Structural and Functional Analysis of the Caspase –dependent and –independent Domains of the X-linked Inhibitor of Apoptosis Protein in Inflammatory Breast Cancer Tumor Biology

Inflammatory breast cancer (IBC) is an extremely rare but highly aggressive form of breast cancer characterized by the rapid development of therapeutic resistance leading to particularly poor survival. Our previous work focused on the elucidation of factors that mediate therapeutic resistance in IBC and identified increased expression of the anti-apoptotic protein, X-linked inhibitor of apoptosis protein (XIAP), to correlate with the development of resistance to chemotherapeutics. Although XIAP is classically thought of as an inhibitor of caspase activation, multiple studies have revealed that XIAP can also function as a signaling intermediate in numerous pathways. Based on preliminary evidence revealing high expression of XIAP in pre-treatment IBC cells rather than only subsequent to the development of resistance, we hypothesized that XIAP could play an important signaling role in IBC pathobiology outside of its heavily published apoptotic inhibition function. Further, based on our discovery of inhibition of chemotherapeutic efficacy, we postulated that XIAP overexpression might also play a role in resistance to other forms of therapy, such as immunotherapy. Finally, we posited that targeting of specific redox adaptive mechanisms, which are observed to be a significant barrier to successful treatment of IBC, could overcome therapeutic resistance and enhance the efficacy of chemo-, radio-, and immuno- therapies. To address these hypotheses our objectives were: 1. to determine a role for XIAP in IBC pathobiology and to elucidate the upstream regulators and downstream effectors of XIAP; 2. to evaluate and describe a role for XIAP in the inhibition of immunotherapy; and 3. to develop and characterize novel redox modulatory strategies that target identified mechanisms to prevent or reverse therapeutic resistance.

Using various genomic and proteomic approaches, combined with analysis of cellular viability, proliferation, and growth parameters both in vitro and in vivo, we demonstrate that XIAP plays a central role in both IBC pathobiology in a manner mostly independent of its role as a caspase-binding protein. Modulation of XIAP expression in cells derived from patients prior to any therapeutic intervention significantly altered key aspects IBC biology including, but not limited to: IBC-specific gene signatures; the tumorigenic capacity of tumor cells; and the metastatic phenotype of IBC, all of which are revealed to functionally hinge on XIAP-mediated NFκB activation, a robust molecular determinant of IBC. Identification of the mechanism of XIAP-mediated NFκB activation led to the characterization of novel peptide-based antagonist which was further used to identify that increased NFκB activation was responsible for redox adaptation previously observed in therapy-resistant IBC cells. Lastly, we describe the targeting of this XIAP-NFκB-ROS axis using a novel redox modulatory strategy both in vitro and in vivo. Together, the data presented here characterize a novel and crucial role for XIAP both in therapeutic resistance and the pathobiology of IBC; these results confirm our previous work in acquired therapeutic resistance and establish the feasibility of targeting XIAP-NFκB and the redox adaptive phenotype of IBC as a means to enhance survival of patients.

Assessment of the Spatial and Temporal Distribution of Functional Connectivity in Resting-State BOLD fMRI

Recent research into resting-state functional magnetic resonance imaging (fMRI) has shown that the brain is very active during rest. This thesis work utilizes blood oxygenation level dependent (BOLD) signals to investigate the spatial and temporal functional network information found within resting-state data, and aims to investigate the feasibility of extracting functional connectivity networks using different methods as well as the dynamic variability within some of the methods. Furthermore, this work looks into producing valid networks using a sparsely-sampled sub-set of the original data.

In this work we utilize four main methods: independent component analysis (ICA), principal component analysis (PCA), correlation, and a point-processing technique. Each method comes with unique assumptions, as well as strengths and limitations into exploring how the resting state components interact in space and time.

Correlation is perhaps the simplest technique. Using this technique, resting-state patterns can be identified based on how similar the time profile is to a seed region’s time profile. However, this method requires a seed region and can only identify one resting state network at a time. This simple correlation technique is able to reproduce the resting state network using subject data from one subject’s scan session as well as with 16 subjects.

Independent component analysis, the second technique, has established software programs that can be used to implement this technique. ICA can extract multiple components from a data set in a single analysis. The disadvantage is that the resting state networks it produces are all independent of each other, making the assumption that the spatial pattern of functional connectivity is the same across all the time points. ICA is successfully able to reproduce resting state connectivity patterns for both one subject and a 16 subject concatenated data set.

Using principal component analysis, the dimensionality of the data is compressed to find the directions in which the variance of the data is most significant. This method utilizes the same basic matrix math as ICA with a few important differences that will be outlined later in this text. Using this method, sometimes different functional connectivity patterns are identifiable but with a large amount of noise and variability.

To begin to investigate the dynamics of the functional connectivity, the correlation technique is used to compare the first and second halves of a scan session. Minor differences are discernable between the correlation results of the scan session halves. Further, a sliding window technique is implemented to study the correlation coefficients through different sizes of correlation windows throughout time. From this technique it is apparent that the correlation level with the seed region is not static throughout the scan length.

The last method introduced, a point processing method, is one of the more novel techniques because it does not require analysis of the continuous time points. Here, network information is extracted based on brief occurrences of high or low amplitude signals within a seed region. Because point processing utilizes less time points from the data, the statistical power of the results is lower. There are also larger variations in DMN patterns between subjects. In addition to boosted computational efficiency, the benefit of using a point-process method is that the patterns produced for different seed regions do not have to be independent of one another.

This work compares four unique methods of identifying functional connectivity patterns. ICA is a technique that is currently used by many scientists studying functional connectivity patterns. The PCA technique is not optimal for the level of noise and the distribution of the data sets. The correlation technique is simple and obtains good results, however a seed region is needed and the method assumes that the DMN regions is correlated throughout the entire scan. Looking at the more dynamic aspects of correlation changing patterns of correlation were evident. The last point-processing method produces a promising results of identifying functional connectivity networks using only low and high amplitude BOLD signals.