Analysis of cellular heterogeneity in breast cancer by single cell sequencing
<p>Breast cancer is a complex heterogenous 3D ecosystem. The heterogenous composition of breast cancer determines disease progression and treatment responses. Triple receptor negative breast cancer (TNBC) is a distinct subtype with poor clinical outcomes. Deconvolution of spatially-regulated t...
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| Format: | Thesis |
| Language: | English |
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2022
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| Summary: | <p>Breast cancer is a complex heterogenous 3D ecosystem. The heterogenous composition of breast cancer determines disease progression and treatment responses. Triple receptor negative breast cancer (TNBC) is a distinct subtype with poor clinical outcomes. Deconvolution of spatially-regulated transcriptomic and microenvironmental drivers unique to TNBC offers the potential to reveal new therapeutic vulnerabilities.</p>
<p>Single cell RNA sequencing (scRNA-seq) and spatial transcriptomic technologies were applied to three treatment naive patient-derived breast cancer samples. New spatial transcriptomic and scRNA-seq experimental pipelines were established. The new technologies were successfully applied to clinical grade biopsy samples. Cellular heterogeneity within the epithelial and non-epithelial compartment was identified across the three samples. The heterogeneity identified is consistent with the published literature.</p>
<p>Knowledge in the theoretical underpinnings for scRNA-seq analysis along with the skills required for data analysis in a small patient cohort were acquired during the DPhil. The application of algebraic topology, manifold learning and graph theory in evaluating and interpreting scRNA-seq has been studied.</p>
<p>The computational tools available for integrating spatial transcriptomics and scRNA-seq data were critically appraised. Future perspectives on approachesfor multimodal integration were explored.</p> |
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