Sarcomas are rare and biologically diverse cancers, comprising more than 70 histological subtypes, each with distinct genomic and molecular characteristics. Their heterogeneity leads to significant clinical uncertainty, with limited reliable biomarkers available to guide diagnosis, prognosis, or treatment decisions. Although traditional short-read sequencing has uncovered a complex mutational landscape, many critical regulatory mechanisms, particularly transcript isoforms, gene fusions, RNA splice variants, and tumour-cell subpopulations, remain poorly characterised. To address these gaps, this project aims to generate an integrative, high-resolution molecular map of sarcoma using a combination of single-cell RNA sequencing (scRNA-seq) and Oxford Nanopore long-read transcriptomics across a diverse cohort of patient samples.
The student will generate and analyse single-cell transcriptomic profiles to identify tumour subclones, immune microenvironment states, stromal interactions, and transcriptional programs associated with metastatic progression and treatment resistance. This fine-grained cellular resolution will allow the discovery of distinct malignant cell states, lineage trajectories, and rare subpopulations that may drive aggressive behaviour. Complementary long-read sequencing will enable full-length transcript reconstruction, detection of novel isoforms, and validation of structural and splice variants that are often missed by conventional short-read approaches. This dual sequencing strategy provides a comprehensive view of both cellular heterogeneity and transcriptomic complexity.
Using advanced bioinformatics, machine learning, and multi-omics integration, the project will:
1. Characterise intra-tumour heterogeneity and identify clinically relevant cellular states.
2. Detect novel fusion transcripts, isoform switches, and long-noncoding RNAs associated with metastasis.
3. Validate biomarkers across the full sample cohort and correlate molecular signatures with available clinical and pathological metadata.
4. Generate a comprehensive biomarker catalogue to inform translational research and potential diagnostic or prognostic assays.
This project provides extensive training in wet-lab methods, multi-omics data analysis, long-read informatics, single-cell analysis pipelines, and computational oncology. The outcomes have the potential to directly inform future sarcoma diagnostic tools, patient stratification strategies, and precision oncology approaches.
References
1. Patel, A.P., Tirosh, I., Trombetta, J.J. et al. (2014). Single-cell RNA-seq highlights intratumoral heterogeneity in cancer. Science, 344(6190), 1396–1401.
2. Cancer Genome Atlas Research Network (2017). Comprehensive and integrated genomic characterization of adult soft tissue sarcomas. Cell, 171(4), 950–965.
3. Ament, I.H., DeBruyne, N., Wang, F. & Lin, L. (2025). Long-read RNA sequencing: A transformative technology for exploring transcriptome complexity in human diseases. Molecular Therapy, 33, 883–894.
