  The aetiology of head and neck squamous cell carcinoma (HNSCC) involves
multiple carcinogens such as alcohol, tobacco and infection with human
papillomavirus (HPV). As the HPV infection influences the prognosis, treatment
and survival of patients with HNSCC, it is important to determine the HPV
status of these tumours. In this paper, we propose a novel triplet-ranking loss
function and a multiple instance learning pipeline for HPV status prediction.
This achieves a new state-of-the-art performance in HPV detection using only
the routine H&E stained WSIs on two HNSCC cohorts. Furthermore, a comprehensive
tumour microenvironment profiling was performed, which characterised the unique
patterns between HPV+/- HNSCC from genomic, immunology and cellular
perspectives. Positive correlations of the proposed score with different
subtypes of T cells (e.g. T cells follicular helper, CD8+ T cells), and
negative correlations with macrophages and connective cells (e.g. fibroblast)
were identified, which is in line with clinical findings. Unique gene
expression profiles were also identified with respect to HPV infection status,
and is in line with existing findings.
