Oncolytic Virus Therapy for the Treatment of Metastatic Ovarian Cancer

The management of patients with epithelial ovarian cancer (EOC) faces two major challenges which standard treatments fail to effectively address: 1) Diffuse metastasis as a consequence of late stage diagnosis and 2) intra-tumoral heterogeneity, which fuels tumor evolution and drives the acquisition of chemotherapeutic resistance. In this thesis, we tested new therapeutic strategies using a 3-dimensional in vitro spheroid culture model that mimics key steps of epithelial ovarian cancer metastasis; and another model that mimics both temporal and cellular heterogeneity by establishing multiple cell lines from a single patient over the course of disease progression. Using these models, we investigated the therapeutic efficacy of three oncolytic viruses for treatment of ovarian cancer: Myxoma virus (MYXV), a modified Vaccinia virus (vvDD), and Maraba virus (MRBV). We determined that all three viruses were capable of inducing some level of oncolysis, but that spheroid formation limited the replication efficiency of poxviruses (MYXV and vvDD), which heavily rely on cell proliferation. However, upon spheroid reattachment, poxvirus oncolysis was restored and prevented cell dispersion. MRBV was least affected by spheroid formation, although there was a capacity for some cell lines to develop resistance to MRBV upon spheroid formation. We discovered MRBV uses the low-density lipoprotein receptor (LDLR) to gain entry to host cells and that entry into spheroids was affected by dynamic changes in LDLR expression. However, we observed that this was only a partial mediator of MRBV tropism. In our in vitro assays of tumor heterogeneity, we observed temporal changes that directly impact MRBV oncolysis and we identified two major groups of subclones in our patient derived cell lines: one that was highly susceptible to MRBV, and another set which exhibited 1000-fold reduced susceptibility to MRBV- iii mediated oncolysis. Differential susceptibility to MRBV virus oncolysis did not strictly depend on LDLR expression. Furthermore, co-culture of virus-sensitive and virus-resistant cells conferred sensitization of virus-resistant cells to MRBV oncolysis. We therefore sought other mechanisms which could impact MRBV tropism and found that oncolysis could be significantly increased through the induction of TGFβ signaling and epithelial-to-mesenchymal transition, commonly activated pathways found during ovarian cancer metastasis. Taken together, these findings not only define the differential therapeutic efficacies between oncolytic viruses for metastatic EOC, but also identify key trophic factors which impact MRBV oncolysis that can be exploited to enhance MRBV-mediated oncolysis for EOC in future therapeutic strategies

Characterization of the HIV-1 Restriction Factor Herc5

Herc5 is a potent HIV-1 restriction factor able to restrict HIV-1 by two mechanisms: 1) conjugation of ISG15 to gag polyprotein via E3 ligase activity, and 2) restricting the production of HIV-1 proteins by an unknown mechanism that is independent of its E3 ligase function. Herc5 mutations of the RCCl-like domain (RLD) and Spacer domains were generated to dissect this mechanism. Based on HIV-1 release assays, the RLD is important for preventing the production of viral proteins. Herc5 transfected cells show defects in cell cycle progression at the G2/M phase as well as abnormal nuclear morphology. C994A and ASpacer constructs did not impact these phenotypes. However, ARLD Herc5 constructs had normal cell cycle and nuclear morphology. Based on the results found, the RLD was found to be an important mediator of Herc5 cellular and antiviral activity

Perspectives of healthcare providers on the nutritional management of patients on haemodialysis in Australia: An interview study

Objective To describe the perspectives of healthcare providers on the nutritional management of patients on haemodialysis, which may inform strategies for improving patient-centred nutritional care. Design Face-to-face semistructured interviews were conducted until data saturation, and thematic analysis based on principles of grounded theory. Setting 21 haemodialysis centres across Australia. Participants 42 haemodialysis clinicians (nephrologists and nephrology trainees (15), nurses (12) and dietitians (15)) were purposively sampled to obtain a range of demographic characteristics and clinical experiences. Results Six themes were identified: responding to changing clinical status (individualising strategies to patient needs, prioritising acute events, adapting guidelines), integrating patient circumstances (assimilating life priorities, access and affordability), delineating specialty roles in collaborative structures (shared and cohesive care, pivotal role of dietary expertise, facilitating access to nutritional care, perpetuating conflicting advice and patient confusion, devaluing nutritional specialty), empowerment for behaviour change (enabling comprehension of complexities, building autonomy and ownership, developing self-efficacy through engagement, tailoring self-management strategies), initiating and sustaining motivation (encountering motivational hurdles, empathy for confronting life changes, fostering non-judgemental relationships, emphasising symptomatic and tangible benefits, harnessing support networks), and organisational and staffing barriers (staffing shortfalls, readdressing system inefficiencies). Conclusions Organisational support with collaborative multidisciplinary teams and individualised patient care were seen as necessary for developing positive patient-clinician relationships, delivering consistent nutrition advice, and building and sustaining patient motivation to enable change in dietary behaviour. Improving service delivery and developing and delivering targeted, multifaceted self-management interventions may enhance current nutritional management of patients on haemodialysis

Statistical hypothesis testing versus machine-learning binary classification: distinctions and guidelines

Making binary decisions is a common data analytical task in scientific research and industrial applications. In data sciences, there are two related but distinct strategies: hypothesis testing and binary classification. In practice, how to choose between these two strategies can be unclear and rather confusing. Here we summarize key distinctions between these two strategies in three aspects and list five practical guidelines for data analysts to choose the appropriate strategy for specific analysis needs. We demonstrate the use of those guidelines in a cancer driver gene prediction example

HybridOctree_Hex: Hybrid Octree-Based Adaptive All-Hexahedral Mesh Generation with Jacobian Control

We present a new software package, "HybridOctree_Hex," for adaptive all-hexahedral mesh generation based on hybrid octree and quality improvement with Jacobian control. The proposed HybridOctree_Hex begins by detecting curvatures and narrow regions of the input boundary to identify key surface features and initialize an octree structure. Subsequently, a strongly balanced octree is constructed using the balancing and pairing rules. Inspired by our earlier preliminary hybrid octree-based work, templates are designed to guarantee an all-hexahedral dual mesh generation directly from the strongly balanced octree. With these pre-defined templates, the sophisticated hybrid octree construction step is skipped to achieve an efficient implementation. After that, elements outside and around the boundary are removed to create a core mesh. The boundary points of the core mesh are connected to their corresponding closest points on the surface to fill the buffer zone and build the final mesh. Coupled with smart Laplacian smoothing, HybridOctree_Hex takes advantage of a delicate optimization-based quality improvement method considering geometric fitting, Jacobian and scaled Jacobian, to achieve a minimum scaled Jacobian that is higher than $0.5$. We empirically verify the robustness and efficiency of our method by running the HybridOctree_Hex software on dozens of complex 3D models without any manual intervention or parameter adjustment. We provide the HybridOctree_Hex source code, along with comprehensive results encompassing the input and output files and statistical data in the following repository: https://github.com/CMU-CBML/HybridOctree_Hex

Neyman-Pearson classification algorithms and NP receiver operating characteristics

In many binary classification applications, such as disease diagnosis and spam detection, practitioners commonly face the need to limit type I error (that is, the conditional probability of misclassifying a class 0 observation as class 1) so that it remains below a desired threshold. To address this need, the Neyman-Pearson (NP) classification paradigm is a natural choice; it minimizes type II error (that is, the conditional probability of misclassifying a class 1 observation as class 0) while enforcing an upper bound, α, on the type I error. Despite its century-long history in hypothesis testing, the NP paradigm has not been well recognized and implemented in classification schemes. Common practices that directly limit the empirical type I error to no more than α do not satisfy the type I error control objective because the resulting classifiers are likely to have type I errors much larger than α, and the NP paradigm has not been properly implemented in practice. We develop the first umbrella algorithm that implements the NP paradigm for all scoring-type classification methods, such as logistic regression, support vector machines, and random forests. Powered by this algorithm, we propose a novel graphical tool for NP classification methods: NP receiver operating characteristic (NP-ROC) bands motivated by the popular ROC curves. NP-ROC bands will help choose α in a data-adaptive way and compare different NP classifiers. We demonstrate the use and properties of the NP umbrella algorithm and NP-ROC bands, available in the R package nproc, through simulation and real data studies

Hierarchical Neyman-Pearson Classification for Prioritizing Severe Disease Categories in COVID-19 Patient Data

COVID-19 has a spectrum of disease severity, ranging from asymptomatic to requiring hospitalization. Understanding the mechanisms driving disease severity is crucial for developing effective treatments and reducing mortality rates. One way to gain such understanding is using a multi-class classification framework, in which patients' biological features are used to predict patients' severity classes. In this severity classification problem, it is beneficial to prioritize the identification of more severe classes and control the "under-classification" errors, in which patients are misclassified into less severe categories. The Neyman-Pearson (NP) classification paradigm has been developed to prioritize the designated type of error. However, current NP procedures are either for binary classification or do not provide high probability controls on the prioritized errors in multi-class classification. Here, we propose a hierarchical NP (H-NP) framework and an umbrella algorithm that generally adapts to popular classification methods and controls the under-classification errors with high probability. On an integrated collection of single-cell RNA-seq (scRNA-seq) datasets for 864 patients, we explore ways of featurization and demonstrate the efficacy of the H-NP algorithm in controlling the under-classification errors regardless of featurization. Beyond COVID-19 severity classification, the H-NP algorithm generally applies to multi-class classification problems, where classes have a priority order