Objective

To evaluate the prevalence, correlates and subgroups at highest risk for suicidal ideation among adults with arthritis.

Methods

We used data on U.S. adults with arthritis, aged ≥40, participating in the 2007–2008 NHANES survey. Suicidal ideation was assessed by item 9 of the Patient Health Questionnaire-9 (PHQ-9). Socio-demographic factors, health behaviors and comorbid conditions were examined as potential correlates. Depression was measured by the PHQ-8 score (range 1–24). We used random forests to identify subgroups at highest risk for suicidal ideation. To determine if any correlates were unique to arthritis, we compared results to those for persons with diabetes mellitus and cancer.

Results

The prevalence (± standard error) of suicidal ideation was 5.6% ± 0.8% among persons with arthritis and 2.4% ± 0.4% among those without. The most important correlates for suicidal ideation in adults with arthritis were depression, anxiety, duration of arthritis, age, income/poverty ratio, number of close friends, pain, alcohol, excessive daytime sleepiness and comorbidities. Eleven of 16 most important contributors for suicidal ideation among adults with arthritis were also important for people with diabetes and cancer. Among persons with arthritis, subgroups at highest risk for suicidal ideation were those with PHQ-8 between 18 and 24 and less than 4.5 years of arthritis (96.5%), and those with PHQ-8 between 7 and 17, ≥1.24 days of binges/month and either income ≥$45,000/year (85.4%) or income <$45,000/year and >3 comorbidities (70.8%).

Conclusion

Depression, short duration of arthritis, binge drinking, income, and >3 comorbidities identified subgroups of adults with arthritis at greatest risk for suicidal ideation.

Genetics Analysis Workshop 17 provided common and rare genetic variants from exome sequencing data and simulated binary and quantitative traits in 200 replicates. We provide a brief review of the machine learning and regression-based methods used in the analyses of these data. Several regression and machine learning methods were used to address different problems inherent in the analyses of these data, which are high-dimension, low-sample-size data typical of many genetic association studies. Unsupervised methods, such as cluster analysis, were used for data segmentation and subset selection. Supervised learning methods, which include regression-based methods (e.g., generalized linear models, logic regression, and regularized regression) and tree-based methods (e.g., decision trees and random forests), were used for variable selection (selecting genetic and clinical features most associated or predictive of outcome) and prediction (developing models using common and rare genetic variants to accurately predict outcome), with the outcome being case-control status or quantitative trait value. We include a discussion of cross-validation for model selection and assessment and a description of available software resources for these methods.

We have recently proposed a new model of cancer metabolism to explain the role of aerobic glycolysis and L-lactate production in fueling tumor growth and metastasis. In this model, cancer cells secrete hydrogen peroxide (H2O2), initiating oxidative stress and aerobic glycolysis in the tumor stroma. This, in turn, drives L-lactate secretion from cancer-associated fibroblasts. Secreted L-lactate then fuels oxidative mitochondrial metabolism (OXPHOS) in epithelial cancer cells, by acting as a paracrine onco-metabolite. We have previously termed this type of two-compartment tumor metabolism the “reverse Warburg effect,” as aerobic glycolysis takes place in stromal fibroblasts, rather than epithelial cancer cells. Here, we used MCT4 immunostaining of human breast cancer tissue microarrays (TMAs; >180 triple-negative patients) to directly assess the prognostic value of the “reverse Warburg effect.” MCT4 expression is a functional marker of hypoxia, oxidative stress, aerobic glycolysis and L-lactate efflux. Remarkably, high stromal MCT4 levels (score = 2) were specifically associated with decreased overall survival (<18% survival at 10 years post-diagnosis). In contrast, patients with absent stromal MCT4 expression (score = 0), had 10-year survival rates of ∼97% (p-value < 10−32). High stromal levels of MCT4 were strictly correlated with a loss of stromal Cav-1 (p-value < 10−14), a known marker of early tumor recurrence and metastasis. In fact, the combined use of stromal Cav-1 and stromal MCT4 allowed us to more precisely identify high-risk triple-negative breast cancer patients, consistent with the goal of individualized risk-assessment and personalized cancer treatment. However, epithelial MCT4 staining had no prognostic value, indicating that the “conventional” Warburg effect does not predict clinical outcome. Thus, the “reverse Warburg effect” or “parasitic” energy-transfer is a key determinant of poor overall patient survival. As MCT4 is a druggable target, MCT4 inhibitors should be developed for the treatment of aggressive breast cancers, and possibly other types of human cancers. Similarly, we discuss how stromal MCT4 could be used as a biomarker for identifying high-risk cancer patients that could likely benefit from treatment with FDA-approved drugs or existing MCT-inhibitors (such as, AR-C155858, AR-C117977 and AZD-3965).

Machine learning approaches are an attractive option for analyzing large-scale data to detect genetic variants that contribute to variation of a quantitative trait, without requiring specific distributional assumptions. We evaluate two machine learning methods, random forests and logic regression, and compare them to standard simple univariate linear regression, using the Genetic Analysis Workshop 17 mini-exome data. We also apply these methods after collapsing multiple rare variants within genes and within gene pathways. Linear regression and the random forest method performed better when rare variants were collapsed based on genes or gene pathways than when each variant was analyzed separately. Logic regression performed better when rare variants were collapsed based on genes rather than on pathways.

G protein-coupled receptor kinase 2 (GRK2), which is upregulated in the failing human myocardium, appears to have a role in heart failure (HF) pathogenesis. In peripheral lymphocytes, GRK2 expression has been shown to reflect myocardial levels. This study represents an attempt to define the role for GRK2 as a potential biomarker of left ventricular function in HF patients. We obtained blood from 24 HF patients before and after heart transplantation and followed them for up to 1 year, also recording hemodynamic data and histological results from endomyocardial biopsies. We determined blood GRK2 protein by Western blotting and enzyme-linked immunosorbent assay. GRK2 levels were obtained before transplant and at first posttransplant biopsy. GRK2 levels significantly declined after transplant and remained low over the course of the study period. After transplantation, we found that blood GRK2 significantly dropped and remained low consistent with improved cardiac function in the transplanted heart. Blood GRK2 has potential as a biomarker for myocardial function in end-stage HF.

Background & Aims

Gucy2c is the intestinal cell receptor for the paracrine hormones guanylin and uroguanylin that converts GTP to cyclic (c)GMP. It functions as a tumor suppressor; its loss disrupts intestinal homeostasis and promotes tumorigenesis. We investigated the effects of Gucy2c loss on intestinal cell proliferation, metabolism, signaling, and tumorigenesis in mice.

Methods

Intestinal cell proliferation and metabolism were examined in Gucy2c−/− and wild-type mice and human colon cancer cells by microscopy, immunoblot, and functional analyses. AKT regulation and signaling were examined and the role of AKT in Gucy2c-dependent tumorigenesis was defined in Gucy2c−/−Akt1−/− mice. Microarray analyses compared gene expression profiles of intestine of Gucy2c−/− and wild-type mice.

Results

The size and number of intestinal crypts increased in Gucy2c−/− mice; the associated epithelial cells exhibited accelerated proliferation, increased glycolysis, and reduced oxidative phosphorylation, which was reversed by oral administration of cGMP. Conversely, activating GUCY2C in human colon cancer cells delayed cell cycle progression (inhibiting DNA synthesis and colony formation), reduced glycolysis, and increased mitochondrial ATP production. AKT signaling pathways were activated in intestines of Gucy2c−/− mice, associated with increased AKT phosphorylation. Disruption of AKT activity, pharmacologically or genetically, reduced DNA synthesis, proliferation, and glycolysis and increased mitochondrial biogenesis. Intestinal tumorigenesis increased following administration of azoxymethane to Gucy2c−/− mice, compared with wild-type mice, but was eliminated in Gucy2c−/−Akt1−/− mice.

Conclusions

Gucy2c is a tumor suppressor that controls proliferation and survival of intestinal epithelial cells by inactivating AKT signaling. This receptor and its ligands, which are paracrine hormones, might be novel candidates for anti-colorectal cancer therapy.

The expression of protein phosphatase 32 (PP32, ANP32A) is low in poorly differentiated pancreatic cancers and is linked to the levels of HuR (ELAV1), a predictive marker for gemcitabine response. In pancreatic cancer cells, exogenous overexpression of pp32 inhibited cell growth, supporting its long-recognized role as a tumor suppressor in pancreatic cancer. In chemotherapeutic sensitivity screening assays, cells overexpressing pp32 were selectively resistant to the nucleoside analogs gemcitabine and cytarabine (ARA-C), but were sensitized to 5-fluorouracil; conversely, silencing pp32 in pancreatic cancer cells enhanced gemcitabine sensitivity. The cytoplasmic levels of pp32 increased after cancer cells are treated with certain stressors, including gemcitabine. pp32 overexpression reduced the association of HuR with the mRNA encoding the gemcitabine-metabolizing enzyme deoxycytidine kinase (dCK), causing a significant reduction in dCK protein levels. Similarly, ectopic pp32 expression caused a reduction in HuR binding of mRNAs encoding tumor-promoting proteins (e.g., VEGF and HuR), while silencing pp32 dramatically enhanced the binding of these mRNA targets. Low pp32 nuclear expression correlated with high-grade tumors and the presence of lymph node metastasis, as compared to patients' tumors with high nuclear pp32 expression. Although pp32 expression levels did not enhance the predictive power of cytoplasmic HuR status, nuclear pp32 levels and cytoplasmic HuR levels associated significantly in patient samples. Thus, we provide novel evidence that the tumor suppressor function of pp32 can be attributed to its ability to disrupt HuR binding to target mRNAs encoding key proteins for cancer cell survival and drug efficacy.

Here, we investigated the possible predictive value of stromal caveolin-1 (Cav-1) as a candidate biomarker for clinical outcome in triple negative (TN) breast cancer patients. A cohort of 85 TN breast cancer patients was available, with the necessary annotation and nearly 12 years of follow-up data. Our primary outcome of interest in this study was overall survival. Interestingly, TN patients with high-levels of stromal Cav-1 had a good clinical outcome, with >50% of the patients remaining alive during the follow-up period. In contrast, the median survival for TN patients with moderate stromal Cav-1 staining was 33.5 months. Similarly, the median survival for TN patients with absent stromal Cav-1 staining was 25.7 months. A comparison of 5-year survival rates yields a similar pattern. TN patients with high stromal Cav-1 had a good 5-year survival rate, with 75.5% of the patients remaining alive. In contrast, TN patients with moderate or absent stromal Cav-1 levels had progressively worse 5-year survival rates, with 40 and 9.4% of the patients remaining alive. In contrast, in a parallel analysis, the levels of tumor epithelial Cav-1 had no prognostic significance. As such, the prognostic value of Cav-1 immunostaining in TN breast cancer patients is compartment-specific, and selective for an absence of Cav-1 staining in the stromal fibroblast compartment. A recursive-partitioning algorithm was used to assess which factors are most predictive of overall survival in TN breast cancer patients. In this analysis, we included tumor size, histologic grade, whether the patient received surgery, radiotherapy or chemotherapy, CK5/6, EGFR, p53 and Ki67 status, as well as the stromal Cav-1 score. This analysis indicated that stromal loss of Cav-1 expression was the most important prognostic factor for overall survival in TN breast cancer. Virtually identical results were obtained with CK5/6 (+) and/or EGFR (+) TN breast cancer cases, demonstrating that a loss of stromal Cav-1 is also a strong prognostic factor for basal-like breast cancers. Our current findings may have important implications for the close monitoring and treatment stratification of TN and basal-like breast cancer patients.

RNA-binding protein HuR binds U- or AU-rich sequences in the 3′-untranslated regions (UTRs) of target mRNAs, stabilizing them and/or modulating their translation. Given HuR’s links with cancer, we studied the consequences of modulating HuR levels in pancreatic cancer cells. HuR-overexpressing cancer cells, in some instances, are up to 30-fold more sensitive to treatment with gemcitabine (GEM), the main chemotherapeutic component of treatment regimens for pancreatic ductal adenocarcinoma (PDA), compared to control cells. In pancreatic cancer cells, HuR associates with deoxycytidine kinase (dCK) mRNA, which encodes the enzyme that metabolizes and thereby activates GEM. GEM exposure to pancreatic cancer cells, enriches the association between HuR and dCK mRNA and increases cytoplasmic HuR levels. Accordingly, HuR overexpression elevates, while HuR silencing reduces, dCK protein expression in pancreatic cancer cells. In a clinical correlate study of GEM treatment, we found a 7-fold increase in risk of mortality in PDA patients with low cytoplasmic HuR levels compared to patients with high HuR levels, after adjusting for other treatments and demographic variables. These data support the notion that HuR is a key mediator of GEM efficacy in cancer cells, at least in part through its ability to regulate dCK levels post-transcriptionally. We propose that HuR levels in PDA modulate the therapeutic efficacy of GEM, thus serving as a marker of the clinical utility of this common chemotherapeutic agent and a potential target for intervention in pancreatic cancer.

Background

Tobacco smoking is responsible for over 90% of lung cancer cases, and yet the precise molecular alterations induced by smoking in lung that develop into cancer and impact survival have remained obscure.

Methodology/Principal Findings

We performed gene expression analysis using HG-U133A Affymetrix chips on 135 fresh frozen tissue samples of adenocarcinoma and paired noninvolved lung tissue from current, former and never smokers, with biochemically validated smoking information. ANOVA analysis adjusted for potential confounders, multiple testing procedure, Gene Set Enrichment Analysis, and GO-functional classification were conducted for gene selection. Results were confirmed in independent adenocarcinoma and non-tumor tissues from two studies. We identified a gene expression signature characteristic of smoking that includes cell cycle genes, particularly those involved in the mitotic spindle formation (e.g., NEK2, TTK, PRC1). Expression of these genes strongly differentiated both smokers from non-smokers in lung tumors and early stage tumor tissue from non-tumor tissue (p<0.001 and fold-change >1.5, for each comparison), consistent with an important role for this pathway in lung carcinogenesis induced by smoking. These changes persisted many years after smoking cessation. NEK2 (p<0.001) and TTK (p = 0.002) expression in the noninvolved lung tissue was also associated with a 3-fold increased risk of mortality from lung adenocarcinoma in smokers.

Conclusions/Significance

Our work provides insight into the smoking-related mechanisms of lung neoplasia, and shows that the very mitotic genes known to be involved in cancer development are induced by smoking and affect survival. These genes are candidate targets for chemoprevention and treatment of lung cancer in smokers.

Background

The epidemiologic literature is replete with conceptual discussions about causal inference, but little is known about how the causal criteria are applied in public health practice. The criteria for causal inference in use today by epidemiologists have been shaped substantially by their use over time in reports of the U.S. Surgeon General on Smoking and Health.

Methods

We reviewed two classic reports on smoking and health from expert committees convened by the US Surgeon General, in 1964 and 1982, in order to evaluate and contrast how the committees applied causal criteria to the available evidence for the different cancer sites at different time periods. We focus on the evidence for four cancer sites in particular that received detailed reviews in the reports: lung, larynx, esophagus and bladder.

Results

We found that strength of association and coherence (especially dose-response, biological plausibility and epidemiologic sense) appeared to carry the most weight; consistency carried less weight, and temporality and specificity were apparently not applied at all in some cases. No causal claim was made for associations with a summary odds ratio of less than 3.0.

Conclusion

Our findings suggest that the causal criteria as described in textbooks and the Surgeon General reports can have variable interpretations and applications in practice. While the authors of these reports may have considered evidential factors that they did not explicitly cite, such lack of transparency of methods undermines the purpose of the causal criteria to promote objective, evidence-based decision making. Further empirical study and critical examination of the process by which causal conclusions are reached can play an important role in advancing the practice of epidemiology by helping public health scientists to better understand the practice of causal inference.