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Description
The concept of vaccination dates back further than Edward Jenner's first vaccine using cowpox pustules to confer immunity against smallpox in 1796. Nevertheless, it was Jenner's success that gave vaccines their name and made vaccinia virus (VACV) of particular interest. More than 200 years later there is still the need to understand vaccination from vaccine design to prediction of vaccine efficacy using mathematical models. Post-exposure vaccination with VACV has been suggested to be effective if administered within four days of smallpox exposure although this has not been definitively studied in humans. The first and second chapters analyze post-exposure prophylaxis of VACV in an animal model using v50ΔB13RMγ, a recombinant VACV expressing murine interferon gamma (IFN-γ) also known as type II IFN. While untreated animals infected with wild type VACV die by 10 days post-infection (dpi), animals treated with v50ΔB13RMγ 1 dpi had decreased morbidity and 100% survival. Despite these differences, the viral load was similar in both groups suggesting that v50ΔB13RMγ acts as an immunoregulator rather than as an antiviral. One of the main characteristics of VACV is its resistance to type I IFN, an effect primarily mediated by the E3L protein, which has a Z-DNA binding domain and a double-stranded RNA (dsRNA) binding domain. In the third chapter a VACV that independently expresses both domains of E3L was engineered and compared to wild type in cells in culture. The dual expression virus was unable to replicate in the JC murine cell line where both domains are needed together for replication. Moreover, phosphorylation of the dsRNA dependent protein kinase (PKR) was observed at late times post-infection which indicates that both domains need to be linked together in order to block the IFN response. Because smallpox has already been eradicated, the utility of mathematical modeling as a tool for predicting disease spread and vaccine efficacy was explored in the last chapter using dengue as a disease model. Current modeling approaches were reviewed and the 2000-2001 dengue outbreak in a Peruvian region was analyzed. This last section highlights the importance of interdisciplinary collaboration and how it benefits research on infectious diseases.
ContributorsHolechek, Susan A (Author) / Jacobs, Bertram L (Thesis advisor) / Castillo-Chavez, Carlos (Committee member) / Frasch, Wayne (Committee member) / Hogue, Brenda (Committee member) / Stout, Valerie (Committee member) / Arizona State University (Publisher)
Created2011
Description
Despite the approval of a Dengue virus (DV) vaccine in five endemic countries, dengue prevention would benefit from an immunization strategy highly immunogenic in young infants and not curtailed by viral interference. Problematically, infants younger than 9 year of age, whom are particularly prone to Dengue severe infection and death, cannot be immunized using current approved DV vaccine. The most important issues documented so far are the lack of efficiency and enhancement of the disease in young seronegative recipients, as well as uneven protection against the four DV serotypes. Based on data from clinical trials that showed enhanced performance of dengue vaccines when the host has previous anti-flaviviral immunity, I proposed here an attractive solution to complement the current vaccine: a recombinant measles vaccine vectoring dengue protective antigens to be administered to young infants. I hypothesized that recombinant measles virus expressing Dengue 2 and 4 antigens would successfully induce neutralizing responses against DV2 and 4 and the vaccine cocktail of this recombinant measles can prime anti-flaviviral neutralizing immunity. For this dissertation, I generated and performed preclinical immune assessment for four novel Measles-Dengue (MV-DV) vaccine candidates. I generated four MVs expressing the pre membrane (prM) and full length or truncated (90%) forms of the major envelope (E) from DV2 and DV4. Two virus, MVvac2-DV2(prME)N and MVvac2-DV4(prME), expressed high levels of membrane associated full-length E, while the other two viruses, MVvac2-DV2(prMEsol)N and MVvac2-DV4(prMEsol)N, expressed and secreted truncated, soluble E protein to its extracellular environment. The last two vectored vaccines proved superior anti-dengue neutralizing responses comparing to its corresponding full length vectors. Remarkably, when MVvac2-DV2/4(prMEsol)N recombinant vaccines were combined, the vaccine cocktail was able to prime cross-neutralizing responses against DV 1 and the relatively distant 17D yellow fever virus attenuated strain. Thus, I identify a promising DV vaccination strategy, MVvac2-DV2/4(prMEsol)N, which can prime broad neutralizing immune responses by using only two of the four available DV serotypes. The current MV immunization scheme can be advantageus to prime broad anti-flaviviral neutralizing immunity status, which will be majorly boosted by subsequent chimeric Dengue vaccine approaches.
ContributorsAbdelgalel, Rowida (Author) / Reyes del Valle, Jorge (Thesis advisor) / Mason, Hugh (Thesis advisor) / Lake, Douglas (Committee member) / Stout, Valerie (Committee member) / Frasch, Wayne (Committee member) / Arizona State University (Publisher)
Created2016
Description
Extracellular vesicles (EVs) represent a heterogeneous population of small vesicles, consisting of a phospholipidic bilayer surrounding a soluble interior cargo. These vesicles play an important role in cellular communication by virtue of their protein, RNA, and lipid content, which can be transferred among cells. Peripheral blood is a rich source of circulating EVs. An analysis of EVs in peripheral blood could provide access to unparalleled amounts of biomarkers of great diagnostic, prognostic as well as therapeutic value. In the current study, a plasma EV enrichment method based on pluronic co-polymer was first established and characterized. Plasma EVs from breast cancer patients were then enriched, profiled and compared to non-cancer controls. Proteins signatures that contributed to the prediction of cancer samples from non-cancer controls were created by a random-forest based cross-validation approach. We found that a large portion of these signatures were related to breast cancer aggression. To verify such findings, KIAA0100, one of the features identified, was chosen for in vitro molecular and cellular studies in the breast cancer cell line MDA-MB-231. We found that KIAA0100 regulates cancer cell aggression in MDA-MB-231 in an anchorage-independent manner and is particularly associated with anoikis resistance through its interaction with HSPA1A. Lastly, plasma EVs contain not only individual proteins, but also numerous molecular complexes. In order to measure millions of proteins, isoforms, and complexes simultaneously, Adaptive Dynamic Artificial Poly-ligand Targeting (ADAPT) platform was applied. ADAPT employs an enriched library of single-stranded oligodeoxynucleotides to profile complex biological samples, thus achieving a deep coverage of system-wide, native biomolecules. Profiling of EVs from breast cancer patients was able to obtain a prediction AUC performance of 0.73 when compared biopsy-positive cancer patient to healthy controls and 0.64 compared to biopsy-negative controls and such performance was not associated with the physical breast condition indicated by BIRAD scores. Taken together, current research demonstrated the potential of profiling plasma EVs in searching for therapeutic targets as well as diagnostic signatures.
ContributorsZhong, Zhenyu (Author) / Spetzler, David (Thesis advisor) / Yan, Hao (Thesis advisor) / Lake, Douglas (Committee member) / Mangone, Marco (Committee member) / Arizona State University (Publisher)
Created2018
Description
Cancer researchers have traditionally used a handful of markers to understand the origin of tumors and to predict therapeutic response. Additionally, performing machine learning activities on disparate data sources of varying quality is fraught with inherent bias. The Caris Life Sciences Molecular Database (CMD) is an immense resource for discovery as it contains over 215,000 molecular profiles of tumors with consistently gathered clinical grade molecular data along with immense amounts of clinical outcomes data. This resource was leveraged to generate two artificial intelligence algorithms aiding in diagnosis and one for therapy selection.
The Molecular Disease Classifier (MDC) was trained on 34,352 cases and tested on 15,473 unambiguously diagnosed cases. The MDC predicted the correct tumor type out of thirteen possibilities in the labeled data set with sensitivity, specificity, PPV, and NPV of 90.5%, 99.2%, 90.5% and 99.2% respectively when considering up to 5 predictions for a case.
The availability of whole transcriptome data in the CMD prompted its inclusion into a new platform called MI GPSai (MI Genomic Prevalence Score). The algorithm trained on genomic data from 34,352 cases and genomic and transcriptomic data from 23,137 cases and was validated on 19,555 cases. MI GPSai can predict the correct tumor type out of 21 possibilities on 93% of cases with 94% accuracy. When considering the top two predictions for a case, the accuracy increases to 97%.
Finally, a 67 gene molecular signature predictive of efficacy of oxaliplatin-based chemotherapy in patients with metastatic colorectal cancer was developed - FOLFOXai. The signature was predictive of survival in an independent real-world evidence (RWE) dataset of 412 patients who had received FOLFOX/BV in 1st line and inversely predictive of survival in RWE data from 55 patients who had received 1st line FOLFIRI. Blinded analysis of TRIBE2 samples confirmed that FOLFOXai was predictive of OS in both oxaliplatin-containing arms (FOLFOX HR=0.629, p=0.04 and FOLFOXIRI HR=0.483, p=0.02).
The Molecular Disease Classifier (MDC) was trained on 34,352 cases and tested on 15,473 unambiguously diagnosed cases. The MDC predicted the correct tumor type out of thirteen possibilities in the labeled data set with sensitivity, specificity, PPV, and NPV of 90.5%, 99.2%, 90.5% and 99.2% respectively when considering up to 5 predictions for a case.
The availability of whole transcriptome data in the CMD prompted its inclusion into a new platform called MI GPSai (MI Genomic Prevalence Score). The algorithm trained on genomic data from 34,352 cases and genomic and transcriptomic data from 23,137 cases and was validated on 19,555 cases. MI GPSai can predict the correct tumor type out of 21 possibilities on 93% of cases with 94% accuracy. When considering the top two predictions for a case, the accuracy increases to 97%.
Finally, a 67 gene molecular signature predictive of efficacy of oxaliplatin-based chemotherapy in patients with metastatic colorectal cancer was developed - FOLFOXai. The signature was predictive of survival in an independent real-world evidence (RWE) dataset of 412 patients who had received FOLFOX/BV in 1st line and inversely predictive of survival in RWE data from 55 patients who had received 1st line FOLFIRI. Blinded analysis of TRIBE2 samples confirmed that FOLFOXai was predictive of OS in both oxaliplatin-containing arms (FOLFOX HR=0.629, p=0.04 and FOLFOXIRI HR=0.483, p=0.02).
ContributorsAbraham, Jim (Author) / Spetzler, David (Thesis advisor) / Frasch, Wayne (Thesis advisor) / Lake, Douglas (Committee member) / Compton, Carolyn (Committee member) / Arizona State University (Publisher)
Created2020