A Virtual Clinical Trial of FDG-PET Imaging of Breast Cancer: Effect of Variability on Response Assessment

INTRODUCTION: There is growing interest in using positron emission tomography (PET) standardized uptake values (SUVs) to assess tumor response to therapy. However, many error sources compromise the ability to detect SUV changes. We explore relationships between these errors and overall SUV variability. METHODS: We used simulations in a virtual clinical trial framework to study impacts of error sources from scanning and analysis effects on assessment of SUV changes. We varied tumor diameter, scan duration, pretherapy SUV, magnitude of change in SUV, image reconstruction filter, and SUV metric. Poisson noise was added to the raw data before image reconstruction. Variance from global sources of error, e.g., scanner calibration, was incorporated. Two thousand independent noisy sinograms per scenario were generated and reconstructed. We used SUVs to create receiver operating characteristic (ROC) curves to quantify ability to assess response. Integrating area under the ROC curve summarized ability to detect SUV changes. RESULTS: Scan duration and image reconstruction method had relatively little impact on ability to measure response. SUV_MAX is nearly as effective as SUV_MEAN, especially with increased image smoothing and despite size-matched region of interest placement. For an effective variability of 15%, we found the Positron Emission Tomography Response Criteria in Solid Tumors criteria for measuring response (±30%) similar to the European Organization for Research and Treatment of Cancer criteria (±25%). CONCLUSIONS: For typical PET variance levels, tumor response must be 30% to 40% to be reliably determined using SUVs. PET scan duration and image reconstruction method had relatively little effect.     

Pheromone-induced morphogenesis and gradient tracking are dependent on the MAPK Fus3 binding to Gα

Mitogen-activated protein kinase (MAPK) pathways control many cellular processes, including differentiation and proliferation. These pathways commonly activate MAPK isoforms that have redundant or overlapping function. However, recent studies have revealed circumstances in which MAPK isoforms have specialized, nonoverlapping roles in differentiation. The mechanisms that underlie this specialization are not well understood. To address this question, we sought to establish regulatory mechanisms that are unique to the MAPK Fus3 in pheromone-induced mating and chemotropic fate transitions of the budding yeast Saccharomyces cerevisiae. Our investigations reveal a previously unappreciated role for inactive Fus3 as a potent negative regulator of pheromone-induced chemotropism. We show that this inhibitory role is dependent on inactive Fus3 binding to the α-subunit of the heterotrimeric G-protein. Further analysis revealed that the binding of catalytically active Fus3 to the G-protein is required for gradient tracking and serves to suppress cell-to-cell variability between mating and chemotropic fates in a population of pheromone-responding cells.     

What Is the Significance of Difference in Phenotypic Variability across SNP Genotypes?

We studied the general problem of interpreting and detecting differences in phenotypic variability among the genotypes at a locus, from both a biological and a statistical point of view. The scales on which we measure interval-scale quantitative traits are man-made and have little intrinsic biological relevance. Before claiming a biological interpretation for genotype differences in variance, we should be sure that no monotonic transformation of the data can reduce or eliminate these differences. We show theoretically that for an autosomal diallelic SNP, when the three corresponding means are distinct so that the variance can be expressed as a quadratic function of the mean, there implicitly exists a transformation that will tend to equalize the three variances; we also demonstrate how to find a transformation that will do this. We investigate the validity of Bartlett’s test, Box’s modification of it, and a modified Levene’s test to test for differences in variances when normality does not hold. We find that, although they may detect differences in variability, these tests do not necessarily detect differences in variance. The same is true for permutation tests that use these three statistics.     

Effects of extrinsic and intrinsic factors on breeding success in a long lived seabird

There is growing concern over the impacts of climate change on animal species. Many studies have demonstrated impacts of climate change at the population level, and density dependent effects of climate are frequently reported. However, there is an increasing recognition of the differential impact of such factors on individuals since there is marked variation in individual performance. We investigated the relationships between breeding success and environmental conditions (winter NAO and one year lagged winter NAO) and intrinsic effects (colony size, pair bond duration, past breeding success rate) in the northern fulmar Fulmarus glacialis , using data from a long-term study commenced in 1950. There was a negative trend in breeding success over time, and a negative relationship with winter NAO and lagged winter NAO, which themselves had shown positive increases over the study period. The effects of lagged winter NAO remained after accounting for the linear trend. There was no evidence of density dependence, with breeding success positively related to colony size. We found strong evidence that breeding success was negatively related to pair bond duration but positively related to past breeding success rate. There was also an interaction between these two intrinsic effects such that those pairs that had historically been successful maintained success with increasing pair bond duration, whereas less successful pairs showed a decline. The prediction that there would be a differential impact of extrinsic factors among pairs was supported by an interaction between past breeding success rate and winter NAO, such that pairs with low past success rate exhibited a sharp decline in breeding success with increasing winter NAO, whereas more successful pairs did not. It is critically important to understand interactions between extrinsic factors and individual heterogeneity since a differential impact on individuals will affect population structure, and hence population dynamics.     

Improvement of mapping accuracy by unifying linkage and association analysis

It is well known that pedigree/family data record information on the coexistence in founder haplotypes of alleles at nearby loci and the cotransmission from parent to offspring that reveal different, but complementary, profiles of the genetic architecture. Either conventional linkage analysis that assumes linkage equilibrium or family-based association tests (FBATs) capture only partial information, leading to inefficiency. For example, FBATs will fail to detect even very tight linkage in the case where no allelic association exists, while a violation of the assumption of linkage equilibrium will result in biased estimation and reduced efficiency in linkage mapping. In this article, by using a data augmentation technique and the EM algorithm, we propose a likelihood-based approach that embeds both linkage and association analyses into a unified framework for general pedigree data. Relative to either linkage or association analysis, the proposed approach is expected to have greater estimation accuracy and power. Monte Carlo simulations support our theoretical expectations and demonstrate that our new methodology: (1) is more powerful than either FBATs or classic linkage analysis; (2) can unbiasedly estimate genetic parameters regardless of whether association exists, thus remedying the bias and less precision of traditional linkage analysis in the presence of association; and (3) is capable of identifying tight linkage alone. The new approach also holds the theoretical advantage that it can extract statistical information to the maximum extent and thereby improve mapping accuracy and power because it integrates multilocus population-based association study and pedigree-based linkage analysis into a coherent framework. Furthermore, our method is numerically stable and computationally efficient, as compared to existing parametric methods that use the simplex algorithm or Newton-type methods to maximize high-order multidimensional likelihood functions, and also offers the computation of Fisher's information matrix. Finally, we apply our methodology to a genetic study on bone mineral density (BMD) for the vitamin D receptor (VDR) gene and find that VDR is significantly linked to BMD at the one-third region of the wrist.     

Extension of the Haseman-Elston regression model to longitudinal data

We propose an extension to longitudinal data of the Haseman and Elston regression method for linkage analysis. The proposed model is a mixed model having several random effects. As response variable, we investigate the sibship sample mean corrected cross-product (smHE) and the BLUP-mean corrected cross product (pmHE), comparing them with the original squared difference (oHE), the overall mean corrected cross-product (rHE), and the weighted average of the squared difference and the squared mean-corrected sum (wHE). The proposed model allows for the correlation structure of longitudinal data. Also, the model can test for gene x time interaction to discover genetic variation over time. The model was applied in an analysis of the Genetic Analysis Workshop 13 (GAW13) simulated dataset for a quantitative trait simulating systolic blood pressure. Independence models did not preserve the test sizes, while the mixed models with both family and sibpair random effects tended to preserve size well.     

Reduction of sample heterogeneity through use of population substructure: an example from a population of African American families with sarcoidosis

Sarcoidosis is a granulomatous inflammatory disorder of complex etiology with significant linkage to chromosome 5, and marginal linkage was observed to five other chromosomes in African Americans (AAs) in our previously published genome scan. Because genetic factors underlying complex disease are often population specific, genetic analysis of samples with diverse ancestry (i.e., ethnic confounding) can lead to loss of power. Ethnic confounding is often addressed by stratifying on self-reported race, a controversial and less-than-perfect construct. Here, we propose linkage analysis stratified by genetically determined ancestry as an alternative approach for reducing ethnic confounding. Using data from the 380 microsatellite markers genotyped in the aforementioned genome scan, we clustered AA families into subpopulations on the basis of ancestry similarity. Evidence of two genetically distinct groups was found: subpopulation one (S1) comprised 219 of the 229 families, subpopulation two (S2) consisted of six families (the remaining four families were a mixture). Stratified linkage results suggest that only the S1 families contributed to previously identified linkage signals at 1p22, 3p21-14, 11p15, and 17q21 and that only the S2 families contributed to those found at 5p15-13 and 20q13. Signals on 2p25, 5q11, 5q35, and 9q34 remained significant in both subpopulations, and evidence of a new susceptibility locus at 2q37 was found in S2. These results demonstrate the usefulness of stratifying on genetically determined ancestry, to create genetically homogeneous subsets--more reliable and less controversial than race-stratified subsets--in which to identify genetic factors. Our findings support the presence of sarcoidosis-susceptibility genes in regions identified elsewhere but indicate that these genes are likely to be ancestry specific.     

The affected-/discordant-sib-pair design can guarantee validity of multipoint model-free linkage analysis of incomplete pedigrees when there is marker-marker disequilibrium

Genomewide linkage studies are tending toward the use of single-nucleotide polymorphisms (SNPs) as the markers of choice. However, linkage disequilibrium (LD) between tightly linked SNPs violates the fundamental assumption of linkage equilibrium (LE) between markers that underlies most multipoint calculation algorithms currently available, and this leads to inflated affected-relative-pair allele-sharing statistics when founders' multilocus genotypes are unknown. In this study, we investigate the impact that the degree of LD, marker allele frequency, and association type have on estimating the probabilities of sharing alleles identical by descent in multipoint calculations and hence on type I error rates of different sib-pair linkage approaches that assume LE. We show that marker-marker LD does not inflate type I error rates of affected sib pair (ASP) statistics in the whole parameter space, and that, in any case, discordant sib pairs (DSPs) can be used to control for marker-marker LD in ASPs. We advocate the ASP/DSP design with appropriate sib-pair statistics that test the difference in allele sharing between ASPs and DSPs.