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Biology subjects

Tashjian, J. Y.

Publications and source records attributed to Tashjian, J. Y..

2 recordsLinked to original sources

TMIGD1, a putative tumor suppressor, induces G2-M cell cycle checkpoint arrest in colon cancer cells

Colorectal cancer (CRC) is a leading non-familial cause of cancer mortality among men and women. Although various genetic and epigenetic mechanisms have been identified, the full molecular mechanisms deriving CRC tumorigenesis remains incompletely understood. In this study, we demonstrate that cell adhesion molecule transmembrane and immunoglobulin domain containing1 (TMIGD1) is highly expressed in mouse and human normal intestinal epithelial cells. We have developed TMIGD1 knockout mice and show that the loss of TMIGD1 in mice results in the development of adenomas in small intestine and colon. Additionally, the loss of TMIGD1 in mouse impaired intestinal epithelium brush border formation, junctional polarity and maturation. Mechanistically, TMIGD1 inhibits tumor cell proliferation, cell migration, arrests cell cycle at G2/M phase and induces expression of p21CIP1 (cyclin-dependent kinase inhibitor 1), and p27KIP1 (cyclin-dependent kinase inhibitor 1B) expression, key cell cycle inhibitor proteins involved in the regulation of the cell cycle. Moreover, we demonstrate that TMIGD1 is progressively downregulated in sporadic human CRC and correlates with poor overall survival. Our findings identify TMIGD1 as a novel tumor suppressor gene and provide insights into the pathogenesis of colorectal cancer and possibilities as a potential therapeutic target.

cancer biology↗

A Computational Solution To Improve Biomarker Reproducibility During Long-term Projects

Biomarkers are fundamental to basic and clinical research outcomes by reporting host responses and providing insight into disease pathophysiology. Measuring biomarkers with research-use ELISA kits is universal, yet lack of kit standardization and unexpected lot-to-lot variability presents analytic challenges for long-term projects. During an ongoing two-year project measuring plasma biomarkers in cancer patients, control concentrations for one biomarker (PF) decreased significantly after changes in ELISA kit lots. A comprehensive operations review pointed to standard curve shifts with the new kits, an analytic variable that jeopardized data already collected on hundreds of patient samples. After excluding other reasonable contributors to data variability, a computational solution was developed to provide a uniform platform for data analysis across multiple ELISA kit lots. The solution (ELISAtools) was developed within open-access R software in which variability between kits is treated as a batch effect. A defined best-fit Reference standard curve is modelled, a unique Shift factor \"S\" is calculated for every standard curve and data adjusted accordingly. The averaged S factors for PF ELISA kit lots #1-5 ranged from -0.086 to 0.735, and reduced control inter-assay variability from 62.4% to <9%, within quality control limits. S factors calculated for four other biomarkers provided a quantitative metric to monitor ELISAs over the 10 month study period for quality control purposes. Reproducible biomarker measurements are essential, particularly for long-term projects with valuable patient samples. Use of research-use ELISA kits is ubiquitous and judicious use of this computational solution maximizes biomarker reproducibility.

pathology↗