Influence of equipment changes on MRI measures of brain atrophy and brain microstructure in a placebo-controlled trial of ibudilast in progressive multiple sclerosis.

BACKGROUND: Hardware changes can be an unavoidable confound in imaging trials. Understanding the impact of such changes may play an important role in the analysis of imaging data. OBJECTIVE: To characterize the effect of equipment changes in a longitudinal, multi-site multiple sclerosis trial. METHODS: Using data from a clinical trial in progressive multiple sclerosis, we explored how major changes in imaging hardware affected data. We analyzed the extent to which these changes affected imaging biomarkers and the estimated treatment effects by including such changes as a time-dependent covariate. RESULTS: Significant differences whole brain atrophy (brain parenchymal fraction, BPF) and microstructure (transverse diffusivity, TD) between scans with and without changes were found and depended on the type of hardware change. A switch from GE HDxt to Siemens Skyra led to significant shifts in BPF (p < 0.04) and TD (p < 0.0001). However, we could not detect the influence of hardware changes on overall trial outcomes- differences between placebo and treatment arms in change over time of BPF and TD (p > 0.5). CONCLUSIONS: The results suggest that differences among hardware types should be considered when planning and analyzing brain atrophy and diffusivity in a longitudinal clinical trial.

Neurofilament light chain in a phase 2 clinical trial of ibudilast in progressive multiple sclerosis.

BACKGROUND: Sensitive and specific biomarkers for use in progressive multiple sclerosis (MS) have not been established. We investigate neurofilament light (NfL) as a treatment response biomarker in progressive MS. OBJECTIVE: To evaluate whether ibudilast 100 mg/day alters serum and cerebrospinal fluid (CSF) levels of NfL in progressive MS. METHODS: In a protocol-defined exploratory analysis from a 2-year, phase 2 clinical trial of ibudilast in progressive MS (NCT01982942), serum samples were collected from 239 subjects and a subset contributed CSF and assayed using single-molecule assay (SIMOA) immunoassay. A mixed model for repeated measurements yielded log(NfL) as the response variable. RESULTS: The geometric mean baseline serum NfL was 31.9 and 28.8 pg/mL in placebo and ibudilast groups, respectively. The geometric mean baseline CSF NfL was 1150.8 and 1290.3 pg/mL in placebo and ibudilast groups, respectively. Serum and CSF NfL correlations were r = 0.52 and r = 0.78 at weeks 48 and 96, respectively. Over 96 weeks, there was no between-group difference in NfL in either serum (p = 0.76) or CSF (p = 0.46). After controlling for factors that may affect NfL, no effect of ibudilast on NfL in either serum or CSF was observed. CONCLUSION: Ibudilast treatment was not associated with a change in either serum or CSF NfL.

Development and Validation of a Test for Competence in Evidence-Based Medicine.

BACKGROUND: Medical educators need valid, reliable, and efficient tools to assess evidence-based medicine (EBM) knowledge and skills. Available EBM assessment tools either do not assess skills or are laborious to grade. OBJECTIVE: To validate a multiple-choice-based EBM test-the Resident EBM Skills Evaluation Tool (RESET). DESIGN: Cross-sectional study. PARTICIPANTS: A total of 304 medicine residents from five training programs and 33 EBM experts comprised the validation cohort. MAIN MEASURES: Internal reliability, item difficulty, and item discrimination were assessed. Construct validity was assessed by comparing mean total scores of trainees to experts. Experts were also asked to rate importance of each test item to assess content validity. KEY RESULTS: Experts had higher total scores than trainees (35.6 vs. 29.4, P < 0.001) and also scored significantly higher than residents on 11/18 items. Cronbach's alpha was 0.6 (acceptable), and no items had a low item-total correlation. Item difficulty ranged from 7 to 86%. All items were deemed "important" by > 50% of experts. CONCLUSIONS: The proposed EBM assessment tool is a reliable and valid instrument to assess competence in EBM. It is easy to administer and grade and could be used to guide and assess interventions in EBM education.

Impact of tissue-based genomic profiling on clinical decision making in the management of patients with metastatic breast cancer at academic centers.

BACKGROUND: Genomic profiling can identify targetable mutations; however, the impact of tissue-based genomic profiling on clinical decision making for patients with metastatic breast cancer has not been well characterized. METHODS: Patients with stage IV breast cancer who had undergone genomic profiling between 7/2013 and 3/2015 were identified at three academic cancer centers. Genomic analysis was determined to have impacted clinical decision if (A) a patient was enrolled onto a genotype-matched clinical trial or (B) prescribed off-label an FDA-approved therapy targeting an identified mutation. The frequency of mutated genes was determined. RESULTS: A total of 117 patients with stage IV breast cancer were identified. Median age was 46 (25-75). Fifty-three patients (45%) had ER-positive/HER2-negative disease, 50 (43%) had ER-negative/HER2-negative disease, and 14 (12%) had ER-any/HER2-positive disease. Median number of previous therapies received prior to genomic profiling was 2 (range 0-15), and median follow-up after testing was obtained after 5.8 months (range 0-24.4 months). Commercial reports indicated that 85 (73%) patients had at least one mutation targetable by an FDA-approved medication, and 112 (96%) patients had at least one clinical trial available; however, clinical management was only affected in 11 patients (9%). The most frequent mutations observed were those in TP53, FGF, PI3KCA, MYC, ZNF, FGFR, CCND, ARID1A, GATA3, and MAP; frequencies of these mutations varied by clinical subtype. CONCLUSIONS: Tumor genomic profiling affected clinical management in a minority of patients with metastatic breast cancer, thus these data do not support the routine use of genomic profiling outside of a clinical trial.

Retrospective study of the efficacy and safety of neoadjuvant docetaxel, carboplatin, trastuzumab/pertuzumab (TCH-P) in nonmetastatic HER2-positive breast cancer.

BACKGROUND: Pertuzumab is FDA approved in the preoperative setting in combination with trastuzumab and chemotherapy, in women with nonmetastatic HER2 + breast cancer. The TRYPHAENA trial (n = 77) reported a pathologic complete response rate (pCR), i.e., ypT0ypN0, of 52 % in patients treated with neoadjuvant (docetaxel, carboplatin, trastuzumab, & pertuzumab) TCH-P. Aside from this study, there is limited information regarding the safety and efficacy of TCH-P in the neoadjuvant setting. Our goal was to evaluate the safety and efficacy of neoadjuvant TCH-P in a non-clinical trial setting. MATERIALS AND METHODS: Cancer data registry was utilized to identify patients with HER2 + nonmetastatic breast cancer that received neoadjuvant TCH-P. pCR was defined as the absence of invasive or noninvasive cancer in breast and lymph nodes, i.e., ypT0ypN0. RESULTS: 70 patients with a median age of 52 years met our inclusion criteria. Clinical staging was I-8.5 %; II-68.5 %; and III-22.8 %. 60 % of patients had hormone receptor (HR)-positive tumors. 23 % (16/71) of patients required dose reduction for rash, diarrhea, neuropathy, or thrombocytopenia. Overall, no patients developed grade 3-4 left ventricular systolic dysfunction(LVSD); an asymptomatic reduction in LVEF of >10 % was observed in three patients. The overall observed pCR rate was 53 %. As expected, the pCR rate was higher in patients with HR-negative breast cancer than for patients with HR+ disease: 69 % (20/29) vs. 42 % (17/41), respectively. The axillary downstaging rate was approximately 53 % (19/36). CONCLUSION: Neoadjuvant TCH-P, in a nonclinical trial setting, was associated with a pCR rate of 53 % similar the reported rate in TRYPHAENA. Toxicity was manageable, with no patients experiencing symptomatic heart failure.

Population structure at different minor allele frequency levels.

Inferring population genetic structure from large-scale genotyping of single-nucleotide polymorphisms or variants is an important technique for studying the history and distribution of extant human populations, but it is also a very important tool for adjusting tests of association. However, the structures inferred depend on the minor allele frequency of the variants; this is very important when considering the phenotypic association of rare variants. Using the Genetic Analysis Workshop 18 data set for 142 unrelated individuals, which includes genotypes for many rare variants, we study the following hypothesis: the difference in detected structure is the result of a "scale" effect; that is, rare variants are likely to be shared only locally (smaller scale), while common variants can be spread over longer distances. The result is similar to that of using kernel principal component analysis, as the bandwidth of the kernel is changed. We show how different structures become evident as we consider rare or common variants.

European American stratification in ovarian cancer case control data: the utility of genome-wide data for inferring ancestry.

We investigated the ability of several principal components analysis (PCA)-based strategies to detect and control for population stratification using data from a multi-center study of epithelial ovarian cancer among women of European-American ethnicity. These include a correction based on an ancestry informative markers (AIMs) panel designed to capture European ancestral variation and corrections utilizing un-thinned genome-wide SNP data; case-control samples were drawn from four geographically distinct North-American sites. The AIMs-only and genome-wide first principal components (PC1) both corresponded to the previously described North or Northwest-Southeast axis of European variation. We found that the genome-wide PCA captured this primary dimension of variation more precisely and identified additional axes of genome-wide variation of relevance to epithelial ovarian cancer. Associations evident between the genome-wide PCs and study site corroborate North American immigration history and suggest that undiscovered dimensions of variation lie within Northern Europe. The structure captured by the genome-wide PCA was also found within control individuals and did not reflect the case-control variation present in the data. The genome-wide PCA highlighted three regions of local LD, corresponding to the lactase (LCT) gene on chromosome 2, the human leukocyte antigen system (HLA) on chromosome 6 and to a common inversion polymorphism on chromosome 8. These features did not compromise the efficacy of PCs from this analysis for ancestry control. This study concludes that although AIMs panels are a cost-effective way of capturing population structure, genome-wide data should preferably be used when available.

Development of a panel of genome-wide ancestry informative markers to study admixture throughout the Americas.

Most individuals throughout the Americas are admixed descendants of Native American, European, and African ancestors. Complex historical factors have resulted in varying proportions of ancestral contributions between individuals within and among ethnic groups. We developed a panel of 446 ancestry informative markers (AIMs) optimized to estimate ancestral proportions in individuals and populations throughout Latin America. We used genome-wide data from 953 individuals from diverse African, European, and Native American populations to select AIMs optimized for each of the three main continental populations that form the basis of modern Latin American populations. We selected markers on the basis of locus-specific branch length to be informative, well distributed throughout the genome, capable of being genotyped on widely available commercial platforms, and applicable throughout the Americas by minimizing within-continent heterogeneity. We then validated the panel in samples from four admixed populations by comparing ancestry estimates based on the AIMs panel to estimates based on genome-wide association study (GWAS) data. The panel provided balanced discriminatory power among the three ancestral populations and accurate estimates of individual ancestry proportions (R² > 0.9 for ancestral components with significant between-subject variance). Finally, we genotyped samples from 18 populations from Latin America using the AIMs panel and estimated variability in ancestry within and between these populations. This panel and its reference genotype information will be useful resources to explore population history of admixture in Latin America and to correct for the potential effects of population stratification in admixed samples in the region.

Analysis of exome sequences with and without incorporating prior biological knowledge.

Next-generation sequencing technology provides new opportunities and challenges in the search for genetic variants that underlie complex traits. It will also presumably uncover many new rare variants, but exactly how these variants should be incorporated into the data analysis remains a question. Several papers in our group from Genetic Analysis Workshop 17 evaluated different methods of rare variant analysis, including single-variant, gene-based, and pathway-based analyses and analyses that incorporated biological information. Although the performance of some of these methods strongly depends on the underlying disease model, integration of known biological information is helpful in detecting causal genes. Two work groups demonstrated that use of a Bayesian network and a collapsing receiver operating characteristic curve approach improves risk prediction when a disease is caused by many rare variants. Another work group suggested that modeling local rather than global ancestry may be beneficial when controlling the effect of population structure in rare variant association analysis.

Replication of GWAS "Hits" by Race for Breast and Prostate Cancers in European Americans and African Americans.

In this study, we assessed association of genome-wide association studies (GWAS) "hits" by race with adjustment for potential population stratification (PS) in two large, diverse study populations; the Carolina Breast Cancer Study (CBCS; N total = 3693 individuals) and the University of Pennsylvania Study of Clinical Outcomes, Risk, and Ethnicity (SCORE; N total = 1135 individuals). In both study populations, 136 ancestry information markers and GWAS "hits" (CBCS: FGFR2, 8q24; SCORE: JAZF1, MSMB, 8q24) were genotyped. Principal component analysis was used to assess ancestral differences by race. Multivariable unconditional logistic regression was used to assess differences in cancer risk with and without adjustment for the first ancestral principal component (PC1) and for an interaction effect between PC1 and the GWAS "hit" (SNP) of interest. PC1 explained 53.7% of the variance for CBCS and 49.5% of the variance for SCORE. European Americans and African Americans were similar in their ancestral structure between CBCS and SCORE and cases and controls were well matched by ancestry. In the CBCS European Americans, 9/11 SNPs were significant after PC1 adjustment, but after adjustment for the PC1 by SNP interaction effect, only one SNP remained significant (rs1219648 in FGFR2); for CBCS African Americans, 6/11 SNPs were significant after PC1 adjustment and after adjustment for the PC1 by SNP interaction effect, all six SNPs remained significant and an additional SNP now became significant. In the SCORE European Americans, 0/9 SNPs were significant after PC1 adjustment and no changes were seen after additional adjustment for the PC1 by SNP interaction effect; for SCORE African Americans, 2/9 SNPs were significant after PC1 adjustment and after adjustment for the PC1 by SNP interaction effect, only one SNP remained significant (rs16901979 at 8q24). We show that genetic associations by race are modified by interaction between individual SNPs and PS.

Rare variant density across the genome and across populations.

Next-generation sequencing allows for a new focus on rare variant density for conducting analyses of association to disease and for narrowing down the genomic regions that show evidence of functionality. In this study we use the 1000 Genomes Project pilot data as distributed by Genetic Analysis Workshop 17 to compare rare variant densities across seven populations. We made the comparisons using regressions of rare variants on total variant counts per gene for each population and Tajima's D values calculated for each gene in each population, using data on 3,205 genes. We found that the populations clustered by continent for both the regression slopes and Tajima's D values, with the African populations (Yoruba and Luhya) showing the highest density of rare variants, followed by the Asian populations (Han and Denver Chinese followed by the Japanese) and the European populations (CEPH [European-descent] and Tuscan) with the lowest densities. These significant differences in rare variant densities across populations seem to translate to measures of the rare variant density more commonly used in rare variant association analyses, suggesting the need to adjust for ancestry in such analyses. The selection signal was high for AHNAK, HLA-A, RANBP2, and RGPD4, among others. RANBP2 and RGPD4 showed a marked difference in rare variant density and potential selection between the Luhya and the other populations. This may suggest that differences between populations should be considered when delimiting genomic regions according to functionality and that these differences can create potential for disease heterogeneity.

Defining genetic determinants of the Metabolic Syndrome in the Framingham Heart Study using association and structural equation modeling methods.

The Metabolic Syndrome (MetSyn), which is a clustering of traits including insulin resistance, obesity, hypertension and dyslipidemia, is estimated to have a substantial genetic component, yet few specific genetic targets have been identified. Factor analysis, a sub-type of structural equation modeling (SEM), has been used to model the complex relationships in MetSyn. Therefore, we aimed to define the genetic determinants of MetSyn in the Framingham Heart Study (Offspring Cohort, Exam 7) using the Affymetrix 50 k Human Gene Panel and three different approaches: 1) an association-based "one-SNP-at-a-time" analysis with MetSyn as a binary trait using the World Health Organization criteria; 2) an association-based "one-SNP-at-a-time" analysis with MetSyn as a continuous trait using second-order factor scores derived from four first-order factors; and, 3) a multivariate SEM analysis with MetSyn as a continuous, second-order factor modeled with multiple putative genes, which were represented by latent constructs defined using multiple SNPs in each gene. Results were similar between approaches in that CSMD1 SNPs were associated with MetSyn in Approaches 1 and 2; however, the effects of CSMD1 diminished in Approach 3 when modeled simultaneously with six other genes, most notably CETP and STARD13, which were strongly associated with the Lipids and MetSyn factors, respectively. We conclude that modeling multiple genes as latent constructs on first-order trait factors, most proximal to the gene's function with limited paths directly from genes to the second-order MetSyn factor, using SEM is the most viable approach toward understanding overall gene variation effects in the presence of multiple putative SNPs.