Primary or adjuvant radiotherapy or chemo-radiotherapy are part of the curative treatment regime in head and neck squamous cell carcinoma (HNSCC). However, in >50% of high-risk patients, treatment resistance leads to loco-regional disease persistence or recurrence. Metastasis to regional lymph nodes is one of the strongest prognostic factors in HNSCC. By using artificial intelligence (AI)-based methods on digitised routine diagnostic pathological images of HNSCC, we have identified histological features indicative of tumour recurrence in patients treated with radical adjuvant chemoradiation following surgery. Examples of these histological images are shown in the figure.
In this project, we will focus on risk-predictive areas encompassing immune cells and aim to characterise in detail the immunoglobulin-producing B cells in primary tumours and lymph nodes. We will use spatial transcriptomics, image CyTOF and multiplex immune-fluorescence staining techniques, as well as B cell receptor sequence analyses. The student will be trained in histological interpretation, as well as computational skills to analyse and integrate these diverse datasets.
1. Spatial transcriptomics and image CyTOF on a selected cohort of HNCC, cancer-free and involved lymph nodes, to characterise the immune cell composition and their activation status.
2. Integrative analyses of molecular and cellular composition of HNCC, cancer-free and involved lymph nodes with and without recurrence.
3. Identified biomarker of recurrence will be tested in larger cohorts. This data will be used in the previously established AI-based methods on digitised pathological images of HNSCC to further improve their detection accuracy.
4. Write paper and thesis.