Department of the Governor's Office of Drug Control Policy, FY 2008

These Facts sheets have been developed to provide a multitude of information about executive branch agencies/departments on a single sheet of paper. The Facts provides general information, contact information, workforce data, leave & benefits information, and affirmative action data. This is the most recent update of information for the fiscal year 2007.

Batch effect confounding leads to strong bias in performance estimates obtained by cross-validation.

BACKGROUND: With the large amount of biological data that is currently publicly available, many investigators combine multiple data sets to increase the sample size and potentially also the power of their analyses. However, technical differences ("batch effects") as well as differences in sample composition between the data sets may significantly affect the ability to draw generalizable conclusions from such studies. FOCUS: The current study focuses on the construction of classifiers, and the use of cross-validation to estimate their performance. In particular, we investigate the impact of batch effects and differences in sample composition between batches on the accuracy of the classification performance estimate obtained via cross-validation. The focus on estimation bias is a main difference compared to previous studies, which have mostly focused on the predictive performance and how it relates to the presence of batch effects. DATA: We work on simulated data sets. To have realistic intensity distributions, we use real gene expression data as the basis for our simulation. Random samples from this expression matrix are selected and assigned to group 1 (e.g., 'control') or group 2 (e.g., 'treated'). We introduce batch effects and select some features to be differentially expressed between the two groups. We consider several scenarios for our study, most importantly different levels of confounding between groups and batch effects. METHODS: We focus on well-known classifiers: logistic regression, Support Vector Machines (SVM), k-nearest neighbors (kNN) and Random Forests (RF). Feature selection is performed with the Wilcoxon test or the lasso. Parameter tuning and feature selection, as well as the estimation of the prediction performance of each classifier, is performed within a nested cross-validation scheme. The estimated classification performance is then compared to what is obtained when applying the classifier to independent data.

Iowa Efficiency Review Report to Governor Chet Culver and Lieutenant Governor Patty Judge, December 18,2009

Public Works is pleased to present the following Iowa Efficiency Review Report to Governor Chet Culver and Lieutenant Governor Patty Judge. This report is the product of a collaboration between our consulting team and very dedicated Iowa state employees who worked with us to share ideas and cost‐saving proposals under very difficult circumstances caused by the national financial crisis that is affecting state budgets throughout the country. For example, during the course of this review, Iowa departments were also asked to develop across‐the‐board cuts to achieve immediate reductions in state spending. It is a credit to Iowa state government that departmental staff continued to work on this Efficiency Review Report despite these challenges of also having to develop across‐the‐board budget cuts to achieve a balanced budget. We hope that these ideas will set the stage for further future budget improvements from achieving efficiencies, eliminating outdated practices, increasing the use of information technology solutions and finding new sources of non‐tax funding. The Efficiency Review Team faced a second challenge. Statewide Efficiency Reviews usually take from nine to 12 months to complete. In Iowa, we worked with dedicated department staff to complete our work in less than 4 months. The Governor challenged all of us to work intensely and to give him our best thinking on efficiency proposals so that he could act as quickly as possible to position state government for success over the next several years.

Strong time dependence of the 76-gene prognostic signature for node-negative breast cancer patients in the TRANSBIG multicenter independent validation series.

PURPOSE: Recently, a 76-gene prognostic signature able to predict distant metastases in lymph node-negative (N(-)) breast cancer patients was reported. The aims of this study conducted by TRANSBIG were to independently validate these results and to compare the outcome with clinical risk assessment. EXPERIMENTAL DESIGN: Gene expression profiling of frozen samples from 198 N(-) systemically untreated patients was done at the Bordet Institute, blinded to clinical data and independent of Veridex. Genomic risk was defined by Veridex, blinded to clinical data. Survival analyses, done by an independent statistician, were done with the genomic risk and adjusted for the clinical risk, defined by Adjuvant! Online. RESULTS: The actual 5- and 10-year time to distant metastasis were 98% (88-100%) and 94% (83-98%), respectively, for the good profile group and 76% (68-82%) and 73% (65-79%), respectively, for the poor profile group. The actual 5- and 10-year overall survival were 98% (88-100%) and 87% (73-94%), respectively, for the good profile group and 84% (77-89%) and 72% (63-78%), respectively, for the poor profile group. We observed a strong time dependence of this signature, leading to an adjusted hazard ratio of 13.58 (1.85-99.63) and 8.20 (1.10-60.90) at 5 years and 5.11 (1.57-16.67) and 2.55 (1.07-6.10) at 10 years for time to distant metastasis and overall survival, respectively. CONCLUSION: This independent validation confirmed the performance of the 76-gene signature and adds to the growing evidence that gene expression signatures are of clinical relevance, especially for identifying patients at high risk of early distant metastases.

Integrated Analysis of High-Resolution Gene Expression and Copy Number Profiling Identified Biallelic Deletion of CDKN2A/2B Tumor Suppressor Locus As the Most Frequent and Unique Genomic Abnormality in Diffuse Large B-Cell Lymphoma (DLBCL) with Strong Prognostic Value in Both GCB and ABC Subtypes and Not Overcome by a Dose-Intensive Immunochemotherapy Regimen Plus Rituximab. Results of a Prospective GELA Clinical Trial Program

Background and aim of the study: Genomic gains and losses play a crucial role in the development and progression of DLBCL and are closely related to gene expression profiles (GEP), including the germinal center B-cell like (GCB) and activated B-cell like (ABC) cell of origin (COO) molecular signatures. To identify new oncogenes or tumor suppressor genes (TSG) involved in DLBCL pathogenesis and to determine their prognostic values, an integrated analysis of high-resolution gene expression and copy number profiling was performed. Patients and methods: Two hundred and eight adult patients with de novo CD20+ DLBCL enrolled in the prospective multicentric randomized LNH-03 GELA trials (LNH03-1B, -2B, -3B, 39B, -5B, -6B, -7B) with available frozen tumour samples, centralized reviewing and adequate DNA/RNA quality were selected. 116 patients were treated by Rituximab(R)-CHOP/R-miniCHOP and 92 patients were treated by the high dose (R)-ACVBP regimen dedicated to patients younger than 60 years (y) in frontline. Tumour samples were simultaneously analysed by high resolution comparative genomic hybridization (CGH, Agilent, 144K) and gene expression arrays (Affymetrix, U133+2). Minimal common regions (MCR), as defined by segments that affect the same chromosomal region in different cases, were delineated. Gene expression and MCR data sets were merged using Gene expression and dosage integrator algorithm (GEDI, Lenz et al. PNAS 2008) to identify new potential driver genes. Results: A total of 1363 recurrent (defined by a penetrance > 5%) MCRs within the DLBCL data set, ranging in size from 386 bp, affecting a single gene, to more than 24 Mb were identified by CGH. Of these MCRs, 756 (55%) showed a significant association with gene expression: 396 (59%) gains, 354 (52%) single-copy deletions, and 6 (67%) homozygous deletions. By this integrated approach, in addition to previously reported genes (CDKN2A/2B, PTEN, DLEU2, TNFAIP3, B2M, CD58, TNFRSF14, FOXP1, REL...), several genes targeted by gene copy abnormalities with a dosage effect and potential physiopathological impact were identified, including genes with TSG activity involved in cell cycle (HACE1, CDKN2C) immune response (CD68, CD177, CD70, TNFSF9, IRAK2), DNA integrity (XRCC2, BRCA1, NCOR1, NF1, FHIT) or oncogenic functions (CD79b, PTPRT, MALT1, AUTS2, MCL1, PTTG1...) with distinct distribution according to COO signature. The CDKN2A/2B tumor suppressor locus (9p21) was deleted homozygously in 27% of cases and hemizygously in 9% of cases. Biallelic loss was observed in 49% of ABC DLBCL and in 10% of GCB DLBCL. This deletion was strongly correlated to age and associated to a limited number of additional genetic abnormalities including trisomy 3, 18 and short gains/losses of Chr. 1, 2, 19 regions (FDR < 0.01), allowing to identify genes that may have synergistic effects with CDKN2A/2B inactivation. With a median follow-up of 42.9 months, only CDKN2A/2B biallelic deletion strongly correlates (FDR p.value < 0.01) to a poor outcome in the entire cohort (4y PFS = 44% [32-61] respectively vs. 74% [66-82] for patients in germline configuration; 4y OS = 53% [39-72] vs 83% [76-90]). In a Cox proportional hazard prediction of the PFS, CDKN2A/2B deletion remains predictive (HR = 1.9 [1.1-3.2], p = 0.02) when combined with IPI (HR = 2.4 [1.4-4.1], p = 0.001) and GCB status (HR = 1.3 [0.8-2.3], p = 0.31). This difference remains predictive in the subgroup of patients treated by R-CHOP (4y PFS = 43% [29-63] vs. 66% [55-78], p=0.02), in patients treated by R-ACVBP (4y PFS = 49% [28-84] vs. 83% [74-92], p=0.003), and in GCB (4y PFS = 50% [27-93] vs. 81% [73-90], p=0.02), or ABC/unclassified (5y PFS = 42% [28-61] vs. 67% [55-82] p = 0.009) molecular subtypes (Figure 1). Conclusion: We report for the first time an integrated genetic analysis of a large cohort of DLBCL patients included in a prospective multicentric clinical trial program allowing identifying new potential driver genes with pathogenic impact. However CDKN2A/2B deletion constitutes the strongest and unique prognostic factor of chemoresistance to R-CHOP, regardless the COO signature, which is not overcome by a more intensified immunochemotherapy. Patients displaying this frequent genomic abnormality warrant new and dedicated therapeutic approaches.

Audit report on the Office of Governor for the year ended June 30, 2008

Audit report on the Office of Governor for the year ended June 30, 2008

Brain Tissue Hypoxia is a Strong Predictor of Outcome after Severe Traumatic Brain Injury Independent from Elevated Intracranial Pressure

Introduction: Low brain tissue oxygen pressure (PbtO2) is associated with worse outcome in patients with severe traumatic brain injury (TBI). However, it is unclear whether brain tissue hypoxia is merely a marker of injury severity or a predictor of prognosis, independent from intracranial pressure (ICP) and injury severity. Hypothesis: We hypothesized that brain tissue hypoxia was an independent predictor of outcome in patients wih severe TBI, irrespective of elevated ICP and of the severity of cerebral and systemic injury. Methods: This observational study was conducted at the Neurological ICU, Hospital of the University of Pennsylvania, an academic level I trauma center. Patients admitted with severe TBI who had PbtO2 and ICP monitoring were included in the study. PbtO2, ICP, mean arterial pressure (MAP) and cerebral perfusion pressure (CPP = MAP-ICP) were monitored continuously and recorded prospectively every 30 min. Using linear interpolation, duration and cumulative dose (area under the curve, AUC) of brain tissue hypoxia (PbtO2 < 15 mm Hg), elevated ICP >20 mm Hg and low CPP <60 mm Hg were calculated, and the association with outcome at hospital discharge, dichotomized as good (Glasgow Outcome Score [GOS] 4-5) vs. poor (GOS 1-3), was analyzed. Results: A total of 103 consecutive patients, monitored for an average of 5 days, was studied. Brain tissue hypoxia was observed in 66 (64%) patients despite ICP was < 20 mm Hg and CPP > 60 mm Hg (72 +/- 39% and 49 +/- 41% of brain hypoxic time, respectively). Compared with patients with good outcome, those with poor outcome had a longer duration of brain hypoxia (1.7 +/- 3.7 vs. 8.3 +/- 15.9 hrs, P<0.01), as well as a longer duration (11.5 +/- 16.5 vs. 21.6 +/- 29.6 hrs, P=0.03) and a greater cumulative dose (56 +/- 93 vs. 143 +/- 218 mm Hg*hrs, P<0.01) of elevated ICP. By multivariable logistic regression, admission Glasgow Coma Scale (OR, 0.83, 95% CI: 0.70-0.99, P=0.04), Marshall CT score (OR 2.42, 95% CI: 1.42-4.11, P<0.01), APACHE II (OR 1.20, 95% CI: 1.03-1.43, P=0.03), and the duration of brain tissue hypoxia (OR 1.13; 95% CI: 1.01-1.27; P=0.04) were all significantly associated with poor outcome. No independent association was found between the AUC for elevated ICP and outcome (OR 1.01, 95% CI 0.97-1.02, P=0.11) in our prospective cohort. Conclusions: In patients with severe TBI, brain tissue hypoxia is frequent, despite normal ICP and CPP, and is associated with poor outcome, independent of intracranial hypertension and the severity of cerebral and systemic injury. Our findings indicate that PbtO2 is a strong physiologic prognostic marker after TBI. Further study is warranted to examine whether PbtO2-directed therapy improves outcome in severely head-injured patients .

Iowa Insurance Division Report to the Governor for Year End 2001

Iowa Insurance Division Report to the Governor for Year End 2001

Iowa Insurance Division Report to the Governor for Year End 2002

Iowa Insurance Division Report to the Governor for Year End 2003

Iowa Insurance Division Report to the Governor for Year End 2003

Iowa Insurance Division Report to the Governor for Year End 2003

Iowa Insurance Division Report to the Governor for Year End 2004

Iowa Insurance Division Report to the Governor for Year End 2004

Iowa Insurance Division Report to the Governor for Year End 2005

Iowa Insurance Division Report to the Governor for Year End 2005

Iowa Insurance Division Report to the Governor for Year End 2006

Iowa Insurance Division Report to the Governor for Year End 2006