Pathway activity analysis of bulk and single-cell RNA-Seq data

Gene expression profiling can produce effective biomarkers that can provide additional information beyond other approaches for characterizing disease. While these approaches are typically performed on standard bulk RNA sequencing data, new methods for RNA sequencing of individual cells have allowed these approaches to be applied at the resolution of a single cell. As these methods enter the mainstream, there is an increased need for user-friendly software that allows researchers without experience in bioinformatics to apply these techniques. In this thesis, I have developed new, user-friendly data resources and software tools to allow researchers to use gene expression signatures in their own datasets. Specifically, I created the Single Cell Toolkit, a user-friendly and interactive toolkit for analyzing single-cell RNA sequencing data and used this toolkit to analyze the pathway activity levels in breast cancer cells before and after cancer therapy. Next, I created and validated a set of activated oncogenic growth factor receptor signatures in breast cancer, which revealed additional heterogeneity within public breast cancer cell line and patient sample RNA sequencing datasets. Finally, I created an R package for rapidly profiling TB samples using a set of 30 existing tuberculosis gene signatures. I applied this tool to look at pathway differences in a dataset of tuberculosis treatment failure samples. Taken together, the results of these studies serve as a set of user-friendly software tools and data sets that allow researchers to rapidly and consistently apply pathway activity methods across RNA sequencing samples

Statistical approaches to harness high throughput sequencing data in diverse biological systems

The development of novel statistical approaches to questions specific to biological systems of interest is becoming more valuable as we tackle increasingly complex problems. This thesis explores three distinct biological systems in which high throughput sequencing data is utilised, varying in research area, organism, number of sequencing platforms and datasets integrated, and structure such as matched samples; showcasing the variety of study designs and thus the need for tailored statistical approaches. First, we characterise allelic imbalance from RNA-Seq data including stringent filtering criteria and a count based likelihood ratio test. This work identified genes of particular importance in livestock genomics such as those related to energy use. Second, we outline a novel methodology to identify highly expressed genes and cells for single cell RNA-Seq data. We derive a gamma-normal mixture model to identify lowly and highly expressed components, and use this to identify novel markers for olfactory sensory neuron (OSN) maturity across publicly available mouse neuron datasets. In addition we estimate single cell networks and find that mature OSN single cell networks are more centralised than immature OSN single cell networks. Third, we develop two novel frameworks for relating information from Whole Exome DNA-Seq and RNA-Seq data when i) samples are matched and when ii) samples are not necessary matched between platforms. In the latter case, we relate functional somatic mutation driver gene scores to transcriptional network correlation disturbance using a permutation testing framework, identifying potential candidate genes for targeted therapies. In the former case, we estimate directed mutation-expression networks for each cancer using linear models, providing a useful exploratory tool for identifying novel relationships among genes. This thesis demonstrates the importance of tailored statistical approaches to further understanding across many biological systems

A systematic assessment of cell type deconvolution algorithms for DNA methylation data

We performed systematic assessment of computational deconvolution methods that play an important role in the estimation of cell type proportions from bulk methylation data. The proposed framework methylDeConv (available as an R package) integrates several deconvolution methods for methylation profiles (Illumina HumanMethylation450 and MethylationEPIC arrays) and offers different cell-type-specific CpG selection to construct the extended reference library which incorporates the main immune cell subsets, epithelial cells and cell-free DNAs. We compared the performance of different deconvolution algorithms via simulations and benchmark datasets and further investigated the associations of the estimated cell type proportions to cancer therapy in breast cancer and subtypes in melanoma methylation case studies. Our results indicated that the deconvolution based on the extended reference library is critical to obtain accurate estimates of cell proportions in non-blood tissues.U01 OH011478/OH/NIOSH CDC HHS/United StatesU01 OH012257/OH/NIOSH CDC HHS/United StatesU01OH011478/ACL HHS/United StatesU01 OH011478/OH/NIOSH CDC HHS/United State

Molecular portraits of patients with intrahepatic cholangiocarcinoma who diverge as rapid progressors or long survivors on chemotherapy

OBJECTIVE: Cytotoxic agents are the cornerstone of treatment for patients with advanced intrahepatic cholangiocarcinoma (iCCA), despite heterogeneous benefit. We hypothesised that the pretreatment molecular profiles of diagnostic biopsies can predict patient benefit from chemotherapy and define molecular bases of innate chemoresistance. DESIGN: We identified a cohort of advanced iCCA patients with comparable baseline characteristics who diverged as extreme outliers on chemotherapy (survival 23 m in long survivors, LS). Diagnostic biopsies were characterised by digital pathology, then subjected to whole-transcriptome profiling of bulk and geospatially macrodissected tissue regions. Spatial transcriptomics of tumour-infiltrating myeloid cells was performed using targeted digital spatial profiling (GeoMx). Transcriptome signatures were evaluated in multiple cohorts of resected cancers. Signatures were also characterised using in vitro cell lines, in vivo mouse models and single cell RNA-sequencing data. RESULTS: Pretreatment transcriptome profiles differentiated patients who would become RPs or LSs on chemotherapy. Biologically, this signature originated from altered tumour-myeloid dynamics, implicating tumour-induced immune tolerogenicity with poor response to chemotherapy. The central role of the liver microenviroment was confrmed by the association of the RPLS transcriptome signature with clinical outcome in iCCA but not extrahepatic CCA, and in liver metastasis from colorectal cancer, but not in the matched primary bowel tumours. CONCLUSIONS: The RPLS signature could be a novel metric of chemotherapy outcome in iCCA. Further development and validation of this transcriptomic signature is warranted to develop precision chemotherapy strategies in these settings

Reading the postcolonial island in Amitav Ghosh's The Hungry Tide.

This paper argues that literature has much to contribute to the theoretical work of island studies, and not just because literary texts provide evidence of the ways islands are conceptualized in different historical and cultural contexts. To this end, it discusses Amitav Ghosh’s The Hungry Tide (2004), a novel which actively theorizes key concepts in island studies. The Hungry Tide is set in the Sundarbans, an “immense archipelago” in the Ganges delta, and tells the largely forgotten history of the forced evacuation of refugees from the island of Morichjhãpi in 1979. The liminal space of the Sundarbans, the “tide country”, is an extraordinary setting for a literary exploration of the relationship between postcolonial island geographies and identities. Ghosh’s depiction of the “watery labyrinth” (Ghosh, 2004: 72) and “storm-tossed islands” (Ghosh, 2004: 164) of the Sundarbans raises and addresses questions, which should be at the heart of the critical meta-discourse of island studies