BCAR4 Expression as a Predictive Biomarker for Endocrine Therapy Resistance in Breast Cancer.

BACKGROUND: Breast cancer, particularly the estrogen receptor positive (ER+) subtype, remains a leading cause of cancer-related death among women. Endocrine therapy is the most effective treatment for ER+ breast cancer; however, the development of resistance presents a significant challenge. This study explored the role of the breast cancer antiestrogen resistance 4 (BCAR4) gene as a potential driver of resistance and a pivotal biomarker in breast cancer. PATIENTS AND METHODS: The researchers undertook a comprehensive analysis of 1743 patients spanning 6 independent cohorts. They examined the association of BCAR4 expression with patient outcomes across all breast cancer types and the PAM50 molecular subtypes. The relationship between elevated BCAR4 expression and resistance to endocrine therapy including AIs, the prevailing standard-of-care for endocrine therapy, was also investigated. RESULTS: This meta-analysis corroborated the link between BCAR4 expression and adverse outcomes as well as resistance to endocrine therapy in breast cancer. Notably, BCAR4 expression is clinically significant in luminal A and B subtypes. Additionally, an association between BCAR4 expression and resistance to AI treatment was discerned. CONCLUSION: This study expands on previous findings by demonstrating that BCAR4 expression is associated with resistance to newer therapies. The identification of patients with intrinsic resistance to hormone therapy is crucial to avoid ineffective treatment strategies. These findings contribute to our understanding of endocrine therapy resistance in breast cancer and could potentially guide the development of more effective treatment strategies.

Pan-Cancer Analysis Reveals Recurrent BCAR4 Gene Fusions across Solid Tumors.

Chromosomal rearrangements often result in active regulatory regions juxtaposed upstream of an oncogene to generate an expressed gene fusion. Repeated activation of a common downstream partner-with differing upstream regions across a patient cohort-suggests a conserved oncogenic role. Analysis of 9,638 patients across 32 solid tumor types revealed an annotated long noncoding RNA (lncRNA), Breast Cancer Anti-Estrogen Resistance 4 (BCAR4), was the most prevalent, uncharacterized, downstream gene fusion partner occurring in 11 cancers. Its oncogenic role was confirmed using multiple cell lines with endogenous BCAR4 gene fusions. Furthermore, overexpressing clinically prevalent BCAR4 gene fusions in untransformed cell lines was sufficient to induce an oncogenic phenotype. We show that the minimum common region to all gene fusions harbors an open reading frame that is necessary to drive proliferation. IMPLICATIONS: BCAR4 gene fusions represent an underappreciated class of gene fusions that may have biological and clinical implications across solid tumors.

LINC00355 regulates p27KIP expression by binding to MENIN to induce proliferation in late-stage relapse breast cancer.

Late-stage relapse (LSR) in patients with breast cancer (BC) occurs more than five years and up to 10 years after initial treatment and has less than 30% 5-year relative survival rate. Long non-coding RNAs (lncRNAs) play important roles in BC yet have not been studied in LSR BC. Here, we identify 1127 lncRNAs differentially expressed in LSR BC via transcriptome sequencing and analysis of 72 early-stage and 24 LSR BC patient tumors. Decreasing expression of the most up-regulated lncRNA, LINC00355, in BC and MCF7 long-term estrogen deprived cell lines decreases cellular invasion and proliferation. Subsequent mechanistic studies show that LINC00355 binds to MENIN and changes occupancy at the CDKN1B promoter to decrease p27Kip. In summary, this is a key study discovering lncRNAs in LSR BC and LINC00355 association with epigenetic regulation and proliferation in BC.

DANSR: A Tool for the Detection of Annotated and Novel Small RNAs.

Existing small noncoding RNA analysis tools are optimized for processing short sequencing reads (17-35 nucleotides) to monitor microRNA expression. However, these strategies under-represent many biologically relevant classes of small noncoding RNAs in the 36-200 nucleotides length range (tRNAs, snoRNAs, etc.). To address this, we developed DANSR, a tool for the detection of annotated and novel small RNAs using sequencing reads with variable lengths (ranging from 17-200 nt). While DANSR is broadly applicable to any small RNA dataset, we applied it to a cohort of matched normal, primary, and distant metastatic colorectal cancer specimens to demonstrate its ability to quantify annotated small RNAs, discover novel genes, and calculate differential expression. DANSR is available as an open source tool.

Estimating the costs of genomic sequencing in cancer control.

BACKGROUND: Despite the rapid uptake of genomic technologies within cancer care, few studies provide detailed information on the costs of sequencing across different applications. The objective of the study was to examine and categorise the complete costs involved in genomic sequencing for a range of applications within cancer settings. METHODS: We performed a cost-analysis using gross and micro-costing approaches for genomic sequencing performed during 2017/2018 across different settings in Brisbane, Australia. Sequencing was undertaken for patients with lung, breast, oesophageal cancers, melanoma or mesothelioma. Aggregated resource data were captured for a total of 1433 patients and point estimates of per patient costs were generated. Deterministic sensitivity analyses addressed the uncertainty in the estimates. Estimated costs to the public health system for resources were categorised into seven distinct activities in the sequencing process: sampling, extraction, library preparation, sequencing, analysis, data storage and clinical reporting. Costs were also aggregated according to labour, consumables, testing, equipment and 'other' categories. RESULTS: The per person costs were AU$347-429 (2018 US$240-297) for targeted panels, AU$871-$2788 (2018 US$604-1932) for exome sequencing, and AU$2895-4830 (2018 US$2006-3347) for whole genome sequencing. Cost proportions were highest for library preparation/sequencing materials (average 76.8% of total costs), sample extraction (8.1%), data analysis (9.2%) and data storage (2.6%). Capital costs for the sequencers were an additional AU$34-197 (2018 US$24-67) per person. CONCLUSIONS: Total costs were most sensitive to consumables and sequencing activities driven by commercial prices. Per person sequencing costs for cancer are high when tumour/blood pairs require testing. Using the natural steps involved in sequencing and categorising resources accordingly, future evaluations of costs or cost-effectiveness of clinical genomics across cancer projects could be more standardised and facilitate easier comparison of cost drivers.

Long non-coding RNA RAMS11 promotes metastatic colorectal cancer progression.

Colorectal cancer (CRC) is the most common gastrointestinal malignancy in the U.S.A. and approximately 50% of patients develop metastatic disease (mCRC). Despite our understanding of long non-coding RNAs (lncRNAs) in primary colon cancer, their role in mCRC and treatment resistance remains poorly characterized. Therefore, through transcriptome sequencing of normal, primary, and distant mCRC tissues we find 148 differentially expressed RNAs Associated with Metastasis (RAMS). We prioritize RAMS11 due to its association with poor disease-free survival and promotion of aggressive phenotypes in vitro and in vivo. A FDA-approved drug high-throughput viability assay shows that elevated RAMS11 expression increases resistance to topoisomerase inhibitors. Subsequent experiments demonstrate RAMS11-dependent recruitment of Chromobox protein 4 (CBX4) transcriptionally activates Topoisomerase II alpha (TOP2α). Overall, recent clinical trials using topoisomerase inhibitors coupled with our findings of RAMS11-dependent regulation of TOP2α supports the potential use of RAMS11 as a biomarker and therapeutic target for mCRC.

Evaluating bio-burden of frequently touched surfaces using Adenosine Triphosphate bioluminescence (ATP): Results from the Researching Effective Approaches to Cleaning in Hospitals (REACH) trial.

BACKGROUND: Environmental cleaning is an important approach to reducing healthcare-associated infection. The aim of this short research paper is to describe changes in the efficacy of post-discharge cleaning by examining the amount of bio-burden on frequent touch points (FTPs) in patient areas, using a validated Adenosine Triphosphate (ATP) bioluminescence sampling method. In so doing, we present findings from a secondary outcome of a recent trial, the Researching Effective Approaches to Cleaning in Hospitals (REACH) study. METHODS: The REACH study used a prospective, stepped-wedge randomised cluster design. Cross sectional ATP sampling was conducted at three of the 11 participating hospitals. At each hospital, during the control and intervention phase of the study, six Frequent Touch Points (FTPs) were sampled: toilet flush, bathroom tap, inside bathroom door handle, patient call button, over bed tray table, and bed rails. RESULTS: Across the three hospitals, 519 surfaces in 49 rooms (control phase) and 2856 surfaces in 251 rooms (intervention phase) were sampled. Bedroom FTP cleaning improved across all three hospitals. The cleaning of bathroom FTPs was generally high from the outset and remained consistent throughout the whole study period. Average cleaning outcomes for bathroom FTPs were consistently high during the control period however outcomes varied between individual FTP. Changes in cleaning performance over time reflected variation in intervention effectiveness at the hospital level. CONCLUSION: Findings confirm improvement in cleaning in the FTPs in bedrooms, demonstrating improvements in discharge cleaning aligned with the improvements seen when using fluorescent marking technology as a marker of performance.

Long non-coding RNA LCAL62 / LINC00261 is associated with lung adenocarcinoma prognosis.

BACKGROUND: More than half of non-small cell lung cancer (NSCLC) patients present with metastatic disease at initial diagnosis with an estimated five-year survival rate of ~5%. Despite advances in understanding primary lung cancer oncogenesis metastatic disease remains poorly characterized. Recent studies demonstrate important roles of long non-coding RNAs (lncRNAs) in tumor physiology and as prognostic markers. Therefore, we present the first transcriptome analysis to identify lncRNAs altered in metastatic lung adenocarcinoma leading to the discovery and characterization of the lncRNA LCAL62 as a prognostic biomarker. PATIENTS AND METHODS: RNA-Seq, microarray, nanoString expression, and clinical data from 1,116 LUAD patients across six independent cohorts and 83 LUAD cell lines were used to discover and evaluate the survival association of metastasis associated lncRNAs. Coexpression and gene set enrichment analyses were used to establish gene regulatory networks and implicate metastasis associated lncRNAs in specific biological processes. RESULTS: Our integrative analysis discovered LCAL62 as the most down-regulated lncRNA in metastasis. Further low LCAL62 expression promoted aggressive phenotypes and regulated genes associated with metastasis (such as metastasis repressor FOXA2). Low LCAL62 expression corresponded to poor overall patient survival across five independent lung adenocarcinoma cohorts (n = 881) including our own nanoString validation cohort. CONCLUSION: We discovered that LCAL62 was down-regulated in lung cancer progression to promote invasive phenotypes, and lower expression was significantly associated with poor patient outcome and aggressive lung adenocarcinoma.

Application of ensemble methods to analyse the decline of organochlorine pesticides in relation to the interactions between age, gender and time.

Organochlorine pesticides (OCPs) are toxic chemicals that persist in human tissue. Short and long term exposure to OCPs have been shown to have adverse effects on human health. This motivates studies into the concentrations of pesticides in humans. However these studies typically emphasise the analysis of the main effects of age group, gender and time of sample collection. The interactions between main effects can distinguish variation in OCP concentration such as the difference in concentrations between genders of the same age group as well as age groups over time. These are less studied but may be equally or more important in understanding effects of OCPs in a population. The aim of this study was to identify interactions relevant to understanding OCP concentrations and utilise them appropriately in models. We propose a two stage analysis comprising of boosted regression trees (BRTs) and hierarchical modelling to study OCP concentrations. BRTs are used to discover influential interactions between age group, gender and time of sampling. Hierarchical models are then employed to test and infer the effect of the interactions on OCP concentrations. Results of our analysis show that the best fitting model of an interaction effect varied between OCPs. The interaction between age group and gender was most influential for hexachlorobenzene (HCB) concentrations. There was strong evidence of an interaction effect between age group and time for ß-hexachlorocyclohexane (ß-HCH) concentrations in >60 year olds as well as an interaction effect between age group and gender for HCB concentrations for adults aged >45 years. This study highlights the need to consider appropriate interaction effects in the analysis of OCP concentrations and provides further insight into the interplay of main effects on OCP concentration trends.

Spatial smoothing in Bayesian models: a comparison of weights matrix specifications and their impact on inference.

BACKGROUND: When analysing spatial data, it is important to account for spatial autocorrelation. In Bayesian statistics, spatial autocorrelation is commonly modelled by the intrinsic conditional autoregressive prior distribution. At the heart of this model is a spatial weights matrix which controls the behaviour and degree of spatial smoothing. The purpose of this study is to review the main specifications of the spatial weights matrix found in the literature, and together with some new and less common specifications, compare the effect that they have on smoothing and model performance. METHODS: The popular BYM model is described, and a simple solution for addressing the identifiability issue among the spatial random effects is provided. Seventeen different definitions of the spatial weights matrix are defined, which are classified into four classes: adjacency-based weights, and weights based on geographic distance, distance between covariate values, and a hybrid of geographic and covariate distances. These last two definitions embody the main novelty of this research. Three synthetic data sets are generated, each representing a different underlying spatial structure. These data sets together with a real spatial data set from the literature are analysed using the models. The models are evaluated using the deviance information criterion and Moran's I statistic. RESULTS: The deviance information criterion indicated that the model which uses binary, first-order adjacency weights to perform spatial smoothing is generally an optimal choice for achieving a good model fit. Distance-based weights also generally perform quite well and offer similar parameter interpretations. The less commonly explored options for performing spatial smoothing generally provided a worse model fit than models with more traditional approaches to smoothing, but usually outperformed the benchmark model which did not conduct spatial smoothing. CONCLUSIONS: The specification of the spatial weights matrix can have a colossal impact on model fit and parameter estimation. The results provide some evidence that a smaller number of neighbours used in defining the spatial weights matrix yields a better model fit, and may provide a more accurate representation of the underlying spatial random field. The commonly used binary, first-order adjacency weights still appear to be a good choice for implementing spatial smoothing.

Concentrations of organochlorine pesticides in pooled human serum by age and gender.

Organochlorine pesticides (OCPs) have been used for many decades in Australia with cessation of selected persistent and bioaccumulative OCPs ranging from the 1970s to as recently as 2007. The specific aims of this study were to use samples representative of an Australian population to assess age and gender differences in the concentration of OCPs in human blood sera and to investigate temporal trends in these chemicals. Serum was collected from de-identified, surplus pathology samples over five time periods (2002/03, 2006/07, 2008/09, 2010/11 and 2012/13), with 183 serum pools made from 12,175 individual samples; 26 pools in 2002/03, 85 pools in 2006/07 and 24 pools each in 2008/09, 2010/11 and 2012/13. Samples were analyzed for hexachlorobenzene (HCB), ß-hexachlorocyclohexane (ß-HCH), γ -hexachlorocyclohexane (lindane) (γ-HCH), oxy-chlordane, trans-nonachlor, p,p'-DDE, o,p'-DDT, p,p'-DDT and Mirex. Stratification criteria included gender and age (0-4; 5-15; 16-30; 31-45; 46-60; and >60 years) with age additionally stratified by adults >16 years and children 0-4 and 5-15 years. All pools from all collection periods had detectable concentrations of OCPs with a detection frequency of >60% for HCB, ß-HCH, trans-nonachlor, p,p'-DDT and p,p'-DDE. The overall OCP concentrations increased with age with the highest concentrations in the >60 years groups. Females did not have higher mean OCP concentrations than males except for HCB concentrations (p=0.0006). Temporal trends showed overall decreasing serum concentrations by collection period with the exception of an increase in OCP concentrations between 2006/07 and 2008/09. Excluding this data point, HCB decreased from year to year by 7-76%; ß-HCH concentrations decreased by 14 - 38%; trans-nonachlor concentrations decreased by 10 - 65%; p,p'-DDE concentrations decreased by 6 - 52%; and p,p'-DDT concentrations decreased by 7 - 30%. The results indicate that OCP concentrations have decreased over time as is to be expected following the phase out of these chemicals in Australia.

Multi-institutional Analysis Shows that Low PCAT-14 Expression Associates with Poor Outcomes in Prostate Cancer.

BACKGROUND: Long noncoding RNAs (lncRNAs) are an emerging class of relatively underexplored oncogenic molecules with biological and clinical significance. Current inadequacies for stratifying patients with aggressive disease presents a strong rationale to systematically identify lncRNAs as clinical predictors in localized prostate cancer. OBJECTIVE: To identify RNA biomarkers associated with aggressive prostate cancer. DESIGN, SETTING, AND PARTICIPANTS: Radical prostatectomy microarray and clinical data was obtained from 910 patients in three published institutional cohorts: Mayo Clinic I (N=545, median follow-up 13.8 yr), Mayo Clinic II (N=235, median follow-up 6.7 yr), and Thomas Jefferson University (N=130, median follow-up 9.6 yr). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The primary clinical endpoint was distant metastasis-free survival. Secondary endpoints include prostate cancer-specific survival and overall survival. Univariate and multivariate Cox regression were used to evaluate the association of lncRNA expression and these endpoints. RESULTS AND LIMITATIONS: An integrative analysis revealed Prostate Cancer Associated Transcript-14 (PCAT-14) as the most prevalent lncRNA that is aberrantly expressed in prostate cancer patients. Down-regulation of PCAT-14 expression significantly associated with Gleason score and a greater probability of metastatic progression, overall survival, and prostate cancer-specific mortality across multiple independent datasets and ethnicities. Low PCAT-14 expression was implicated with genes involved in biological processes promoting aggressive disease. In-vitro analysis confirmed that low PCAT-14 expression increased migration while overexpressing PCAT-14 reduced cellular growth, migration, and invasion. CONCLUSIONS: We discovered that androgen-regulated PCAT-14 is overexpressed in prostate cancer, suppresses invasive phenotypes, and lower expression is significantly prognostic for multiple clinical endpoints supporting its significance for predicting metastatic disease that could be used to improve patient management. PATIENT SUMMARY: We discovered that aberrant prostate cancer associated transcript-14 expression during prostate cancer progression is prevalent across cancer patients. Prostate cancer associated transcript-14 is also prognostic for metastatic disease and survival highlighting its importance for stratifying patients that could benefit from treatment intensification.

Making the most of spatial information in health: a tutorial in Bayesian disease mapping for areal data.

Disease maps are effective tools for explaining and predicting patterns of disease outcomes across geographical space, identifying areas of potentially elevated risk, and formulating and validating aetiological hypotheses for a disease. Bayesian models have become a standard approach to disease mapping in recent decades. This article aims to provide a basic understanding of the key concepts involved in Bayesian disease mapping methods for areal data. It is anticipated that this will help in interpretation of published maps, and provide a useful starting point for anyone interested in running disease mapping methods for areal data. The article provides detailed motivation and descriptions on disease mapping methods by explaining the concepts, defining the technical terms, and illustrating the utility of disease mapping for epidemiological research by demonstrating various ways of visualising model outputs using a case study. The target audience includes spatial scientists in health and other fields, policy or decision makers, health geographers, spatial analysts, public health professionals, and epidemiologists.

Bayesian spatiotemporal modelling for identifying unusual and unstable trends in mammography utilisation.

OBJECTIVES: To compare two Bayesian models capable of identifying unusual and unstable temporal patterns in spatiotemporal data. SETTING: Annual counts of mammography screening users from each statistical local area (SLA) in Brisbane, Australia, recorded between 1997 and 2008 inclusive. PRIMARY OUTCOME MEASURES: Mammography screening counts. RESULTS: The temporal trends of 91 SLAs (58%) were dissimilar from the overall common temporal trend. SLAs that followed the common temporal trend also tended to have stable temporal trends. SLAs with unstable temporal trends tended to be situated farther from the city and farther from mammography screening facilities. CONCLUSIONS: This paper demonstrates the usefulness of the two models in identifying unusual and unstable temporal trends, and the synergy obtained when both models are applied to the same data set. An analysis of these models has provided interesting insights into the temporal trends of mammography screening counts and has shown several possible avenues for further research, such as extending the models to allow for multiple common temporal trends and accounting for additional spatiotemporal heterogeneity.

KLK6-regulated miRNA networks activate oncogenic pathways in breast cancer subtypes.

KLK6 is expressed in normal mammary tissues and is aberrantly regulated in breast cancer. At physiological levels of expression, i.e. those found in normal mammary tissues, KLK6 acts as a tumor suppressor in human breast cancer. However, aberrant overexpression of KLK6 (i.e. 50-100-fold higher than normal), a characteristic of a subset of human breast cancers is associated with increased tumorigenicity (Pampalakis et al. Cancer Res 69:3779-3787, 2009). Here, we stably transfected KLK6-non-expressing MDA-MB-231 breast cancer cells with the full-length KLK6 cDNA to overexpress KLK6 at levels comparable to those observed in patients, and investigated potential oncogenic miRNA networks regulated by these abnormally high KLK6 expression levels and increased activity of this serine protease. A number of miRNAs that are upregulated (e.g. miR-146a) or downregulated (e.g. miR-34a) via KLK6-induced alterations in the miRNA biogenesis machinery were identified. Integrated experimental and bioinformatics analyses identified convergent miRNA networks targeting the cell cycle, MYC, MAPK, and other signaling pathways. In large clinical datasets, significant correlations between KLK6 and downstream MAPK and MYC targets at both the RNA and protein levels was confirmed, as well as negative correlation with GATA3. It was also demonstrated that KLK6 overexpression and likely its proteolytic activity is associated with alterations in downstream miRNAs and their targets, and these differ with the molecular subtypes of breast cancer. The data partly explains the different characteristics of breast cancer subtypes. Importantly, we introduce a combined KLK6-CDKN1B+MYC+CDKN1C score for prediction of long-term patient survival outcomes, with higher scores indicating poor survival.

INTEGRATE: gene fusion discovery using whole genome and transcriptome data.

While next-generation sequencing (NGS) has become the primary technology for discovering gene fusions, we are still faced with the challenge of ensuring that causative mutations are not missed while minimizing false positives. Currently, there are many computational tools that predict structural variations (SV) and gene fusions using whole genome (WGS) and transcriptome sequencing (RNA-seq) data separately. However, as both WGS and RNA-seq have their limitations when used independently, we hypothesize that the orthogonal validation from integrating both data could generate a sensitive and specific approach for detecting high-confidence gene fusion predictions. Fortunately, decreasing NGS costs have resulted in a growing quantity of patients with both data available. Therefore, we developed a gene fusion discovery tool, INTEGRATE, that leverages both RNA-seq and WGS data to reconstruct gene fusion junctions and genomic breakpoints by split-read mapping. To evaluate INTEGRATE, we compared it with eight additional gene fusion discovery tools using the well-characterized breast cell line HCC1395 and peripheral blood lymphocytes derived from the same patient (HCC1395BL). The predictions subsequently underwent a targeted validation leading to the discovery of 131 novel fusions in addition to the seven previously reported fusions. Overall, INTEGRATE only missed six out of the 138 validated fusions and had the highest accuracy of the nine tools evaluated. Additionally, we applied INTEGRATE to 62 breast cancer patients from The Cancer Genome Atlas (TCGA) and found multiple recurrent gene fusions including a subset involving estrogen receptor. Taken together, INTEGRATE is a highly sensitive and accurate tool that is freely available for academic use.

Pan-cancer transcriptome analysis reveals long noncoding RNAs with conserved function.

A growing number of gene-centric studies have highlighted the emerging significance of lncRNAs in cancer. However, these studies primarily focus on a single cancer type. Therefore, we conducted a pan-cancer analysis of lncRNAs comparing tumor and matched normal expression levels using RNA-Seq data from ∼ 3,000 patients in 8 solid tumor types. While the majority of differentially expressed lncRNAs display tissue-specific expression we discovered 229 lncRNAs with outlier or differential expression across multiple cancers, which we refer to as 'onco-lncRNAs'. Due to their consistent altered expression, we hypothesize that these onco-lncRNAs may have conserved oncogenic and tumor suppressive functions across cancers. To address this, we associated the onco-lncRNAs in biological processes based on their co-expressed protein coding genes. To validate our predictions, we experimentally confirmed cell growth dependence of 2 novel oncogenic lncRNAs, onco-lncRNA-3 and onco-lncRNA-12, and a previously identified lncRNA CCAT1. Overall, we discovered lncRNAs that may have broad oncogenic and tumor suppressor roles that could significantly advance our understanding of cancer lncRNA biology.

Inferring lung cancer risk factor patterns through joint Bayesian spatio-temporal analysis.

BACKGROUND: Preventing risk factor exposure is vital to reduce the high burden from lung cancer. The leading risk factor for developing lung cancer is tobacco smoking. In Australia, despite apparent success in reducing smoking prevalence, there is limited information on small area patterns and small area temporal trends. We sought to estimate spatio-temporal patterns for lung cancer risk factors using routinely collected population-based cancer data. METHODS: The analysis used a Bayesian shared component spatio-temporal model, with male and female lung cancer included separately. The shared component reflected lung cancer risk factors, and was modelled over 477 statistical local areas (SLAs) and 15 years in Queensland, Australia. Analyses were also run adjusting for area-level socioeconomic disadvantage, Indigenous population composition, or remoteness. RESULTS: Strong spatial patterns were observed in the underlying risk factor estimates for both males (median Relative Risk (RR) across SLAs compared to the Queensland average ranged from 0.48 to 2.00) and females (median RR range across SLAs 0.53-1.80), with high risks observed in many remote areas. Strong temporal trends were also observed. Males showed a decrease in the underlying risk across time, while females showed an increase followed by a decrease in the final 2 years. These patterns were largely consistent across each SLA. The high underlying risk estimates observed among disadvantaged, remote and indigenous areas decreased after adjustment, particularly among females. CONCLUSION: The modelled underlying risks appeared to reflect previous smoking prevalence, with a lag period of around 30 years, consistent with the time taken to develop lung cancer. The consistent temporal trends in lung cancer risk factors across small areas support the hypothesis that past interventions have been equally effective across the state. However, this also means that spatial inequalities have remained unaddressed, highlighting the potential for future interventions, particularly among remote areas.

Transcriptome sequencing reveals altered long intergenic non-coding RNAs in lung cancer.

BACKGROUND: Long intergenic non-coding RNAs (lncRNAs) represent an emerging and under-studied class of transcripts that play a significant role in human cancers. Due to the tissue- and cancer-specific expression patterns observed for many lncRNAs it is believed that they could serve as ideal diagnostic biomarkers. However, until each tumor type is examined more closely, many of these lncRNAs will remain elusive. RESULTS: Here we characterize the lncRNA landscape in lung cancer using publicly available transcriptome sequencing data from a cohort of 567 adenocarcinoma and squamous cell carcinoma tumors. Through this compendium we identify over 3,000 unannotated intergenic transcripts representing novel lncRNAs. Through comparison of both adenocarcinoma and squamous cell carcinomas with matched controls we discover 111 differentially expressed lncRNAs, which we term lung cancer-associated lncRNAs (LCALs). A pan-cancer analysis of 324 additional tumor and adjacent normal pairs enable us to identify a subset of lncRNAs that display enriched expression specific to lung cancer as well as a subset that appear to be broadly deregulated across human cancers. Integration of exome sequencing data reveals that expression levels of many LCALs have significant associations with the mutational status of key oncogenes in lung cancer. Functional validation, using both knockdown and overexpression, shows that the most differentially expressed lncRNA, LCAL1, plays a role in cellular proliferation. CONCLUSIONS: Our systematic characterization of publicly available transcriptome data provides the foundation for future efforts to understand the role of LCALs, develop novel biomarkers, and improve knowledge of lung tumor biology.

Quantitative proteomic analysis reveals potential diagnostic markers and pathways involved in pathogenesis of renal cell carcinoma.

There are no serum biomarkers for the accurate diagnosis of clear cell renal cell carcinoma (ccRCC). Diagnosis and decision of nephrectomy rely on imaging which is not always accurate. Non-invasive diagnostic biomarkers are urgently required. In this study, we preformed quantitative proteomics analysis on a total of 199 patients including 30 matched pairs of normal kidney and ccRCC using isobaric tags for relative and absolute quantitation (iTRAQ) labeling and LC-MS/MS analysis to identify differentially expressed proteins. We found 55 proteins significantly dysregulated in ccRCC compared to normal kidney tissue. 54 were previously reported to play a role in carcinogenesis, and 39 are secreted proteins. Dysregulation of alpha-enolase (ENO1), L-lactate dehydrogenase A chain (LDHA), heat shock protein beta-1 (HSPB1/Hsp27), and 10 kDa heat shock protein, mitochondrial (HSPE1) was confirmed in two independent sets of patients by western blot and immunohistochemistry. Pathway analysis, validated by PCR, showed glucose metabolism is altered in ccRCC compared to normal kidney tissue. In addition, we examined the utility of Hsp27 as biomarker in serum and urine. In ccRCC patients, Hsp27 was elevated in the urine and serum and high serum Hsp27 was associated with high grade (Grade 3-4) tumors. These data together identify potential diagnostic biomarkers for ccRCC and shed new light on the molecular mechanisms that are dysregulated and contribute to the pathogenesis of ccRCC. Hsp27 is a promising diagnostic marker for ccRCC although further large-scale studies are required. Also, molecular profiling may help pave the road to the discovery of new therapies.