Decoding Treatment Resistance and Genotype-Phenotype Interactions at Single Cell Resolution

<p>Understanding how cancer cells and their tumor microenvironment (TME) mediate chemoradiation (CRT) resistance in esophageal adenocarcinoma (EAC) is key for improving the low treatment response rate in the clinic. Here, we generated single cell transcriptomics data of 302,035 cells from N=83 longitudinal samples that was used to identify TME and cancer cell programs associated with treatment responses along the course of CRT. Our data showed that interferon-activated immune cells acquired higher tumor-reactivate phenotypes. We also found a large expansion of myeloid cells with different infiltration frequencies between the good-responders and non-responders during and after CRT. Furthermore, we identified four TME ecotypes and four cancer expression subtypes that were strongly associated with CRT responses. This data was used to construct a prognostic 12-gene panel that stratified patients into clinically meaningful survival subgroups. Collectively, our data provided valuable insights into the biology of EAC CRT responses and identified novel predictive biomarkers and actionable targets to enhance CRT efficacy. Another important biological question is whether the intra-tumor heterogeneity (ITH) in cancer cell gene expression programs stem from genomic alteration events, or alternatively, from other layers of gene regulation (i.e. epigenetic regulation). Due to the sparsity of scRNA-seq data, it is very challenging to identify gnomic alteration events (i.e. copy number alterations). To address this question, we developed wellDR-seq, a high-throughput single cell sequencing method capable of profiling the whole genome and transcriptome simultaneously of thousands of single cells. By applying this method to estrogen receptor positive breast tumor samples, we were able to identify somatic genomic alterations in normal cell types, cancer ancestral cells, and cancer cells. Furthermore, this method allowed us to link genomic aberrations to their corresponding gene expression programs. We directly quantified the gene-dosage effects of subclonal genomic aberrations on gene expression levels and provided evidence for the impact of the interactions between genotypes and phenotypes on cancer initiation and progression. In summary, this dissertation provided valuable insights into the biology of EAC CRT response and identified novel predictive biomarkers and actionable targets to enhance CRT efficacy. Moreover, we reported a high-throughput single cell DNA/RNA co-assay method which enabled direct mapping of genotypes to phenotypes at an unprecedented resolution and scale.</p>

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation (PhD)

Year dc:date.available

2024

Authors dc:creator

• Ye, Rui
• <p>https://orcid.org/0000-0002-4021-8093</p>

Contributors dc:contributor

• Steven Lin & Nicholas Navin
• Guillermina Lozano
• Ken Chen

• Esophageal cancer
• chemoradiation
• single cell genomics
• treatment resistance
• Bioinformatics
• Biology
• Biotechnology
• Genetics and Genomics

Repository record dc:identifier

https://digitalcommons.library.tmc.edu/utgsbs_dissertations/1340

OAI identifier oai:identifier

oai:digitalcommons.library.tmc.edu:utgsbs_dissertations-2397