GMMchi: gene expression clustering using Gaussian mixture modeling.

BACKGROUND: Cancer evolution consists of a stepwise acquisition of genetic and epigenetic changes, which alter the gene expression profiles of cells in a particular tissue and result in phenotypic alterations acted upon by natural selection. The recurrent appearance of specific genetic lesions across individual cancers and cancer types suggests the existence of certain "driver mutations," which likely make up the major contribution to tumors' selective advantages over surrounding normal tissue and as such are responsible for the most consequential aspects of the cancer cells' gene expression patterns and phenotypes. We hypothesize that such mutations are likely to cluster with specific dichotomous shifts in the expression of the genes they most closely control, and propose GMMchi, a Python package that leverages Gaussian Mixture Modeling to detect and characterize bimodal gene expression patterns across cancer samples, as a tool to analyze such correlations using 2 × 2 contingency table statistics. RESULTS: Using well-defined simulated data, we were able to confirm the robust performance of GMMchi, reaching 85% accuracy with a sample size of n = 90. We were also able to demonstrate a few examples of the application of GMMchi with respect to its capacity to characterize background florescent signals in microarray data, filter out uninformative background probe sets, as well as uncover novel genetic interrelationships and tumor characteristics. Our approach to analysing gene expression analysis in cancers provides an additional lens to supplement traditional continuous-valued statistical analysis by maximizing the information that can be gathered from bulk gene expression data. CONCLUSIONS: We confirm that GMMchi robustly and reliably extracts bimodal patterns from both colorectal cancer (CRC) cell line-derived microarray and tumor-derived RNA-Seq data and verify previously reported gene expression correlates of some well-characterized CRC phenotypes. AVAILABILITY: The Python package GMMchi and our cell line microarray data used in this paper is available for downloading on GitHub at https://github.com/jeffliu6068/GMMchi .

Critical roles for EGFR and EGFR-HER2 clusters in EGF binding of SW620 human carcinoma cells.

Epidermal growth factor (EGF) signalling regulates normal epithelial and other cell growth, with EGF receptor (EGFR) overexpression reported in many cancers. However, the role of EGFR clusters in cancer and their dependence on EGF binding is unclear. We present novel single-molecule total internal reflection fluorescence microscopy of (i) EGF and EGFR in living cancer cells, (ii) the action of anti-cancer drugs that separately target EGFR and human EGFR2 (HER2) on these cells and (iii) EGFR-HER2 interactions. We selected human epithelial SW620 carcinoma cells for their low level of native EGFR expression, for stable transfection with fluorescent protein labelled EGFR, and imaged these using single-molecule localization microscopy to quantify receptor architectures and dynamics upon EGF binding. Prior to EGF binding, we observe pre-formed EGFR clusters. Unexpectedly, clusters likely contain both EGFR and HER2, consistent with co-diffusion of EGFR and HER2 observed in a different model CHO-K1 cell line, whose stoichiometry increases following EGF binding. We observe a mean EGFR : EGF stoichiometry of approximately 4 : 1 for plasma membrane-colocalized EGFR-EGF that we can explain using novel time-dependent kinetics modelling, indicating preferential ligand binding to monomers. Our results may inform future cancer drug developments.

The Association Between Sleep Disorders and the Risk of Colorectal Cancer in Patients: A Population-based Nested Case-Control Study.

AIM: To investigate the risk of colorectal cancer in patients with sleep disorders. MATERIALS AND METHODS: We identified 7,355 participants with colorectal cancer between January 1, 2000, and December 31, 2013, from the Longitudinal Health Insurance Database 2005 of the Taiwan National Health Insurance Research Database; 29,420 controls were also identified from the same database based on frequency matching on age, sex, and index date of the cases. Diagnoses of sleep disorders by International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) in the cases and controls prior to the index date were assessed. The risk of colorectal cancer in patients with sleep disorders was estimated with multivariate logistic regression analyses. RESULTS: The mean age of the 36,775 patients was 63.05 years, and 56% of them were males. The risk of colorectal cancer was higher in patients with sleep disorders compared to those without [adjusted odds ratio (OR)=1.29, 95% confidence interval (CI)=1.13-1.47]. The risk of colorectal cancer was higher in patients having sleep disorders with depression compared to those without the condition (adjusted OR=5.69, 95% CI=4.01-6.98). CONCLUSION: The risk of colorectal cancer in patients with sleep disorders was found to be significantly higher by case-control study and particularly pronounced among those with sleep disorders with depression, exhibiting a joint effect on colorectal cancer risk.

Risk of pneumonia in patients with insomnia: A nationwide population-based retrospective cohort study.

Evidence is lacking regarding whether insomnia increases the risk of infectious disease. Accordingly, the present study examined the risk of pneumonia in patients with insomnia. This study was a population-based retrospective cohort study on a cohort of 8061 patients with insomnia and a control cohort of 16,112 patients (matched by age, sex, and year of diagnosis) from the Taiwan National Health Insurance Research Database for the 2000-2010 period. Overall incidence of pneumonia was 50.6 per 1000 person-years in the insomnia cohort, which was significantly higher than that in the control cohort (30.9 per 1000 person-years). Overall, the insomnia cohort exhibited a higher risk of pneumonia (HR=2.43; CI, 2.24-2.62). By age group, the risk of pneumonia was significantly higher in the insomnia cohort for those aged ≤40 years (HR=3.23, CI: 1.38-7.57), 41-65 years (HR=2.62, CI: 2.07-3.32), and >65years (CI: 2.21-2.61). Compared with the controls, the insomnia cohort exhibited a higher risk of pneumonia, particularly in young adults.