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.

Size-Based Differentiation of Cancer and Normal Cells by a Particle Size Analyzer Assisted by a Cell-Recognition PC Software

“…[1][2][3][4][5][6][7] Localized cancers can be detected by a histopathological examination of suspicious tissue, but this technique has constraints due to the fact that the analysis varies from person to person according to their visual acuity and experience. 8) On the other hand, it has been reported that non-adherent abnormal CTCs can be detected based on the expression of tumor antigens and biophysical deformities. 9,[11][12][13][14][15][16][17][18] The positive expression of CTC protein markers such as cytokeratin -8, -18 and -19, CD44 and the epithelial cell adhesion molecule (EpCAM) is often found in a CTC.…”

Similarity and diversity of the tumor microenvironment in multiple metastases: critical implications for overall and progression-free survival of high-grade serous ovarian cancer

“…Our previous work [25] demonstrated how fully automated image analysis using hematoxylin and eosin (H&E) slides of ovary tumors can enable the identification of ovarian cancer subtypes that were consistent with molecular subtyping previously reported [26, 48]. These subtypes were defined based on proportions of cells in histological ovary tumor sections alone: a high lymphocyte ratio group with good prognosis and a high stromal ratio group with poor prognosis [25].…”

“…Debulking status is known as a key clinical variable for advanced ovarian cancer [7, 10, 50], and was found to be prognostic in our previous study that included patients with only ovary tumor samples (OS and PFS p < 0.01) [25]. However, it was not found to be associated with OS or PFS in the patient subset with more than one local metastasis under study here ( p > 0.05, Table 3).…”

“…These subtypes were defined based on proportions of cells in histological ovary tumor sections alone: a high lymphocyte ratio group with good prognosis and a high stromal ratio group with poor prognosis [25]. However, different types of cells interact and co-exist with high spatial heterogeneity within a patient, yet the collective effect of cell diversity has not been studied.…”

When machine vision meets histology: A comparative evaluation of model architecture for classification of histology sections

“…Recent studies on quantitative histology analysis (Lan et al, 2015; Rogojanu et al, 2015; Huijbers et al, 2013; de Kruijf et al, 2011) reveal that the tumor-stroma ratio is a prognostic factor in many different tumor types, and it is therefore interesting and desirable to know how such an index plays its role in KIRC, which can be fulfilled with two steps as follows, (i) identification/classification of tumor/stromal regions in tissue histology sections for the construction of tumor-stroma ratio; and (ii) correlative analysis of the derived tumor-stroma ratio with clinical outcome. Therefore, in this study, we aim to validate the model architecture for the three-category classification (i.e., Tumor, Normal, and Stromal) on the KIRC dataset, where the images are curated from the whole slide images (WSI) scanned with a 40 X objective (0.252 micron/pixel).…”