A digital score of tumour-associated stroma infiltrating lymphocytes predicts survival in head and neck squamous cell carcinoma

Muhammad Shaban, Shan E Ahmed Raza, Mariam Hassan, Arif Jamshed, Sajid Mushtaq, Asif Loya, Nikos Batis, Jill Brooks, Paul Nankivell, Neil Sharma, Max Robinson, Hisham Mehanna, Syed Ali Khurram, Nasir Rajpoot

Research output: Contribution to journalArticlepeer-review

Abstract

The infiltration of T-lymphocytes in the stroma and tumour is an indication of an effective immune response against the tumour, resulting in better survival. In this study, our aim is to explore the prognostic significance of tumour-associated stroma infiltrating lymphocytes (TASILs) in head and neck squamous cell carcinoma (HNSCC) through an AI based automated method. A deep learning based automated method was employed to segment tumour, tumour-associated stroma, and lymphocytes in digitally scanned whole slide images of HNSCC tissue slides. The spatial patterns of lymphocytes and tumour-associated stroma were digitally quantified to compute the tumour-associated stroma infiltrating lymphocytes score (TASIL-score). Finally, the prognostic significance of the TASIL-score for disease-specific and disease-free survival was investigated with the Cox proportional hazard analysis. Three different cohorts of Haematoxylin & Eosin (H&E) stained tissue slides of HNSCC cases (n=537 in total) were studied, including publicly available TCGA head and neck cancer cases. The TASIL-score carries prognostic significance (p=0.002) for disease-specific survival of HNSCC patients. The TASIL-score also shows a better separation between low- and high-risk patients as compared to the manual Tumour Infiltrating Lymphocytes (TILs) scoring by pathologists for both disease-specific and disease-free survival. A positive correlation of TASIL-score with molecular estimates of CD8+ T cells was also found, which is in line with existing findings. To the best of our knowledge, this is the first study to automate the quantification of TASIL from routine H&E slides of head and neck cancer. Our TASIL-score based findings are aligned with the clinical knowledge with the added advantages of objectivity, reproducibility, and strong prognostic value. Although we validated our method on three different cohorts (n=537 cases in total), a comprehensive evaluation on large multicentric cohorts is required before the proposed digital score can be adopted in clinical practice.
Original languageEnglish
JournalJournal of Pathology
Early online date26 Oct 2021
DOIs
Publication statusE-pub ahead of print - 26 Oct 2021

Keywords

  • digital pathology
  • deep learning
  • tumour-associated stroma
  • survival analysis
  • head and neck squamous cell carcinoma
  • artificial intelligence
  • machine learning

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