Eric W. Klee

Preemptive genotyping for personalized medicine: design of the right drug, right dose, right time-using genomic data to individualize treatment protocol.

OBJECTIVE To report the design and implementation of the Right Drug, Right Dose, Right Time-Using Genomic Data to Individualize Treatment protocol that was developed to test the concept that prescribers can deliver genome-guided therapy at the point of care by using preemptive pharmacogenomics (PGx) data and clinical decision support (CDS) integrated into the electronic medical record (EMR). PATIENTS AND METHODS We used a multivariate prediction model to identify patients with a high risk of initiating statin therapy within 3 years. The model was used to target a study cohort most likely to benefit from preemptive PGx testing among the Mayo Clinic Biobank participants, with a recruitment goal of 1000 patients. We used a Cox proportional hazards model with variables selected through the Lasso shrinkage method. An operational CDS model was adapted to implement PGx rules within the EMR. RESULTS The prediction model included age, sex, race, and 6 chronic diseases categorized by the Clinical Classifications Software for International Classification of Diseases, Ninth Revision codes (dyslipidemia, diabetes, peripheral atherosclerosis, disease of the blood-forming organs, coronary atherosclerosis and other heart diseases, and hypertension). Of the 2000 Biobank participants invited, 1013 (51%) provided blood samples, 256 (13%) declined participation, 555 (28%) did not respond, and 176 (9%) consented but did not provide a blood sample within the recruitment window (October 4, 2012, through March 20, 2013). Preemptive PGx testing included CYP2D6 genotyping and targeted sequencing of 84 PGx genes. Synchronous real-time CDS was integrated into the EMR and flagged potential patient-specific drug-gene interactions and provided therapeutic guidance. CONCLUSION This translational project provides an opportunity to begin to evaluate the impact of preemptive sequencing and EMR-driven genome-guided therapy. These interventions will improve understanding and implementation of genomic data in clinical practice.

Integrated genomic characterization reveals novel, therapeutically relevant drug targets in FGFR and EGFR pathways in sporadic intrahepatic cholangiocarcinoma.

Advanced cholangiocarcinoma continues to harbor a difficult prognosis and therapeutic options have been limited. During the course of a clinical trial of whole genomic sequencing seeking druggable targets, we examined six patients with advanced cholangiocarcinoma. Integrated genome-wide and whole transcriptome sequence analyses were performed on tumors from six patients with advanced, sporadic intrahepatic cholangiocarcinoma (SIC) to identify potential therapeutically actionable events. Among the somatic events captured in our analysis, we uncovered two novel therapeutically relevant genomic contexts that when acted upon, resulted in preliminary evidence of anti-tumor activity. Genome-wide structural analysis of sequence data revealed recurrent translocation events involving the FGFR2 locus in three of six assessed patients. These observations and supporting evidence triggered the use of FGFR inhibitors in these patients. In one example, preliminary anti-tumor activity of pazopanib (in vitro FGFR2 IC50≈350 nM) was noted in a patient with an FGFR2-TACC3 fusion. After progression on pazopanib, the same patient also had stable disease on ponatinib, a pan-FGFR inhibitor (in vitro, FGFR2 IC50≈8 nM). In an independent non-FGFR2 translocation patient, exome and transcriptome analysis revealed an allele specific somatic nonsense mutation (E384X) in ERRFI1, a direct negative regulator of EGFR activation. Rapid and robust disease regression was noted in this ERRFI1 inactivated tumor when treated with erlotinib, an EGFR kinase inhibitor. FGFR2 fusions and ERRFI mutations may represent novel targets in sporadic intrahepatic cholangiocarcinoma and trials should be characterized in larger cohorts of patients with these aberrations.

Global Methylation Profiling for Risk Prediction of Prostate Cancer

Purpose: The aim of this study was to investigate the promoter hypermethylation as diagnostic markers to detect malignant prostate cells and as prognostic markers to predict the clinical recurrence of prostate cancer. Experimental Design: DNA was isolated from prostate cancer and normal adjacent tissues. After bisulfite conversion, methylation of 14,495 genes was evaluated using the Methylation27 microarrays in 238 prostate tissues. We analyzed methylation profiles in four different groups: (i) tumor (n = 198) versus matched normal tissues (n = 40), (ii) recurrence (n = 123) versus nonrecurrence (n = 75), (iii) clinical recurrence (n = 80) versus biochemical recurrence (n = 43), and (iv) systemic recurrence (n = 36) versus local recurrence (n = 44). Group 1, 2, 3, and 4 genes signifying biomarkers for diagnosis, prediction of recurrence, clinical recurrence, and systemic progression were determined. Univariate and multivariate analyses were conducted to predict risk of recurrence. We validated the methylation of genes in 20 independent tissues representing each group by pyrosequencing. Results: Microarray analysis revealed significant methylation of genes in four different groups of prostate cancer tissues. The sensitivity and specificity of methylation for 25 genes from 1, 2, and 4 groups and 7 from group 3 were shown. Validation of genes by pyrosequencing from group 1 (GSTP1, HIF3A, HAAO, and RARβ), group 2 (CRIP1, FLNC, RASGRF2, RUNX3, and HS3ST2), group 3 (PHLDA3, RASGRF2, and TNFRSF10D), and group 4 (BCL11B, POU3F3, and RASGRF2) confirmed the microarray results. Conclusions: Our study provides a global assessment of DNA methylation in prostate cancer and identifies the significance of genes as diagnostic and progression biomarkers of prostate cancer. Clin Cancer Res; 18(10); 2882–95. ©2012 AACR.

Gene Panel Model Predictive of Outcome in Men at High-Risk of Systemic Progression and Death From Prostate Cancer After Radical Retropubic Prostatectomy

PURPOSE In men who are at high-risk of prostate cancer, progression and death from cancer after radical retropubic prostatectomy (RRP), limited prognostic information is provided by established prognostic features. The objective of this study was to develop a model predictive of outcome in this group of patients. METHODS Candidate genes were identified from microarray expression data from 102 laser capture microdissected prostate tissue samples. Candidates were overexpressed in tumor compared with normal prostate and more frequently in Gleason patterns 4 and 5 than in 3. A case control study of 157 high-risk patients, matched on Gleason score and stage with systemic progression or death of prostate cancer as the end point, was used to evaluate the expression of candidate genes and build a multivariate model. Tumor was collected from the highest Gleason score in paraffin-embedded blocks and the gene expression was quantified by real-time reverse transcription polymerase chain reaction. Validation of the final model was performed on a separate case-control study of 57 high-risk patients who underwent RRP. RESULTS A model incorporating gene expression of topoisomerase-2a, cadherin-10, the fusion status based on ERG, ETV1, and ETV4 expression, and the aneuploidy status resulted in a 0.81 area under the curve (AUC) in receiver operating characteristic statistical analysis for the identification of men with systemic progression and death from high grade prostate cancer. The AUC was 0.79 in the independent validation study. CONCLUSION The model can identify men with high-risk prostate cancer who may benefit from more intensive postoperative follow-up and adjuvant therapies.

Adrenomedullin is Up-regulated in Patients With Pancreatic Cancer and Causes Insulin Resistance in β Cells and Mice

BACKGROUND & AIMS New-onset diabetes in patients with pancreatic cancer is likely to be a paraneoplastic phenomenon caused by tumor-secreted products. We aimed to identify the diabetogenic secretory product(s) of pancreatic cancer. METHODS Using microarray analysis, we identified adrenomedullin as a potential mediator of diabetes in patients with pancreatic cancer. Adrenomedullin was up-regulated in pancreatic cancer cell lines, in which supernatants reduced insulin signaling in beta cell lines. We performed quantitative reverse-transcriptase polymerase chain reaction and immunohistochemistry on human pancreatic cancer and healthy pancreatic tissues (controls) to determine expression of adrenomedullin messenger RNA and protein, respectively. We studied the effects of adrenomedullin on insulin secretion by beta cell lines and whole islets from mice and on glucose tolerance in pancreatic xenografts in mice. We measured plasma levels of adrenomedullin in patients with pancreatic cancer, patients with type 2 diabetes mellitus, and individuals with normal fasting glucose levels (controls). RESULTS Levels of adrenomedullin messenger RNA and protein were increased in human pancreatic cancer samples compared with controls. Adrenomedullin and conditioned media from pancreatic cell lines inhibited glucose-stimulated insulin secretion from beta cell lines and islets isolated from mice; the effects of conditioned media from pancreatic cancer cells were reduced by small hairpin RNA-mediated knockdown of adrenomedullin. Conversely, overexpression of adrenomedullin in mice with pancreatic cancer led to glucose intolerance. Mean plasma levels of adrenomedullin (femtomoles per liter) were higher in patients with pancreatic cancer compared with patients with diabetes or controls. Levels of adrenomedullin were higher in patients with pancreatic cancer who developed diabetes compared those who did not. CONCLUSIONS Adrenomedullin is up-regulated in patients with pancreatic cancer and causes insulin resistance in β cells and mice.

Evaluating eukaryotic secreted protein prediction

BackgroundImprovements in protein sequence annotation and an increase in the number of annotated protein databases has fueled development of an increasing number of software tools to predict secreted proteins. Six software programs capable of high throughput and employing a wide range of prediction methods, SignalP 3.0, SignalP 2.0, TargetP 1.01, PrediSi, Phobius, and ProtComp 6.0, are evaluated.ResultsPrediction accuracies were evaluated using 372 unbiased, eukaryotic, SwissProt protein sequences. TargetP, SignalP 3.0 maximum S-score and SignalP 3.0 D-score were the most accurate single scores (90–91% accurate). The combination of a positive TargetP prediction, SignalP 2.0 maximum Y-score, and SignalP 3.0 maximum S-score increased accuracy by six percent.ConclusionSingle predictive scores could be highly accurate, but almost all accuracies were slightly less than those reported by program authors. Predictive accuracy could be substantially improved by combining scores from multiple methods into a single composite prediction.

Genome-Wide Reverse Genetics Framework to Identify Novel Functions of the Vertebrate Secretome

Background Understanding the functional role(s) of the more than 20,000 proteins of the vertebrate genome is a major next step in the post-genome era. The approximately 4,000 co-translationally translocated (CTT) proteins – representing the vertebrate secretome – are important for such vertebrate-critical processes as organogenesis. However, the role(s) for most of these genes is currently unknown. Results We identified 585 putative full-length zebrafish CTT proteins using cross-species genomic and EST-based comparative sequence analyses. We further investigated 150 of these genes (Figure 1) for unique function using morpholino-based analysis in zebrafish embryos. 12% of the CTT protein-deficient embryos resulted in specific developmental defects, a notably higher rate of gene function annotation than the 2%–3% estimate from random gene mutagenesis studies. Conclusion(s) This initial collection includes novel genes required for the development of vascular, hematopoietic, pigmentation, and craniofacial tissues, as well as lipid metabolism, and organogenesis. This study provides a framework utilizing zebrafish for the systematic assignment of biological function in a vertebrate genome.

TREAT: a bioinformatics tool for variant annotations and visualizations in targeted and exome sequencing data

Summary: TREAT (Targeted RE-sequencing Annotation Tool) is a tool for facile navigation and mining of the variants from both targeted resequencing and whole exome sequencing. It provides a rich integration of publicly available as well as in-house developed annotations and visualizations for variants, variant-hosting genes and host-gene pathways. Availability and implementation: TREAT is freely available to non-commercial users as either a stand-alone annotation and visualization tool, or as a comprehensive workflow integrating sequencing alignment and variant calling. The executables, instructions and the Amazon Cloud Images of TREAT can be downloaded at the website: http://ndc.mayo.edu/mayo/research/biostat/stand-alone-packages.cfm Contact: Hossain.Asif@mayo.edu; Kocher.JeanPierre@mayo.edu Supplementary information: Supplementary data are provided at Bioinformatics online.

Identification of Prognostic Biomarkers for Prostate Cancer

Purpose: This paper describes a process for the identification of genes that can report on the aggressiveness of prostate tumors and thereby add to the information provided by current pathologic analysis. Materials and Methods: Expression profiling data from over 100 laser capture microdissection derived samples from nonneoplastic epithelium; Gleason patterns 3, 4, and 5 and node metastasis prostate cancer were used to identify genes at abnormally high levels in only some tumors. These variably overexpressed genes were stratified by their association with aggressive phenotypes and were subsequently filtered to exclude genes with redundant expression patterns. Selected genes were validated in a case-control study in which cases (systemic progression within 5 years) and controls (no systemic progression at 7 years of follow-up) were matched for all clinical and pathologic criteria from time of prostatectomy (n = 175). Both cases and controls, therefore, could have nodal invasion or seminal vesicle involvement at the time of initial treatment. Results: A number of candidate variably overexpressed genes selected for their association with aggressive prostate cancer phenotype were evaluated in the case control study. The most prominent candidates were SSTR1 and genes related to proliferation, including TOP2A. Conclusions: The process described here identified genes that add information not available from current clinical measures and can improve the prognosis of prostate cancer.

Expanding DNA diagnostic panel testing: is more better?

During the last 25 years, a small number of meaningful DNA-based diagnostic tests have been available to aid in the diagnosis and subsequent treatment of heritable disorders. These tests have targeted a limited number of genes and are often ordered in serial testing strategies in which results from one preliminary test dictate the subsequent test orders. This approach can be both time and resource intensive when a patient requires several genes to be sequenced. Recently, there has been much discussion regarding how ‘massively parallel’ or ‘next-generation’ DNA sequencing will impact clinical care. While the technology promises to reduce the cost of sequencing an entire human genome to less than US