Citations

Background— Coronary artery disease (CAD) is the major cause of death in patients with type 2 diabetes mellitus. Although demographic and clinical factors associated with extent of CAD in patients with diabetes mellitus have been described, genetic factors have not. We hypothesized that genetic variation in peroxisome proliferator-activated receptor (PPAR) pathway genes, important in diabetes mellitus and atherosclerosis, would be associated with extent of CAD in patients with diabetes mellitus. Methods and Results— We genotyped 1043 patients (702 white, 175 blacks) from the Bypass Angioplasty Revascularization Investigation 2 Diabetes (BARI 2D) genetic cohort for 3351 variants in 223 PPAR pathway genes using a custom targeted-genotyping array. Angiographic end points were determined by a core laboratory. In whites, a single variant (rs1503298) in TLL1 was significantly ( P =5.5×10 −6 ) associated with extent of CAD, defined as number of lesions with percent diameter stenosis ≥20%, after stringent Bonferroni correction for all 3351 single nucleotide polymorphisms. This association was validated in the diabetic subgroups of 2 independent cohorts, the Translational Research Investigating Underlying Disparities in Acute Myocardial Infarction Patients' Health Status (TRIUMPH) post–myocardial infarction registry and the prospective Family Heart Study (FHS) of individuals at risk for CAD. TLL1 rs1503298 was also significantly associated with extent of severe CAD (≥70% diameter stenosis; P =3.7×10 −2 ) and myocardial jeopardy index ( P =8.7×10 −4 ). In general linear regression modeling, TLL1 rs1503298 explained more variance of extent of CAD than the previously determined clinical factors. Conclusions— We identified a variant in a single PPAR pathway gene, TLL1 , that is associated with the extent of CAD independently of clinical predictors, specifically in patients with type 2 diabetes mellitus and CAD. Clinical Trial Registration— URL: http://www.clinicaltrials.gov . Unique identifier: NCT00006305.

Abstract Background: Controversy exists regarding the association between CYP2D6 enzyme activity and tamoxifen effectiveness in the adjuvant treatment of invasive breast cancer; however, this association in the primary prevention of breast cancer is unknown. Methods: We conducted a nested case–control study in the context of the NSABP P1 and P2 prevention trials to determine the impact of CYP2D6 genotype, CYP2D6 inhibitor use, and metabolizer status (CYP2D6 genotype combined with CYP2D6 inhibitor use), on breast cancer events. Women who developed breast cancer (both noninvasive and invasive) while on 5 years of selective estrogen receptor modulators therapy (cases) were matched to controls free of breast cancer. Comprehensive CYP2D6 genotyping was conducted for alleles associated with absent (*3, *4, *5, and *6), reduced (*10, *17, and *41), and increased (*1XN and *2XN) enzyme activity. Information regarding the use of CYP2D6 inhibitors was recorded. Results: A total of 591 cases were matched to 1,126 controls and DNA was genotyped in more than 97%. In patients treated with tamoxifen, there was no association of CYP2D6 genotype [OR (extensive/poor metabolizer): 0.90; 95% CI: 0.46–1.74, P = 0.74), use of a potent CYP2D6 inhibitor (OR 0.92; 95% CI: 0.575–1.486), or CYP2D6 metabolizer status (OR 1.03; 95% CI: 0.669–1.607) with breast cancer occurrence. Likewise, there was no association between any CYP2D6 metabolism parameter with breast cancer events in raloxifene-treated patients. Conclusion: In the NSABP P1 and P2 clinical trials, alterations in CYP2D6 metabolism are not associated with either tamoxifen or raloxifene efficacy. Clin Cancer Res; 17(21); 6944–51. ©2011 AACR.

Abstract Purpose: The categorical definition of response assessed via the Response Evaluation Criteria in Solid Tumors has documented limitations. We sought to identify alternative metrics for tumor response that improve prediction of overall survival. Experimental Design: Individual patient data from three North Central Cancer Treatment Group trials (N0026, n = 117; N9741, n = 1,109; and N9841, n = 332) were used. Continuous metrics of tumor size based on longitudinal tumor measurements were considered in addition to a trichotomized response [TriTR: response (complete or partial) vs. stable disease vs. progression). Cox proportional hazards models, adjusted for treatment arm and baseline tumor burden, were used to assess the impact of the metrics on subsequent overall survival, using a landmark analysis approach at 12, 16, and 24 weeks postbaseline. Model discrimination was evaluated by the concordance (c) index. Results: The overall best response rates for the three trials were 26%, 45%, and 25%, respectively. Although nearly all metrics were statistically significantly associated with overall survival at the different landmark time points, the concordance indices (c-index) for the traditional response metrics ranged from 0.59 to 0.65; for the continuous metrics from 0.60 to 0.66; and for the TriTR metrics from 0.64 to 0.69. The c-indices for TriTR at 12 weeks were comparable with those at 16 and 24 weeks. Conclusions: Continuous tumor measurement–based metrics provided no predictive improvement over traditional response–based metrics or TriTR; TriTR had better predictive ability than best TriTR or confirmed response. If confirmed, TriTR represents a promising endpoint for future phase II trials. Clin Cancer Res; 17(20); 6592–9. ©2011 AACR.

Background— Few data exist on long-term outcomes of elderly patients after aortic valve replacement. We evaluated latest follow-up information for patients ≥70 years of age after aortic valve replacement. Methods and Results— Late overall survival of 2890 consecutive patients ≥70 years of age who underwent aortic valve replacement between January 1993 and December 2007 was reviewed retrospectively, analyzed, and stratified by preoperative and intraoperative variables. Observed 5-, 10-, and 15-year late postoperative survival was lower than generally expected (68%, 34%, and 8% versus 70%, 42%, and 20%, respectively; P <0.001). Independent predictors of late death included older age, renal failure, diabetes mellitus, stroke, myocardial infarction, immunosuppression, prior coronary artery bypass grafting, implanted pacemaker, lower ejection fraction, hypertension, and New York Heart Association class III or IV. After stratification by age–comorbidity risk score, 10-year survival for the lowest-risk group (n=946 [33%]) was similar to expected survival (55% versus 55%; P =0.50), but for the highest-risk group (n=564 [20%]), survival was significantly lower than expected (9% versus 26%; P <0.001). For 229 pairs of propensity-matched patients with mechanical or biological prostheses, survival was not significantly different (67%, 40%, and 19% versus 71%, 45%, and 7% at 5, 10, and 15 years, respectively; P =0.81). Structural deterioration of bioprostheses occurred in 64 patients (2.4%). Conclusions— Survival of elderly patients after aortic valve replacement is influenced by age and preoperative comorbidities; 33% at lowest risk had overall survival similar to that of an age- and sex-matched general population. There was no sufficient evidence that valve type affected survival. Structural deterioration of aortic bioprostheses was rare.

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.