Clinical analysis of NSCLC patients reveals lack of association between EGFR mutation and TET1 downregulation.

Lung cancer is one of the leading causes of death from cancer worldwide, with a poor prognosis in advanced cases. In the past decade, epidermal growth factor receptor (EGFR) inhibitors have shown significant efficacy towards treatment for EGFR mutant lung cancer. Expanding our knowledge of oncogenic EGFR signaling pathways is therefore of highly importance for the cancer field. Recently it has been proposed that mutant EGFR transcriptionally silences the TET1 (ten-eleven translocation methylcytosine dioxygenase 1) gene in cellular and animal models of lung cancer. Since TET1 is a known DNA demethylase, EGFR-mediated TET1 silencing therefore downregulates demethylation of tumor suppressor genes, which then leads to tumor growth inhibition, potentiating the role of TET1 as a tumor suppressor gene in NSCLC. In our study, we examined the role of EGFR-TET1 silencing in NSCLC patient samples. By independently analyzing the TCGA (The Cancer Genome Atlas) NSCLC data set as well as a cohort of patient samples from our hospital and a data set from publicly deposited databases, we did not observe the aforementioned mutant EGFR silencing of TET1. Conversely, in our cohort, TET1 expression levels were significantly elevated in EGFR mutant samples (P=0.007). Patients with higher TET1 levels showed a trend of better response rates to EGFR inhibitors compared to low TET1 staining levels, although the result was not significant (P=0.08). Furthermore, we did not observe a correlation between TET1 expression levels and patient survival. We conclude that while oncogenic EGFR suppression of TET1 is established in cellular and animal models of lung cancer, its role in patient outcome and prognosis remains inconclusive and warrants further investigation.

Evaluating environmental performance using new process capability indices for autocorrelated data.

Traditionally, the process capability index is developed by assuming that the process output data are independent and follow normal distribution. However, in most environmental cases, the process data are autocorrelated. The autocorrelated process, if unrecognized as an independent process, can lead to erroneous decision making and unnecessary quality loss. In this paper, three new capability indices with unbiased estimators are proposed to relieve the independence assumption for the-nominal-the-best and the-smaller-the-better cases. Furthermore, we use mean squared error (MSE) and mean absolute percent error (MAPE) to compare the accuracy of our proposed indices to previous autocorrelated indices. The results show that our proposed capability indices outperform the predecessors.

Correlation-feedback technique in optical flow determination.

In this correspondence, we present a new algorithm to determine optical flow that utilizes a correlation-feedback technique. Several experiments are presented to demonstrate that our method performs generally better than some standard correlation and gradient-based methods in terms of accuracy.