Filtering by
![154070-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-09/154070-Thumbnail%20Image.png?versionId=BE9fYV5UvAnmMh471E.u_vazT027mA97&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240616/us-west-2/s3/aws4_request&X-Amz-Date=20240616T024648Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=f38bdb253b9a24337d3c38b1e42a270379f376acd2a8c4adcd7cb305ac832554&itok=Y4QAUBYo)
The deluge of next-generation sequencing data nowadays has shifted the bottleneck of cancer research from multiple “-omics” data collection to integrative analysis and data interpretation. In this dissertation, I attempt to address two distinct, but dependent, challenges. The first is to design specific computational algorithms and tools that can process and extract useful information from the raw data in an efficient, robust, and reproducible manner. The second challenge is to develop high-level computational methods and data frameworks for integrating and interpreting these data. Specifically, Chapter 2 presents a tool called Snipea (SNv Integration, Prioritization, Ensemble, and Annotation) to further identify, prioritize and annotate somatic SNVs (Single Nucleotide Variant) called from multiple variant callers. Chapter 3 describes a novel alignment-based algorithm to accurately and losslessly classify sequencing reads from xenograft models. Chapter 4 describes a direct and biologically motivated framework and associated methods for identification of putative aberrations causing survival difference in GBM patients by integrating whole-genome sequencing, exome sequencing, RNA-Sequencing, methylation array and clinical data. Lastly, chapter 5 explores longitudinal and intratumor heterogeneity studies to reveal the temporal and spatial context of tumor evolution. The long-term goal is to help patients with cancer, particularly those who are in front of us today. Genome-based analysis of the patient tumor can identify genomic alterations unique to each patient’s tumor that are candidate therapeutic targets to decrease therapy resistance and improve clinical outcome.
![152847-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-08/152847-Thumbnail%20Image.png?versionId=OoXmrMqi6gYT9vXtrmWDCY.SAzyjtiBu&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240616/us-west-2/s3/aws4_request&X-Amz-Date=20240616T024648Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=886a0e8cf7bd3af8da41caae45e64543c4357fad4c2f1507850c4f7894d1eb89&itok=HqKUGwJX)
![152740-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-09/152740-Thumbnail%20Image.png?versionId=khLvjguuCK_52NaJav082JpBFuTtccAt&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240616/us-west-2/s3/aws4_request&X-Amz-Date=20240616T024648Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=538d3716487bc771eb029558c735835a262fa26d2e780d19257ee8b377682afd&itok=Mbbs9KkD)
![155725-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-09/155725-Thumbnail%20Image.png?versionId=BZ6zzIg9TMfLoPBj5oIXWQ6Z71J2wxjR&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240616/us-west-2/s3/aws4_request&X-Amz-Date=20240616T075537Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=12de5b3f1c928d11eb46486b9d6bbb1025a7f0a58eb9bb8a968a51c4abc15a9c&itok=wc6asWJd)
Firstly, a biodosimetry is developed using RF to determine absorbed radiation dose from gene expression measured from blood samples of potentially exposed individuals. To improve the prediction accuracy of the biodosimetry, day-specific models were built to deal with day interaction effect and a technique of nested modeling was proposed. The nested models can fit this complex data of large variability and non-linear relationships.
Secondly, a panel of biomarkers was selected using a data-driven feature selection method as well as handpick, considering prior knowledge and other constraints. To incorporate domain knowledge, a method called Know-GRRF was developed based on guided regularized RF. This method can incorporate domain knowledge as a penalized term to regulate selection of candidate features in RF. It adds more flexibility to data-driven feature selection and can improve the interpretability of models. Know-GRRF showed significant improvement in cross-species prediction when cross-species correlation was used to guide selection of biomarkers. The method can also compete with existing methods using intrinsic data characteristics as alternative of domain knowledge in simulated datasets.
Lastly, a novel non-parametric method, RFerr, was developed to generate prediction interval using RF regression. This method is widely applicable to any predictive models and was shown to have better coverage and precision than existing methods on the real-world radiation dataset, as well as benchmark and simulated datasets.
![155994-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-08/155994-Thumbnail%20Image.png?versionId=sTLdy1U3u6bsNuT8vAaAtKnJSfBtDAXJ&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240616/us-west-2/s3/aws4_request&X-Amz-Date=20240616T023934Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=f106e78fc94e744e362ab9ab2b7e59380c1eec5b5572007fd0153ea31afdbcbc&itok=3SbKzrRr)
This dissertation proposes two PageRank-based analytical methods, Pathways of Topological Rank Analysis (PoTRA) and miR2Pathway, discussed in Chapter 1 and Chapter 2, respectively. PoTRA focuses on detecting pathways with an altered number of hub genes in corresponding pathways between two phenotypes. The basis for PoTRA is that the loss of connectivity is a common topological trait of cancer networks, as well as the prior knowledge that a normal biological network is a scale-free network whose degree distribution follows a power law where a small number of nodes are hubs and a large number of nodes are non-hubs. However, from normal to cancer, the process of the network losing connectivity might be the process of disrupting the scale-free structure of the network, namely, the number of hub genes might be altered in cancer compared to that in normal samples. Hence, it is hypothesized that if the number of hub genes is different in a pathway between normal and cancer, this pathway might be involved in cancer. MiR2Pathway focuses on quantifying the differential effects of miRNAs on the activity of a biological pathway when miRNA-mRNA connections are altered from normal to disease and rank disease risk of rewired miRNA-mediated biological pathways. This dissertation explores how rewired gene-gene interactions and rewired miRNA-mRNA interactions lead to aberrant activity of biological pathways, and rank pathways for their disease risk. The two methods proposed here can be used to complement existing genomics analysis methods to facilitate the study of biological mechanisms behind disease at the systems-level.
![157966-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-09/157966-Thumbnail%20Image.png?versionId=YQ70Qb938z6qwGzju5mwwe9lBMkJkS5I&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240616/us-west-2/s3/aws4_request&X-Amz-Date=20240616T071529Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=b6136d6633d671c75a548416089594119c788452cd4502ae238512e71c8273a6&itok=_eZXnAFq)
designing personalized treatments and improving clinical outcomes of cancers. Such
investigations require accurate delineation of the subclonal composition of a tumor, which
to date can only be reliably inferred from deep-sequencing data (>300x depth). The
resulting algorithm from the work presented here, incorporates an adaptive error model
into statistical decomposition of mixed populations, which corrects the mean-variance
dependency of sequencing data at the subclonal level and enables accurate subclonal
discovery in tumors sequenced at standard depths (30-50x). Tested on extensive computer
simulations and real-world data, this new method, named model-based adaptive grouping
of subclones (MAGOS), consistently outperforms existing methods on minimum
sequencing depth, decomposition accuracy and computation efficiency. MAGOS supports
subclone analysis using single nucleotide variants and copy number variants from one or
more samples of an individual tumor. GUST algorithm, on the other hand is a novel method
in detecting the cancer type specific driver genes. Combination of MAGOS and GUST
results can provide insights into cancer progression. Applications of MAGOS and GUST
to whole-exome sequencing data of 33 different cancer types’ samples discovered a
significant association between subclonal diversity and their drivers and patient overall
survival.
![158771-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-09/158771-Thumbnail%20Image.png?versionId=64.R10SUWEL_AxkWDD4lICRxoR_gU2eo&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240616/us-west-2/s3/aws4_request&X-Amz-Date=20240616T071529Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=66e5758cd1cc54ed9a8cf2652013046f0379bee24535cb7c34d73fa3fd00f303&itok=pzfZs3rU)
![161916-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-11/161916-Thumbnail%20Image.png?versionId=nHWAScIfIC3rTQR0bK77xR9glYO7cZ6T&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240615/us-west-2/s3/aws4_request&X-Amz-Date=20240615T025046Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=f447934c917752e8480b064d9083601f9cba8cc3d05103ef19c4216e82e4b3f7&itok=_1j9bU6J)
![129573-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-04/129573-Thumbnail%20Image.png?versionId=1J0lcC9w1T0IGwStNeFbO4C27UHEiYRk&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240602/us-west-2/s3/aws4_request&X-Amz-Date=20240602T224638Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=8abb716aa291752cf83aee6c8a35177c428a8c2805cd185830a885104f40b199&itok=sg7uRARO)
Bacterial lipopolysaccharides (LPS) are structural components of the outer membranes of Gram-negative bacteria and also are potent inducers of inflammation in mammals. Higher vertebrates are extremely sensitive to LPS, but lower vertebrates, like fish, are resistant to their systemic toxic effects. However, the effects of LPS on the fish intestinal mucosa remain unknown. Edwardsiella ictaluri is a primitive member of the Enterobacteriaceae family that causes enteric septicemia in channel catfish (Ictalurus punctatus). E. ictaluri infects and colonizes deep lymphoid tissues upon oral or immersion infection. Both gut and olfactory organs are the primary sites of invasion. At the systemic level, E. ictaluri pathogenesis is relatively well characterized, but our knowledge about E. ictaluri intestinal interaction is limited. Recently, we observed that E. ictaluri oligo-polysaccharide (O-PS) LPS mutants have differential effects on the intestinal epithelia of orally inoculated catfish. Here we evaluate the effects of E. ictaluri O-PS LPS mutants by using a novel catfish intestinal loop model and compare it to the rabbit ileal loop model inoculated with Salmonella enterica serovar Typhimurium LPS. We found evident differences in rabbit ileal loop and catfish ileal loop responses to E. ictaluri and S. Typhimurium LPS. We determined that catfish respond to E. ictaluri LPS but not to S. Typhimurium LPS. We also determined that E. ictaluri inhibits cytokine production and induces disruption of the intestinal fish epithelia in an O-PS-dependent fashion. The E. ictaluri wild type and ΔwibT LPS mutant caused intestinal tissue damage and inhibited proinflammatory cytokine synthesis, in contrast to E. ictaluri Δgne and Δugd LPS mutants. We concluded that the E. ictaluri O-PS subunits play a major role during pathogenesis, since they influence the recognition of the LPS by the intestinal mucosal immune system of the catfish. The LPS structure of E. ictaluri mutants is needed to understand the mechanism of interaction.
![129510-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-04/129510-Thumbnail%20Image.png?versionId=kxHHeSyIKU0AW88qqAkQVI521K40gKpA&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240530/us-west-2/s3/aws4_request&X-Amz-Date=20240530T155452Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=b7fa1254983d2d4d9fc4ac8be2e75eb1dd25222c21f3d3f8253f2f777db81c1c&itok=jsLM6zCZ)
Contemporary vaccine development relies less on empirical methods of vaccine construction, and now employs a powerful array of precise engineering strategies to construct immunogenic live vaccines. In this review, we will survey various engineering techniques used to create attenuated vaccines, with an emphasis on recent advances and insights. We will further explore the adaptation of attenuated strains to create multivalent vaccine platforms for immunization against multiple unrelated pathogens. These carrier vaccines are engineered to deliver sufficient levels of protective antigens to appropriate lymphoid inductive sites to elicit both carrier-specific and foreign antigen-specific immunity. Although many of these technologies were originally developed for use in Salmonella vaccines, application of the essential logic of these approaches will be extended to development of other enteric vaccines where possible. A central theme driving our discussion will stress that the ultimate success of an engineered vaccine rests on achieving the proper balance between attenuation and immunogenicity. Achieving this balance will avoid over-activation of inflammatory responses, which results in unacceptable reactogenicity, but will retain sufficient metabolic fitness to enable the live vaccine to reach deep tissue inductive sites and trigger protective immunity. The breadth of examples presented herein will clearly demonstrate that genetic engineering offers the potential for rapidly propelling vaccine development forward into novel applications and therapies which will significantly expand the role of vaccines in public health.