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.

SAFE: A Declarative Trust-Agile System with Linked Credentials

Secure Access For Everyone (SAFE), is an integrated system for managing trust

using a logic-based declarative language. Logical trust systems authorize each

request by constructing a proof from a context---a set of authenticated logic

statements representing credentials and policies issued by various principals

in a networked system. A key barrier to practical use of logical trust systems

is the problem of managing proof contexts: identifying, validating, and

assembling the credentials and policies that are relevant to each trust

decision.

SAFE addresses this challenge by (i) proposing a distributed authenticated data

repository for storing the credentials and policies; (ii) introducing a

programmable credential discovery and assembly layer that generates the

appropriate tailored context for a given request. The authenticated data

repository is built upon a scalable key-value store with its contents named by

secure identifiers and certified by the issuing principal. The SAFE language

provides scripting primitives to generate and organize logic sets representing

credentials and policies, materialize the logic sets as certificates, and link

them to reflect delegation patterns in the application. The authorizer fetches

the logic sets on demand, then validates and caches them locally for further

use. Upon each request, the authorizer constructs the tailored proof context

and provides it to the SAFE inference for certified validation.

Delegation-driven credential linking with certified data distribution provides

flexible and dynamic policy control enabling security and trust infrastructure

to be agile, while addressing the perennial problems related to today's

certificate infrastructure: automated credential discovery, scalable

revocation, and issuing credentials without relying on centralized authority.

We envision SAFE as a new foundation for building secure network systems. We

used SAFE to build secure services based on case studies drawn from practice:

(i) a secure name service resolver similar to DNS that resolves a name across

multi-domain federated systems; (ii) a secure proxy shim to delegate access

control decisions in a key-value store; (iii) an authorization module for a

networked infrastructure-as-a-service system with a federated trust structure

(NSF GENI initiative); and (iv) a secure cooperative data analytics service

that adheres to individual secrecy constraints while disclosing the data. We

present empirical evaluation based on these case studies and demonstrate that

SAFE supports a wide range of applications with low overhead.

Methods for Imputing Missing Values and Synthesizing Confidential Values for Continuous and Magnitude Data

Abstract

Continuous variable is one of the major data types collected by the survey organizations. It can be incomplete such that the data collectors need to fill in the missingness. Or, it can contain sensitive information which needs protection from re-identification. One of the approaches to protect continuous microdata is to sum them up according to different cells of features. In this thesis, I represents novel methods of multiple imputation (MI) that can be applied to impute missing values and synthesize confidential values for continuous and magnitude data.

The first method is for limiting the disclosure risk of the continuous microdata whose marginal sums are fixed. The motivation for developing such a method comes from the magnitude tables of non-negative integer values in economic surveys. I present approaches based on a mixture of Poisson distributions to describe the multivariate distribution so that the marginals of the synthetic data are guaranteed to sum to the original totals. At the same time, I present methods for assessing disclosure risks in releasing such synthetic magnitude microdata. The illustration on a survey of manufacturing establishments shows that the disclosure risks are low while the information loss is acceptable.

The second method is for releasing synthetic continuous micro data by a nonstandard MI method. Traditionally, MI fits a model on the confidential values and then generates multiple synthetic datasets from this model. Its disclosure risk tends to be high, especially when the original data contain extreme values. I present a nonstandard MI approach conditioned on the protective intervals. Its basic idea is to estimate the model parameters from these intervals rather than the confidential values. The encouraging results of simple simulation studies suggest the potential of this new approach in limiting the posterior disclosure risk.

The third method is for imputing missing values in continuous and categorical variables. It is extended from a hierarchically coupled mixture model with local dependence. However, the new method separates the variables into non-focused (e.g., almost-fully-observed) and focused (e.g., missing-a-lot) ones. The sub-model structure of focused variables is more complex than that of non-focused ones. At the same time, their cluster indicators are linked together by tensor factorization and the focused continuous variables depend locally on non-focused values. The model properties suggest that moving the strongly associated non-focused variables to the side of focused ones can help to improve estimation accuracy, which is examined by several simulation studies. And this method is applied to data from the American Community Survey.