Citations

Background Censoring that is dependent on covariates associated with survival can arise in randomized trials due to changes in recruitment and eligibility criteria to minimize withdrawals, potentially leading to biased treatment effect estimates. Imputation approaches have been proposed to address censoring in survival analysis; while these approaches may provide unbiased estimates of treatment effects, imputation of a large number of outcomes may over- or underestimate the associated variance based on the imputation pool selected. Purpose We propose an improved method, risk-stratified imputation, as an alternative to address withdrawal related to the risk of events in the context of time-to-event analyses. Methods Our algorithm performs imputation from a pool of replacement subjects with similar values of both treatment and covariate(s) of interest, that is, from a risk-stratified sample. This stratification prior to imputation addresses the requirement of time-to-event analysis that censored observations are representative of all other observations in the risk group with similar exposure variables. We compared our risk-stratified imputation to case deletion and bootstrap imputation in a simulated dataset in which the covariate of interest (study withdrawal) was related to treatment. A motivating example from a recent clinical trial is also presented to demonstrate the utility of our method. Results In our simulations, risk-stratified imputation gives estimates of treatment effect comparable to bootstrap and auxiliary variable imputation while avoiding inaccuracies of the latter two in estimating the associated variance. Similar results were obtained in analysis of clinical trial data. Limitations Risk-stratified imputation has little advantage over other imputation methods when covariates of interest are not related to treatment. Risk-stratified imputation is intended for categorical covariates and may be sensitive to the width of the matching window if continuous covariates are used. Conclusions The use of the risk-stratified imputation should facilitate the analysis of many clinical trials, in which one group has a higher withdrawal rate that is related to treatment.

Abstract Microarray gene expression profiling has facilitated identification of molecular subtypes which can be associated with disease outcome and treatment response. However current platforms lack disease focus, potentially missing vital information contained in patient tissue samples. Moreover, a wide range of FFPE sample cohorts are available but their use is limited by the mRNA degradation inherent to the sample fixation process. By combining the Breast Cancer DSA® with powerful data analysis methodology, we reproduced the five major molecular subtypes in breast cancer based on a cohort of 107 FFPE samples. Furthermore, we showed that the established subtypes do not extend to BRCA1 and BRCA2 mutant tumours, necessitating expansion of the original breast cancer subtypes definition. Following RMA pre-processing of the 107 raw profiles, the data was transformed to overcome the association between profile quality and expression variability. If left untreated, this technical variability, often observed with FFPE tissues, can impair the study of inherent biological variations. Using the intrinsic list of 277 genes previously identified as differentiating breast cancer subtypes in fresh frozen samples [Sorlie et al., 2003], initially the sporadic samples were examined using hierarchical agglomerative clustering. All five original subtypes were identified, verifying the ability to detect subtypes in FFPE samples using the breast cancer DSA®. When the intrinsic list was applied to all 107 samples including BRCA1 and BRCA2 mutant samples, the subtypes were still distinguishable, although 31% of the mutant tumors were unclassified suggesting that specific biological processes defining BRCA1/BRCA2 mutant tumours are not captured by the original breast cancer molecular classification. We have successfully overcome the limitation imposed by FFPE sample microarray profiling by using the Breast Cancer DSA® and advanced data manipulation and exploration to reproduce molecular subtypes previously identified using fresh frozen samples. Further investigation to extend the previously identifed subtypes will progress the investigation of the heterogeneous biology underlying breast cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 326. doi:10.1158/1538-7445.AM2011-326

ABSTRACT Underlying mechanisms of individual variation in severity of influenza infection and response to vaccination are poorly understood. We investigated the effect of reduced heme oxygenase‐1 (HO‐1) expression on vaccine response and outcome of influenza infection. HO‐1‐deficient and wild‐type (WT) mice (kingdom, Animalia; phylum, Chordata; genus/species, Mus musculus ) were infected with influenza virus A/PR/8/34 with or without prior vaccination with an adeno‐viral‐based influenza vaccine. A genome‐wide association study evaluated the expression of single‐nucleotide polymorphisms (SNPs) in the HO‐1 gene and the response to influenza vaccination in healthy humans. HO‐1‐deficient mice had decreased survival after influenza infection compared to WT mice (median survival 5.5 vs. 6.5 d, P =0.016). HO‐1‐deficient mice had impaired production of antibody following influenza vaccination compared to WT mice (mean antibody titer 869 vs. 1698, P =0.02). One SNP in HO‐1 and one SNP in the constitutively expressed isoform HO‐2 were independently associated with decreased antibody production after influenza vaccination in healthy human volunteers ( P =0.017 and 0.014, respectively). HO‐1 deficient mice were paired with sex‐ and age‐matched WT controls. HO‐1 affects the immune response to both influenza infection and vaccination, suggesting that therapeutic induction of HO‐1 expression may represent a novel adjuvant to enhance influenza vaccine effectiveness.—Cummins, N. W., Weaver, E. A., May, S. M., Croatt, A. J., Foreman, O., Kennedy, R. B., Poland, G. A., Barry, M. A., Nath, K. A., Badley, A. D. Heme oxygenase‐1 regulates the immune response to influenza virus infection and vaccination in aged mice. FASEB J. 26, 2911–2918 (2012). www.fasebj.org

Abstract Despite the tremendous success of the classical “isolate, inactivate, and inject” approach to vaccine development, new breakthroughs in vaccine research are increasingly reliant on novel approaches that incorporate cutting edge technology and advances in innate and adaptive immunology, microbiology, virology, pathogen biology, genetics, bioinformatics, and many other disciplines in order to: (1) deepen our understanding of the key biological processes that lead to protective immunity, (2) observe vaccine responses on a global, systems level, and (3) directly apply the new knowledge gained to the development of next-generation vaccines with improved safety profiles, enhanced efficacy, and even targeted utility in select populations. Here we highlight five key components foundational to vaccinomics efforts: applied immunogenomics, next generation sequencing and other cutting-edge “omics” technologies, advanced bioinformatics and analysis techniques, and finally, systems biology applied to immune profiling and vaccine responses. We believe these “game changers” will play a critical role in moving us toward the rational and directed development of new vaccines in the 21st century.

Abstract In this article we define vaccinomics as the integration of immunogenetics and immunogenomics with systems biology and immune profiling. Vaccinomics is based on the use of cutting edge, high-dimensional (so called “omics”) assays and novel bioinformatics approaches to the development of next-generation vaccines and the expansion of our capabilities in individualized medicine. Vaccinomics will allow us to move beyond the empiric “isolate, inactivate, and inject” approach characterizing past vaccine development efforts, and toward a more detailed molecular and systemic understanding of the carefully choreographed series of biological processes involved in developing viral vaccine-induced “immunity.” This enhanced understanding will then be applied to overcome the obstacles to the creation of effective vaccines to protect against pathogens, particularly hypervariable viruses, with the greatest current impact on public health. Here we provide an overview of how vaccinomics will inform vaccine science, the development of new vaccines and/or clinically relevant biomarkers or surrogates of protection, vaccine response heterogeneity, and our understanding of immunosenescence.

Abstract Background The BRCA 1 tumour suppressor gene is mutated in a significant proportion of hereditary breast cancer cases. Additionally, downregulation of BRCA1 mRNA and protein expression is reported in approximately one third of sporadic breast cancers. BRCA1 is strongly implicated in the maintenance of genomic stability by its involvement in multiple cellular pathways including: DNA damage signalling, DNA repair, cell cycle regulation, protein ubiquitination, chromatin remodelling, transcriptional regulation and apoptosis. To date, gene expression profiling has identified: (1) at least five breast cancer subtypes and (2) that BRCA1 mutant tumours segregate with basal-like breast cancers. These studies also provide evidence that breast cancers with germline mutations in BRCA1 are different from non BRCA1-related tumours. The main aim of this study is to investigate the underlying biology of BRCA1-mutated breast cancer. Methods Extensive gene expression profiling and data analysis were performed on a cohort of 70 FFPE (Formalin Fixed Paraffin Embedded) derived BRCA 1 mutated breast tumours and matched sporadic controls using the Almac Breast Cancer DSATM research tool. Functional analysis was performed with DAVID and METACORE. Validation of gene targets was performed by qRT-PCR and Western blotting. Results A list of differentially expressed transcripts has been derived from the comparison of these BRCA1 mutant breast tumours and matched sporadic controls. Functional analysis of this gene list has identified the key genes and molecular pathways that are deregulated in these tumours. BRCA1 deficiency was associated with deregulation of pathways involved in: (1) immune response, (2) metastasis and invasion, (3) cytoskeletal remodelling, (4) spindle assembly and chromosome separation, (5) apoptosis and survival. Validation of the key genes underlying this BRCA1-deficient breast cancer profile has been performed. Conclusions This approach has revealed a set of transcripts that could potentially be used to identify both hereditary and sporadic breast cancer patients with BRCA1- deficiency. The ability to perform gene expression profiling from FFPE derived breast tissue could also have significant clinical application. Citation Information: Cancer Res 2010;70(24 Suppl):Abstract nr P6-04-02.