Disruption of clock gene expression in human colorectal liver metastases

The circadian timing system controls about 40 % of the transcriptome and is important in the regulation of a wide variety of biological processes including metabolic and proliferative functions. Disruption of the circadian clock could have significant effect on human health and has an important role in the development of cancer. Here, we compared the expression levels of core clock genes in primary colorectal cancer (CRC), colorectal liver metastases (CRLM), and liver tissue within the same patient. Surgical specimens of 15 untreated patients with primary CRC and metachronous CRLM were studied. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to measure the expression of 10 clock genes: CLOCK, BMAL1, PER1, PER2, PER3, CRY1, CRY2, CSNK1E, TIM, TIPIN, and 2 clock-controlled genes: Cyclin-D1, and WEE1. Expression levels of 7 core clock genes were downregulated in CRLM: CLOCK (p = 0.006), BMAL1 (p = 0.003), PER1 (p = 0.003), PER2 (p = 0.002), PER3 (p < 0.001), CRY1 (p = 0.002), and CRY2 (p < 0.001). In CRC, 5 genes were downregulated: BMAL1 (p = 0.02), PER1 (p = 0.004), PER2 (p = 0.008), PER3 (p < 0.001), and CRY2 (p < 0.001). CSNK1E was upregulated in CRC (p = 0.02). Cyclin-D1 and WEE1 were both downregulated in CRLM and CRC. Related to clinicopathological factors, a significant correlation was found between low expression of CRY1 and female gender, and low PER3 expression and the number of CRLM. Our data demonstrate that the core clock is disrupted in CRLM and CRC tissue from the same patient. This disruption may be linked to altered cell-cycle dynamics and carcinogenesis

Genomics Virtual Laboratory: a practical bioinformatics workbench for the cloud

Analyzing high throughput genomics data is a complex and compute intensive task, generally requiring numerous software tools and large reference data sets, tied together in successive stages of data transformation and visualisation. A computational platform enabling best practice genomics analysis ideally meets a number of requirements, including: a wide range of analysis and visualisation tools, closely linked to large user and reference data sets ; workflow platform(s) enabling accessible, reproducible, portable analyses, through a flexible set of interfaces ; highly available, scalable computational resources ; and flexibility and versatility in the use of these resources to meet demands and expertise of a variety of users. Access to an appropriate computational platform can be a significant barrier to researchers, as establishing such a platform requires a large upfront investment in hardware, experience, and expertise

T1 vs. T2 weighted magnetic resonance imaging to assess total kidney volume in patients with autosomal dominant polycystic kidney disease

Purpose: In ADPKD patients total kidney volume (TKV) measurement using MRI is performed to predict rate of disease progression. Historically T1 weighted images (T1) were used, but the methodology of T2 weighted imaging (T2) has evolved. We compared the performance of both sequences. Methods: 40 ADPKD patients underwent an abdominal MRI at baseline and follow-up. TKV was measured by manual tracing with Analyze Direct 11.0 software. Three readers established intra- and interreader coefficients of variation (CV). T1 and T2 measured kidney volumes and growth rates were compared with ICC and Bland-Altman analyses. Results: Participants were 49.7 +/- 7.0 years of age, 55.0% female, with estimated GFR of 50.1 +/- 11.5 mL/min/1.73 m(2). CVs were low and comparable for T2 and T1 (intrareader: 0.83% [0.48-1.79] vs. 1.15% [0.34-1.77], P = 0.9, interreader: 2.18% [1.59-2.61] vs. 1.69% [1.07-3.87], P = 0.9). TKV was clinically similar, but statistically significantly different between T2 and T1: 1867 [1172-2721] vs. 1932 [1180-2551] mL, respectively (P = 0.006), with a bias of only 0.8% and high agreement (ICC 0.997). Percentage kidney growth during 2.2 +/- 0.3 years was similar for T2 and T1 (9.3 +/- 10.6% vs. 7.8 +/- 9.9%, P = 0.1, respectively), with a bias of 1.5% and high agreement (ICC 0.843). T2 was more often of sufficient quality for volume measurement (86.7% vs. 71.1%, P <0.001). Conclusions: In patients with ADPKD, measurement of kidney volume and growth rate performs similarly when using T2 compared to T1 weighted images, although T2 performs better on secondary outcome parameters; they are more often of sufficient quality for volume measurement and result in slightly lower intra- and interreader variability

Circadian variation in tamoxifen pharmacokinetics in mice and breast cancer patients

The anti-estrogen tamoxifen is characterized by a large variability in response, partly due to pharmacokinetic differences. We examined circadian variation in tamoxifen pharmacokinetics in mice and breast cancer patients. Pharmacokinetic analysis was performed in mice, dosed at six different times (24-h period). Tissue samples were used for mRNA expression analysis of drug-metabolizing enzymes. In patients, a cross-over study was performed. During three 24-h periods, after tamoxifen dosing at 8 a.m., 1 p.m., and 8 p.m., for at least 4 weeks, blood samples were collected for pharmacokinetic measurements. Differences in tamoxifen pharmacokinetics between administration times were assessed. The mRNA expression of drug-metabolizing enzymes showed circadian variation in mouse tissues. Tamoxifen exposure seemed to be highest after administration at midnight. In humans, marginal differences were observed in pharmacokinetic parameters between morning and evening administration. Tamoxifen Cmax and area under the curve (AUC)0–8 h were 20 % higher (P max was shorter (2.1 vs. 8.1 h; P = 0.001), indicating variation in absorption. Systemic exposure (AUC0–24 h) to endoxifen was 15 % higher (P < 0.001) following morning administration. The results suggest that dosing time is of marginal influence on tamoxifen pharmacokinetics. Our study was not designed to detect potential changes in clinical outcome or toxicity, based on a difference in the time of administration. Circadian rhythm may be one of the many determinants of the interpatient and intrapatient pharmacokinetic variability of tamoxifen

Relationship Between Sunitinib Pharmacokinetics and Administration Time: Preclinical and Clinical Evidence

Background and Objective: Circadian rhythms may influence the pharmacokinetics of drugs. This study aimed to elucidate whether the pharmacokinetics of the orally administered drug sunitinib are subject to circadian variation. Methods: We performed studies in male FVB-mice aged 8–12 weeks, treated with single-dose sunitinib at six dosing times. Plasma and tissue samples were obtained for pharmacokinetic analysis and to monitor messenger RNA (mRNA) expression of metabolizing enzymes and drug transporters. A prospective randomized crossover study was performed in which patients took sunitinib once daily at 8 a.m., 1 p.m., and 6 p.m at three subsequent courses. Patients were blindly randomized into two groups, which determined the sequence of the sunitinib dosing time. The primary endpoint in both studies was the difference in plasma area under the concentration–time curve (AUC) of sunitinib and its active metabolite SU12662 between dosing times. Results: Sunitinib and SU12662 plasma AUC in mice followed an ~12-h rhythm as a function of administration time (p ≤ 0.04). The combined AUC from time zero to 10 h (AUC10) was 14–27 % higher when sunitinib was administered at 4 a.m. and 4 p.m. than at 8 a.m. and 8 p.m. Twenty-four-hour rhythms were seen in the mRNA levels of drug transporters and metabolizing enzymes. In 12 patients, sunitinib trough concentrations (Ctrough) were higher when the drug was taken at 1 p.m. or 6 p.m. than when taken at 8 a.m. (Ctrough-1 p.m. 66.0 ng/mL; Ctrough-6 p.m. 58.9 ng/mL; Ctrough-8 a.m. 50.7 ng/mL; p = 0.006). The AUC was not significantly different between dosing times. Conclusions: Our results indicate that sunitinib pharmacokinetics follow an ~12-h rhythm in mice. In humans, morning dosing resulted in lower Ctrough values, probably resulting from differences in elimination. This can have implications fo