Despite improved outcomes in the past 30 years, less than half of all women diagnosed with epithelial ovarian cancer live five years beyond their diagnosis. Although typically treated as a single disease, epithelial ovarian cancer includes several distinct histological subtypes, such as papillary serous and endometrioid carcinomas. To address whether the morphological differences seen in these carcinomas represent distinct characteristics at the molecular level we analyzed DNA methylation patterns in 11 papillary serous tumors, 9 endometrioid ovarian tumors, 4 normal fallopian tube samples and 6 normal endometrial tissues, plus 8 normal fallopian tube and 4 serous samples from TCGA. For comparison within the endometrioid subtype we added 6 primary uterine endometrioid tumors and 5 endometrioid metastases from uterus to ovary. Data was obtained from 27,578 CpG dinucleotides occurring in or near promoter regions of 14,495 genes. We identified 36 locations with significant increases or decreases in methylation in comparisons of serous tumors and normal fallopian tube samples. Moreover, unsupervised clustering techniques applied to all samples showed three major profiles comprising mostly normal samples, serous tumors, and endometrioid tumors including ovarian, uterine and metastatic origins. The clustering analysis identified 60 differentially methylated sites between the serous group and the normal group. An unrelated set of 25 serous tumors validated the reproducibility of the methylation patterns. In contrast, >1,000 genes were differentially methylated between endometrioid tumors and normal samples. This finding is consistent with a generalized regulatory disruption caused by a methylator phenotype. Through DNA methylation analyses we have identified genes with known roles in ovarian carcinoma etiology, whereas pathway analyses provided biological insight to the role of novel genes. Our finding of differences between serous and endometrioid ovarian tumors indicates that intervention strategies could be developed to specifically address subtypes of epithelial ovarian cancer. The majority of cases (75%) are diagnosed at an advanced stage (III or IV) wherein the 5-year survival rate is less than 30% Ovarian cancer has an incidence of 21,500 cases per year in the United States and 204,000 worldwide, with an estimated annual mortality of 125,000 women. The condition ranks as the 5Of the four major histopathologic subtypes, serous is the most common, followed by endometrioid, mucinous and clear cell types. These subtypes have distinctive gene expression profiles Like serous tumors, the origin of endometrioid tumors is controversial TP53 gene WNT and PIK3CA pathways Gene expression studies of ovarian tumors intended to detect cancer-specific profiles have yielded modest success and limited reproducibility In concert with gene expression and mutational profiles, delineating the epigenome of tumor cells should reveal relationships among samples reflecting common embryological origins, similar histopathological outcomes, or shared mutational events. In ovarian tumors, DNA methylation silences expression of critical genes de novo classification using unsupervised computational clustering. To improve the strength of the epigenetic profiling technique, we included raw methylation data from serous tumors and normal fallopian tube generated through The Cancer Genome Atlas (TCGA). These samples were analyzed using the same methylation platform, and performed by independent research laboratories using independent tumor specimens.We hypothesized that DNA methylation patterns in ovarian tumors would resemble cells from their putative tissue of origin, with a small number of changes representing events associated with malignancy, that uniquely represent each tumor subtype. Moreover, we also hypothesized that uterine and ovarian endometrioid tumors were related by pathogenic mechanisms, which would be observed in DNA methylation patterns. To address these ideas, we examined methylation profiles of 27,578 target CpG sites representing 14,495 genes in the human genome, using DNA derived from serous and endometrioid ovarian tumors, normal fallopian tube and normal endometrium, and primary and metastatic endometrioid endometrial tumors. This large dataset was analyzed using a supervised analysis followed by We analyzed the DNA methylation status of genomic samples using the Illumina Infinium platform. DNA was treated with bisulfite to convert unmethylated cytosines to uracil, leaving methylated cytosines unchanged. The hybridization reaction on the HumanMethylation27 Illumina BeadChip provided signal specific to the methylated and unmethylated states, using the Illumina single base extension assay protocol To assess gross changes in degree of methylation, we examined aggregate methylation levels of all samples. Assay values are reported as the proportion of fluorescence arising from the probe for the methylated state, from 0  to 1 . Comparing all samples, the vast majority showed consistent methylation profiles. Across the genome, most assayed sites had methylation levels between 0 and 0.2, small numbers of sites had levels between 0.2 and 0.8, and a slightly larger number had levels of 0.8 to 1 .In contrast, considering only the X-chromosome, single-copy silencing by random X-inactivation was expected to produce a methylation level of 0.5. The observed pattern showed a broad peak centered at 0.5 for most samples, even though the tumor samples had more extensive heterogeneity. Five serous tumor samples showed a distinct profile, with many loci being unmethylated , indicating either a failure to maintain X-inactivation or copy number alterations with relative excess of the active X We first considered whether the tumor subtypes (defined by histopathology) corresponded to specific methylation profiles. With the inclusion of the TCGA samples, 12 normal fallopian tube samples and 16 ovarian serous tumors yielded sufficient statistical power for a direct comparison. Probes with poor quality control, high variability in the controls, or located on X or Y-chromosomes were not considered see . Using aTo address whether other sample divisions with shared molecular phenotypes existed and to gain a broader picture of the relationships between the sample types, we moved to unsupervised clustering. Utilizing the complete set of 31 primary tumor samples and 18 normal tissues , we limited this analysis to probes that were in the top 500 when ranked by variance, as reported by Houseman et al. The consensus groupings, as shown in The cluster of endometrioid samples, including tumors from both ovarian and uterine sites, displays a remarkably altered profile, with methylation at numerous sites that are normally unmethylated CpG islands, and a loss of methylation at sites that are normally methylated. The extent and reproducibility of these changes is strongly reminiscent of the methylator phenotypes noted in other cancers These data confirm the hypothesis that endometrioid type tumors, whether at ovarian or uterine sites, share similarities at the molecular level. To further address this finding, we analyzed five ovarian metastases derived from primary endometrial tumors using a nested log-likelihood-ratio test. This test addressed consistency of clustering with the primary endometrioid samples versus the combined serous tumors and controls, and secondarily enabled classification within the serous or control groups when necessary. Four of the five samples were strongly identified as endometrioid-type, whereas the fifth was more similar to the control samples , RBAK1 (an RB1 interaction partner), MAPK15 and MAP2K2 kinases, and histone deacetylase HDAC1 were also on the list  from the originals. For each site and for each validation sample, we assessed whether the methylation more closely resembled the normal sample cluster or the serous tumor sample cluster. Of the 25 tumor samples, 4 closely resembled the methylation pattern of normal samples, seven were altered at 21 or more of the 60 loci, and 14 samples showed the altered pattern at 40 or more of the 60 loci .BRCA1 was hypermethylated in 2 of 16 (12.5%) of ovarian serous samples. The tumor suppressor RASSF1A showed evidence of complete methylation in 11 tumors, and single-copy methylation in 4 more . These changes, and others, are likely to be important transformative events, but are restricted to smaller subsets of the samples.Our analysis of differential methylation focused on changes that defined characteristics of each group and were shared among all or nearly all samples. Many important changes in methylation state, previously reported in the literature, have lower prevalence and are not directly identified by our approach. When we assessed our samples for patterns of known methylation, our data were consistent with published results. For instance, 23 or 1024 probability of either network occurring from a random list of genes  analysis and an IPA analysis of the differentially methylated genes from the cluster-based comparison of serous tumors versus controls. The genes corresponding to the methylated loci in our list showed a statistically significant enrichment for GO terms involving regulation of cell cycle . The topof genes ,S5. NotaThis work represents one of the largest studies of methylation using several normal and tumor subtypes of gynecologic cancers. We initially examined the methylation status of 27,578 sites for 49 samples including normal fallopian tube and endometrium, serous ovarian cancer, endometrioid ovarian cancer, primary endometrioid endometrial cancer, and ovarian metastasis of endometrial cancer. Regardless of tumor or normal status, all samples showed similar profiles in the overall distribution of methylated sites. Although we did not find global shifts toward hyper- or hypomethylation across the assayed samples, a subset of samples showed drastically altered methylation for the X chromosome, consistent with loss of the inactive X chromosome, amplification of the remaining active X, or both Our data confirm that different histological subtypes have distinct patterns of methylation. Moreover, ovarian serous tumors are more similar to normal ovarian and endometrial tissues than to ovarian or endometrial endometrioid tumors, which are highly similar to each other and display drastic and consistent changes in their methylation. This result is consistent with a methylator phenotype and in agreement with a model of ovarian endometrioid tumors arising from endometriosis, where the cells ultimately derive from a uterine lineage. Endometrioid tumors from the ovary and uterus share several common somatic mutations PDPK1 is in the PI3K signaling pathway involved in serous ovarian cancer PDPK1 and PLEKHF1 share a pleckstrin homology domain, capable of binding inositol polyphosphates. PARP3 is involved in DNA repair and genome stability. Given the reproducible signal from these genes regardless of method, we conclude that uncharacterized genes in this list are strongly implicated in ovarian tumor development and require additional characterization.For the identification of differentially methylated loci, we used known labels and blinded (data-directed) subgroups. Known labels identified a few dozen genes, some with characterized roles in ovarian cancer. However, given the stringent bar for statistical significance in testing very large numbers of sites, we found that a few outliers within a group could obscure important patterns. By clustering data in an unbiased approach, we found similar methylation patterns among normal samples and some tumor outliers, indicating that current histologic subtyping strategies may miss important molecular distinctions between tumors. This point was further supported in metastatic endometrioid tumors, which also contained an outlier that looked like a normal sample in its methylation patterns. Our clustering approach clearly identified a set of 500 genes that could separate the majority of serous samples from endometrioid samples and normal controls. Although the clustering was distinctive for the three main classes of samples, its use precluded a statistical evaluation of the significance of genes within the set. Nevertheless, the increased power of clustering 49 samples identified an additional 60 loci that were independent of the clustering set and segregated samples into normal or serous subtypes with statistical significance. Several of these genes correspond to networks implicated in the development of ovarian cancer  and S5. RB1 and RBAK are differentially methylated between the papillary serous and normal fallopian tube samples. RB1 was recently reported by TCGA to be involved in serous tumor etiology, through mutation or deletion in 67% of tumors RB1 pathway is consistent with concurrent Rb1 and Tp53 mutation in mice, which simulates characteristics of aggressive serous ovarian cancers, including formation of ascites and metastasis A limitation of our analysis is that we did not screen the tumor DNA for gene mutations or ascertain gene expression levels; nevertheless, we found that Our analysis of methylation profiles in ovarian and endometrial tumors indicates value in characterizing tumors at the molecular level. The methylator phenotype indicates an aberration in the molecular function of enzymes regulating DNA methylation levels and suggests that a molecule acting upstream of the candidate genes is responsible for the cascade of events leading to tumor development. Studies in hepatocellular carcinoma have identified mutations in the beta-catenin gene in association with a methylator phenotype. Mutations in beta-catenin are also common in endometrial tumors The consistency of the methylation profiles, despite independent sample preparation and data collection for TCGA samples, was used to validate and extend our results. These data show that sample batch effects are minimal and do not disrupt data consistency. Our data provide a foundation for future genomic and genetic analyses of endometrial and serous tumors for diagnostic and treatment applications. Notably, our results show that methylation levels in serous tumors are less consistent than endometrioid tumors, but increase and decrease in a target-dependent way. In contrast endometrioid tumors show extensive changes that are likely linked to a common upstream mechanism gone awry.Ovarian, endometrial and fallopian tube tissues were received from the Magee-Womens Hospital Tissue Procurement Program . The tissues were snap frozen after surgery and stored at −80°C. Genomic DNA was isolated using the Puregene Blood Kit (Qiagen) following the manufacturer's instructions. DNA quality was assessed using a SmartSpec Plus spectrophotometer .Tissue samples were provided by the Cooperative Human Tissue Network, which is funded by the National Cancer Institute. Samples are from post-menopausal individuals with atrophic endometrium and were obtained from routine hysterectomy or pelvic resection for non-endometrial cancers. DNA was isolated following the protocol of Trizol reagent (Invitrogen).The use of human subject material was approved by the University of Pittsburgh and the Office of Human Subjects Research at the NIH.http://tcga-data.nci.nih.gov/tcga/dataAccessMatrix.htm). Data from the same batch contained unmatched serous tumors and normal fallopian tube samples. Normals: TCGA-01-0639-11A-01D-0383-05, TCGA-01-0631-11A-01D-0383-05, TCGA-01-0642-11A-02D-0383-05, TCGA-01-0628-11A-01D-0383-05, TCGA-01-0637-11A-01D-0383-05, TCGA-01-0633-11A-01D-0383-05, TCGA-01-0630-11A-01D-0383-05, TCGA-01-0636-11A-01D-0383-05. Tumors: TCGA-09-0367-01A-01D-0359-05, TCGA-09-0365-01A-02D-0359-05, TCGA-09-0366-01A-01D-0359-05, TCGA-09-0369-01A-01D-0359-05.TCGA data were downloaded at the time of this analysis from the data portal , with slight modification. One half microgram of DNA was used for each conversion reaction. The hybridization reaction was performed according to the HumanMethylation27 Illumina BeadChip protocol and scanned using an Illumina iScan System.Experimental confidence levels were recorded as p-value estimates for each methylation ratio measurement; all readings with a corresponding p-value >0.05 were censored. These low-confidence values were not uniformly distributed in the data, therefore a few loci had an unusually large number of exclusions; we chose to completely eliminate from consideration any probe location at which values for ten or more samples were unavailable. This step eliminated 61 loci.We performed a comparison between normal fallopian tube (control) and ovarian serous tumors. We excluded probes with poor quality control metrics from the Illumina analysis software (61 probes), and probes that had high variability within the control samples . We also censored all data from the X and Y chromosomes . Some overlap in these sets resulted in eliminating a total of 2,489 loci. Differential analysis by Wilcoxon summed-rank test was performed with the R function wilcox.test, followed by Bonferroni correction for the 25,102 loci tested.Unsupervised clustering was performed in R. To select a subset of loci to use, all primary samples were pooled, and the loci were ranked by sample variance. The number of probes considered was determined empirically, based on bootstrap support for clustering results obtained for data sets of 50, 100, 250, or 500 probes. Based on apparent stability of results with 250 or more probes, the top 500 probes were used for all clustering analyses. Model-based top-down clustering was conducted with the β-mixture model described in Houseman, 2008 To create a classifier from the clustering results, a beta distribution was estimated for each of the 500 loci used for clustering. These distributions were estimated separately for each of four groups of samples: the endometrioid-type tumor cluster, the serous-type tumor cluster, the control-type cluster, and a cluster including both the control-type and serous-type tumor samples. These groupings allowed a nested binary decision first between endometrioid-type and all other samples, and then a second division between the serous tumor and control clusters. The test metric was calculated as the log of the ratio of probability densities at the observed methylation level for the new sample, summed over all 500 loci.In a pairwise comparison of a tumor cluster versus the control-type cluster, we selected cases showing consistent signal in the controls and alteration in the tumors. We again excluded probes with poor quality, high variability in controls, and sex chromosome location; we also excluded all probes used in clustering for the definition of classes . Differential analysis was again done with Wilcoxon summed-rank test, correction for 24,766 independent tests.For each probe in the set of differentially methylated sites, a threshold was chosen that maximized the discrimination between the previously identified control and serous clusters, minimizing the total number of classification errors . 25 additional samples from ovarian serous tumors were analyzed for DNA methylation as described above, and were assessed for which group they were classified with at each of the differential sites.Figure S1Comparison of methylation intensity plots from independent replicates and samples. Technical replicates of methylation signals in normal fallopian tube, endometrial tumors or ovarian metastases from primary endometrial samples, with best linear fit.(TIF)Click here for additional data file.Figure S2Clustering results for hierarchical and non-hierarchical methods. At top, the tree shows the result of top-down partitioning under a beta-mixture model. Below, the six trees show results of hierarchical clustering under either complete linkage (middle row) or average linkage (bottom row), for each of 3 distance metrics. For each method, color bocks beneath the tree show the correspondence to the consensus clusters, with the control-type cluster in blue, the serous-type cluster in black, and the endometrioid cluster in red. For hierarchical methods, black dots on tree nodes indicate ≥95% confidence in that grouping under bootstrap analysis.(EPS)Click here for additional data file.Figure S3Box plot graph of differentially methylated loci. Each differentially methylated gene, listed from top to bottom by p-value is represented by a box plot for the methylation in the control cluster (black) and the methylation in the serous tumor cluster (red).(EPS)Click here for additional data file.Figure S4IPA network for Cell Cycle and Cell Morphology. Differentially methylated genes participating in the network are colored red with increasing intensity representing smaller p-values. Shapes of molecules indicate distinct molecular functions. Arrows represent direct and indirect interactions. Designations for biomarkers are highlighted in green.(TIFF)Click here for additional data file.Figure S5IPA network for Inflammatory Response. Differentially methylated genes participating in the network are colored red with increasing intensity representing smaller p-values. Shapes of molecules indicate distinct molecular functions. Arrows represent direct and indirect interactions. Designations for biomarkers are highlighted in green.(TIFF)Click here for additional data file.


Acute care surgical services provide timely comprehensive emergency general surgical care while optimizing the use of limited resources. At our institution, 50% of the daily dedicated operating room (OR) time allocated to the Acute Care Emergency Surgery Service (ACCESS) came from previous elective general surgery OR time. We assessed the impact of this change in resource allocation on wait-times for elective general surgery cancer cases.We retrospectively reviewed adult patients who underwent elective cancer surgeries in the pre-ACCESS (September 2009 to June 2010) and post-ACCESS (September 2010 to June 2011) eras. Wait-times, calculated as the time between booking and actual dates of surgery, were compared within assigned priority classifications. Categorical and continuous variables were compared using chi-square and Mann–Whitney U tests respectively.A total of 732 cases (367 pre-ACCESS and 365 post-ACCESS) were identified, with no difference in median wait-times (25 versus 23 days) between the eras. However, significantly fewer cases exceeded wait-time targets in the post-ACCESS era (p <0.0001). There was a significant change (p = 0.027) in the composition of cancer cases, with fewer breast cancer operations (22% versus 28%), and more colorectal (41% versus 32%) and hepatobiliary cancer cases (5% versus 2%) in the post-ACCESS era.These results suggest that shifting OR resources towards emergency surgery does not affect the timeliness of surgical cancer care. This study may encourage more centres to adopt acute care surgical services alongside their elective or subspecialty practices. Acute care surgery (ACS) is a distinct surgical care model that provides dedicated comprehensive care for general surgical emergencies such as acute appendicitis, cholecystitis, bowel obstruction, perineal sepsis, and perforated viscus -3. This de novo. At the London Health Sciences Centre (LHSC) - Victoria Hospital, the Acute Care and Emergency Surgery Service (ACCESS) was established in July 2010 when the growing need for organized emergency general surgery coverage was recognized by the Division of General Surgery, the Emergency Department, and hospital leadership. In this model, a single staff surgeon suspends their elective practice while covering ACCESS for one week at a time (Monday to Monday), and their previously-allocated elective OR time for the week (15 hours) is subsumed into the daily dedicated ACCESS OR time. An additional 15 hours of OR time per week was funded by the South West Local Health Integration Network  specifically for the ACCESS initiative, for a total of 30 hours per week dedicated to the service. The team is also assigned a full complement of housestaff.The local delivery and structure of ACS services can vary significantly from hospital to hospital, particularly in terms of the availability of dedicated ACS operating room (OR) time. Because of the financial constraints associated with a publicly-funded healthcare system, Canadian hospitals have typically funded dedicated ACS OR time by reallocating existing OR resources, rather than providing additional funding In 2004, Ontario’s Ministry of Health and Long-Term Care (MOHLTC) implemented a Wait Time Strategy -13 to imAll clinical activity reviewed occurred at Victoria Hospital (VH), LHSC in London, Canada, which serves as a regional tertiary-care hospital and Level I trauma centre for Southwestern Ontario. The Division of General Surgery at VH is a diverse group of sub-specialists, including colorectal, hepatobiliary, endocrine, surgical oncology, trauma, and minimally invasive surgeons. All eight general surgeons at Victoria Hospital were involved with ACCESS during the study period, and performed oncological surgeries as part of their subspecialty practices, including thyroid, breast, colorectal, hepatobiliary (HPB), foregut , endocrine, and melanoma surgery. Other surgical specialties, including plastic, orthopaedic, urologic, gynecologic, and head and neck surgery, also routinely perform cancer operations at VH.Ethics approval for this single-centre retrospective cohort study was provided by the Western University Research and Ethics Board (REB Number 102988). The LHSC-VH operative database was queried for all elective cancer operations performed by all surgical specialties between September 1, 2009 and June 30, 2010 (pre-ACCESS) and between September 1, 2010 and June 30, 2011 (post-ACCESS). Cancer surgeries were defined as oncological operations booked electively. As part of the provincial Wait-Time Strategy initiative, all cancer operations were assigned a certain priority status by the surgeon at the time of booking based on the perceived urgency of the intervention (Table All adults (age 18 and older) undergoing elective cancer surgery with curative intent and whose decision-to-treat and operation dates fell within the defined study periods were included. We excluded patients whose cases were booked in emergency or ACCESS OR time, and patients who were assigned a P1 priority status, since they required an imminent operation and thus were typically operated on non-electively. We also excluded patients who underwent surgery to remove benign or pre-malignant tumours, to correct or repair defects from previous cancer operations (reconstructive surgery), or to provide palliation.Analyses were carried out on the basis of surgeries performed by general surgeons, as well as the overall patient population. Continuous variables were compared using the Mann–Whitney U-test. Categorical variables were compared using chi-square or Fisher’s exact tests where indicated. P-values less than 0.05 were considered statistically significant. Statistical analysis was performed using Graphpad Prism Version 5 .We identified a total of 732 patients who underwent cancer surgery by the general surgeons at VH across the two study periods  between the eras: in the post-ACCESS period, P2 and P3 cases declined by 95% and 40%, respectively, while P4 cases rose by 185%. There was no significant change in wait-times for elective general surgery cancer cases pre- and post-ACCESS, according to priority status. However, the proportion of cases that exceeded assigned wait-time targets in the post-ACCESS era declined by 100% and 55% for P2 and P3 cases, respectively (p < 0.0001), while the proportion of P4 cases that exceeded wait-time targets did not change  was shifted to a nearby ambulatory-care centre. During the study period, CCO also mandated a shift in the treatment of select malignancies  away from community hospitals to high-volume tertiary-care centres such as VH. Consequently, there was a significant change observed in the composition of cancer surgeries performed at VH after the implementation of ACCESS, with fewer breast and melanoma surgeries, and increased proportions of colorectal and hepatobiliary cases.Interestingly, we observed a significant change in the distribution of cancer cases by priority post-ACCESS, for all surgeons  at Victoria Hospital: the proportion of P2 and P3 cases declined, while the proportion of P4 cases increased significantly. Since the general surgeons participating in ACCESS also perform cancer surgeries during their elective practices, they may have been performing P2 and P3 cancer cases on standby during ACCESS time , thereby contributing to the decline in P2 and P3 cases electively. If this was the case, surgeons may have had more time during their elective OR time to operate on patients with P4 cancers. This possible change may also partially explain the significant reduction in the number of general surgery cancer cases that exceeded the wait-time targets. Alternatively, surgeons at VH may have become more conservative in assigning priority levels for cancer patients in order to avoid missing wait-time targets and the associated penalties. This explanation may be more likely given the down-grading present across all surgical specialties at VH, although a case–control analysis of cancer patients may determine if this has been occurring since the implementation of the Wait Time Strategy. One of the limitations of this study was our inability to accurately determine the number of cancer surgeries performed during ACCESS time because standby cancer operations were usually reported as emergency cases rather than elective surgeries. With the recent integration of operative databases for emergency and elective cases at our institution, however, future prospective analyses may provide this important information.Overall, there was no significant change in cancer surgery wait times pre- versus post-ACCESS. Therefore, the implementation of ACCESS, and the resultant reallocation of OR time from elective to emergency case loads, did not negatively impact wait times for elective cancer surgery. Additionally, wait-times remained unchanged despite the significant increase in the performance of hepatobiliary and colorectal surgeries post-ACCESS, which are typically longer and more complex than the breast cancer and melanoma cases that were moved off-site. Finally, while general surgery OR resources were redistributed to accommodate ACCESS at a single institution (VH), other hospitals within the city and region may have also adjusted their practices, contributing to keeping wait-times on target.et al.[et al.[To our knowledge, this is the first study to examine the impact of implementing an ACS service on wait-times for elective surgeries. Miller et al. and Barnl.[et al. observedl.[et al.. HoweverOne of the limitations of this study is that the effect of ACCESS on wait-times for non-cancer elective operations, such as elective bowel resections for non-malignant pathology or hernia repair, was not explored. Because of the lack of organized databases to measure wait-times for elective non-cancer operations, it was difficult to ascertain the impact of ACCESS on wait-times for these cases. However, surgeons are given the discretion to book elective cases during ACCESS OR time if there are no emergency cases on the board. Most have reported excellent patient satisfaction with the development of “standby lists”, whereby patients who are booked for elective non-cancer surgeries are called into the hospital on the day of their operation. Additionally, as discussed earlier, the recent integration of elective and emergency operating databases, which also include non-cancer operations, may allow for future prospective studies to address this important issue.In conclusion, the reallocation of operating room resources from elective surgical practice towards an ACS service did not appear to affect the timeliness of care provided to patients waiting for elective cancer surgeries, and thus such concerns should not serve as a barrier for centres considering implementing an ACS service.The authors do not have any actual or potential conflicts of interest to declare.RVA, NP, and KL conceived and designed the study. RVA and KV collected the data and performed the statistical analysis. RVA drafted the manuscript. DP helped to draft the manuscript. RVA, DP, KV, SC, NP, and KL provided critical revisions of the manuscript for important intellectual content. All authors read and approved the final manuscript.


The tumor microenvironment contains normal, non-neoplastic cells that may contribute to tumor growth and maintenance. Within PDGF-driven murine gliomas, tumor-associated astrocytes (TAAs) are a large component of the tumor microenvironment. The function of non-neoplastic astrocytes in the glioma microenvironment has not been fully elucidated; moreover, the differences between these astrocytes and normal astrocytes are unknown. We therefore sought to identify genes and pathways that are increased in TAAs relative to normal astrocytes and also to determine whether expression of these genes correlates with glioma behavior.We compared the gene expression profiles of TAAs to normal astrocytes and found the Antigen Presentation Pathway to be significantly increased in TAAs. We then identified a gene signature for glioblastoma (GBM) TAAs and validated the expression of some of those genes within the tumor. We also show that TAAs are derived from the non-tumor, stromal environment, in contrast to the Olig2+ tumor cells that constitute the neoplastic elements in our model. Finally, we validate this GBM TAA signature in patients and show that a TAA-derived gene signature predicts survival specifically in the human proneural subtype of glioma.Our data identifies unique gene expression patterns between populations of TAAs and suggests potential roles for stromal astrocytes within the glioma microenvironment. We show that certain stromal astrocytes in the tumor microenvironment express a GBM-specific gene signature and that the majority of these stromal astrocyte genes can predict survival in the human disease. Gliomas are the most common primary malignant brain tumor in adults Recent advances in characterizing gene expression profiles from patient samples have divided gliomas into four main subgroups  each highlighted by the activation or loss of specific signaling pathways Astrocytes are often identified by their expression of Glial Fibrillary Acidic Protein (GFAP), which increases in response to injury. Astrocytes were originally thought to play a passive role in the brain, but they are now known to play critical roles in supporting the integrity of the blood brain barrier In addition to reactive astrocytes, adult neural stem cells (aNSC) also express GFAP Recent studies have highlighted the plasticity of astrocytes with regard to their functions in injury and as stem cells. In addition to shared markers and ultrastructural characteristics between reactive astrocytes and aNSC, recent work has demonstrated that reactive astrocytes derived from an injury setting possess characteristics specific to aNSC, including the expression of aNSC markers as well as the ability to proliferate and generate neurospheres The role of astrocytes can vary significantly among gliomas. In PDGF-driven gliomas where the majority of transformed cells closely resemble oligodendrocytes, astrocytes appear to play a supportive role in the perivascular stem cell niche and the peri-tumoral (invasive) edge of these tumors. In contrast, the bulk of the tumor cells in astrocytomas are astrocytic in morphology. The biology of transformed astrocytes derived from astrocytomas, including the genetic alterations and gene expression patterns in these cells, has been studied in depth. However, little is known about the TAAs found in PDGF-driven oligodendrogliomas. Considering the growth-promoting and stem cell properties of astrocytic cells in normal physiologic states and brain injuries and the possibility that these properties can be co-opted by the tumor to promote proliferation and progression, we were compelled to study the gene-expression properties of TAAs in PDGF-driven gliomas with the goal of identifying stromal factors that contribute to tumorigenesis.To study TAAs, we used a GFAP-GFP transgenic mouse that expresses GFP under the control of the human GFAP promoter, which allows for isolation of astrocytes using fluorescence activated cell sorting (FACS) from normal and tumor brains. When compared to normal astrocytes, samples enriched for TAAs show markedly increased expression of members of the antigen presentation pathway. Additionally, we identified genes specific to TAAs in GBM and validate their expression. We also demonstrate that TAAs are derived from the stromal microenvironment and that many of the gene expression changes seen in TAAs are specifically present in stroma and not in tumor cells. Further, we demonstrate that many of these differentially expressed genes predict survival in the human proneural subtype of glioma. These data suggest a role for stromal astrocyte-expressed genes in tumor progression and identifies potential therapeutic targets in the treatment of proneural gliomas.Ink4a/Arf heterozygous mice that express the RCAS receptor tv-a under the control of the Nestin promoter. Using this model, Ink4a/Arf heterozygous mice injected with RCAS-PDGF develop tumors of varying grades within four to eight weeks. Upon manifestation of brain tumor symptoms such as weight loss and poor grooming, mice were sacrificed and their brains were fixed and embedded in paraffin for histologic analysis and grading. Using RCAS-PDGF in the Ink4a/Arf heterozygous background allows for the generation of tumors of varying grades within the same litter  and GBM as tumors exhibiting both hypervascularity and pseudo-pallisading necrosis (WHO grade IV). We then used ingenuity pathway analysis (IPA) to query pathways associated with the resulting genes and identified the antigen presentation pathway as the most significantly represented pathway increased in TAAs relative to normal astrocytes . While pIn order to identify stromal genes important for tumorigenesis, we next looked specifically for genes expressed at much higher levels in GBM TAAs when compared to TAAs from low-grade gliomas. Having established that the astrocytes from low-grade glioma and GBM were relatively similar, we hypothesized that there may be a subset of genes differentially expressed between the two populations and that these genes may play a role in tumor progression. In order to identify genes specific to GBM TAAs we plotted gene expression data in TAAs from low-grade gliomas and compared it to that of TAAs from GBMs . Since tBecause astrocytes are an abundant component of the tumor microenvironment, we asked whether TAAs were derived from the RCAS-transduced tumor cell of origin or from the stromal microenvironment. To achieve this goal, we utilized fluorescently labeled RCAS-PDGF vectors combined with immunofluorescence and FACS analysis. We generated gliomas using RCAS-PDGF-GFP in vitro and found that they generated neurospheres under the appropriate conditions  TAAs were also identified as arising only in the stromal compartment of the tumor and not in the Olig2-expressing tumor cells . In factOnce we confirmed the stromal nature of the GBM TAAs signature genes, we were interested in knowing whether these stromal genes play a role in tumorigenesis. Moreover, given the differences in the astrocytic component of proneural gliomas compared to non-proneural tumors, we were interested in exploring the possibility that proneural TAAs play a role in the molecular pathophysiology of proneural glioma. In order to understand whether our GBM TAA signature is active in human tumors, we turned to the TCGA dataset Recent data has identified the presence of a CpG island methylator phenotype (CIMP) within the Proneural subgroup Our understanding of astrocyte function in neurological disease has increased vastly in recent years. Whereas astrocytes were once thought to be post-mitotic, negative regulators of tissue growth, we now know that astrocytes possess proliferative and progenitor cell functions Astrocytes respond to injury by changing their morphology, proliferating and migrating to the site of injury ex vivo and in models of brain injury TAAs are not only structurally similar to injury-response astrocytes but are also similar on a molecular level. Specifically, they are thought to express Sonic Hedgehog In addition to the antigen presentation pathway, we also identified a gene signature that distinguishes GBM-associated astrocytes from those in lower grade gliomas. These genes are mainly expressed in the stromal compartment of the tumor, and are associated with survival in the proneural subtype of human glioma. Two of these markers, CD44 and Tenascin C, which are expressed in the stem cell niches of normal brains To further understand characteristics of peritumoral and perivascular TAAs, we investigated whether these cells were derived from transformed cells, stroma, or both cellular compartments. We expected to find that peritumoral astrocytes were stromal in origin, but were intrigued to learn that perivascular astrocytes found in the center of these tumors were also stromal in origin. While it is tempting to attribute functional roles to these astrocytes that exist at the core of the tumor stem cell niche, more investigation is necessary before such conclusion can be reached.It is not currently known where the stromal-derived perivascular astrocytes originate. Numerous studies have shown that normal neural stem cells demonstrate a tropism towards tumors In addition to identifying stromal genes expressed in TAAs of the PDGF-driven mouse gliomas, we identified that a subset of these stromal genes are prognostic of survival in the human proneural GBMs, where high expression was associated with worse outcome. In fact, almost half of the genes identified were able to predict a difference in survival of at least 1 year. Moreover, the predictive value of these genes is specific to the proneural subtype of glioma, indicating the possibility of a TAA effect within the proneural subtype of human glioma. Interestingly, the predictive value of these genes was lost when the g-CIMP patients were removed. Three of the stromal genes  were methylated at a significantly higher level in g-CIMP tumors compared to non-CIMP tumors Our data highlight the importance of the tumor microenvironment, specifically TAAs, in the proneural subtype of glioma. These TAAs respond to the tumor in the microenvironment and express genes that are associated with overall patient survival. As more is learned about the various pathways driving the neoplastic growth of gliomas, we must also consider the role of the stromal cells within these tumors. If these cells are capable of promoting tumor growth, they may need to be targeted in treatment protocols. Because these cells are not neoplastic, conventional mutagenic treatments may not be effective and thus, the ability to target specific growth-promoting pathways in these cells is imperative. It will be important to identify other genes and pathways active in stromal cells, but the identification of the antigen presentation pathway as well as GBM TAAs signature genes provides us with a starting point in elucidating the functional roles of tumor microenvironment in progression and malignancy.N-tva) transgenic mice were injected with DF1 cells (purchased from ATCC) producing RCAS-PDGFB Ntv-a, Ink4a/Arf null background to generate tumors that were heterozygous for Ink4a/Arf and GFAP-GFP. Olig2-rp-GFP Ntv-a, Ink4a/Arf null background to generate tumors that were heterozygous for Ink4a/Arf and Olig2-rp-GFP. For human orthotopic tumors, 100,000 cells from human tumorsphere cultures Nestin-tv-a  supplemented with 5% proliferation supplement (Stem Cell Technologies), EGF and FGF, as previously described For paraffin embedded samples, tumors were fixed in 10% formalin and embedded in paraffin. Histological grading was determined based on Hematoxylin and Eosin staining and the presence of high-grade structures. DAB staining was performed using the Ventana DAB-MAP system. The following antibodies were used for DAB staining: GFAP , CD44 . Immunofluorescence was performed on 10% formalin fixed, paraffin embedded samples as well as on 4% paraformaldehyde fixed tissues embedded in OCT and frozen. For paraffin embedded tissue: tissues were deparaffinized and rehydrated in graded alcohols. Antigen unmasking was performed at 93° for 15 minutes. Tissues were then permeabilized in 0.3% triton-x/PBS, blocked in 5% serum and stained overnight in primary antibody. The next morning, samples were washed in 0.1% triton-X/PBS and stained in the appropriate alexa-fluor tagged secondary antibody for 2 hours at room temperature. Samples were then counterstained with DAPI and mounted in 70% glycerol or prolong gold (Invitrogen). For frozen samples: tissues were air-dried and then permeabilized, blocked and stained as described above. The following antibodies were used for fluorescent staining: GFAP , CD44 , MHCII , IBA-1 (1∶500 Wako), Nestin  Tenascin C .For analysis and sorting of tumors, tumors were enzymatically digested in EBSS containing 12% papain (Worthington) and 10 mg/ml DNase at 37°C for 15 minutes. The digestion was stopped with 1 mg/ml ovomucoid (Worthington). Cells were washed and resuspended in basal medium and sorted using a Cytomation MoFlo cell sorter. Cells to be used on the array were collected in PBS, centrifuged and resuspended in trizol. Cells that were to be cultured were collected directly into the appropriate media. For analysis and sorting of primary neurospheres, spheres were mechanically dissociated, resuspended in basal media and sorted using a Beckton-Dickinson FACSCalibur flow cytometer. Antibodies used for FACS include CD44-PE , CD44-Pacific blue , APC-conjugated MHC-II , CD45-APC , CD31-APC .Ink4a/Arf heterozygous mice with or without PDGF-driven gliomas. Prior to sorting, a piece of the tumor was removed for histological analysis of grade. Six samples each of Normal, low-grade-associated and GBM-associated astrocytes were collected, pelleted and then resuspended in trizol. Three samples of Olig2-rpGFP cells and three samples of whole tumors were resuspended in trizol. RNA was extracted from normal astrocytes as well as from samples enriched for TAAs and Illumina-6 arrays were run by the genomics core at MSKCC. For the Olig2-rpGFP versus whole tumor array, Illumina-8 arrays were used. Array data was analyzed using Partek analysis software. Data was imported as a text file and was quantile and then log2 normalized prior to analysis. ANOVA statistics were run to determine significantly changed genes. Gene lists were uploaded into Ingenuity Pathway Analysis for analysis of relevant pathways.GFAP-GFP-expressing astrocytes were collected by FACS from http://www.cbioportal.org/cgx/index.do, which has annotated TCGA data. Patient survival data was obtained from published TCGA information Expression values for each gene of interest were obtained from the MSKCC computational biology cancer genomics portal, Array statistics were performed using ANOVA in the Partek analysis software. Comparison of the changes in MHC-II expression in astrocytes and CD44 expression in differentiated and neurosphere conditions, as well as the differences in gene expression between subtypes was performed using a two-tailed t-test in prism software.Figure S1Astrocytes in Glioma. A) Examples of Low-grade Gliomas and GBMs at low- and high-magnification. B) Immunofluorescence of tumors from Lectin-GFP injected mice stained with GFAP (red) demonstrates that astrocytes within the tumor reside close to blood vessels. Scale bars = 10 µm.(TIF)Click here for additional data file.Figure S2Gfap-GFP Reporter Recapitulates Endogenous GFAP Expression. A) Immunofluorescence for GFAP (red) and GFP (green) in the hippocampus of a normal brain from a GFAP-GFP mouse shows colocalization of the GFP reporter with GFAP protein. Scale bars = 50 µm. B) Immunofluorescence for GFAP (red) and GFP (green) in a tumor-bearing brain shows colocalization of the GFP reporter with GFAP protein. Scale bars = 10 µm C) FACS analysis indicates that GFAP-GFP cells do not colocalize with CD31-expressing endothelial cells. GFAP-GFP expression is on the x-axis; CD31 expression is on the y-axis. D) FACS analysis indicates that GFAP-GFP cells do not colocalize with CD45-expressing immune cells. GFAP-GFP expression is on the x-axis; CD45 expression is on the y-axis.(TIF)Click here for additional data file.Figure S3MHC Class II Expression is Increased in Tumor-associated Astrocytes When Compared to Normal Astrocytes. A) Immunofluorescence for MHC II (red), GFAP (green) and Iba-1 (white) in low grade- and GBM-associated astrocytes shows co-localization of MHC II with GFAP-expressing astrocytes. Arrowheads point to MHC II-expressing astrocytes that are Iba-1 negative and thus not immune cells. Scale bars = 10 µm B) FACS analysis of normal and tumor-bearing brains for expression of MHC class II (y-axis) and GFAP-GFP (x-axis). In normal brains, there is very little MHC II expression and virtually no expression on astrocytes. However, in a tumor-bearing brain, MHCII expression is increased in the tumor and specifically within the astrocyte population. C) Quantification of results of FACS analysis: in a normal brain, approximately 1.5% of astrocytes express MHCII and in a tumor brain, approximately 17.5% of astrocytes express MHCII, p = 0.0351.(TIF)Click here for additional data file.Figure S4CD44 Expression in Astrocytes. A) Immunofluorescent staining for CD44 (red) and GFAP (green) in the sub-ventricular zone of a normal brain. Arrows point to CD44-expressing astrocytes near the ventricle (V). In addition to expressing CD44, these astrocytes have a bipolar morphology, which is a hallmark of adult astrocyte-like stem cells (TIF)Click here for additional data file.Figure S5GBM TAAs Genes can Predict Survival in Human Proneural Glioma. Representative Kaplan Meier curves for the GBM TAAs genes in each subtype as well as G-CIMP only tumors. Blue line represents survival of patients with expression below the median for each gene in each subtype and black line represent survival for patients with expression above the median for each gene in each subtype, * = p<0.05, ** = p<0.01, *** = p<0.001. Survival is represented in months to death after diagnosis.(TIF)Click here for additional data file.Figure S6G-CIMP Patients Have Lower Expression of GBM TAAs Genes. Plot of average expression of the GBM TAAs signature genes in each subtype and in proneural G-CIMP and non-G-CIMP patients. The average expression for all GBM TAAs genes was lowest in the proneural subtype and lower in proneural G-CIMP patients when compared to proneural non-G-CIMP patients. * = p<0.05, ** = p<0.01, *** = p<0.001.(TIF)Click here for additional data file.Table S1MHC Class II Pathway is increased in Tumor-associated Astrocytes Relative to Normal Astrocytes. List of MHC Class II pathway genes that are increased in TAAs compared to normal astrocytes. Values listed as fold change in expression when TAAs were compared to normal astrocytes.(TXT)Click here for additional data file.


To examine the associations between area-level socioeconomic attributes and stage of esophageal adenocarcinoma diagnoses in 16 SEER cancer registries during 2000-2007.Odds ratios (OR) and 95% confidence intervals (CI) were calculated using multivariable logistic regression models to assess the relationship between distant-stage esophageal adenocarcinoma and individual, census tract, and county-level attributes.Among cases with data on birthplace, no significant association was seen between reported birth within versus outside the United States and distant-stage cancer . Living in an area with a higher percentage of residents born outside the United States than the national average was associated with distant-stage esophageal adenocarcinoma; census tract level: >11.8%, , county level: >11.8%, . No association was observed between median household income and distant-stage cancer at either census tract or county levels.The finding of greater odds of distant-stage esophageal adenocarcinoma among cases residing in SEER areas with higher proportion of non-U.S. Natives suggests local areas where esophageal cancer control efforts might be focused. Missing data at the individual level was a limitation of the present study. Furthermore, inconsistent associations with foreign birth at individual- versus area-levels cautions against using area-level attributes as proxies for case attributes. In the United States, it is estimated that 14,440 men and 3,550 women will be diagnosed with and 15,210 people will die of cancer of the esophagus in 2013 [Because the esophagus is located near other vital organs, lesions are most treatable when detected as the premalignant condition of Barrett’s esophagus  or as eaLow socioeconomic status may be a risk factor for esophageal adenocarcinoma ,8. SpeciSocial support may encourage lifestyle and screening behaviors that reduce the risk of esophageal adenocarcinoma . PositivThe NCI IRB and the IRB of the contributing SEER registries have granted a waiver from HIPAA requirements because the SEER data used in this analysis have been de-identified, protecting against disclosure of personal information. The study was restricted to incident cases from 16 Surveillance, Epidemiology and End Results Registries that were diagnosed between the years 2000 and 2007. The Registries included eight states  and eight areas , which together represent approximately 20 percent of the United States . Census Cancer site and histology were defined by the International Classification of Diseases for Oncology (ICD-O) (3rd edition) codes: esophagus (C15.0-C15.9) and adenocarcinoma (8140-8573). These criteria identified 14,264 esophageal adenocarcinoma cases in SEER registries during the surveillance period.Of 14,264 reported esophageal adenocarcinoma cases, 11,233 had a single primary tumor. Only cases with a single primary tumor were included in this study. Of these, 1,134 cases were excluded due to missing stage. The resulting analytic dataset was comprised of 10,099 cases with localized-, regional-, or distant-stage esophageal adenocarcinoma at initial diagnosis; 71% of all esophageal adenocarcinoma cases in the SEER dataset.The analysis linked esophageal adenocarcinoma cases to census tract- and county-level data regarding the percent of the population born outside the United States. Areas were stratified into groups with less than or equal to 11.8% foreign-born population: the estimated national mean percent foreign-born population in 2002  with senThe overall distribution of esophageal adenocarcinoma cases by individual and area-level attributes was explored, as well as by stage at diagnosis. Logistic regression models were developed to estimate odds ratios and 95% confidence intervals for associations between individual and area-level attributes and distant-stage esophageal adenocarcinoma diagnosis. Crude and adjusted models accounted for age group , year of diagnosis (2000-2007), sex, and race . Statistical analyses were conducted using SAS .Individual case-level demographic data for esophageal adenocarcinoma cases are presented by stage at diagnosis . The peaAt both the census tract and county level, the majority of cases lived in areas with equal or lower percent foreign-born population than the U.S. average in 2002, 11.8% . Nearly Cases 70 years of age and older were less likely than younger cases to be diagnosed with distant-stage esophageal adenocarcinoma (adjusted odds ratio (OR)=0.65, 95% confidence interval (95% CI) from 0.60 to 0.71), . In addiAt the census tract level, cases living in areas with a greater proportion of foreign-born population than the U.S. average (11.8%) had higher odds of distant versus localized-stage esophageal adenocarcinoma compared with cases living in census tracts with a lower proportion of foreign-born residents (adjusted odds ratio (OR)=1.10, 95% CI from 1.01 to 1.19). The association was statistically significant for cases residing in the highest quartile areas of foreign-born population (data not shown). Median household income was not associated with stage at diagnosis at the census tract level . At the county level, cases living in areas with greater than 11.8% of the population born outside the United States also had statistically significant higher odds ratios of distant-stage esophageal adenocarcinoma compared to cases living in counties with a lower proportion of foreign-born residents . The association was statistically significant for the highest two quartiles of area foreign-born population (data not shown). Median household income was not associated with stage at diagnosis at the county level . The association between distant-stage EAC and socioeconomic attributes persisted when compared separately with localized then regional stage cases (data not shown), however, the effect was most clearly illustrated in the final model . In a subanalysis including cases with multiple primary tumors, crude and adjusted models for all socioeconomic attributes remained essentially unchanged (data not shown). Finally, in analyses restricted to cases born outside the United States, area-level associations with foreign born populations were no longer statistically significant (data not shown).In this study, no association was seen between reported birth inside the United States versus outside the country and distant-stage esophageal cancer. While the majority of cases were born in the United States, at both the census tract and county level, cases that lived in areas with a high percentage of non-U.S. born residents had significantly higher odds of distant-stage esophageal adenocarcinoma. This finding has potential implications for cancer control because screening to detect early-stage cancer may improve prognosis . The finAt the individual level, the risk of developing esophageal adenocarcinoma is associated with limited social support and low socioeconomic status ,8. We weKeegan and colleagues hypothesized that the surplus of distant-stage cancer in enclave communities was a function of limited access to screening . Low incStrengths of the study included the large sample size and linkage to area-level attributes. There was, however, limited availability of individual-level socioeconomic data, and nearly 30% of cases were missing data regarding place of birth. A large proportion of cases with missing place of birth had localized/regional stage of disease, were younger than 70 years of age, and were diagnosed in more recent years. We suspect that place of birth is most complete among cases with worse outcomes because death certificates are a common source of this information. Nonetheless, stratified analyses in registries with the most complete data on nativity supported the null finding. A limited amount of demographic data is typically collected in cancer registries. To overcome this limitation of cancer surveillance, area-level data are sometimes used as proxies for individual data. This practice is based on the assumption that people residing in a common area often share similar socioeconomic attributes. As our findings suggest, caution is warranted against this practice, because area-level measures can operate differently from individual-level measures. First, attributes of residents can differ from prevailing attributes. In addition, while area-level socioeconomic status affects social support and quality of care within a community , it occuIn summary, while residing in an area with a high percent of non-U.S. born individuals was associated with distant-stage esophageal adenocarcinoma, no such association was evident among cases with individual level data on place of birth. The findings suggest a need for caution regarding the use of area-level measures as proxies for individual level attributes. The findings of this study could also help target resources toward specific neighborhoods and individuals at risk for late-stage esophageal adenocarcinoma.


P < 0.05; family-wise error-corrected), mainly in the uncinate, cingulum and corpus callosum, whereas responders were indistinguishable from healthy control subjects. After 12 weeks, there was an increase in fractional anisotropy in both responders and non-responders, positively correlated with antipsychotic exposure. This represents one of the largest, controlled investigations of white matter integrity and response to antipsychotic treatment early in psychosis. These data, together with earlier findings on cortical grey matter, suggest that grey and white matter integrity at the start of treatment is an important moderator of response to antipsychotics. These findings can inform patient stratification to anticipate care needs, and raise the possibility that antipsychotics may restore white matter integrity as part of the therapeutic response.The integrity of brain white matter connections is central to a patient’s ability to respond to pharmacological interventions. This study tested this hypothesis using a specific measure of white matter integrity, and examining its relationship to treatment response using a prospective design in patients within their first episode of psychosis. Diffusion tensor imaging data were acquired in 63 patients with first episode psychosis and 52 healthy control subjects (baseline). Response was assessed after 12 weeks and patients were classified as responders or non-responders according to treatment outcome. At this second time-point, they also underwent a second diffusion tensor imaging scan. Tract-based spatial statistics were used to assess fractional anisotropy as a marker of white matter integrity. At baseline, non-responders showed lower fractional anisotropy than both responders and healthy control subjects ( Response to treatment in first episode of psychosis is heterogeneous, with approximately 55% of patients responding to antipsychotics in the first 12 months , attentin = 8), as assessed 28 days after the brain imaging, had reduced white matter microstructural integrity compared with non-responders (n = 5), who were in turn no different from control subjects (n = 26) had lower frontal and parietal fractional anisotropy compared with responders (n = 23)  of patients with first episode psychosis, at baseline and again at 12 weeks follow-up. To our knowledge, this is the first longitudinal diffusion tensor imaging study in first episode psychosis. To evaluate the relationship between white matter integrity and treatment response, we used a whole-brain skeleton-based technique  . Body coil was used for radiofrequency transmission, with 8-channel head coil for signal reception, allowing a parallel imaging (ASSET) speed-up factor of two. Each volume was acquired using a multi-slice peripherally-gated doubly refocused spin echo-echo planar imaging sequence, optimized for precise measurement of the diffusion tensor in parenchyma, from 60 contiguous near-axial slice locations with isotropic (2.4 × 2.4 × 2.4 mm) voxels. Echo time was 104.5 ms and repetition time varied between 12–20 R–R intervals. Maximum diffusion-weighting was 1300 s/mmhttp://www.fmrib.ox.ac.uk/fsl/tbss/)  (l/tbss/)  to compaTo investigate whether differences in white matter microstructural integrity were related to changes in white matter volume, a tractography analysis of the tracts identified as most affected in the whole-brain comparisons between responders and non-responders was performed using TrackVis, with placement of regions of interest described elsewhere . For eacP-values were corrected for multiple comparisons . Age, gender and handedness were de-meaned before analysis and used as covariates of no-interest within the voxel-based analysis. Because of differences in the duration of untreated psychosis between groups, duration of untreated psychosis was calculated and added as a covariate after logarithmic transformation. To localize significant voxel effects, contrast maps were subdivided according to the 48 regions of the JHU-ICBM-DTI-81 white matter atlas. This allowed quantification of the number of significant voxels within each regional mask, and spatial identification of the peak voxel. We then extracted the individual’s mean fractional anisotropy value for clusters identified as significantly different between responders and non-responders at baseline, and for the same clusters at follow-up, to quantify progressive fractional anisotropy change in these regions. For these clusters we also estimated the values of other white matter diffusion tensor imaging parameters: mean diffusivity ; axial diffusivity ; and perpendicular diffusivity, suggested to be a marker of reduction in the myelin content, hence representing an index of axonal demyelination) . Longitudinal changes in fractional anisotropy were assessed by subtracting the follow-up skeletonized fractional anisotropy image from each individual’s baseline image. Statistical comparisons used the threshold-free cluster enhancement (TFCE), using a non-parametric permutation test, in which group membership was permuted 5000 times to generate a null distribution for each contrast. Significant clusters were identified where voxel-wise ination) .Socio-demographic and clinical characteristics were analysed using one-way ANOVA for continuous variables, Mann-Whitney for skewed distributions and chi-square for categorical variables. Repeated measures ANOVA were used for the analysis of extracted fractional anisotropy values and tractography volumes. Analyses were conducted using SPSS v.18.0.A total of 63 patients and 52 healthy control subjects underwent an MRI scan at baseline, and their demographic and clinical characteristics are presented in P < 0.05; FWE-corrected), with the most affected tracts including corpus callosum, superior longitudinal fasciculus, corona radiata and left cingulum and thalamic radiation (Supplementary Table 1).We first compared the white matter maps of the whole first episode psychosis patient group with the maps of the healthy controls at baseline. This comparison revealed areas of reduced fractional anisotropy values across the brain in first episode psychotic patients   A. The morrected) . We re-rP < 0.05; FWE-corrected). The most affected tracts included associative tracts, such as the left uncinate, cingulum and superior longitudinal fasciculus, and commissural tracts such as the corpus callosum  There were no white matter regions where controls had significantly lower fractional anisotropy values than non-responders.Similarly, non-responders also had a significantly lower fractional anisotropy across multiple white matter regions when compared to healthy controls  .We compared cross-sectionally the white matter microstructural integrity at follow-up, in those 42 subjects with two usable scans: 20 responders and 22 non-responders. Non-responders continued to display lower fractional anisotropy compared with responders, mostly in the same brain tracts where they differed at baseline, including the uncinate, corona radiata, fornix, external and internal capsule and corpus callosum, although the differences appeared less widespread (rrected) . In addiF = 16.7; P < 0.001]. Interestingly, there was no Group (responders versus non-responders) × Time (baseline versus follow-up) interaction for these fractional anisotropy changes , suggesting that changes were broadly in the same direction in both groups.We then evaluated longitudinal within-group changes over the 12 weeks. A whole-brain analysis of within-group, and Group × Time interaction for fractional anisotropy change did not identify any significant difference between responders and non-responders. We then explored whether the regions identified at baseline as being significantly different between responders and non-responders at baseline had changed over time, and we found an overall increase in fractional anisotropy over the 12-weeks . There was also no Group × Time interaction for white matter tracts volume (P = 0.44 to P = 0.78) and no significant correlations were observed between longitudinal changes in volumes and longitudinal changes in fractional anisotropy or exposure to antipsychotics.To explore if changes in white matter microstructural integrity were driven by volumetric changes, we explored whether there had been longitudinal changes in the volume of the most affected tracts. However, there was no significant longitudinal change in volume in any of the tracts analysed . In contrast, there were no differences in axial diffusivity . Furthermore, we found an overall increase in axial diffusivity  and a decrease in mean diffusivity and perpendicular diffusivity .We further explored whether the fractional anisotropy differences we observed between responders and non-responders were accompanied by a change in other diffusion tensor imaging measures in these brain regions. Results showed that at baseline responders had significant lower mean and perpendicular diffusivity than non-responders in the white matter regions that differentiated these two groups [P = 0.02], indicating that the higher the exposure to antipsychotic medication over the 12-week period, the larger the increase in fractional anisotropy   within these brain regions was positively correlated with the cumulative antipsychotic dose at follow-up , as well as between PANSS total scores and follow-up fractional anisotropy values in these regions . However, there was no correlation between change in psychotic symptoms and change in fractional anisotropy.Finally, in the regions that discriminated responders from non-responders at baseline, a significant negative correlation was observed between fractional anisotropy and baseline PANSS total score [r(63) = −0.273, To our knowledge, this is the first longitudinal study investigating white matter microstructural changes and treatment response in first episode psychosis. In this large sample, we found that non-responders already have significantly impaired white matter microstructure at baseline, when compared with both healthy control subjects and future responders. In contrast, responders are not distinguishable from controls. Furthermore, fractional anisotropy differences between responders and non-responders remain present, albeit to a lower extent, even at follow-up. Interestingly, both patient groups show an increase in white matter microstructural integrity over time in these regions, which seems associated with the extent of exposure to antipsychotics.Our main finding is that at the time of the first episode of illness, subsequent non-responders already show altered white matter microstructural integrity than responders. So far, results from the few existing studies assessing white matter microstructural integrity and clinical outcome have been inconsistent, possibly because of small samples, different follow-up periods and longer duration of illness. However, consistent with our findings, a cross-sectional tractography study showed that first episode patients who do not respond to 6 months of treatment have lower fractional anisotropy than responders in the superior longitudinal fasciculus and the uncinate  Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. The study was also partially supported by a King’s College London Translational Research Grant to P. Dazzan. P. Dazzan’s research is also supported by the Psychiatry Research Trust. Tiago Reis Marques and P. Dazzan’s research are supported by NARSAD. A.A.T. Simone Reinders is supported by the Netherlands Organization for Scientific Research (NWO-VENI grant no. 451-07-009).This work was supported by the Supplementary material is available at Brain online.


The performance of immunoassays for the detection of autoantibodies is of critical importance in the diagnosis and assessment of patients with autoimmune connective tissue diseases (ACTD). Our objective was to compare the features of two multiplexed assays—INNO-LIA ANA and Gennova-PictArray ENA ELISA—for measurement of multiple autoantibodies and their utility as a clinical tool in ACTD diagnosis. The antigens included SS-A/Ro (60 and 52), SSB/La, Sm, Sm/RNP, CENP-B, Jo-1, and Scl-70. Stored sera from 85 ACTD patients and 80 controls consisting of patients with vasculitis, rheumatoid arthritis and infectious diseases, as well as healthy subjects were analyzed jointly with clinical and laboratory data. Agreement between the two methods varied between 58 and 99% (Cohen's kappa: 0.21–0.71) mostly for SSA and SSB. The frequency of specific autoantibodies measured using the two methods was more variable for SSA, SSB, and RNP/Sm. There were a higher number of ambiguous results when using INNO-LIA. The optimized cut-off values of the Gennova-PictArray resulted in over 99% specificities in samples obtained from the control group. Sensitivity patterns were more accurate in Gennova-PictArray than in INNO-LIA, as suggested in previously reported studies. A third method could be applied to determine which of the two methods is more accurate. The detection of antinuclear antibodies (ANA) has long been an important tool in the early diagnosis of autoimmune connective tissue diseases . The antAutoantibodies play a role as biomarkers assisting in diagnosis and monitoring of disease activity, predicting disease onset, classifying disease subsets, and defining prognosis . Their dThe study included 85 serum samples submitted to our reference laboratory for autoimmune testing from patients with ACTD. Patients were recruited through input clinical diagnosis according to criteria of SLE diagnosis issued in 1997 by American College of Rheumatology . SamplesThe study also included 80 serum samples from a control group to determine the clinical specificity of both tests. The control group consisted of samples from patients with vasculitis, rheumatoid arthritis, infectious diseases, and healthy subjects. Vasculitis is an accompanying or atypical symptom for a number of rheumatic diseases such as rheumatoid arthritis, systemic lupus erythematosus, and dermatomyositis. Infectious diseases are characterized by a high titre of serum antibodies which may result in false positive results in tests measuring serum antibodies to autoantigens.The most commonly measured ENA specificities are anti-SS-A/Ro (60 and 52), anti-SSB/La, anti-Sm, anti-Sm/RNP, anti-CENP-B, anti-Jo-1, and anti-Scl-70 [INNO-LIA  is a qualitative line blot for identifying the reactivity of serum antibodies to specific antigens coated as discrete lines on a nylon membrane with a plastic backing. Briefly, the strips are incubated in buffer containing blocking protein, followed by incubation with prediluted serum samples. Autoantibodies present in the sample bind to specific antigens deposited on the membrane, which are identified by alkaline phosphatase conjugated anti-human-IgG antibodies showing blue stained bands on the strips. The results are interpreted by comparing the intensity of the reaction with control lines and measured by image analysis. The strips can be scanned and analyzed using software . LIA strμL antigen spots along with a number of control spots included to ensure that the test has been performed correctly. The spots are deposited at the bottom of sixteen specially designed nanowell slides containing a protein binding membrane. The array layout has been shown in Microarrays are prepared by depositing 300 Diluted serum samples were added to the wells and after incubating at 37°C, the nonbound material was washed followed by sequential incubation with biotin-conjugated anti-human IgG and horseradish peroxidase-conjugated Streptavidin. The amount of IgG bound to antigens was measured by adding diaminobenzidine (DAB) to each well. The peroxidase reaction is stopped by a single wash to remove excess substrate and the wells dried. The colour intensity is proportional to the amount of autoantigens present in the original serum sample. The intensity of spots images was analysed by Pictorial Software which was developed to analyze spot intensities for Gennova-PictArrays.The method offers the advantages of enabling automated sample processing using off-the-shelf ELISA processors, consistent performance, cost-effectiveness, and precision in measurement of antibody levels.For INNO-LIA results, are reported as negative (<67% of control line), equivocal (between 67% and 100% of control line), or positive (>100% of control line) according to the manufacturer's recommendations.Gennova-PictArray results were quantified and reported as IU/mL. This unit reports the fold difference in signal for the patient sample as compared to samples that are negative for the antibodies. The units are interpreted with the following ranges: negative (0–0.8 IU/mL), weak positive (0.9–1.3 IU/mL) or ambiguous result, positive (1.4–25 IU/mL), and strong positive (>25.1 IU/mL).The analysis was performed using SPSS statistics, version 19.0 . Agreement between the INNO-LIA and Gennova-PictArrays was assessed using Kappa coefficient as previously described . A recomThe frequency of autoantibodies detected by the two immunoassays is summarized in There were a higher number of ambiguous results when using INNO-LIA . Of all As seen in The agreement between the two assays for autoantibody detection is summarized in The estimated Kappa coefficient for specific agreement between the different assays was “fair” for anti-RNP/Sm, anti-Ro60, and anti-Sm Antigen; “substantial” for anti-Ro52, anti-SSB, anti-Scl70, and anti-CENP-B; and “undefined” for anti-Jo1, due to the high proportion of negative results with INNO-LIA and Gennova-PictArrays.Quantification of autoantibodies is essential for the diagnosis of autoimmune diseases such as ACTD. Clinicians can use these results as guidance for classifying patients and/or for assessing their response to specific therapies, especially in cases where development of targeted biological therapies is possible –14.There are many factors involved in the measurement of autoantibodies. Patients with ACTD have a high concentration of serum antibodies to autoantigens. New assays need to be developed for measuring newly reported biomarkers for the diagnosis and pathogenesis of this disease –17. BiomRecent technologies have provided a new approach for autoantibody quantification based on multiplex testing which represents an advantage compared to earlier methods such as line blot assays and conventional one well-one test microtiter ELISA , 19. TheμL of a 200-fold diluted sample and follows the same steps as a traditional ELISA assay. Together with low setup costs and a fast sample processing time, Gennova-PictArrays provide an affordable alternative to currently available multiplex testing systems. These cost-saving features enable small laboratories with limited budgets to use this technology without a large capital outlay and training of laboratory technicians. Gennova-PictArrays have demonstrated an excellent analytical specificity and sensitivity when compared with established ELISA assays [Gennova-PictArrays enable the testing of eight antigens in parallel from individual serum samples with high specificity (99.8%). This method provides significant savings in time, avoiding the conventional first screening and post-confirmation algorithm currently being used in most clinical testing laboratories for ACTD sample testing. Gennova-PictArrays require only 50 The lack of a substantial agreement between techniques and the differences in sensitivities for detection of autoantibodies was relevant in SSA and SSB, the most frequent biomarkers in ACTD according to other studies . This hiAmbiguous results from a diagnostic test leads to uncertain diagnosis and are generally disregarded by clinicians in making treatment decisions. The decrease in the number of ambiguous results by Gennova-PictArray as compared to INNO-LIA assay (36.5% versus 48.2%) may help clinicians make better clinical decisions when using Gennova-PictArray technology.The results of this study indicate that microarray assays are superior to line immunoassays since selection of assay methodology by a laboratory depends upon several factors such as differences in protocol time, simplicity of use, and cost. It is important to note that clinical service laboratories select tests which can provide results with reliability and confidence to assure correct results, avoiding platforms which require a second test to confirm results .For future analysis of microarray technique, longitudinal cohort studies may be performed which provide new information about ACTD. To determine which method is more accurate, a third method or “positive” reference samples from patients with confirmed Sjogren Syndrome should be tested. This was an exploratory study to assess the use of microarrays in autoimmune diagnostic testing from a clinical angle.In conclusion, the optimized cut-off values of the Gennova-PictArray resulted in high specificities in samples obtained from healthy donors and other controls (average of 99.8%). Sensitivity patterns were more accurate in Gennova-PictArray than in the line immunoassay, in accordance with the high degree of variability found in other studies. A poor agreement between techniques was observed. The most significant discordance (58–88%) occurred with autoantibodies to SSA and SSB which are the most prevalent specificities identified in ENA profiling and most frequently detected in patients with ACTD . Prospec


This study seeks to determine if implementing a culturally-appropriate early childhood caries (ECC) intervention reduces dental disease burden and oral health inequalities among Indigenous children living in South Australia, Australia.This paper describes the study protocol for a randomised controlled trial conducted among Indigenous children living in South Australia with an anticipated sample of 400. The ECC intervention consists of four components: (1) provision of dental care; (2) fluoride varnish application to the teeth of children; (3) motivational interviewing and (4) anticipatory guidance. Participants are randomly assigned to two intervention groups, immediate (n = 200) or delayed (n = 200). Provision of dental care (1) occurs during pregnancy in the immediate intervention group or when children are 24-months in the delayed intervention group. Interventions (2), (3) and (4) occur when children are 6-, 12- and 18-months in the immediate intervention group or 24-, 30- and 36-months in the delayed intervention group. Hence, all participants receive the ECC intervention, though it is delayed 24 months for participants who are randomised to the control-delayed arm. In both groups, self-reported data will be collected at baseline (pregnancy) and when children are 24- and 36-months; and child clinical oral health status will be determined during standardised examinations conducted at 24- and 36-months by two calibrated dental professionals.Expected outcomes will address whether exposure to a culturally-appropriate ECC intervention is effective in reducing dental disease burden and oral health inequalities among Indigenous children living in South Australia. Early childhood caries is the presence of one or more decayed, missing, or filled primary teeth in children aged 71 months 5 years) or younger . Early c years orIndigenous Australians are disadvantaged on almost every health and social indicator compared to non-Indigenous Australians . Marked The intervention undertaken in this study is comprised of four approaches reported to successfully prevent early childhood caries: dental care provided during pregnancy ,11; fluoThis study seeks to determine if implementing a culturally-appropriate early childhood caries (ECC) intervention reduces dental disease burden and oral health inequalities among Indigenous children living in South Australia, Australia. We hypothesize that exposure to a culturally-appropriate ECC intervention will reduce dental disease burden among Indigenous children and reduce oral health inequalities between such children with their non-Indigenous counterparts. The South Australian study described in this paper is part of a larger investigation including research teams in New Zealand and Canada; we will compare findings when they become available.In this randomised, controlled trial participants are offered four intervention components: 1) provision of dental care; (2) fluoride varnish application to the teeth of children; (3) motivational interviewing and (4) anticipatory guidance. A schema outlining the study design is presented in Figure  provisio1. Provision of dental careIn the test-immediate intervention group, dental care is arranged during pregnancy or up to six weeks after birth. For participants in the control-delayed intervention group, dental care is arranged when their child is 24 months of age. Care will comprise of extractions, restorations, scaling and prophylaxis and will take as many visits as required to achieve a non-diseased mouth. Participants holding an Australian healthcare or pensioner concession card will be eligible for treatment through the South Australian Dental Service; participants who do not hold this entitlement will receive treatment from private dentists limited to $1265 AUD. Dentists are asked to focus on reducing bacterial load, such as filling or extracting teeth with frank carious lesions. Study staff remind participants of dental appointments by telephoning, sending text messages via mobile phone and sending letters, and attempt to mitigate obstacles such as dental fear and lack of childcare by attending appointments when helpful. Free transport to and from the dentist is also available through culturally-sensitive transport providers.2. Fluoride varnish applicationsFluoride varnish is applied to children by trained study staff in a setting convenient to participants, often their homes. In the test-immediate intervention group, this occurs when children are aged 6-, 12- and 18-months. Children in the control-delayed intervention group receive fluoride varnish applications at 24-, 30- and 36 months. Given the obstacles in meeting participants, these age targets are extended by one month prior and three months following target age, e.g. the six-month meeting can occur anywhere from five to eight months of age.3. Motivational interviewingParticipants in the test-immediate intervention group receive four motivational interviewing (MI) sessions by trained study staff, with each session focussing on a specific directive. For participants in the test-immediate intervention group, the MI sessions occur at baseline (pregnancy) and when the child is aged 6-, 12- and 18-months. The directives for each of these sessions are: (1) Baseline/pregnancy; encourage participants to attend dental appointments; (2) Child aged 6-months; encourage participants to learn more about the importance of non-cariogenic foods and drinks for their child; (3) Child aged 12-months; emphasise to participants the importance of fluoride in early childhood caries prevention; and 4) Child aged 18-months; encourage participants to enrol their child for a dental check. Participants in the control-delayed intervention group receive three motivational interviews when the child is aged 24-, 30-, and 36 months, with the first session combining directives one and two.4. Anticipatory guidanceAnticipatory guidance (AG) for those in the test-immediate intervention group will occur during pregnancy and when the child is aged 6-, 12-, and 18-months. Participants in the control-delayed-intervention group will receive anticipatory guidance when the child is aged at 24-, 30-, and 36 months. The anticipatory guidance topics reflect and compliment the directives of the MI sessions.Participants must be pregnant residents of South Australia expecting an Indigenous Australian baby or babies, or who have recently birthed an Indigenous Australian baby or babies less than six weeks of age. Although interventions are timed differently , all participants are eligible to receive free dental care including transportation, fluoride varnish for children’s teeth, dental-related sample bags as well as gift cards valued at fifty dollars as reimbursement for time spent completing each questionnaire at baseline, 24- and 36- months. Participants are recruited through referrals from a variety of sources including Indigenous groups, community services and hospitals. Recruitment commenced January 31, 2011 and will end May 4, 2012.This study received approval from the University of Adelaide Human Research Ethics Committee, the Aboriginal Health Council of South Australia, the Government of South Australia, the Human Research Ethics Committee of Child, Youth, and Women’s Health Service, and the Human Research Ethics Committees of participating Adelaide hospitals . Researchers receive guidance from an Indigenous reference group and heed the World Health Organisation principles that health research among Indigenous populations needs to be designed and carried out in a manner that takes account of cultural differences based on mutual respect, and is beneficial to both parties . The stuPotential participants are provided information about the study face-to-face and/or over the phone from health service providers and study staff. If potential participants consent to meeting, researchers explain the project in detail, answering any questions which arise. Those who want to participate are then asked to complete and sign a form expressing consent for various elements of the study including participation, access to child’s dental records, access to child’s hospital records and audio recording for the purposes of assessing researchers’ proficiency in motivational interviewing. Consent is obtained using the NHMRC Guidelines for Ethical Conduct in Aboriginal and Torres Strait Islander Health Research . All parFour staff have been employed for the Australian arm of the study; one Indigenous and three non-Indigenous.The Australian arm of this international collaborative grant is provided by the National Health and Medical Research Council of Australia (NHMRC ICIHRP Grant application ID # 627350).Clinical and self-reported data collected during interventions are entered into password-protected Microsoft Excel and Access databases at the Australian Research Centre for Population Oral Health, University of Adelaide.Based on a recent ECC intervention study of Australian Northern Territory-dwelling Indigenous children, it was estimated that a sample size of 250  would be necessary to detect a 25 percent difference in ECC prevalence between the two groups, at the significance criterion of 0.05 and a power of 0.80 . The litParticipants are randomly assigned on a 1:1 basis to either a test-immediate or control-delayed intervention group. A computer-generated permuted block randomisation sequence is used, stratified by six recruitment sites, i.e. Women’s and Children’s hospital, Flinders Medical Centre and Southern metropolitan, Lyell McEwin and Northern metropolitan, Whyalla and Port Pirie, Port Augusta, and non-metro regions including Murray Bridge, Mount Gambier, Port Lincoln and Ceduna. The randomisation was developed by biostatisticians at the Australian Research Centre for Population Oral Health (ARCPOH) using a random number generator. Randomly selected block sizes of 4, 6 and 8 were used, such that there is an equal number of participants in each intervention arm within the blocks. This ensures that if the study is stopped at any particular time there will be approximately an equal number of participants in each intervention arm.Self-report information in the form of a questionnaire will be administered by staff at baseline (pregnancy) and when children are aged 24- and 36-months. The questionnaire was pilot-tested using focus groups comprised of members of participating Indigenous communities and Aboriginal Maternal Infant Care workers, with the questions revisited and refined during Year 1 of the research. Questionnaire items were modelled on those used in both Australian  and inteCovariates include socio-demography, oral health and general health. Socio-demographic covariates (collected at baseline only) include age, Indigenous affiliation, residential location, education level, employment, gross household income, house ownership, number of children, number of people who stayed in house the previous night, and car ownership. Oral health covariates include carer self-reported oral health, oral health-related quality of life, and history and experience of dental services of both the parent and child. General health covariates include parent behaviours such as tobacco smoking, alcohol consumption and self-reported general health.Information about child clinical oral health status will be collected during standardised examinations conducted at the 24- and 36-months follow-ups by two calibrated dental professionals. Before examination, a new toothbrush will be used to clean the teeth, and the teeth dried with gauze. Examiners will follow a standardised protocol. Procedures appropriate for young children will be used during the examinations, for example, children examined in the ‘knee-to-knee’ position on their carers’ lap. Standard infection control procedures will be followed with a fibre-optic light used as a light source. No sharp instruments will be used during examinations. The status of all teeth and tooth surfaces will be examined and recorded. Non-cavitated and cavitated lesions will be recorded for all teeth. The study will focus on the earliest manifestation of ECC. Two ECC case definitions will be used: (1) one or more upper incisor teeth labial surfaces being carious, either non-cavitated or cavitated and; (2) one or more non-cavitated or cavitated carious, missing of filled surfaces. Lesions will be diagnosed as ‘non-cavitated’ if an area of demineralization is without loss of surface continuity detected visually. Lesions will be diagnosed as ‘cavitated’ if a loss of continuity of the enamel is detected [Test-immediate and control-delayed intervention groups will be compared at baseline, 24- and 36- months. Results will be compared using the Generalized Linear Mixed Model (GLMM) approach to focus on variation in the primary outcome: early childhood caries levels. All participants who completed the 24-month follow-up ECC-related outcome data will be included in multivariate modelling. Analysis will include fitting GLMM models for analysis of the correlated data in order to determine factors associated with ECC-related changes. Missing data will be addressed by the GLMM estimating mechanism, which assumes interdependence between repeated measures of participants, copes well with random missing data, allows for different intercepts and rates of change, and provides good predictive models. All variables showing associations at p < 0.15 in the GLMM univariate analyses will be entered into multivariate models using a stepwise approach. All effects will be estimated with 95% confidence intervals, with statistical significance taken as a two-sided p-value less than 0.05. This data will contribute to the inter-country analysis.This study could provide a foundation for preventive dental care and oral health education among Indigenous children, reducing oral disease in childhood. The unique approach of combining four interventions of known efficacy in preventing or reducing early childhood caries will potentially enable greater benefits. The control-delayed intervention design satisfies many of the ethical concerns raised, in Australia at least, when conducting traditional randomised controlled trials among Indigenous populations  ,29.This study is timely, as most countries recognize the oral health of Indigenous children to be an important yet often over-looked area of both research and policy. Because each country participating in the study  has unique Indigenous population groups in terms of history and geographic isolation, our data will enable country, community and individual-level comparisons. The governments of Australia, New Zealand and Canada recognize Indigenous child oral health to be a priority in their respective national oral health plans; and Indigenous groups in these nations emphasise that children’s oral health is of key importance. Developing culturally-appropriate interventions which utilise Indigenous frameworks and research methodologies may provide evidence which leads to more appropriate oral health initiatives for Indigenous children, reducing global oral health inequalities.AG, Anticipatory guidance; dmft/s, Decayed, missing or filled teeth/surfaces in the primary dentition; ECC, Early childhood caries; GLMM, Generalized linear mixed model; MI, Motivational interviewing; MS, Mutans streptococci; NHMRC, National Health and Medical Research Council of Australia.The authors declare that they have no competing interests.JM participated in data collection, data management and manuscript preparation. AC, EP, KRT, GM, AJS, JB and HL provided important intellectual input into the study design and revision of the manuscript. LJ participated in study design, ethics applications, coordinated data collection and data management, and participated in manuscript preparation. All authors were involved in revising the manuscript for important intellectual content and read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2458/12/323/prepub