a significant increase in heterogeneity of AR-expression in epithelium compared to stroma in PCa samples . Inversely, heterogeneity of ER-expression significantly decreases in epithelium of PCa samples .The stromal expression of αSMA and CD34 see Figs  and 8 siCav-1 expression is not significantly different between PCa and PNT tissue. No significant differences in CAF expression profiles were found between ISUP GG2 and GG3.The expression of the vascular marker CD31 see Fig.  significSince the 3 GS groups exhibit the same trends in all the Spearman correlation analyses ). Similar trends were obtained for ER .Cross correlating the different SHR expression levels, positive correlations were found in tumour areas between epithelial AR and stromal ER  and, to a lesser extent, the stromal expression of both ER (r_s = −0.41) and PR (r_s = −0.40).We completed our correlation analysis by comparing marker expression levels and two patient features: their age and PSA level. We carried out these analyses in each GS group and found significant correlations in GS7 only; these data are summarised in Table 1In this study we investigated the relation between different GS and the expression of several stromal cell markers, some of which are also expressed in epithelial cells and show interesting variations between these two cell types. Stromal cell markers were selected based on a high prevalence in PCa research, their possible clinical relevance and/or biomarker potential and the availability of specific and reliable antibodies. Thus, we applied a transversal approach, using several established stromal cell markers instead of a vertical approach, focussing on a specific signalling pathway but often without reliable/validated antibodies for clinical application.2 and therefore the recent ISUP grade group system subdivides GS7 in GG2 (3 + 4) and GG3 (4 + 3)2. However, we were unable to find specific stromal cell changes in GG2 compared to GG3. We found multiple strong correlations between several of the studied biomarkers, which were however always independent from the GS group. When we looked for correlations with the patients age and PSA we found some GS-dependent differences, mostly for the GS7 group, where stromal SMA expression ratio tends to decrease when PSA and/or age increase. We also carried out additional multivariate analysis based on the biomarker panel investigated in this study without obtaining additional information related to GS-related stromal cell profiles. The essential reason is that the extracted data appear as constituting a continuum across the different GS groups : GS8 PCa was characterised by decreased stromal expression of AR and PR, increased epithelium/stroma ratio of AR-expression, increased expression of the CAF-marker CD34 and increased stromal vascularity. Low grade GS6) PCa did not show any grade-dependent changes in stromal cell profile. The intermediate grade groups (GS7) showed a stromal cell profile similar to GS8, with a decreased stromal expression of AR and PR and increased expression of the CAF-marker CD34, although the changes were less significant and mainly related to PNT tissue. It is known that GS7 (4 + 3) has a much worse prognosis than GS7 (3 + 4) PCa did 18, and some of them have also found an association between this decrease and a higher GS18. The decreased expression of PR in PCa tumour stroma has also been reported in previous studies19, but the association with higher GS was not found10, which might be due to pooling of GS6 and GS7 as one study cohort10. Concerning conflicting reports respectively showing a decrease7 and increase20 in stromal expression of ERα in PCa, several methodological differences, like cohort characteristics, pre-analytical tissue sample characteristics, antibody clones, scoring methodologies,… could be involved. We were unable to show any significant difference. In several studies epithelial expression of ERα was only found in PCa and HGPIN and not in PNT tissue22. We also observed very limited expression of ERα in epithelial cells of PNT samples, with a very low epithelial to stroma ratio, which has been reported in another study20. The observed limited and focal expression of ERα in luminal epithelial cells might have been easily overlooked by semi-quantitative assessments, as carried out in previous reports22. Despite the increasing amount of reports showing a functional role for ERβ in the onset of PCa23, we did not investigate the ERβ isoform, due to the lack of reliable and specific ERβ antibodies25.The present study provides new data on stromal cell changes in PCa and largely confirms previous reports, although some differences are apparent and need to be discussed. The decreased expression of AR in tumour stroma has been reported in several studies26. Data on Cav-1 expression levels in PCa stroma are not uniform, with several studies reporting on decreased expression levels26, sometimes related with high GS12, but with other studies lacking obvious changes11. Apart from a negative correlation with age (Cav-1 expression decreases when age increases), we were unable to find any other significant change in Cav-1 expression levels in PCa. Possible explanations for these conflicting data are methodological differences in delineation of the stromal compartments and in scoring procedures . There have also been reports of epithelial overexpression of Cav-1 in high grade PCa28, but we were unable to show significant epithelial Cav-1 staining in our PCa samples, possibly due to different selection criteria for our patient samples.Several studies have investigated the expression of Cav-1 in PCa stroma, and the majority of them has found a relation between low expression levels and poor clinical outcome30. An increased presence of αSMA+ and CD34+ CAF’s was repetitively reported31, but a relation with GS was not established yet. Earlier analysis of the relation between GS and microvessel density (MVD) resulted in conflicting reports. While some studies report a higher MVD in high grade PCa, others did not find any significant relation (for review see32). A critical study-parameter is the selected marker to study MVD32. Comparative studies have shown that CD31 is a reliable vascular marker in PCa, although CD105 (endoglin) might perform slightly better in certain conditions34. Preliminary studies in our lab with CD105 showed weak and limited vascular staining, motivating our selection of CD31 for this study.The particular properties of reactive tumour stroma in PCa have been described in multiple studies32. We tried to ensure as much as possible the validation and reproducibility of our staining assays, but we are well aware that in most cases no consensus exists on several methodological parameters (e.g. the choice of antibody clones) since comparative studies are mostly lacking. A major methodological novelty in this study was the use of whole slide imaging and automated image analysis tools to characterize IHC. Compared to the generally used semi-quantitative and observer-dependent visual scoring systems of IHC, annotating the regions of interest to compartmentalise the quantitative analysis is a very time-consuming approach, but it has the major advantages to generate robust and reproducible quantitative data and to avoid the effects of human subjectivity in visual evaluation38. Recent developments in machine learning, and more specifically in deep learning, propose methods for automatically segmenting glandular epithelium in H&E slide images from non-tumor and tumor colorectal tissue samples40. This kind of tools will greatly help to refine quantitative IHC analysis when they outperform manually compartmentalization, as done for example in the present study.Reliable assessment of tissue biomarkers is crucial in PCa research, but it is clear that many methodological differences between studies often hamper an adequate interpretation and comparison of the results. Amongst major issues are differences in antibody clones, staining assays and image analysis methodologies17. Similar findings have been reported for stromal PR41. The exact role of the reactive tumour stroma in modulating tumour progression is still under debate, but several studies suggest that the damage response biology of reactive stroma is likely to be tumour-promoting42. In this context the high amounts of CD34+ and SMA+ CAF’s in high grade PCa might reflect increased tumorigenic properties of the reactive stroma. Some studies have suggested that CD34+ stromal cells exhibit mesenchymal stem cell properties and are recruited from the microvasculature to contribute to the formation of reactive stroma in tumours like PCa31.Although we cannot draw direct functional conclusions based on the present study design, the finding of specific stromal cell marker changes in high grade PCa is likely to reflect a different stromal cell behaviour depending on tumour grade and differentiation. The decreased expression levels of stromal AR and PR in high grade PCa could reflect a tumour suppressive role for these SHR. Previous studies have shown an association between low stromal AR and death from PCa, suggesting that stromal AR prevents metastasis of evolving epithelial cancer cellsIn conclusion, we have found several GS-specific alterations in stromal cell marker profiles, but we were unable to find a global GS-specific multi-marker stromal cell profile in PCa. The present data need to be confirmed in a larger and prospective series with an additional focus on multi-marker interactions. Automated image analysis tools will be crucial to obtain reproducible and robust biomarker data in future studies.The study was approved by the Institution’s Ethical Committee and Biobank Board as a satellite project to the prostate enabling protocol for studies on a large retrospective prostate tissue database (S55860). The study protocol was in accordance to the EU guidelines. All patient-related sample-data were fully anonymized in the study analysis. Informed consent could not be obtained since research was performed on a retrospective tissue database. Therefore a waiver of consent was issued by the institution’s ethical committee.Matched patient cohorts were composed of different GS (GS6 (n = 28)/ GS7 (n = 32)/ GS8 (n = 31)). Patient cohorts were matched for clinical recurrence , lymph node-status, margin-status, p-stage and age. All cases were revised for correct GS by an experienced urogenital pathologist. Paired PNT samples were made up of tissue blocks from the same patient cohorts containing histologically normal prostate tissue (n = 91).Tissue micro-array (TMA) layout designs were used to develop the TMAs from donor paraffin blocks. Donor paraffin blocks were collected from a large database with tissue blocks from radical prostatectomy specimens. Based on haematoxylin and eosin (H&E) stained slides, two representative paraffin blocks were selected per patient: one with PCa and one with histologically normal prostate (i.e. PNT) tissue. Per block six cylindrical cores were harvested and inserted into a recipient paraffin block with Alphelys minicore . A total amount of 6 TMAs was carried out from 91 PCa patients and consisting of paired PCa and PNT samples.www.nordiq.com). Prior to enrolment in the study, antibodies were validated on control tissue for staining specificity and reliability. Antibodies were directed against SHR , CAF markers  and the vascular marker CD31.Antibody-clones were selected for their epitope-selectivity  for 10 min. Nuclear counterstaining was done with haematoxylin.TMAs were cut in serial slides of 5μm to have all markers evidenced on similar tissue areas. IHC stains were done with the automated Leica Bond-Max system . The automated procedure consisted of: blocking endogenous peroxidase activity using 0.3% HTo avoid bleaching, within 2 weeks after staining, the TMA slides were digitized at 20x using a calibrated whole slide scanner . The calibration concerns light intensity, white balance and shading and is done every day automatically, using a special slide provided by the manufacturer. Annotations were then performed by an experienced urogenital pathologist using the Visiopharm software package  following stringent parameters, as detailed below.30, annotations were restricted to the 2–3 stromal cell layers adherent to the prostate glands  can be expressed by both epithelial and stromal cells, we used different types of annotations to distinguish epithelial and stromal expression , the LI is the percentage of the immunostained (i.e. positive) tissue area, whereas for the IHC markers with nuclear expression , LI is computed on the nuclear area only43. High LI values (expressed in percentages) are indicative of high percentages of positive cells. In view of the different tissue types considered for each marker , differences and ratios of quantitative expression (LI) values were also computed to refine the analysis of the expression variations between the different tissue components. While the differences, which are at the basis of the sign test, evidence the directions of the variations between two components, the ratios enable to take into account the proportional increase or decrease with respect to a basal expression level, e.g. in PNT tissue.A quantitative staining analysis was then performed within the annotated areas using the Visiomorph software package . For each IHC marker we computed the labeling index (LI). These measurements were computed per patient, as detailed previouslyWe also characterised marker expression heterogeneity in each histological compartment for each patient. To this aim we computed the LI per TMA core and their value range (=max - min) observed across the available cores per patient. We used the value range instead of the standard deviation because of the low number of cores per patient.All of the statistical analyses were performed using Statistica software . Paired and independent groups of quantitative data were compared using non-parametric tests, i.e. the sign test and the Kruskal-Wallis test (this latter with associated post-hoc tests), respectively. To take into account the difference extends in paired group analysis, we also checked whether the difference symmetry condition required for applying Wilcoxon matched pair test was satisfied. Correlation between staining features was investigated using Spearman correlation analysis.Supplementary Figure 1


Metastasis to bone occurs in over 70% of patients with advanced breast cancer resulting in skeletal complications, including pathological fractures, hypercalcaemia, and bone pain. Significant advances have been made in the treatment of bone metastases, including the use of antiresorptive drugs, such as bisphosphonates, as well as antibody-based therapies targeting key signalling intermediates within the process of cancer-mediated bone destruction. Despite these advances, treatment is not without side effects, including osteonecrosis of the jaw therefore biomarkers predictive of which patients are at high risk of developing bone spread are required to enable personalized medicine initiatives within this important disease area. We used proteomic analysis to compare the protein expression within (1) a parental triple negative human breast cancer cell line, (2) a fully bone homing cell line and (3) a lung homing cell line. The bone and lung homing cell-lines were derived by intra-cardiac injection of fluorescently labelled cells within immune-compromised mice. Proteomics identified Dedicator of Cytokinesis 4 as a biomarker predictive of bone spread, and this finding was further supported by the observation that high levels of Dedicator of Cytokinesis 4 within primary breast tumours were predictive of breast cancer spread to bone. Here, we provide an overview of this study and put the findings into context. Comment on: Westbrook JA, Wood SL, Cairns DA, et al. Identification and validation of DOCK4 as a potential biomarker for risk of bone metastasis development in patients with early breast cancer. J Pathol. 2019;247(3):381-391. doi:10.1002/path.5197. PubMed PMID: 30426503. https://www.ncbi.nlm.nih.gov/pubmed/30426503.1 we have described the discovery of the role of Dedicator of Cytokinesis 4 (DOCK4) as a potential biomarker protein predictive of bone metastasis within breast cancer. Bone is a common site of metastasis within advanced breast cancer, and over 70% of advanced breast cancer patients develop bone metastases.2 Patients present with symptoms of bone metastasis in many cases years after apparently successful treatment, with disseminated tumour cells remaining dormant within bone prior to their activation. The cellular mechanisms leading to both breast cancer metastasis to bone, and the eventual reactivation of dormant breast cancer cells, are currently the subject of intensive research. Bone is the subject of constant turnover with bone homeostasis being regulated by the activity of bone-resorbing osteoclasts and bone-forming osteoblasts.3 Disseminated and reactivated breast cancer cells secrete parathyroid hormone–related protein (PTHrP), which triggers osteoblasts to increase their synthesis and secretion of receptor activator of NF-κB ligand (RANKL), as well as reduce their secretion of osteoprotegerin (OPG) (an orphan receptor for RANKL). This results in increased binding of RANKL to the receptor for RANKL (RANK) resulting in osteoclast activation and bone destruction. Degradation of bone releases growth factors trapped in the bone matrix, which in turn stimulate breast cancer cell proliferation and release of PTHrP, resulting in a ‘vicious cycle’ of bone destruction.4In our recent publication,5 and antibody-based therapies, eg, denosumab.6 These treatments have greatly improved patient quality of life, with zoledronic acid conferring a statistically significant improvement in invasive disease–free survival within women who were >5 years post-menopausal.7 Despite these improvements in patient treatment, these drugs are not without side effects, including the case of bisphosphonate treatment of osteonecrosis of the jaw.8 To date, there are no effective biomarkers available to predict which patients are at greatest risk of developing bone spread, enabling a personalised medicine approach towards the treatment of skeletal metastasis within breast cancer.Current treatments for bone metastasis within breast cancer include bisphosphonate drugs (such as zoledronic acid)9 Our proteomic analysis of bone-homing and parental triple negative breast cancer cells derived from intracardiac injection murine models has identified a panel of potential bone metastasis biomarker candidates including macrophage actin-capping protein (CapG), the PDZ-domain containing adaptor protein GIPC1,10 and the cell motility regulator DOCK4.1We are engaged in research aiming to elucidate the molecular basis of cancer spread to the skeleton. This work has benefitted from access to unique patient sample banks, including samples from the large, open-label, international, multicentre, randomised, controlled, parallel-group phase 3 trial AZURE, which asks the question ‘Does adjuvant zoledronic acid reduce recurrence in patients with high risk localised breast cancer?’. The AZURE trial has provided both tissue samples (tissue microarrays) as well as patient serum and plasma samples from patients receiving standard adjuvant therapy either with or without addition of zoledronic acid. In addition, proteomic methods have been used to characterise murine models of breast cancer spread to bone, including intracardiac injection models of bone metastasis.12 DOCK4 expression was elevated 2.5-fold within bone homing cells and was not elevated within a lung-homing control cell line, suggesting bone specificity. Immunohistochemical assessment of DOCK4 expression levels within AZURE-patient derived tissue microarrays (n = 689) demonstrated that DOCK4 levels were significantly prognostic for first recurrence within bone in control arm standard chemotherapy) patients , an effect not observed within the zoledronate treatment arm .1 There was no association of DOCK4 levels with metastatic spread to nonbone sites suggesting that DOCK4 levels within primary breast tumours predict aggressive disease and risk of metastasis specifically to bone. The absence of a predictive effect within the zoledronate-treated arm suggests that DOCK4 levels may also inform patient treatment decisions.1 There was no statistically significant difference in overall survival between patients with high and low DOCK4 levels within this study.DOCK4 was identified as a potential biomarker predicting bone metastasis risk via quantitative proteomic analysis of bone-homing and parental MDA-MB-231 cells using stable isotope labelling by amino acids in cell culture (SILAC).1 This effect was not observed within the zoledronic acid treatment arm, suggesting that zoledronate may remove an advantageous effect of high DOCK4 levels upon the incidence of nonskeletal metastases. The mechanism of action of zoledronate on nonbone metastases is currently unknown; however, it has been previously described that zoledronic acid can inhibit the proliferation, survival, and migration of breast cancer cells.13 DOCK4 levels may therefore have prognostic potential for nonskeletal as well as skeletal metastases.Our study aimed primarily to look into the role of DOCK4 as a potential predictive marker for bone metastasis. Within our study, high DOCK4 levels reduced the incidence of nonskeletal metastases within patients in the control arm of the AZURE trial.14 The mechanism of action of DOCK4 within the regulation of cell migration is still the subject of active research. Previous studies have demonstrated that the proangiogenic growth factor vascular endothelial growth factor (VEGF) promotes the interaction of DOCK4 with DOCK9 (a GEF for the GTPase Cdc42).15 In addition, VEGF-dependent Rac activation by DOCK4 is required for the activation of the GTPase Cdc42, and VEGF-dependent Rac activation is itself dependent on the activation of a further GTPase RhoG.15 In this way, the activity of a GTPase signalling cascade functions to both activate DOCK4 and transduce signals downstream of this important GEF. DOCK4 activation leads to the formation of lateral filopodia within epithelial cells within tumours, and the formation of blood vessel lumen within tumour angiogenesis.15 DOCK4 thus plays a pivotal role within tumour angiogenesis; however, its precise role within bone-specific metastasis is still to be elucidated.The role of DOCK4 as a prognostic biomarker for metastatic spread to bone was a unique finding within our published study. DOCK4 is a known guanine nucleotide exchange factor (GEF) responsible for activation of several GTPases inside the cell, including the GTPase protein Rac1, a pivotal regulator of cell migration expressed at the leading edge of motile cells.16 Discovered biomarker proteins may thus achieve their effects via functions that differ from the primary gene annotation, and, thus, the pro–bone-metastatic role of DOCK4 may involve additional functions to the regulation of cell motility via GTPases.While elucidating the role of DOCK4 within cancer spread to bone, we must be aware that proteins often play pleiotropic roles within the cell. Research within our group previously identified actin capping protein (CapG) as a prognostic biomarker for risk of breast cancer spread to bone. Actin-capping proteins do not appear to offer an obvious bone-specific function for metastatic spread; however, subsequent research revealed that CapG can act as an epigenetic regulator responsible for the increased expression of a prometastatic gene stanniocalcin-I (STC-I), required for metastasis to bone.17 TGFβ acts via Smad phosphorylation to increase the expression of numerous gene products, including c-MAF, a transcription factor responsible for promoting bone metastasis within breast cancer.18 Previous research within our group has also identified GIPC1 (a scaffolding protein and binding partner of the TGFβ receptor) as a potential biomarker prognostic for increased risk of bone metastasis within breast cancer.10 DOCK4 expression levels may thus be positively correlated with the expression levels of c-MAF,19 suggesting that, within bone metastasis of breast cancer, TGFβ increases expression of both c-MAF and DOCK4. DOCK4 may thus act as a functional mediator of a c-MAF response panel. The precise role of DOCK4 function within metastasis to bone is still to be elucidated. Studies conducted within our laboratory demonstrated that stable knockdown of DOCK4 expression reduced breast cancer cell motility; however, this effect was transient, being significant at 6 hours post knockdown, but not observed as significant levels 12 hours post knockdown.1 Cell motility may be the only aspect of DOCK4’s role that is altered in bone metastasis of breast cancer.The expression of DOCK4 is induced by the cytokine transforming growth factor-β (TGFβ), a pivotal cytokine within bone metastasis.20 and have a greater ability to deal with diseases with known molecular heterogeneity, such as breast cancer. Research efforts are therefore currently underway to further supplement our protein panel with additional proteins, thus increasing the sensitivity and specificity of this clinical test when eventually applied. There are many aspects of DOCK4’s role within bone metastasis of breast cancer, which remain to be elucidated, including the possible role of DOCK4 within nonbone metastasis, as well as the interaction of DOCK4 with bisphosphonate treatment and its potential role as an informative biomarker for patient treatment decisions.Our research to date has clearly identified a series of proteins with elevated expression specifically within bone metastasis of breast cancer. Functional study of these proteins has identified that they are frequently downstream of key cytokines and growth factors within skeletal metastasis, including TGFβ. We anticipate that DOCK4 will be just one protein within a panel of proteins ultimately forming a prognostic test for clinical application. Multiprotein tests have greater utility for informing patient treatment than single-protein tests,


DEIN (chromosome 4q34.1). Similar CNVs strongly support a common clonal origin and metastatic spread from one twin to the other. DEIN is a long-coding RNA (IncRNA) that has been found highly expressed in stage MS neuroblastoma and is likely involved in biological processes such as cell migration and metastasis.Fetoplacental neuroblastoma metastasis has been postulated as a mechanism accounting for concordant cases where one twin develops a primary tumour and the second twin manifests the disease without an identifiable primary site. These tumours may originate and spread concomitantly due to the same genetic background shared by monozygotic twins. This study investigated the molecular profile of stage MS neuroblastoma presenting concomitantly in monozygotic twins. Comparative genomic hybridisation (aCGH) was done for each of the twin liver tumour and peripheral blood samples at diagnosis. Comparison of copy-number variation (CNV) regions revealed a set of CNVs that were common to both tumour specimens and not apparent in the blood. The CNV signature in both twins’ tumours was highly similar, suggesting a common clonal origin. Additional findings included large deletion of chromosome 10 and amplification of chromosome 17. Notably, both liver samples had amplification of a short region involving  We present a case of stage MS NBL presenting concomitantly in monozygotic monochorionic twins.Neuroblastoma (NBL) is the most common extracranial solid tumour of childhood. Concordant or discordant occurrence of NBL in monozygotic twins has been described in the literature.3 Following two cycles of chemotherapy, interim-staging scans revealed the interval decrease in right adrenal (Twin B) and liver (Twins A and B) masses, declining urine vanillylmandelic acid/homovanillic acid (uVMA/HVA) and minimal (Twin B) or no (Twin A) bone marrow tumor infiltration. Following four cycles of chemotherapy, both twins had normalisation of uVMA/HVA and complete or near-complete NBL image resolution. Both twins are now healthy long-term survivors.Twin B was a 2-month-old who presented with one-week history of abdominal distention and decreased oral intake. Twin A presented with similar complaints one day after Twin B. Both twins were diagnosed with stage MS NBL  and were treated with cycles of carboplatin, etoposide alternating with carboplatin, cyclophosphamide and doxorubicin.4 Microarray images were analysed by using linear and lowess normalisation to provide log ratio values for each probe, with the Promega standard used as the reference channel. aCGH data were imported into Genomic Workbench software (Agilent Technologies©) for identification of structural variants. Regions of copy-number variation (CNV) were detected in each sample by using the ADM-1 algorithm, threshold = 5.0, and human genome build hg19. Normalised log ratios of probes in CNV regions were then exported to CIRCA software (omgenomics.com) for visual comparison between samples.Array comparative genomic hybridisation (aCGH) for each of the twin liver tumour and peripheral blood (PB) samples was performed as per the manufacturer’s protocol.5 Conventional CNV detection algorithms are susceptible to noise in aGCH data and have a high rate of false negatives and positives, particularly for short CNVs. To identify high-confidence short CNVs, we applied an algorithm that was developed in-house to the aCGH data. A notable finding was a short region of amplification on locus NBLA00301, representing the DEIN gene   Fig. .Fig. 1a 7 The occurrence of synchronous tumour in monozygotic twins may suggest the presence of a common genetic background as a predisposition factor for NBL genesis. Furthermore, fetoplacental metastasis is considered when monozygotic monochorionic twins have similar tumour pathology, but only one with identifiable primary tumour, presenting spontaneously, or shortly after.2 A similar mechanism was postulated for concordance of leukaemia in twins.9 Several molecular markers are considered evidence for common clonality and common origin of tumourigenesis.9 Nearly identical CGH and CNV profiles provide substantial evidence of the clonal origin of the malignant cells.12Stage MS NBL is usually a sporadic disease with distinct clinical and molecular features that are different from neuroblastoma stage IV.2 In our report, similar CNVs between both twin tumour samples also strongly support a common clonal origin.Previous aCGH analysis of NBL in a set of monozygotic twins revealed few differences between the primary adrenal versus liver tumours, supporting metastatic spread.DEIN. DEIN is a long noncoding RNA (IncRNA) tightly linked to HAND2, a gene that functions as a key regulator of neurogenesis in the developing sympathetic nervous system.13DEIN has also be found to be a candidate IncRNA related to metastasis in hepatocellular carcinoma.14 Stage MS NBL is an entity defined by its metastatic pattern confined to the skin, liver and bone marrow, in addition to the tumour unique capability to undergo spontaneous regression in young children.16 High expression of DEIN variants has been found to occur exclusively in stage MS NBL, potentially implicating this gene and its variants in biological processes such as cell migration and metastasis in stage MS NBL.13Interestingly, both twin liver tumours had a 4q34.1 amplification, involving PHOX2B), and the anaplastic lymphoma kinase (ALK) were identified here, also supporting its sporadic nature.17 It is interesting that for the twins, tumour preferentially metastasised to similar organs , which is the pattern for stage MS NBL. Additional studies are needed to clarify the role of IncRNA in metastatic spread of stage MS NBL.Of note, no abnormalities involving genes previously described in familial cases of NBL, such as paired-like homeobox 2B  for identification of structural variants. Regions of CNV were detected in each sample by using the ADM-1 algorithm, threshold = 5.0, and human genome build hg19. Normalised log ratios of probes in CNV regions were then exported to CIRCA software (omgenomics.com) for visual comparison between samples.Comparative genomic hybridisation (CGH) was performed for each of the twin tumour specimens and their respective peripheral blood samples. Agilent Human Genome aCGH 1 × 244k (p/n G4411B) and Agilent Genomic DNA Enzymatic labeling Kit (p/n 5190-0453) were used in the direct method of the oligo aCGH workflow as described in Agilent protocol “Oligonucleotide Array-Based CGH for Genomic DNA Analysis”.


To identify clinical and biomarker risk factors for preeclampsia in women with obesity and to explore interactions with gestational diabetes, a condition associated with preeclampsia.2) from the UK Pregnancies Better Eating and Activity Trial (UPBEAT), we examined 8 clinical factors  and 7 biomarkers ) in the early second trimester for association with later development of preeclampsia using logistic regression. Factors were selected based on prior association with preeclampsia. Interaction with gestational diabetes was assessed.In women with obesity . Factors independently associated with preeclampsia were higher mean arterial blood pressure (Odds Ratio (OR) 2.22; 95% Confidence Interval (CI) 1.58–3.12, per 10 mmHg) and lower PlGF . The association of PlGF with preeclampsia was present amongst obese women without gestational diabetes , but not in those with GDM , p = 0.04 for interaction.The relationship between PlGF and preeclampsia differed in women with obesity according to gestational diabetes status, which may suggest different mechanistic pathways to preeclampsia. Whilst replication is required in other populations, this study suggests that performance of prediction models for preeclampsia should be confirmed in pre-specified subgroups. By 2025, it is expected that one in every five women of reproductive age across the world will have obesity of whom at least 7–9% are likely to develop preeclampsia The aim of this study was to identify clinical and biomarker risk factors for preeclampsia in women with obesity and to explore interaction with GDM status. This was undertaken as a secondary analysis of a large cohort of pregnant women with obesity, the UK Pregnancies Better Eating and Activity Trial (UPBEAT), from whom a detailed clinical history had been obtained and blood samples taken in the early second trimester, prior to the development of preeclampsia 22) with singleton pregnancies at 15+0–18+6 weeks’ gestation. Women were excluded if they had underlying disorders, including pre-pregnancy diagnosis of essential hypertension, diabetes, renal disease, systemic lupus erythematosus, antiphospholipid syndrome, sickle cell disease, thalassaemia, coeliac disease, thyroid disease, and current psychosis; or if they were currently being prescribed metformin. Detailed information was collected at trial entry (15+0–18+6 weeks gestation) on maternal clinical characteristics, including socio-demographic information and anthropometric measures; non-fasting blood samples were also taken. Extensive information on maternal and infant outcomes was recorded. Research ethics committee approval was obtained  and all women provided written consent.This study was a prospective cohort using data from UPBEAT, a randomized controlled trial of a complex behavioral intervention aimed at reducing GDM and large for gestational age infants For the purpose of the present study, women with incomplete information on preeclampsia status, oral glucose tolerance test (OGTT) results, clinical and biomarker data were excluded. The UPBEAT intervention was not associated with a difference in the prevalence of preeclampsia or GDM The primary outcome of the present study was preeclampsia which was defined according to the International Society for the study of Hypertension in Pregnancy (ISSHP) criteria: two measures of systolic (≥140 mmHg) or diastolic blood pressure (≥90 mmHg) taken at least four hours apart and the presence of proteinuria (spot urine protein/creatinine ≥30 mg/mmol [0.3 mg/mg] or ≥300 mg/day or at least 1 g/L [‘2 + ’] on dipstick testing) Potential clinical risk factors included 8 variables selected on the basis of previously identified associations with preeclampsia and ability to translate to clinical practice. These were: age, nulliparity, BMI, waist circumference, known history of preeclampsia, first degree family history of hypertension and mean arterial blood pressure. Maternal sum of skinfold thicknesses  was also explored due to its known association with BMI. Blood pressure was recorded using the pregnancy validated Microlife BP3BT0-A blood pressure monitor  and maternal skinfold thicknesses  were measured in triplicate, using Harpenden skinfold Calipers  2 were performed so that odds ratios showed the effect associated with doubling the concentration. As low values of PlGF, are associated with preeclampsia, the scale was reversed, so that the odds ratio (OR) represented the increase in odds of preeclampsia associated with a reduction in PlGF. Clinical factors and biomarkers did not vary with gestational age at sampling, so additional correction for gestational age was not performed.A complete case analysis was undertaken and we compared socio-demographic characteristics between the study population and women excluded from this analysis because of missing data. Assumptions for normality were checked for all predictors. Candidate biomarkers that showed positive skew were log transformed . Transformation into logDescriptive statistics were presented as means (SD), median (IQR) and frequency of observations (percentages), as appropriate. Comparisons of socio demographic characteristics and pregnancy outcomes between women with and without preeclampsia were performed using Chi-square tests, Wilcoxon rank-sum tests and t-tests as appropriate. Univariable logistic regression was used to investigate the association between clinical risk factors and biomarkers and the risk of development of preeclampsia later in pregnancy. Assessment of biomarkers was corrected for multiple testing using the False Discovery Rate (FDR) 3Of the 1554 participants available for analysis, a total of 730 (47%) were excluded because of missing data for evaluation of preeclampsia diagnosis (n = 47), OGTT results (n = 251), clinical risk factors (n = 43), and biomarker measurement (n = 537) . Althougvs. 34.8%, p < 0.01) and had a higher rate of preterm delivery , whilst their infants had lower median birth weight , lower Apgar scores , and were more likely to be admitted to neonatal intensive care unit (NICU) . Women with preeclampsia also had a higher rate of stillbirth or neonatal mortality . Amongst women with preeclampsia, the prevalence of any neonatal morbidity  was 34.8% (8/23) in women with GDM compared to 13.9% (5/36) in women without GDM (p = 0.06).Socio-demographic characteristics of women with and without preeclampsia are shown in 2), higher sum of skinfolds , waist circumference  and mean arterial blood pressure  (2) in univariable analyses. In the multivariable regression, factors independently associated with preeclampsia were higher mean arterial blood pressure  and lower PlGF   . Correcter log2) .Table 2C2); no association was evident in women with GDM  (2) but not in women without GDM , although this was not significant in the interaction test (p = 0.18). Other clinical factors and biomarkers associated with preeclampsia in the full cohort did not differ by GDM status.The prevalence of GDM was 29.6% (n = 241/824). The rate of preeclampsia in women with GDM was 9.5% (n = 23/241) and in women without GDM, 6.2% (n = 36/583). Lower PlGF in the early second trimester increased the odds of preeclampsia only in women without GDM  . This dier log2) . Higher The size of associations (odds ratios) and the confidence intervals were broadly similar in the complete case analysis and in the additional sensitivity analyses of imputed cases  and stra4Whilst it is widely accepted that the etiology of preeclampsia is heterogeneous, evidence to suggest that risk factors for preeclampsia may differ according to specific high-risk subgroups is relatively new, and presents opportunities for personalized intervention strategies Although we confirmed preeclampsia risk factors previously reported in a heterogeneous BMI population, notably raised mean arterial blood pressure and a reduced concentration of PlGF in early second trimester, we also report that PlGF, increasingly used in prediction or diagnosis of preeclampsia 2) and 3106 lean nulliparous women (BMI 20–25 kg/m2) from the international Screening for Pregnancy Endpoints study (SCOPE) and in whom 53 relevant biomarkers were measured, we found no evidence of an association between markers of insulin resistance or inflammatory mediators measured at 14+0–16+0 weeks’ gestation with later preeclampsia in either lean women or women with obesity Whilst numerous epidemiological studies have demonstrated that obesity increases the risk of preeclampsia, the mechanisms have yet to be fully elucidated The association of low PlGF with preeclampsia, which in the women in this study occurred predominantly at term, confirms our previous observations in women with obesity from the SCOPE cohort Raised IL-6 has been associated previously with later development of GDM and has been described in preeclampsia at the time of disease in unselected populations Early second trimester mean arterial blood pressure was strongly associated with preeclampsia in this multi-ethnic heterogeneous population , confirmThe strengths of our study include the prospective detailed data collection ensuring almost complete ascertainment of preeclampsia and GDM and the wide range of clinical factors, anthropometric measures and biomarkers available. Another strength is the precise definition of GDM using universal screening with a 75 g OGTT in accordance with IADPSG and WHO recommendations. Measurement of sFlt-1 was not performed as the association with preeclampsia is confined to later pregnancy (after 20 weeks) 5The study of subgroups of women at high risk for preeclampsia has contributed to improved understanding of the heterogeneous etiology of this condition. Our results suggest that the relationship between PlGF and preeclampsia differs in women with obesity according to GDM status, which may indicate different pathways to disease. Studies seeking to translate the use of PlGF as predictive test for preeclampsia into clinical practice should consider sub-group analysis according to obesity status.


A first-generation amperometric galactose biosensor has been systematically developed utilizing layer-by-layer (LbL) construction of xerogels, polymers, and carbon nanotubes toward a greater fundamental understanding of sensor design with these materials and the potential development of a more efficient galactosemia diagnostic tool for clinical application. The effect of several parameters  outer layer) on galactose sensitivity were investigated with the critical nature of xerogel selection being demonstrated. Xerogels formed from silanes with medium, aliphatic side chains were shown to exhibit significant enhancements in sensitivity with the addition of PU due to decreased enzyme leaching. Semi-permeable membranes of diaminobenzene and resorcinol copolymer and Nafion were used for selective discrimination against interferent species and the accompanying loss of sensitivity with adding layers was countered using functionalized, single-walled carbon nanotubes (CNTs). Optimized sensor performance included effective galactose sensitivity (0.037 μA/mM) across a useful diagnostic concentration range (0.5 mM to 7 mM), fast response time (~30 s), and low limits of detection (~80 μM) comparable to literature reports on galactose sensors. Additional modification with anionic polymer layers and/or nanoparticles allowed for galactose detection in blood serum samples and additional selectivity effectiveness. The resulting peroxide is subsequently oxidized at a working electrode, and the oxidation generates a current that is directly proportional to the amount of analyte present. Biosensor development continues to be a relevant field of research across many disciplines of science, most notably for the detection of analytes implicated in various disease states ,2,3,4. BA critical feature of first-generation biosensors is the development of immobilization scaffolds that will preserve an enzyme’s structure and function. Optimal sensors must adequately immobilize an enzyme while allowing smaller analyte and product molecules to diffuse freely. While a wide array of enzymes immobilization strategies via layer-by-layer (LbL) constructs have been investigated ,11,12, tAn additional challenge in biosensor design is ensuring high sensitivity while simultaneously maintaining selectivity for the analyte of interest. Endogenous interferent species with low oxidation potentials pose challenges to selectivity. Thus, minimization of interferent response is particularly important for clinical testing which uses whole blood or urine samples that may contain electroactive species. A common strategy employed in biosensor design is the inclusion of a semi-permeable outer layer capable of minimizing such interferent response ,16. A seAs ad-layers of material are employed to gain selectivity of the target analyte over interferent species, sensitivity is sometimes compromised. A major strategy to combat this problem in biosensing schemes is the incorporation of nanomaterials (NMs) such as nanoparticles (NPs) or carbon nanotubes (CNTs). NMs of this nature offer unique properties with regard to biosensing including their high conductivity, favorable interaction with biological materials, and surface area–to–volume ratio. NMs have demonstrated enhancement of amperometric–based signals at electrode interfaces ,17 incluIn this work, the development and optimization of a first-generation amperometric biosensor scheme for the detection of galactose is presented. This monosaccharide is a primary diagnostic marker for galactosemia, a potentially lethal autosomal recessive disorder. In individuals with galactosemia, total blood galactose are >10 mg/dL (~0.56 mM), typically ranging between 5–20 mM for classic galactosemia, while normal levels are <1 mg/dL (~0.056 mM) . While cI-t) curves were recorded with an eight-channel potentiostat  and used to evaluate biosensor performance. Electrochemical cells were composed of a Ag/AgCl reference electrode (saturated KCl), Pt wire counter electrode (CH Instruments) and a modified Pt working electrode . Silane precursors used in xerogel formation were stored in a desiccator and subsequently used/deposited in a relative humidity (RH)-controlled chamber . UV-Vis measurements were recorded with a Jasco V-570 spectrophotometer by depositing xerogel-enzyme layers (15 μL) onto glass slides and measuring absorbance over time upon exposure to phosphate buffered solution (PBS).All chemicals were purchased from Sigma-Aldrich  unless specifically stated. Tecoflex SG-80A polyurethane (TPU) was obtained from Lubrizol  and Hydroethane AL25-80A polyurethane (HPU) was obtained from AdvanSource Biomaterials . Carboxylic acid functionalized single-walled carbon nanotubes  were purchased from Nano Lab Incorporated . Sheep blood serum was purchased from Hemostat . All solutions were prepared using 18 MΩ·cm ultrapurified water. Amperometric current-time . Polished electrodes were then electrochemically cleaned via cycling in 0.1 M sulfuric acid between 1.2 and −0.25 V at 0.25 V/s for 500 s. For biosensor fabrication, a solution of tetrahydrof-tran  and silane (25 μL) was prepared in one Eppendorf tube, while galactose oxidase  from Dactylium dendroides dissolved in water (75 μL) was prepared in a second tube. Each tube was shaken on a vortex for 10 min inside a RH controlled chamber (50% RH). A portion of the GaOx solution (50 μL) was then added to the silane/THF mixture, and the resulting combination was shaken on a vortex for an additional 10 min. A 3 μL aliquot of the final sol-gel mixture was deposited onto the platinum electrode surface and allowed to dry at 50% for 24–48 h. to form xerogel layers  . The. Thet-teI-t curves of these systems  were calculated to provide conservative quantitative assessment of selectivity as has been done in previous studies using the following equation:jI and ∆gI are the measured currents for a specific interferent species (j) and galactose and jC and gC are concentrations of the interferent species and galactose, respectively. Negative values of ampK indicate that the interferent is inconsequential  whereas species with near zero or slightly positive values are selected for by the sensor. The selectivity coefficient graph  and the lower limit of quantification (373 μM) are maintained for effective diagnostic galactose concentrations. Calibrated sensor constructs were used on a limited number of blood serum samples spiked with 2 mM galactose (n = 3). A critical aspect of any potential biomedical device of this nature is its effective operation in bodily fluids. In the case of galactosemia diagnostic tests, the media most likely to be screened is blood or blood serum. alactose  and showThe performance of the sensors designed in this study equal or exceed published reports on amperometric galactose biosensors ,29,30,31The design of a first-generation galactose biosensor utilizing xerogel matrices, CNTs for signal enhancement, and polymeric layers for selectivity has been shown to exhibit high sensitivity, clinically relevant limits of detection and linear sensing ranges, as well as discrimination against common interferents. While the presence of the outer PU layer enhanced galactose sensitivity, the study revealed that the choice of silane precursor for the xerogel matrix was critical for retaining immobilized enzyme to achieve the enhancement. This work suggests that minimization of enzyme leaching is particularly important when incorporating smaller enzymes into 1st generation schemes of this nature. The addition of polymers to the LbL construction is effective for gaining discrimination against interferents while the use of CNTs compensated for the loss of sensitivity with the addition of selective layers. Taken together with other biosensors built using the same strategy, this work demonstrates the promise of an adaptable template for the detection of clinically relevant molecules in a variety of disease states. Ultimately, the biosensor presented here could improve upon current diagnostic methods for galactosemia providing fast and accurate measurements with clinical implications.


Our objective is to examine the layer and spectrotemporal architecture and laminar distribution of high-frequency oscillations (HFOs) in a neonatal freeze lesion model of focal cortical dysplasia (FCD) associated with a high prevalence of spontaneous spike-wave discharges (SWDs). Electrophysiological recording of local field potentials (LFPs) in control and freeze lesion animals were obtained with linear micro-electrode arrays to detect presence of HFOs as compared to changes in spectral power, signal coherence, and single-unit distributions during “hyper-excitable” epochs of anesthesia-induced burst-suppression (B-S). Result were compared to HFOs observed during spontaneous SWDs in animals during sleep. Micro-electrode array recordings from the malformed cortex indicated significant increases in the presence of HFOs above 100 Hz and associated increases in spectral power and altered LFP coherence of recorded signals across cortical lamina of freeze-lesioned animals with spontaneous bursts of high-frequency activity, confined predominately to granular and supragranular layers. Spike sorting of well-isolated single-units recorded from freeze-lesioned cortex indicated an increase in putative excitatory cell activity in the outer cortical layers that showed only a weak association with HFOs while deeper inhibitory units were strongly phase-locked to high-frequency ripple (HFR) oscillations (300–800 Hz). Both SWDs and B-S show increases in HFR activity that were phase-locked to the high-frequency spike pattern occurring at the trough of low frequency oscillations. The spontaneous cyclic spiking of cortical inhibitory cells appears to be the driving substrate behind the HFO patterns associated with SWDs and a hyperexcitable supragranular layer near the malformed cortex may play a key role in epileptogenesis in our model. These data, derived from a mouse model with a distinct focal cortical malformation, support recent clinical data that HFOs, particularly fast ripples, is a biomarker to help define the cortical seizure zone, and provide limited insights toward understanding cellular level changes underlying the HFOs. Focal epilepsy is associated with the derangement of local neural network activity leading to hypersynchronous neuronal discharges (ictogenesis) in the affected brain tissues  is a common form of cortical malformation associated with a high incidence of epilepsy  of the University of Wyoming. Animals were housed in a vivarium maintained at 22–23°C on a 12:12 h light-dark cycle. Food and water were available Neonatal freeze lesions were induced in P0–1 pups  using a modification of the model described by Dvorak et al.  to inducTM arrays  from the right S1cortical region of anesthetized control (sham) and freeze lesion mice as previously described . These electrodes typically have a impedance of around 1.5 MΩ. The array was positioned perpendicular to the cortical surface to allow recording from both cortical and subcortical brain structures. Each signal was digitally filtered , and recorded at a sample rate of 30 kHz using Smartbox 2.0 software (NeuroNexus). Additional off-line digital filtering was used to define LFPs (1–100 Hz) and multi-unit activity . All recordings were obtained during B-S due to the hyperexcitable state of the brain during this level of anesthesia  near the recording site. The multi-mode fiber optic patch cable was coupled to a blue laser which is triggered by a custom written program.Virus injection was performed in sham or FL mice aged P14-P16. Mice were anesthetized with 2% isoflurane (vol/vol) and maintained with oxygenated 1% isoflurane throughout surgery procedure. AAV2.1.CAG.hChR2(H134R)-mCherry (University of Pennsylvania Vector Core) was used. Viral vector (2 μl) was loaded into the tip (~10 μm in diameter) of a beveled glass micropipette . A custom stereotactic apparatus was used to deliver viral vector to cortex through a small hole drilled into the skull. For local vS1 injection, virus was injected at two depths: 400 μm and 800 μm; for L4 injection, 400 μm and 600 μm. For each depth, a volume of ~150 nl was injected within 1–2 min using a micromanipulator . Injection pipette was kept in place for 5 min for each depth after injection. Injected mice were put back to dam after 5–10 min recovery from anesthesia in a separate cage with a heating pad underneath. After weaning day, pups were separated and housed by gender until experiments. All recorded electrographic signal files were exported to NeuroExplorer  for off-line data analysis and visual inspection by an experimenter blinded to the test group. Each signal was digitally filtered using finite impulse response filters to define changes across a continuous array of frequency bands dependent on the sampling rate of the signals; low frequency <25 Hz), gamma (25–100 Hz), low-frequency ripple , high-frequency ripple , and MUA . HFOs were identified as amplitude increases in the digitally filtered low and HFR bands  spike field coherence (SFC) analysis was used to compare single-unit firing rate to specific frequencies across the LFP derived from the same signal; and (2) zero-phase oscillation timestamps were computed for each frequency band of interest and compared to individual single-unit spike rates to construct spike-phase histograms.post hoc analysis to evaluate significant differences between individual groups. A Fisher’s Exact Test was used to assess significant differences in the relative proportion of responses between experimental groups. A P value of < 0.05 was considered significant.Following primary analysis, raw data was exported to Microsoft Excel for tabulation of statistical averages and standard error values or Sigmaplot  for graphical display  and statistical analysis between groups. A multifactorial ANOVA was used to evaluate main effects and interactions when multiple independent variables were present followed by a Holm-Sidak n = 7) exhibited a distinct microgyrus within the right S1 region of the brain as compared to the normal cortical morphology of control animals , in a total of 45 mice of either sex on a CD-1 background  ranging in age from 5 to 11 months old (8 ± 1 months). More detailed animal information for each experiment is provided in relevant “Results” section. Upon completion of the electrophysiological recordings, brain tissue was collected from a subset of animals to verify that animals exposed to a neonatal FL  and 8.67 ± 0.54 months for FL animals . SWDs were defined as 10–12 Hz repetitive SWDs above baseline activity of at least 1 s in duration that occurred primarily during slow-wave sleep (SWS) as previously described in detail for this mouse model , contralateral S1 (cS1), and VPM, see Figure Cortical LFP recordings were band-pass filtered to evaluate changes in HFOs across vigilance state and during SWDs Figure . ControlThe increase in HF activity (>100 Hz) that occurred during SWDs presented as oscillatory wax-wane amplitude alterations locked to the spike-and-wave pattern of the SWD Figure .Both SWDs and hyperexcitable B-S both show increases in HFR activity that were phase-locked to the high-frequency spike pattern of hyperexcitable brain waves occurring at the trough of low-frequency waves.unanesthetized animals  exposed to neonatal freeze lesion injury, and seven sham-treated controls  were evaluated for the presence of SWDs and associated HFOs for comparison to the results obtained in the B-S model . SWDs were defined as 10–12 Hz repetitive SWD patterns above baseline activity of at least 1 s in duration that occurred primarily during SWS as previously described in detail for this mouse model  as demonstrated in Figure P > 0.05 between groups). The location and orientation of the microelectrode array in relation to the S1 region is demonstrated in Figure P < 0.05 between groups, Fisher’s Exact test) with a similar profile of activity across cortical level for the HFO frequency bands above 100 Hz as summarized in Figure Acute cortical field potential recordings were obtained using linear microelectrode arrays during hyperexcitable epochs of B-S in anesthetized mice  was used to evaluate the similarity in waveform patterns across cortical lamina. Channel 1 was chosen as the reference channel as is represents the upper most cortical layer that would dominate cortical EEG recordings and also indicated the most robust B-S activity in both control and freeze lesion animals  or putative inhibitory (n = 8) cells based on their waveform pattern  were recorded from multiple sites in 7/7 sham treated animals  and 12/12 FL animals  during the spontaneous B-S period. The MUA in each animal were numerous and were not able to be separated for further analysis . A Pearson’s correlation analysis also indicated a significant increase in maximum SFC values across recording electrode depth  and the lower inhibitory cells recorded from channel 4 (n = 4) that represent the cortical location of the highest percentage of cells recorded for each cell type  but not for the lower gamma range  although the results were dependent on cell type . Post hoc analysis indicated that the INH (CH04) cells had significantly higher firing probabilities across the 100–300 and 300–800 Hz oscillations, confined to the downward slope of the individual waveform  and INH (CH04) cells exhibited increases in firing rate during periods of B-S as demonstrated in Figure via a fiber optic positioned next to the recording site reliably evoked SUAs in 5/5 mice in majority of the electrode sites in the cortex , presumably because the suppression of activities associated with the anesthesia. To further validate if this was the case, we expressed channel-rhodopsin 2 (ChR2) in S1 of shame treated mice and examined the evoked single-units activities . The differential distribution of cell types and stronger SFC values within the deep supragranular and granular layers has not been previously reported in FCD models or in patients with cortical dysplasia. Further analysis of excitatory cells recorded from the most superficial cortical layers indicated only weak phase-locking to HFOs in comparison to the strong phase-locking of inhibitory cells located in the lower supragranular layer to fast ripple band frequencies (possibly emanating from cells within the granular layer). These data suggest that the deeper inhibitory cells may be the driving substrate of ripple waves near the site of the microgyric lesion. It should be noted that these results were obtained under isoflurane anesthesia and should be verified in awake animals as well. However, in line with these results, the concept of abnormal glutamatergic excitation as the driving force behind initiation of epileptic discharges has been challenged in favor of a possible role of GABAergic inhibition in shaping ictal activity (de Curtis and Avoli, Q-QS designed the experiments, co-wrote the manuscript. AW designed the experiments, performed data analysis and wrote the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


In this article, we present a dataset containing word embeddings and document topic distribution vectors generated from MOOCs video lecture transcripts. Transcripts of 12,032 video lectures from 200 courses were collected from Coursera learning platform. This large corpus of transcripts was used as input to two well-known NLP techniques, namely Word2Vec and Latent Dirichlet Allocation (LDA) to generate word embeddings and topic vectors, respectively. We used Word2Vec and LDA implementation in the Gensim package in Python. The data presented in this article are related to the research article entitled “Integrating word embeddings and document topics with deep learning in a video classification framework” [ Video transcripts length variation illustrated in box plot and the distribution of tokens among the entire video transcripts corpus represented by a density function are shown in In A visual representation of word embeddings generated from the MOOC video lectures corpora using principal component analysis (PCA) projected in a geometric space is illustrated in 2A new real-world dataset from the education domain is presented in this article. The dataset contains word embeddings and document topic distribution vectors generated by a corpus of 12,032 video lecture transcripts. The steps involved in collecting and creating the WET dataset are illustrated in As a first step, we downloaded video lecture transcripts from Coursera learning platform and annotated them. For annotation, we used a two-level hierarchical organizational structure of Coursera where each downloaded video transcript is associated with one fine-grained category and one general-level category of the structure. 8 general-level and 40 fine-grained categories constitute the dataset and the distribution of lecture transcripts among these categories is given in Prior to creating corpus and dictionary for generating word embeddings and topic distribution vectors, video lecture transcripts have undergone some preprocessing tasks including converting text to lowercase, removing stop words, punctuations, and removing words that are not purely comprised of alphabetical characters and those that are only one character. In addition, WordNetLemmatizer is used to lemmatize all words in transcripts. An open source Python Library for symbolic and statistical natural language processing called Natural Language Toolkit (NLTK) is used for performing preprocessing tasks.2.1To train and generate word embeddings, we used the Word2Vec  word embWe set various parameters for Word2Vec as shown in Each of four csv files contains 68176 lines comprising of a unique word followed by either 50, 100, 200, or 300 real numbers that correspond to 50, 100, 200, 300 respective dimensions.2.2We conducted unsupervised topic modeling on the MOOC lecture transcript corpus. A latent Dirichlet allocation (LDA)  conventiEach csv file contains 12,032 lines comprising of a unique word followed by either 50, 100, 200, or 300 real numbers that correspond to 50, 100, 200, 300 respective number of topics.Concatenation of document topics obtained from LDA model with either general-level or fine-grained categories produces the final csv file as shown in The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.