Communications Medicine (Jan 2024)

Computational pathology identifies immune-mediated collagen disruption to predict clinical outcomes in gynecologic malignancies

  • Arpit Aggarwal,
  • Sirvan Khalighi,
  • Deepak Babu,
  • Haojia Li,
  • Sepideh Azarianpour-Esfahani,
  • Germán Corredor,
  • Pingfu Fu,
  • Mojgan Mokhtari,
  • Tilak Pathak,
  • Elizabeth Thayer,
  • Susan Modesitt,
  • Haider Mahdi,
  • Stefanie Avril,
  • Anant Madabhushi

DOI
https://doi.org/10.1038/s43856-023-00428-0
Journal volume & issue
Vol. 4, no. 1
pp. 1 – 12

Abstract

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Abstract Background The role of immune cells in collagen degradation within the tumor microenvironment (TME) is unclear. Immune cells, particularly tumor-infiltrating lymphocytes (TILs), are known to alter the extracellular matrix, affecting cancer progression and patient survival. However, the quantitative evaluation of the immune modulatory impact on collagen architecture within the TME remains limited. Methods We introduce CollaTIL, a computational pathology method that quantitatively characterizes the immune-collagen relationship within the TME of gynecologic cancers, including high-grade serous ovarian (HGSOC), cervical squamous cell carcinoma (CSCC), and endometrial carcinomas. CollaTIL aims to investigate immune modulatory impact on collagen architecture within the TME, aiming to uncover the interplay between the immune system and tumor progression. Results We observe that an increased immune infiltrate is associated with chaotic collagen architecture and higher entropy, while immune sparse TME exhibits ordered collagen and lower entropy. Importantly, CollaTIL-associated features that stratify disease risk are linked with gene signatures corresponding to TCA-Cycle in CSCC, and amino acid metabolism, and macrophages in HGSOC. Conclusions CollaTIL uncovers a relationship between immune infiltration and collagen structure in the TME of gynecologic cancers. Integrating CollaTIL with genomic analysis offers promising opportunities for future therapeutic strategies and enhanced prognostic assessments in gynecologic oncology.