Matching Items (5)

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Clonal Analysis of a Human Breast Cancer

Description

Breast and other solid tumors exhibit high and varying degrees of intra-tumor heterogeneity resulting in targeted therapy resistance and other challenges that make the management and treatment of these diseases

Breast and other solid tumors exhibit high and varying degrees of intra-tumor heterogeneity resulting in targeted therapy resistance and other challenges that make the management and treatment of these diseases rather difficult. Due to the presence of admixtures of non-neoplastic cells with polyclonal cell populations, it is difficult to define cancer genomes in patient samples. By isolating tumor cells from normal cells, and enriching distinct clonal populations, clinically relevant genomic aberrations that drive disease can be identified in patients in vivo. An in-depth analysis of clonal architecture and tumor heterogeneity was performed in a stage II chemoradiation-naïve breast cancer from a sixty-five year old patient. DAPI-based DNA content measurements and DNA content-based flow sorting was used to to isolate nuclei from distinct clonal populations of diploid and aneuploid tumor cells in surgical tumor samples. We combined DNA content-based flow cytometry and ploidy analysis with high-definition array comparative genomic hybridization (aCGH) and next-generation sequencing technologies to interrogate the genomes of multiple biopsies from the breast cancer. The detailed profiles of ploidy, copy number aberrations and mutations were used to recreate and map the lineages present within the tumor. The clonal analysis revealed driver events for tumor progression (a heterozygous germline BRCA2 mutation converted to homozygosity within the tumor by a copy number event and the constitutive activation of Notch and Akt signaling pathways. The highlighted approach has broad implications in the study of tumor heterogeneity by providing a unique ultra-high resolution of polyclonal tumors that can advance effective therapies and clinical management of patients with this disease.

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Created

Date Created
  • 2015-05

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Targeted proteomics studies: design, development and translation of mass spectrometric immunoassays for diabetes and kidney disease

Description

In an effort to begin validating the large number of discovered candidate biomarkers, proteomics is beginning to shift from shotgun proteomic experiments towards targeted proteomic approaches that provide solutions to

In an effort to begin validating the large number of discovered candidate biomarkers, proteomics is beginning to shift from shotgun proteomic experiments towards targeted proteomic approaches that provide solutions to automation and economic concerns. Such approaches to validate biomarkers necessitate the mass spectrometric analysis of hundreds to thousands of human samples. As this takes place, a serendipitous opportunity has become evident. By the virtue that as one narrows the focus towards "single" protein targets (instead of entire proteomes) using pan-antibody-based enrichment techniques, a discovery science has emerged, so to speak. This is due to the largely unknown context in which "single" proteins exist in blood (i.e. polymorphisms, transcript variants, and posttranslational modifications) and hence, targeted proteomics has applications for established biomarkers. Furthermore, besides protein heterogeneity accounting for interferences with conventional immunometric platforms, it is becoming evident that this formerly hidden dimension of structural information also contains rich-pathobiological information. Consequently, targeted proteomics studies that aim to ascertain a protein's genuine presentation within disease- stratified populations and serve as a stepping-stone within a biomarker translational pipeline are of clinical interest. Roughly 128 million Americans are pre-diabetic, diabetic, and/or have kidney disease and public and private spending for treating these diseases is in the hundreds of billions of dollars. In an effort to create new solutions for the early detection and management of these conditions, described herein is the design, development, and translation of mass spectrometric immunoassays targeted towards diabetes and kidney disease. Population proteomics experiments were performed for the following clinically relevant proteins: insulin, C-peptide, RANTES, and parathyroid hormone. At least thirty-eight protein isoforms were detected. Besides the numerous disease correlations confronted within the disease-stratified cohorts, certain isoforms also appeared to be causally related to the underlying pathophysiology and/or have therapeutic implications. Technical advancements include multiplexed isoform quantification as well a "dual- extraction" methodology for eliminating non-specific proteins while simultaneously validating isoforms. Industrial efforts towards widespread clinical adoption are also described. Consequently, this work lays a foundation for the translation of mass spectrometric immunoassays into the clinical arena and simultaneously presents the most recent advancements concerning the mass spectrometric immunoassay approach.

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Agent

Created

Date Created
  • 2011

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The sensitivity of confirmatory factor analytic fit indices to violations of factorial invariance across latent classes: a simulation study

Description

Although the issue of factorial invariance has received increasing attention in the literature, the focus is typically on differences in factor structure across groups that are directly observed, such as

Although the issue of factorial invariance has received increasing attention in the literature, the focus is typically on differences in factor structure across groups that are directly observed, such as those denoted by sex or ethnicity. While establishing factorial invariance across observed groups is a requisite step in making meaningful cross-group comparisons, failure to attend to possible sources of latent class heterogeneity in the form of class-based differences in factor structure has the potential to compromise conclusions with respect to observed groups and may result in misguided attempts at instrument development and theory refinement. The present studies examined the sensitivity of two widely used confirmatory factor analytic model fit indices, the chi-square test of model fit and RMSEA, to latent class differences in factor structure. Two primary questions were addressed. The first of these concerned the impact of latent class differences in factor loadings with respect to model fit in a single sample reflecting a mixture of classes. The second question concerned the impact of latent class differences in configural structure on tests of factorial invariance across observed groups. The results suggest that both indices are highly insensitive to class-based differences in factor loadings. Across sample size conditions, models with medium (0.2) sized loading differences were rejected by the chi-square test of model fit at rates just slightly higher than the nominal .05 rate of rejection that would be expected under a true null hypothesis. While rates of rejection increased somewhat when the magnitude of loading difference increased, even the largest sample size with equal class representation and the most extreme violations of loading invariance only had rejection rates of approximately 60%. RMSEA was also insensitive to class-based differences in factor loadings, with mean values across conditions suggesting a degree of fit that would generally be regarded as exceptionally good in practice. In contrast, both indices were sensitive to class-based differences in configural structure in the context of a multiple group analysis in which each observed group was a mixture of classes. However, preliminary evidence suggests that this sensitivity may contingent on the form of the cross-group model misspecification.

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Agent

Created

Date Created
  • 2011

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Designing For Interest: Heterogeneity as a Design Tool and a Catalyst in a Networked STEM Club

Description

There has been growing interest among learning scientists in the design and study of out-of-school time (OST) learning environments to support equitable development of science, technology, engineering, and math (STEM)

There has been growing interest among learning scientists in the design and study of out-of-school time (OST) learning environments to support equitable development of science, technology, engineering, and math (STEM) interests among youth from groups that are underrepresented in STEM fields. Most of these design studies assumed the youth came to the learning environments without well-developed STEM interests. I challenged this assumption by enacting a social design participatory study to engage youth (aged 11 to 14), from groups that are underrepresented in STEM fields, as partners in designing an OST networked club to support the youth in growing their own STEM interests. Based on longitudinal ethnographic data, I report a three-year iterative design of this networked club. I characterize the heterogeneity of STEM interests that emerged and grew across the networked club. Building on ecological theories of interest development, and leveraging the cultural assets of the nondominant community, I argue that heterogeneity of interests, resources, and practices served as a design tool and a catalyst for the development of STEM interest in the OST networked club.

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Created

Date Created
  • 2019

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Dielectric response of glass-forming liquids in the nonlinear regime

Description

Broadband dielectric spectroscopy is a powerful technique for understanding the dynamics in supercooled liquids. It generates information about the timescale of the orientational motions of molecular dipoles within the liquid.

Broadband dielectric spectroscopy is a powerful technique for understanding the dynamics in supercooled liquids. It generates information about the timescale of the orientational motions of molecular dipoles within the liquid. However, dynamics of liquids measured in the non-linear response regime has recently become an area of significant interest, because additional information can be obtained compared with linear response measurements.

The first part of this thesis describes nonlinear dielectric relaxation experiments performed on various molecular glass forming-liquids, with an emphasis on the response at high frequencies (excess wing). A significant nonlinear dielectric effect (NDE) was found to persist in these modes, and the magnitude of this NDE traces the temperature dependence of the activation energy. A time resolved measurement technique monitoring the dielectric loss revealed that for the steady state NDE to develop it would take a very large number of high amplitude alternating current (ac) field cycles. High frequency modes were found to be ‘slaved’ to the average structural relaxation time, contrary to the standard picture of heterogeneity. Nonlinear measurements were also performed on the Johari-Goldstein β-relaxation process. High ac fields were found to modify the amplitudes of these secondary modes. The nonlinear features of this secondary process are reminiscent of those found for the excess wing regime, suggesting that these two contributions to dynamics have common origins.

The second part of this thesis describes the nonlinear effects observed from the application of high direct current (dc) bias fields superposed with a small amplitude sinusoidal ac field. For several molecular glass formers, the application of a dc field was found to slow down the system via reduction in configurational entropy (Adam-Gibbs relation). Time resolved measurements indicated that the rise of the non-linear effect is slower than its decay, as observed in the electro-optical Kerr effect. A model was discussed which quantitatively captures the observed magnitudes and time dependencies of the NDE. Asymmetry in these rise and decay times was demonstrated as a consequence of the quadratic field dependence of the entropy change. It was demonstrated that the high bias field modifies the polarization response to the field, even including the zero field limit.

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Agent

Created

Date Created
  • 2016