Rank the triplets: A ranking-based multiple instance learning framework for detecting HPV infection in head and neck cancers using routine H&E images

TL;DR

This paper introduces a novel triplet-ranking loss function and multiple instance learning framework that significantly improves HPV status prediction in head and neck cancers from routine H&E images, while also profiling tumor microenvironment differences.

Contribution

It presents a new ranking-based learning method for HPV detection and provides comprehensive tumor microenvironment analysis correlated with HPV status.

Findings

Achieved state-of-the-art HPV detection accuracy on two cohorts.

Identified immune cell and gene expression patterns associated with HPV status.

Correlated proposed scores with immune cell subtypes and gene expression profiles.

Abstract

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.