Lung Adenocarcinoma Intratumoral Expression Heterogeneity Profiling Using Single-Cell RNA-seq Data

The heterogeneity of tumors frequently challenge cancer treatment and diagnosis efficacy. Features of genes in tumors such as expression profiles can vary between tumors and across cells of the same tumor. This biological variation is known to give rise to treatment failures and disease recurrence events in cancer patients. However, a vast amount of biological complexity in cancer has not been sufficiently profiled. An increasingly popular method for interrogating features of tumor heterogeneity is single-cell transcriptome analysis by RNA-seq. Single-cell RNA-sequencing (scRNA-seq) can capture expression levels of individual cells at a given time under given conditions. To address the issue of unknown biological complexity in lung adenocarcinoma, single-cell RNA counts from lung adenocarcinoma cells were analyzed in this experiment to identify distinct cells groups and their profiles of differential gene expression. For each cell group identified by similar expression, a Wilcoxon rank-sum test identified the top 10 genes that were differentially expressed across all other identified cell groups as candidate marker genes. Candidate marker gene sets for each cell group were analyzed for enriched pathways using the Reactome and Gene Ontology (GO) databases. PEM scores were computed for each gene in each cell group. Qualitative terms of preferential expression were computationally determined for each PEM score to provide additional annotation for interpretation of preferential gene expression scoring. Performance of the analysis pipeline featured in this experiment was characterized with simulated data featuring known differential expression (DE) of genes. DE profiles of identified cell groups were integrated into the OncoMX cancer biomarker resource and presented with intuitive graphics for public accessibility of data from this experiment. The assemblage of works in this experiment provided a freely available profile of lung adenocarcinoma gene expression heterogeneity for the verification and detection of cell subpopulation biomarkers.

Author

• Holmes, Evan

Language

• en

Keyword

• scRNA-seq
• cancer
• adenocarcinoma
• lung
• heterogeneity
• single-cell

Date created

• 2020

Type of Work

• Master's Thesis

License

• All rights reserved

Rights statement

• In Copyright

Degree

• M.S.

Advisor

• Mazumder, Raja

Committee Member(s)

• Johnson, William E

Persistent URL

•  https://scholarspace.library.gwu.edu/etd/6969z153r