Computational tumor stroma reaction evaluation led to novel prognosis-associated fibrosis and molecular signature discoveries in high-grade serous ovarian carcinoma

Abstract

BackgroundAs one of the key criteria to differentiate benign vs. malignant tumors in ovarian and other solid cancers, tumor-stroma reaction (TSR) is long observed by pathologists and has been found correlated with patient prognosis. However, paucity of study aims to overcome subjective bias or automate TSR evaluation for enabling association analysis to a large cohort.Materials and methodsServing as positive and negative sets of TSR studies, H&E slides of primary tumors of high-grade serous ovarian carcinoma (HGSOC) (n = 291) and serous borderline ovarian tumor (SBOT) (n = 15) were digitally scanned. Three pathologist-defined quantification criteria were used to characterize the extents of TSR. Scores for each criterion were annotated (0/1/2 as none-low/intermediate/high) in the training set consisting of 18,265 H&E patches. Serial of deep learning (DL) models were trained to identify tumor vs. stroma regions and predict TSR scores. After cross-validation and independent validations, the trained models were generalized to the entire HGSOC cohort and correlated with clinical characteristics. In a subset of cases tumor transcriptomes were available, gene- and pathway-level association studies were conducted with TSR scores.ResultsThe trained models accurately identified the tumor stroma tissue regions and predicted TSR scores. Within tumor stroma interface region, TSR fibrosis scores were strongly associated with patient prognosis. Cancer signaling aberrations associated 14 KEGG pathways were also found positively correlated with TSR-fibrosis score.ConclusionWith the aid of DL, TSR evaluation could be generalized to large cohort to enable prognostic association analysis and facilitate discovering novel gene and pathways associated with disease progress.

Spatiotemporal architecture of immune cells and cancer-associated fibroblasts in high-grade serous ovarian carcinoma

“…Our results show that early relapse is associated with a significant reduction in epithelial cancer cells and increases in fibroblasts in fibroblast-enriched zones. While the observation of a diminished proportion of cancer cells amidst an increased fibroblast population may initially appear paradoxical, it aligns consistently with findings derived from numerous investigations using whole-slide images in large cohorts of patients with ovarian cancer ( 76 – 81 ). A plausible interpretation of the correlation between early relapse and a lower cancer-fibroblast ratio is that cytotoxic chemotherapy and/or cytotoxic immune cells cannot access cancer cells embedded within the stromal matrix.…”

The prognostic value of tumor-stroma ratio and a newly developed computer-aided quantitative analysis of routine H&E slides in high-grade serous ovarian cancer

“…( 15) Furthermore, the results from our automated image analyses are consistent with Lan et al and Jiang et al, who described an automated image analysis of TSR, using the WT, in which higher percentages of stromal cells were associated with worse survival. (16,17) In addition to the previous studies, we indicated that TSR is a prognostic parameter in both the MI and WT region and that both regions can be computationally scored generating similar results to images scored by pathologists. We also depicted that the TSR is not a prognostic parameter in patients treated with NACT, which could be a result of stromal recomposition.…”

“…Additionally, studies that have investigated the in uence of manually determined TSR have focused on the most invasive (MI) tumor region, (14,15) while the few studies based on automated image analysis have focused on the whole tumor (WT), limiting comparability between both scoring techniques. (16,17) Advances in the infrastructure of digital pathology, improvement of deep learning models, and the existence of national databases result in the opportunity to further develop automated image analysis methods.…”