Augmenting cancer registry data with health survey data with no cases in common: the relationship between pre-diagnosis health behaviour and post-diagnosis survival in oesophageal cancer.

BACKGROUND: For epidemiological research, cancer registry datasets often need to be augmented with additional data. Data linkage is not feasible when there are no cases in common between data sets. We present a novel approach to augmenting cancer registry data by imputing pre-diagnosis health behaviour and estimating its relationship with post-diagnosis survival time. METHODS: Six measures of pre-diagnosis health behaviours (focussing on tobacco smoking, 'at risk' alcohol consumption, overweight and exercise) were imputed for 28,000 cancer registry data records of US oesophageal cancers using cold deck imputation from an unrelated health behaviour dataset. Each data point was imputed twice. This calibration allowed us to estimate the misclassification rate. We applied statistical correction for the misclassification to estimate the relative risk of dying within 1 year of diagnosis for each of the imputed behaviour variables. Subgroup analyses were conducted for adenocarcinoma and squamous cell carcinoma separately. RESULTS: Simulated survival data confirmed that accurate estimates of true relative risks could be retrieved for health behaviours with greater than 5% prevalence, although confidence intervals were wide. Applied to real datasets, the estimated relative risks were largely consistent with current knowledge. For example, tobacco smoking status 5 years prior to diagnosis was associated with an increased age-adjusted risk of all cause death within 1 year of diagnosis for oesophageal squamous cell carcinoma (RR = 1.99 95% CI 1.24,3.12) but not oesophageal adenocarcinoma RR = 1.61, 95% CI 0.79,2.57). CONCLUSIONS: We have demonstrated a novel imputation-based algorithm for augmenting cancer registry data for epidemiological research which can be used when there are no cases in common between data sets. The algorithm allows investigation of research questions which could not be addressed through direct data linkage.

Using estimated probability of pre-diagnosis behavior as a predictor of cancer survival time: an example in esophageal cancer.

BACKGROUND: Information on the associations between pre-diagnosis health behavior and post-diagnosis survival time in esophageal cancer could assist in planning health services but can be difficult to obtain using established study designs. We postulated that, with a large data set, using estimated probability for a behavior as a predictor of survival times could provide useful insight as to the impact of actual behavior. METHODS: Data from a national health survey and logistic regression were used to calculate the probability of selected health behaviors from participant's demographic characteristics for each esophageal cancer case within a large cancer registry data base. The associations between survival time and the probability of the health behaviors were investigated using Cox regression. RESULTS: Observed associations include: a 0.1 increase in the probability of smoking 1 year prior to diagnosis was detrimental to survival (Hazard Ratio (HR) 1.21, 95% CI 1.19,1.23); a 0.1 increase in the probability of hazardous alcohol consumption 10 years prior to diagnosis was associated with decreased survival in squamous cell cancer (HR 1.29, 95% CI 1.07, 1.56) but not adenocarcinoma (HR 1.08, 95% CI 0.94,1.25); a 0.1 increase in the probability of physical activity outside the workplace is protective (HR 0.83, 95% CI 0.81,0.84). CONCLUSIONS: We conclude that probability for health behavior estimated from demographic characteristics can provide an initial assessment of the association between pre-diagnosis health behavior and post-diagnosis health outcomes, allowing some sharing of information across otherwise unrelated data collections.

Impact of pre-diagnosis behavior on risk of death from esophageal cancer: a systematic review and meta-analysis.

PURPOSE: Most people diagnosed with esophageal cancer will die from their disease, but it is not known whether survival is influenced by pre-morbid behavior. We undertook a systematic review and meta-analysis to investigate the impact of pre-diagnosis behavior on risk of death for esophageal cancer. METHODS: We performed a systematic review of studies reporting on the relationship between pre-diagnosis smoking, alcohol consumption, overweight and obesity, physical activity and regular consumption of nonsteroidal anti-inflammatory drugs, and risk of death from esophageal squamous cell carcinoma (ESCC) and adenocarcinomas (EACs). Study characteristics are presented and aggregate results are compiled using meta-analysis. RESULTS: From an initial pool of 644 non-duplicate records, 13 articles arising from 12 studies met the inclusion criteria. Considerable variation was observed between studies in location, measurement categories, adjustment for other risks, and results. Pooled estimates suggested that for ESCC pre-diagnosis smoking was associated with a 1.19 times [95 % confidence interval (CI) 1.04-1.36] increased risk of death and pre-diagnosis alcohol consumption with a 1.36 times increased risk of death (95 % CI 1.15-1.61). No significant effects were observed for EAC. We observed a lower risk of death for both ESCC and EAC associated with high pre-diagnosis body mass index (BMI) ≥25 kg/m(2) (ESCC hazard ratio 0.80, 95 % CI 0.67-0.95; EAC 0.80, 95 % CI 0.68-0.95), although there was significant heterogeneity across studies. CONCLUSIONS: Our findings suggest that a number of modifiable pre-diagnosis risk factors have a carryover effect on the risk of death from esophageal cancer. These include smoking, drinking alcohol, and BMI.

Shining a light on dark sequencing: characterising errors in Ion Torrent PGM data.

The Ion Torrent Personal Genome Machine (PGM) is a new sequencing platform that substantially differs from other sequencing technologies by measuring pH rather than light to detect polymerisation events. Using re-sequencing datasets, we comprehensively characterise the biases and errors introduced by the PGM at both the base and flow level, across a combination of factors, including chip density, sequencing kit, template species and machine. We found two distinct insertion/deletion (indel) error types that accounted for the majority of errors introduced by the PGM. The main error source was inaccurate flow-calls, which introduced indels at a raw rate of 2.84% (1.38% after quality clipping) using the OneTouch 200 bp kit. Inaccurate flow-calls typically resulted in over-called short-homopolymers and under-called long-homopolymers. Flow-call accuracy decreased with consecutive flow cycles, but we also found significant periodic fluctuations in the flow error-rate, corresponding to specific positions within the flow-cycle pattern. Another less common PGM error, high frequency indel (HFI) errors, are indels that occur at very high frequency in the reads relative to a given base position in the reference genome, but in the majority of instances were not replicated consistently across separate runs. HFI errors occur approximately once every thousand bases in the reference, and correspond to 0.06% of bases in reads. Currently, the PGM does not achieve the accuracy of competing light-based technologies. However, flow-call inaccuracy is systematic and the statistical models of flow-values developed here will enable PGM-specific bioinformatics approaches to be developed, which will account for these errors. HFI errors may prove more challenging to address, especially for polymorphism and amplicon applications, but may be overcome by sequencing the same DNA template across multiple chips.

Introduction to the special issue: How COVID-19 has affected students' health, achievement, and mental health.

The worldwide pandemic caused by COVID-19 was an event that has left an indelible mark upon the people who lived through this period. This special issue of Psychology in the Schools is devoted to exploring the wide range of issues that children, families, and schools dealt with during the pandemic. The following topics are discussed: (1) an overview of how children were affected in both physical and psychological terms, (2) the range of problems that professionals working with youth during the pandemic had to address, (3) the special issues that impacted college-age students during the pandemic, and finally (4) the various factors that influenced the level of impact that COVID-19 had on children and their families.

Insulin-like growth factor role in determining the anti-cancer effect of metformin: RCT in prostate cancer patients.

Objective: Androgen deprivation therapy (ADT), a principal therapy in patients with prostate cancer, is associated with the development of obesity, insulin resistance, and hyperinsulinemia. Recent evidence indicates that metformin may slow cancer progression and improves survival in prostate cancer patients, but the mechanism is not well understood. Circulating insulin-like growth factors (IGFs) are bound to high-affinity binding proteins, which not only modulate the bioavailability and signalling of IGFs but also have independent actions on cell growth and survival. The aim of this study was to investigate whether metformin modulates IGFs, IGF-binding proteins (IGFBPs), and the pregnancy-associated plasma protein A (PAPP-A) - stanniocalcin 2 (STC2) axis. Design and methods: In a blinded, randomised, cross-over design, 15 patients with prostate cancer on stable ADT received metformin and placebo treatment for 6 weeks each. Glucose metabolism along with circulating IGFs and IGFBPs was assessed. Results: Metformin significantly reduced the homeostasis model assessment as an index of insulin resistance (HOMA IR) and hepatic insulin resistance. Metformin also reduced circulating IGF-2 (P < 0.05) and IGFBP-3 (P < 0.01) but increased IGF bioactivity (P < 0.05). At baseline, IGF-2 correlated significantly with the hepatic insulin resistance (r2= 0.28, P < 0.05). PAPP-A remained unchanged but STC2 declined significantly (P < 0.05) following metformin administration. During metformin treatment, change in HOMA IR correlated with the change in STC2 (r2= 0.35, P < 0.05). Conclusion: Metformin administration alters many components of the circulating IGF system, either directly or indirectly via improved insulin sensitivity. Reduction in IGF-2 and STC2 may provide a novel mechanism for a potential metformin-induced antineoplastic effect.

ChIPseqR: analysis of ChIP-seq experiments.

BACKGROUND: The use of high-throughput sequencing in combination with chromatin immunoprecipitation (ChIP-seq) has enabled the study of genome-wide protein binding at high resolution. While the amount of data generated from such experiments is steadily increasing, the methods available for their analysis remain limited. Although several algorithms for the analysis of ChIP-seq data have been published they focus almost exclusively on transcription factor studies and are usually not well suited for the analysis of other types of experiments. RESULTS: Here we present ChIPseqR, an algorithm for the analysis of nucleosome positioning and histone modification ChIP-seq experiments. The performance of this novel method is studied on short read sequencing data of Arabidopsis thaliana mononucleosomes as well as on simulated data. CONCLUSIONS: ChIPseqR is shown to improve sensitivity and spatial resolution over existing methods while maintaining high specificity. Further analysis of predicted nucleosomes reveals characteristic patterns in nucleosome sequences and placement.

ScreenClust: Advanced statistical software for supervised and unsupervised high resolution melting (HRM) analysis.

BACKGROUND: High resolution melting (HRM) is an emerging new method for interrogating and characterizing DNA samples. An important aspect of this technology is data analysis. Traditional HRM curves can be difficult to interpret and the method has been criticized for lack of statistical interrogation and arbitrary interpretation of results. METHODS: Here we report the basic principles and first applications of a new statistical approach to HRM analysis addressing these concerns. Our method allows automated genotyping of unknown samples coupled with formal statistical information on the likelihood, if an unknown sample is of a known genotype (by discriminant analysis or "supervised learning"). It can also determine the assortment of alleles present (by cluster analysis or "unsupervised learning") without a priori knowledge of the genotypes present. CONCLUSION: The new algorithms provide highly sensitive and specific auto-calling of genotypes from HRM data in both supervised an unsupervised analysis mode. The method is based on pure statistical interrogation of the data set with a high degree of standardization. The hypothesis-free unsupervised mode offers various possibilities for de novo HRM applications such as mutation discovery.

Development and validation of a novel molecular biomarker diagnostic test for the early detection of sepsis.

INTRODUCTION: Sepsis is a complex immunological response to infection characterized by early hyper-inflammation followed by severe and protracted immunosuppression, suggesting that a multi-marker approach has the greatest clinical utility for early detection, within a clinical environment focused on Systemic Inflammatory Response Syndrome (SIRS) differentiation. Pre-clinical research using an equine sepsis model identified a panel of gene expression biomarkers that define the early aberrant immune activation. Thus, the primary objective was to apply these gene expression biomarkers to distinguish patients with sepsis from those who had undergone major open surgery and had clinical outcomes consistent with systemic inflammation due to physical trauma and wound healing. METHODS: This was a multi-centre, prospective clinical trial conducted across four tertiary critical care settings in Australia. Sepsis patients were recruited if they met the 1992 Consensus Statement criteria and had clinical evidence of systemic infection based on microbiology diagnoses (n = 27). Participants in the post-surgical (PS) group were recruited pre-operatively and blood samples collected within 24 hours following surgery (n = 38). Healthy controls (HC) included hospital staff with no known concurrent illnesses (n = 20). Each participant had minimally 5 ml of PAXgene blood collected for leucocyte RNA isolation and gene expression analyses. Affymetrix array and multiplex tandem (MT)-PCR studies were conducted to evaluate transcriptional profiles in circulating white blood cells applying a set of 42 molecular markers that had been identified a priori. A LogitBoost algorithm was used to create a machine learning diagnostic rule to predict sepsis outcomes. RESULTS: Based on preliminary microarray analyses comparing HC and sepsis groups, a panel of 42-gene expression markers were identified that represented key innate and adaptive immune function, cell cycling, WBC differentiation, extracellular remodelling and immune modulation pathways. Comparisons against GEO data confirmed the definitive separation of the sepsis cohort. Quantitative PCR results suggest the capacity for this test to differentiate severe systemic inflammation from HC is 92%. The area under the curve (AUC) receiver operator characteristics (ROC) curve findings demonstrated sepsis prediction within a mixed inflammatory population, was between 86 and 92%. CONCLUSIONS: This novel molecular biomarker test has a clinically relevant sensitivity and specificity profile, and has the capacity for early detection of sepsis via the monitoring of critical care patients.

Imputing pre-diagnosis health behaviour in cancer registry data and investigating its relationship with oesophageal cancer survival time.

BACKGROUND: As oesophageal cancer has short survival, it is likely pre-diagnosis health behaviours will have carry-over effects on post-diagnosis survival times. Cancer registry data sets do not usually contain pre-diagnosis health behaviours and so need to be augmented with data from external health surveys. A new algorithm is introduced and tested to augment cancer registries with external data when one-to-one data linkage is not available. METHODS: The algorithm is to use external health survey data to impute pre-diagnosis health behaviour for cancer patients, estimate misclassification errors in these imputed values and then fit misclassification corrected Cox regression to quantify the association between pre-diagnosis health behaviour and post-diagnosis survival. Data from US cancer registries and a US national health survey are used in testing the algorithm. RESULTS: It is demonstrated that the algorithm works effectively on simulated smoking data when there is no age confounding. But age confounding does exist (risk of death increases with age and most health behaviours change with age) and interferes with the performance of the algorithm. The estimate of the hazard ratio (HR) of pre-diagnosis smoking was HR = 1.32 (95% CI 0.82,2.68) with HR = 1.93 (95% CI 1.08,7.07) in the squamous cell sub-group and pre-diagnosis physical activity was protective of survival with HR = 0.25 (95% CI 0.03, 0.81). But the method failed for less common behaviours (such as heavy drinking). CONCLUSIONS: Further improvements in the I2C2 algorithm will permit enrichment of cancer registry data through imputation of new variables with negligible risk to patient confidentiality, opening new research opportunities in cancer epidemiology.

k-link EST clustering: evaluating error introduced by chimeric sequences under different degrees of linkage.

MOTIVATION: The clustering of expressed sequence tags (ESTs) is a crucial step in many sequence analysis studies that require a high level of redundancy. Chimeric sequences, while uncommon, can make achieving the optimal EST clustering a challenge. Single-linkage algorithms are particularly vulnerable to the effects of chimeras. To avoid chimera-facilitated erroneous merges, researchers using single-linkage algorithms are forced to use stringent sequence-similarity thresholds. Such thresholds reduce the sensitivity of the clustering algorithm. RESULTS: We introduce the concept of k-link clustering for EST data. We evaluate how clustering error rates vary over a range of linkage thresholds. Using k-link, we show that Type II error decreases in response to increasing the number of shared ESTs (ie. links) required. We observe a base level of Type II error likely caused by the presence of unmasked low-complexity or repetitive sequence. We find that Type I error increases gradually with increased linkage. To minimize the Type I error introduced by increased linkage requirements, we propose an extension to k-link which modifies the required number of links with respect to the size of clusters being compared. AVAILABILITY: The implementation of k-link is available under the terms of the GPL from http://www.bioinformatics.csiro.au/products.shtml. k-link is licensed under the GNU General Public License, and can be downloaded from http://www.bioinformatics.csiro.au/products.shtml. k-link is written in C++.

Author Correction: Long-term impacts of Bt cotton in India.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Long-term impacts of Bt cotton in India.

Most scholarship on the closely-watched case of genetically modified Bacillus thuringiensis (Bt) cotton in India has focused on short-term impacts and has also ignored other major changes in India's cotton agriculture. This Perspective combines several data sources over a 20-year span to provide long-term comparisons of Bt adoption with yields and other inputs at both countrywide and state-specific scales. Bt cotton adoption is shown to be a poor indicator of yield trends but a strong indicator of initial reductions in pesticide use. Yield increases correspond to changes in fertilizer and other inputs. Bt cotton has continued to control one major cotton pest, but with Bt resistance in another pest and surging populations of non-target pests, farmers now spend more on pesticides today than before the introduction of Bt. Indications are that the situation will continue to deteriorate.

Androgen deprivation in prostate cancer: benefits of home-based resistance training.

INTRODUCTION: Androgen deprivation therapy (ADT) has detrimental effects on body composition, metabolic health, physical functioning, bone mineral density (BMD) and health-related quality of life (HRQOL) in men with prostate cancer. We investigated whether a 12-month home-based progressive resistance training (PRT) programme, instituted at the start of ADT, could prevent these adverse effects. METHODS: Twenty-five patients scheduled to receive at least 12 months of ADT were randomly assigned to either usual care (UC) (n = 12) or PRT (n = 13) starting immediately after their first ADT injection. Body composition, body cell mass (BCM; a functional component of lean body mass), BMD, physical function, insulin sensitivity and HRQOL were measured at 6 weeks and 6 and 12 months. Data were analysed by a linear mixed model. RESULTS: ADT had a negative impact on body composition, BMD, physical function, glucose metabolism and HRQOL. At 12 months, the PRT group had greater reductions in BCM by - 1.9 ± 0.8 % (p = 0.02) and higher gains in fat mass by 3.1 ± 1.0 % (p = 0.002), compared to the UC group. HRQOL domains were maintained or improved in the PRT versus UC group at 6 weeks (general health, p = 0.04), 6 months (vitality, p = 0.02; social functioning, p = 0.03) and 12 months (mental health, p = 0.01; vitality, p = 0.02). A significant increase in the Matsuda Index in the PRT versus UC group was noted at 6 weeks (p = 0.009) but this difference was not maintained at subsequent timepoints. Between-group differences favouring the PRT group were also noted for physical activity levels (step count) (p = 0.02). No differences in measures of BMD or physical function were detected at any time point. CONCLUSION: A home-based PRT programme instituted at the start of ADT may counteract detrimental changes in body composition, improve physical activity and mental health over 12 months. TRIAL REGISTRATION: Australian and New Zealand Clinical Trials Registry, ACTRN12616001311448.

Alignment using variable penalty dynamic time warping.

In this article we highlight a novel variation on dynamic time warping (DTW) for aligning chromatogram signals. We are interested in sets of signals that can be aligned well locally, but not globally, by shifting individual signals in time. This kind of alignment is often sufficient for aligning gas chromatography data. Regular DTW often "over-warps" signals and introduces artificial features into the aligned data. To overcome this we introduce a variable penalty into the DTW process. The penalty is added to the distance metric whenever a nondiagonal step is taken. We select our penalty based on a morphological dilation of the two signals. We showcase our method by aligning GC/MS datafiles from 712 blood plasma samples processed in 23 batches over the course of 6 months. The use of variable penalty DTW significantly reduces the number of nondiagonal moves. In the examples presented here, this reduction is by a factor of 30, with no cost to visual quality of the alignment.

Loss of prostaglandin D2 synthase: a key molecular event in the transition of a low-grade astrocytoma to an anaplastic astrocytoma.

Reduction in the mRNA and protein expression of lipocalin-like prostaglandin D(2) (PGD(2)) synthase (PGDS), the main arachidonic acid metabolite produced in neurons and glial cells of the central nervous system, is a significant biological event involved in the malignant progression of astrocytomas and is predictive of poor survival. In vitro, the addition of the main PGDS metabolite, PGD(2), to A172 glioblastoma cells devoid of PGDS resulted in antiproliferative activity and cell death. In vitro PGD(2) substitution also enhanced the efficacy of cyclo-oxygenase-2 inhibitors. This finding has exciting implications for early interventional efforts for the grade 2 and 3 astrocytomas.

A potent liver-mediated mechanism for loss of muscle mass during androgen deprivation therapy.

CONTEXT: Androgen deprivation therapy (ADT) in prostate cancer results in muscular atrophy, due to loss of the anabolic actions of testosterone. Recently, we discovered that testosterone acts on the hepatic urea cycle to reduce amino acid nitrogen elimination. We now hypothesize that ADT enhances protein oxidative losses by increasing hepatic urea production, resulting in muscle catabolism. We also investigated whether progressive resistance training (PRT) can offset ADT-induced changes in protein metabolism. OBJECTIVE: To investigate the effect of ADT on whole-body protein metabolism and hepatic urea production with and without a home-based PRT program. DESIGN: A randomized controlled trial. PATIENTS AND INTERVENTION: Twenty-four prostate cancer patients were studied before and after 6 weeks of ADT. Patients were randomized into either usual care (UC) (n = 11) or PRT (n = 13) starting immediately after ADT. MAIN OUTCOME MEASURES: The rate of hepatic urea production was measured by the urea turnover technique using 15N2-urea. Whole-body leucine turnover was measured, and leucine rate of appearance (LRa), an index of protein breakdown and leucine oxidation (Lox), a measure of irreversible protein loss, was calculated. RESULTS: ADT resulted in a significant mean increase in hepatic urea production (from 427.6 ± 18.8 to 486.5 ± 21.3; P < 0.01) regardless of the exercise intervention. Net protein loss, as measured by Lox/Lra, increased by 12.6 ± 4.9% (P < 0.05). PRT preserved lean body mass without affecting hepatic urea production. CONCLUSION: As early as 6 weeks after initiation of ADT, the suppression of testosterone increases protein loss through elevated hepatic urea production. Short-term PRT was unable to offset changes in protein metabolism during a state of profound testosterone deficiency.

Parameter estimation for robust HMM analysis of ChIP-chip data.

BACKGROUND: Tiling arrays are an important tool for the study of transcriptional activity, protein-DNA interactions and chromatin structure on a genome-wide scale at high resolution. Although hidden Markov models have been used successfully to analyse tiling array data, parameter estimation for these models is typically ad hoc. Especially in the context of ChIP-chip experiments, no standard procedures exist to obtain parameter estimates from the data. Common methods for the calculation of maximum likelihood estimates such as the Baum-Welch algorithm or Viterbi training are rarely applied in the context of tiling array analysis. RESULTS: Here we develop a hidden Markov model for the analysis of chromatin structure ChIP-chip tiling array data, using t emission distributions to increase robustness towards outliers. Maximum likelihood estimates are used for all model parameters. Two different approaches to parameter estimation are investigated and combined into an efficient procedure. CONCLUSION: We illustrate an efficient parameter estimation procedure that can be used for HMM based methods in general and leads to a clear increase in performance when compared to the use of ad hoc estimates. The resulting hidden Markov model outperforms established methods like TileMap in the context of histone modification studies.

Pharmacodynamics of growth hormone abuse biomarkers and the influence of gender and testosterone: a randomized double-blind placebo-controlled study in young recreational athletes.

CONTEXT: IGF axis proteins and collagen peptides are promising markers of GH abuse. OBJECTIVE: Our objective was to investigate whether responses of serum IGF axis and collagen markers to GH differ between men and women, and are influenced by testosterone (T). DESIGN: This was a randomized, double-blind, placebo-controlled study of 8-wk treatment followed by 6-wk washout. SETTING: The study was performed at a clinical research facility. PARTICIPANTS: A total of 96 recreationally trained healthy athletes (63 men, 33 women), aged 18-40 yr, were studied. INTERVENTION: All subjects received GH (2 mg/d sc) or placebo for 8 wk; men also received T (250 mg/wk im) or placebo for 5 wk. MAIN OUTCOME MEASURES: Serum IGF axis proteins (IGF-I, IGF binding protein-3, and acid labile subunit) and collagen peptides (N-terminal propeptide of type I procollagen, C-terminal telopeptide of type I collagen, and N-terminal propeptide of type III procollagen) were measured. RESULTS: GH induced significant increases in IGF axis and collagen markers that were greater in men than women (P < 0.001). Of the IGF axis markers, IGF-I showed the greatest increase. The relative incremental responses of the collagen markers in general were greater than the IGF markers, especially for PIIINP. The collagen markers increased and decreased more slowly with most remaining elevated (P < 0.01) after 6 wk, in comparison to IGF markers, which returned to baseline within 1 wk. Addition of T to GH amplified the response of PIIINP by more than 1.5-fold but did not affect any other marker. T alone did not affect IGF axis markers but modestly increased collagen markers. CONCLUSIONS: These markers of GH abuse are less responsive in women. The increases in collagen markers have a different time course to the IGF markers and extend the window of detection in both sexes. The response of PIIINP is increased by coadministration of T.