Ribonucleotide reductase M1 (RRM1) is required for mammalian deoxyribonucleotide (dNTP) metabolism. It is the primary target of the antimetabolite drug gemcitabine, which is among the most efficacious and most widely used cancer therapeutics. Gemcitabine directly binds to RRM1 and irreversibly inactivates ribonucleotide reductase. Intra-tumoral RRM1 levels are predictive of gemcitabine’s therapeutic efficacy. The mechanisms that regulate intracellular RRM1 levels are largely unknown. Here, we identified the E3 ubiquitin-protein ligases RNF2 and Bmi1 to associate with RRM1 with subsequent poly-ubiquitination at either position 48 or 63 of ubiquitin. The lysine residues 224 and 548 of RRM1 were identified as major ubiquitination sites. We show that ubiquitinated RRM1 undergoes proteasome-mediated degradation and that targeted post-transcriptional silencing of RNF2 and Bmi1 results in increased RRM1 levels and resistance to gemcitabine. Immunohistochemical analyses of 187 early-stage lung cancer tumor specimens revealed a statistically significant co-expression of RRM1 and Bmi1. We were unable to identify suitable reagents for in situ quantification of RNF2. Our findings suggest that Bmi1 and possibly RNF2 may be attractive biomarkers of gemcitabine resistance in the context of RRM1 expression. They also provide novel information for the rational design of gemcitabine-proteasome inhibitor combination therapies, which so far have been unsuccessful if given to patients without taking the molecular context into account.

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Idelalisib, a potent phosphatidylinositol-3-kinase delta (PI3Kδ) inhibitor, is metabolized primarily by aldehyde oxidase to form GS-563117 and to a lesser extent by cytochrome P450 (CYP) 3A and uridine 5′-diphospho-glucuronosyltransferase 1A4. In vitro, idelalisib inhibits P-glycoprotein (P-gp) and organic anion transporting polypeptides 1B1 and 1B3, and GS-563117 is a time-dependent CYP3A inhibitor. This study enrolled 24 healthy subjects and evaluated (1) the effect of idelalisib on the pharmacokinetics (PK) of digoxin, a P-gp probe substrate, rosuvastatin, a breast cancer resistance protein, and OATP1B1/OATP1B3 substrate, and midazolam, a CYP3A substrate; and (2) the effect of a strong inducer, rifampin, on idelalisib PK. On treatment, the most common clinical adverse events (AEs) were headache and pyrexia. Grade 3 transaminase increases were observed in 5 of 24 subjects and were reversible. Two subjects had serious AEs after treatment completion (grade 3 pyrexia and/or drug-induced liver injury). Idelalisib coadministration did not affect digoxin and rosuvastatin PK. Coadministration with idelalisib increased plasma exposures of midazolam (138% and 437% for maximum observed plasma concentration [Cmax ] and area under the plasma concentration-time curve from time 0 extrapolated to infinity [AUCinf ], respectively), consistent with the in vitro finding of CYP3A inhibition by GS-563117. Rifampin caused a substantial decrease in idelalisib (58% and 75%, Cmax and AUCinf , respectively) and GS-563117 exposures, indicating an enhanced contribution of CYP3A to idelalisib metabolism under a strongly induced state.
© 2015, The American College of Clinical Pharmacology.

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A single-nucleotide polymorphism (SNP) is a variation in the DNA sequence that occurs when a single nucleotide in the genome differs across members of the same species. Variations in the DNA sequences of humans are associated with human diseases. This makes SNPs as a key to open up the door of personalized medicine. SNP(s) can also be used for human identification and forensic applications. Compared to short tandem repeat (STR) loci, SNPs have much lower statistical testing power for individual recognition due to the fact that there are only 3 possible genotypes for each SNP marker, but it may provide sufficient information to identify the population to which a certain samples may belong. In this report, using eight SNP markers for 641 samples, we performed a standard statistical classification procedure and found that 86% of the samples could be classified accurately under a two-population model. This study suggests the potential use of SNP(s) in population classification with a small number (n ≤ 8) of genetic markers for forensic screening, biodiversity and disaster victim controlling.

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Prognostic markers that identify patients with stage II colon cancers (CC) who are at risk of recurrence are essential to personalize therapy. We evaluated the potential of GIV/Girdin as a predictor of recurrence risk in such patients.
Expression of full-length GIV was evaluated by immunohistochemistry (IHC) using a newly developed monoclonal antibody together with a mismatch repair (MMR)-specific antibody panel in three stage II CC patient cohorts, ie. a training (n=192), test (n=317), and validation (n=181) cohort, with clinical follow-up data. Recurrence risk stratification models were established in the training cohort of T3, proficient MMR (pMMR) patients without chemotherapy and subsequently validated.
For T3 pMMR tumors, GIV expression and the presence of lymphovascular invasion (LVI) were the only factors predicting recurrence in both training (GIV: HR:2.78, p=0.013; LVI: HR 2.54, p=0.025) and combined test and validation (pooled) cohorts (GIV, HR:1.85, p=0.019; LVI, HR:2.52, p=0.0004). A risk model based on GIV expression and LVI-status classified patients into high- or low-risk groups; 3-year recurrence-free survival was significantly lower in the high-risk versus low-risk group across all cohorts (Training: 52.3% versus 84.8%; HR:3.74, 95%CI: 1.50-9.32; Test: 85.9% versus 97.9%, HR:7.83, 95%CI:1.03-59.54; Validation: 59.4% versus 84.4%, HR:3.71, 95%CI: 1.24-11.12).
GIV expression status predicts recurrence risk in patients with T3 pMMR stage II CC. A risk model combining GIV expression and LVI-status information further enhances prediction of recurrence. Further validation studies are warranted before GIV status can be routinely included in patient management algorithms.
Copyright ©2016, American Association for Cancer Research (AACR) (Free AACR Whitepaper).

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We describe the identification of a novel, tumor-specific missense mutation in the active site of casein kinase 1α (CSNK1A1) using activity-based proteomics. Matched normal and tumor colon samples were analyzed using an ATP acyl phosphate probe in a kinase-targeted LC-MS2 platform. An anomaly in the active-site peptide from CSNK1A1 was observed in a tumor sample that was consistent with an altered catalytic aspartic acid. Expression and analysis of the suspected mutant verified the presence of asparagine in the probe-labeled, active-site peptide for CSNK1A1. Genomic sequencing of the colon tumor samples confirmed the presence of a missense mutation in the catalytic aspartic acid of CSNK1A1 (GAC→AAC). To our knowledge, the D163N mutation in CSNK1A1 is a newly defined mutation to the conserved, catalytic aspartic acid of a protein kinase and the first missense mutation identified using activity-based proteomics. The tumorigenic potential of this mutation remains to be determined.

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