The crossroads of GIS and health information: a workshop on developing a research agenda to improve cancer control

Cancer control researchers seek to reduce the burden of cancer by studying interventions, their impact in defined populations, and the means by which they can be better used. The first step in cancer control is identifying where the cancer burden is elevated, which suggests locations where interventions are needed. Geographic information systems (GIS) and other spatial analytic methods provide such a solution and thus can play a major role in cancer control. This report presents findings from a workshop held June 16–17, 2005, to bring together experts and stakeholders to address current issues in GIScience and cancer control. A broad range of areas of expertise and interest was represented, including epidemiology, geography, statistics, environmental health, social science, cancer control, cancer registry operations, and cancer advocacy. The goals of this workshop were to build consensus on important policy and research questions, identify roadblocks to future progress in this field, and provide recommendations to overcome these roadblocks

Using Gini coefficient to determining optimal cluster reporting sizes for spatial scan statistics

Background: Spatial and space–time scan statistics are widely used in disease surveillance to identify geographical areas of elevated disease risk and for the early detection of disease outbreaks. With a scan statistic, a scanning window of variable location and size moves across the map to evaluate thousands of overlapping windows as potential clusters, adjusting for the multiple testing. Almost always, the method will find many very similar overlapping clusters, and it is not useful to report all of them. This paper proposes to use the Gini coefficient to help select which of the many overlapping clusters to report. Methods: The Gini coefficient provides a quick and intuitive way to evaluate the degree of the heterogeneity of the collection of clusters, which is useful to explain how well the cluster collection reveal the underlying true cluster patterns. Using simulation studies and real cancer mortality data, it is compared with the traditional approach for reporting non-overlapping clusters. Results: The Gini coefficient can identify a more refined collection of non-overlapping clusters to report. For example, it is able to determine when it makes more sense to report a collection of smaller non-overlapping clusters versus a single large cluster containing all of them. It also fulfils a set of desirable theoretical properties, such as being invariant under a uniform multiplication of the population numbers by the same constant. Conclusions: The Gini coefficient can be used to determine which set of non-overlapping clusters to report. It has been implemented in the free SaTScan™ software version 9.3 (www.satscan.org)

Heregulin β1 drives gefitinib-resistant growth and invasion in tamoxifen-resistant MCF-7 breast cancer cells

Introduction Resistance to anti-epidermal growth factor receptor (anti-EGFR) therapies is an emerging clinical problem. The efficacy of anti-EGFR therapies can be influenced by the presence of heregulins (HRGs), which can bind erbB3/4 receptors and can activate alternative signalling pathways. In the present study we have examined whether HRG signalling can circumvent EGFR blockade in an EGFR-positive tamoxifen-resistant MCF-7 (Tam-R) breast cancer cell line. Methods Tam-R cells, incubated with the selective EGFR tyrosine kinase inhibitor gefitinib ('Iressa', ZD1839), were exposed to HRGβ1 and the effects on erbB receptor dimerization profiles and on activation of associated downstream signalling components were assessed by immunoprecipitation, western blotting and immunocytochemistry. The effects of HRGβ1 on gefitinib-treated Tam-R cell growth and invasion were also examined, and HRGβ1 expression levels were assessed in breast cancer tissue by immunohistochemistry to address the potential clinical relevance of such a resistance mechanism. Results In Tam-R cells, HRGβ1 promoted erbB3/erbB2 and erbB3/EGFR heterodimerization, promoted ERK1/2 and AKT pathway activation and increased cell proliferation and invasion. Gefitinib prevented HRGβ1-driven erbB3/EGFR heterodimerization, ERK1/2 activation and Tam-R cell proliferation, but HRGβ1-driven erbB3/erbB2 heterodimerization, AKT activation and Tam-R cell invasion were maintained. A combination of gefitinib and the phosphatidylinositol 3-kinase inhibitor LY294002 effectively blocked HRGβ1-mediated intracellular signalling activity, growth and invasion in Tam-R cells. Similarly, targeting erbB2 with trastuzumab in combination with gefitinib in Tam-R cells reduced HRGβ1-induced erbB2 and ERK1/2 activity; however, HRGβ1-driven AKT activity and cell growth were maintained while cell invasion was significantly enhanced with this combination. In clinical tissue all samples demonstrated cytoplasmic tumour epithelial HRGβ1 protein staining, with expression correlating with EGFR positivity and activation of both AKT and ERK1/2. Conclusion HRGβ1 can overcome the inhibitory effects of gefitinib on cell growth and invasion in Tam-R cells through promotion of erbB3/erbB2 heterodimerization and activation of the phosphatidylinositol 3-kinase/AKT signalling pathway. This may have implications for the effectiveness of anti-EGFR therapies in breast cancer as HRGβ1 is enriched in many EGFR-positive breast tumours

Analysis of shared heritability in common disorders of the brain

ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders

Recent Spatiotemporal Patterns of US Lung Cancer by Histologic Type

BackgroundAfter a period of increasing rates, lung cancer incidence is declining in the US for men and women. We investigated lung cancer rate patterns by gender, geographic location, and histologic subtype, and for total lung cancer (TLC), for the entire study period, and for 2000–2011 from 17 surveillance, epidemiology, and end results areas.MethodsFor each gender–histologic type combination, time trend plots and maps of age-adjusted rates are presented. Time trend significance was tested by joinpoint regression analysis. Spatial random effects models were applied to examine effects of sociodemographic factors, health insurance coverage, smoking, and physician density at the county level. Linked micromap plots illustrate patterns for important model predictors.ResultsDeclining incidence trends occurred for TLC (p < 0.05, entire period). Squamous cell carcinoma trends increased for females only (p < 0.05). Small cell carcinoma trends declined overall, p < 0.05, but recently increased faster for females than males. Adenocarcinoma rates initially declined, but were significantly increasing by 2004, p < 0.05. Counties with higher current smoking and family poverty were strongly associated with higher risk for all gender–histologic types (p < 0.0001, for both variables). County socioeconomic status was associated with higher risk for all lung cancer subtypes for females, p < 0.02. Counties with more diagnostic radiologists were associated with higher TLC rates (p < 0.03); counties with greater primary care physician access were associated with lower TLC rates (p < 0.03). TLC incidence rates were higher in eastern and southern states than western areas. Male rates were higher than female rates along the West Coast. Males and females had similar small cell rate patterns, with higher rates in the Midwest and southeast. Squamous cell carcinoma and adenocarcinoma rate patterns were similar to TLC patterns, except for relatively higher female adenocarcinoma rates in the northeast and northwest.ConclusionGeographic patterns and declining time trends for incident lung cancer are consistent with previous mortality patterns. Male–female time trend and geographic pattern differences occur by histologic type. Time trends remain significant, even after adjustment for significant covariates. Knowledge of the variation of lung cancer incidence by region and histologic type is useful for surveillance and for implementing lung cancer control efforts