Quantitative histology analysis of the ovarian tumour microenvironment.
Abstract
Concerted efforts in genomic studies examining RNA transcription and DNA methylation patterns have revealed profound insights in prognostic ovarian cancer subtypes. On the other hand, abundant histology slides have been generated to date, yet their uses remain very limited and largely qualitative. Our goal is to develop automated histology analysis as an alternative subtyping technology for ovarian cancer that is cost-efficient and does not rely on DNA quality. We developed an automated system for scoring primary tumour sections of 91 late-stage ovarian cancer to identify single cells. We demonstrated high accuracy of our system based on expert pathologists' scores (cancer = 97.1%, stromal = 89.1%) as well as compared to immunohistochemistry scoring (correlation = 0.87). The percentage of stromal cells in all cells is significantly associated with poor overall survival after controlling for clinical parameters including debulking status and age (multivariate analysis p = 0.0021, HR = 2.54, CI = 1.40-4.60) and progression-free survival (multivariate analysis p = 0.022, HR = 1.75, CI = 1.09-2.82). We demonstrate how automated image analysis enables objective quantification of microenvironmental composition of ovarian tumours. Our analysis reveals a strong effect of the tumour microenvironment on ovarian cancer progression and highlights the potential of therapeutic interventions that target the stromal compartment or cancer-stroma signalling in the stroma-high, late-stage ovarian cancer subset.
Collections
Subject
Stromal Cells
Humans
Ovarian Neoplasms
Disease Progression
Neoplasm Staging
Prognosis
Disease-Free Survival
Immunohistochemistry
Survival Rate
Multivariate Analysis
Proportional Hazards Models
Automation
Adult
Aged
Aged, 80 and over
Middle Aged
Female
Tumor Microenvironment
Research team
Computational Pathology & Integrated Genomics
Language
eng
Date accepted
2015-10-12
License start date
2015-11-17
Citation
Scientific reports, 2015, 5 pp. 16317 - ?
Publisher
NATURE PUBLISHING GROUP