{"database":"EGA","file_versions":[],"scores":null,"additional":{"omics_type":["Genomics"],"contact_person":["Yinyin Yuan"],"full_dataset_link":["https://ega-archive.org/dacs/EGAC00001000433"],"host":["EGA"],"description":["EGA DAC EGAC00001000433"],"repository":["EGA"],"email":["yinyin.yuan@icr.ac.uk"],"pubmed_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.","The tumor microenvironment is pivotal in influencing cancer progression and metastasis. Different cells co-exist with high spatial diversity within a patient, yet their combinatorial effects are poorly understood. We investigate the similarity of the tumor microenvironment of 192 local metastatic lesions in 61 ovarian cancer patients. An ecologically inspired measure of microenvironmental diversity derived from multiple metastasis sites is correlated with clinicopathological characteristics and prognostic outcome. We demonstrate a high accuracy of our automated analysis across multiple sites. A low level of similarity in microenvironmental composition is observed between ovary tumor and corresponding local metastases (stromal ratio r = 0.30, lymphocyte ratio r = 0.37). We identify a new measure of microenvironmental diversity derived from Shannon entropy that is highly predictive of poor overall (p = 0.002, HR = 3.18, 95% CI = 1.51-6.68) and progression-free survival (p = 0.0036, HR = 2.83, 95% CI = 1.41-5.7), independent of and stronger than clinical variables, subtype stratifications based on single cell types alone and number of sites. Although stromal influence in ovary tumors is known to have significant clinical implications, our findings reveal an even stronger impact orchestrated by diverse cell types. Quantitative histology-based measures can further enable objective selection of patients who are in urgent need of new therapeutic strategies such as combinatorial treatments targeting heterogeneous tumor microenvironment."],"pubmed_title":["Quantitative histology analysis of the ovarian tumour microenvironment.","Similarity and diversity of the tumor microenvironment in multiple metastases: critical implications for overall and progression-free survival of high-grade serous ovarian cancer."],"pubmed_authors":["Lan Chunyan C, Heindl Andreas A, Huang Xin X, Xi Shaoyan S, Banerjee Susana S, Liu Jihong J, Liu Jihong J, Yuan Yinyin Y","Heindl Andreas A, Lan Chunyan C, Rodrigues Daniel Nava DN, Koelble Konrad K, Yuan Yinyin Y"],"additional_accession":[]},"is_claimable":false,"name":"Yuanlab","description":"Data Access Committee EGAC00001000433","dates":{"output":"2025-1-9"},"accession":"EGAC00001000433","cross_references":{"TAXONOMY":["9606"],"pubmed":["27661102","26573438"],"EGA":["EGAS00001001694","EGAS00001002065","EGAD00010001099","EGAD00010000881"]}}