SUMMARY Akt is a central regulator of cell growth. Its activity can be negatively regulated by the phosphatase PHLPP that specifically dephosphorylates the hydrophobic motif of Akt (Ser473 in Akt1). However, how PHLPP is targeted to Akt is not clear. Here we show that FKBP51 (FK506-binding protein 51) acts as a scaffolding protein for Akt and PHLPP and promotes dephosphorylation of Akt. Furthermore, FKBP51 is downregulated in pancreatic cancer tissue samples and several cancer cell lines. Decreased FKBP51 expression in cancer cells results in hyperphosphorylation of Akt and decreased cell death following genotoxic stress. Overall, our findings identify FKBP51 as a negative regulator of the Akt pathway, with potentially important implications for cancer etiology and response to chemotherapy.

Background-Antidepressant response is likely influenced by genetic constitution, but the actual genes involved have yet to be determined. We have carried out a genome-wide association study to determine if common DNA variation influences antidepressant response.

Two cytidine analogues, gemcitabine (dFdC) and 1-B-Darabinofuranosylcytosine (AraC), show significant therapeutic effect in a variety of cancers. However, response to these drugs varies widely. Evidence from tumor biopsy samples shows that expression levels for genes involved in the cytidine transport, metabolism, and bioactivation pathway contribute to this variation in response. In the present study, we set out to test the hypothesis that variation in gene expression both within and outside of this ''pathway'' might influence sensitivity to gemcitabine and AraC. Specifically, Affymetrix U133 Plus 2.0 GeneChip and cytotoxicity assays were performed to obtain basal mRNA expression and IC 50 values for both drugs in 197 ethnically defined Human Variation Panel lymphoblastoid cell lines. Genes with a high degree of association with IC 50 values were involved mainly in cell death, cancer, cell cycle, and nucleic acid metabolism pathways. We validated selected significant genes by performing real-time quantitative reverse transcription-PCR and selected two representative candidates, NT5C3 (within the pathway) and FKBP5 (outside of the pathway), for functional validation. Those studies showed that down-regulation of NT5C3 and FKBP5 altered tumor cell sensitivity to both drugs. Our results suggest that cell-based model system studies, when combined with complementary functional characterization, may help to identify biomarkers for response to chemotherapy with these cytidine analogues.

This GWAS identified SNPs associated with MS-AEs in women treated with AIs and with a gene (TCL1A) which, in turn, was related to a cytokine (IL17). These findings provide a focus for further research to identify patients at risk for MS-AEs and to explore the mechanisms for these adverse events.

Radiation therapy is used to treat half of all cancer patients. Response to radiation therapy varies widely among patients. Therefore, we performed a genome-wide association study (GWAS) to identify biomarkers to help predict radiation response using 277 ethnically defined human lymphoblastoid cell lines (LCLs). Basal gene expression levels and 1.3 million genome-wide single nucleotide polymorphism (SNP) markers from both Affymetrix and Illumina platforms were assayed for all 277 human LCLs. MTS [3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium] assays for radiation cytotoxicity were also performed to obtain area under the curve (AUC) as a radiation response phenotype for use in the association studies. Functional validation of candidate genes, selected from an integrated analysis that used SNP, expression, and AUC data, was performed with multiple cancer cell lines using specific siRNA knockdown, followed by MTS and colony-forming assays. A total of 27 loci, each containing at least two SNPs within 50 kb with P-values less than 10−4 were associated with radiation AUC. A total of 270 expression probe sets were associated with radiation AUC with P < 10−3. The integrated analysis identified 50 SNPs in 14 of the 27 loci that were associated with both AUC and the expression of 39 genes, which were also associated with radiation AUC (P < 10−3). Functional validation using siRNA knockdown in multiple tumor cell lines showed that C13orf34, MAD2L1, PLK4, TPD52, and DEPDC1B each significantly altered radiation sensitivity in at least two cancer cell lines. Studies performed with LCLs can help to identify novel biomarkers that might contribute to variation in response to radiation therapy and enhance our understanding of mechanisms underlying that variation.

Little is known about the spectrum or frequency of comorbidities in patients with chronic lymphocytic leukemia (CLL). We investigated the prevalence and prognostic implications of comorbidities in patients with newly diagnosed CLL. Local/non-referred patients with CLL evaluated by a hematologist at Mayo Clinic within 1 year of diagnosis were eligible for this retrospective review. Of 1195 individuals evaluated for newly-diagnosed CLL between 1995 and 2006, 373 (31%) were local/non-referred. At diagnosis, 89% of these patients had one or more comorbidities, and 46% had at least one major comorbidity. Twenty-six percent of patients failed to meet NCI working group guidelines to participate in a clinical trial. On multi-factor analysis, Rai risk category (1.39 per each risk category increase; p < 0.0001) and age (1.056 per year increase; p < 0.0001) were the only factors associated with overall survival. We conclude that, although common, comorbid conditions are less important than age and stage in predicting prognosis in newly diagnosed patients with CLL. Clinical trials evaluating treatments that are designed to be tolerated by patients who do not meet traditional clinical trial eligibility criteria are needed.

Major depressive disorder (MDD) is a common psychiatric disease. Selective serotonin reuptake inhibitors (SSRIs) are an important class of drugs used to treat MDD. However, many patients do not respond adequately to SSRI therapy. We used a pharmacometabolomics-informed pharmacogenomic research strategy to identify citalopram/escitalopram treatment outcome biomarkers. Metabolomic assay of plasma samples from 20 escitalopram remitters and 20 nonremitters showed that glycine was negatively associated with treatment outcome (p=0.0054). That observation was pursued by genotyping tag single nucleotide polymorphisms (SNPs) for genes encoding glycine synthesis and degradation enzymes using 529 DNA samples from SSRI-treated MDD patients. The rs10975641 SNP in the glycine dehydrogenase gene was associated with treatment outcome phenotypes. Rs10975641 was then genotyped and was significant (p=0.02) in DNA from 1245 MDD patients in the STAR*D depression study. These results highlight both a possible role for glycine in SSRI response and the use of pharmacometabolomics to "inform" pharmacogenomics. Major depressive disorder (MDD) is a common psychiatric disorder worldwide (1). The majority of these patients receive antidepressant medications as first-line treatment, but there are large variations in the efficacy of all antidepressants, including the widely prescribed selective serotonin reuptake inhibitors (SSRIs) (2). On average, 40% of patients do not "respond" to these drugs, defined as a 50% or greater reduction in symptoms, and over two thirds do not achieve complete "remission" of symptoms after antidepressant therapy (2,3). Therefore, there is a need to identify biomarkers that might help to predict treatment outcomes prior to antidepressant therapy and might also provide insight into drug response mechanisms.Metabolomics is a rapidly developing discipline that represents an attempt to capture global biochemical events by assaying the metabolome, the total repertoire of small molecules in biological samples, to define metabolomic "signatures" (4,5). The emerging field of pharmacometabolomics is focused on metabolomic signatures for drug exposure and/or efficacy, with the goal of using these signatures to better individualize drug therapy (4,6). Pharmacogenomics shares the goals of pharmacometabolomics but utilizes genomic rather than metabolomic data (7). Many pharmacogenetic studies of antidepressant drugs, particularly SSRIs, have been performed. Those studies have generally focused on polymorphisms in candidate genes, including those encoding the serotonin transporter; a variety of serotonin receptors; enzymes involved in serotonin biosynthesis; and drug metabolizing enzymes specific to the particular SSRI being studied (8-10). However, these candidate gene-based studies, and even recently published genome-wide association studies (GWAS), have failed to provide reliable biomarkers for SSRI treatment outcome (11-13).In the present study, a "pharmacometabolomics-informed pharmacogenomic" research strategy ( ...

Multiple factors adversely affected morbidity and mortality after pneumonectomy for malignant disease. Appropriate selection and meticulous perioperative care are paramount to minimize risks in those patients who require pneumonectomy.