BackgroundThe cross talk between the stroma and cancer cells plays a major role in phenotypic modulation. During peritoneal carcinomatosis ovarian cancer cells interact with mesenchymal stem cells (MSC) resulting in increased metastatic ability. Understanding the transcriptomic changes underlying the phenotypic modulation will allow identification of key genes to target. However in the context of personalized medicine we must consider inter and intra tumoral heterogeneity. In this study we used a pathway-based approach to illustrate the role of cell line background in transcriptomic modification during a cross talk with MSC.MethodsWe used two ovarian cancer cell lines as a surrogate for different ovarian cancer subtypes: OVCAR3 for an epithelial and SKOV3 for a mesenchymal subtype. We co-cultured them with MSCs. Genome wide gene expression was determined after cell sorting. Ingenuity pathway analysis was used to decipher the cell specific transcriptomic changes related to different pro-metastatic traits (Adherence, migration, invasion, proliferation and chemoresistance).ResultsWe demonstrate that co-culture of ovarian cancer cells in direct cellular contact with MSCs induces broad transcriptomic changes related to enhance metastatic ability. Genes related to cellular adhesion, invasion, migration, proliferation and chemoresistance were enriched under these experimental conditions. Network analysis of differentially expressed genes clearly shows a cell type specific pattern.ConclusionThe contact with the mesenchymal niche increase metastatic initiation and expansion through cancer cells’ transcriptome modification dependent of the cellular subtype. Personalized medicine strategy might benefit from network analysis revealing the subtype specific nodes to target to disrupt acquired pro-metastatic profile.
Background: The 2019 NCCN guidelines and the College of American Pathology (CAP) endorse consistent, unambiguous comprehensive pathology reporting for invasive breast cancer. Challenges surrounding inter-pathologist variability and the lack of quantitative, standardized approaches to histologic grade are significant and critical to patient management. We developed an automated multi-network machine learning platform for histologic grading and examined performance with clinical outcome. Methods: Using the Cancer Genome Atlas (TCGA) breast cancer (BCA) image dataset and clinical data as a training cohort, we evaluated 420 conventional H&E images (one slide per patient). An artificial intelligent (AI) / deep-learning based workflow is used to normalize staining differences, identify regions of tumor vs. normal and characterize individual cellular attributes of breast cancer grading systems including overall gland structure, nuclear morphology, and mitotic figures. Individual features were correlated with overall survival using the concordance index (c-index). Support vector models and Kaplan-Meier incidence curves were used to further understand feature importance. Results: Using the AI platform, 88 image features representing BCA gland morphology and cellular-nuclear attributes were generated from 420 patients with 36 events including metastasis and or death with a median overall survival (OS) of 5-years. Three clinical variables were available including AJCC stage, grade and age at diagnosis. Image features were prioritized on C-index <0.5 or >0.5 which reflects increased or reduced risk of poor outcome, respectively. A training model consisting of image features + clinical data produced a C-index of 0.85, Hazards ratio 11, p<0.001. Only 1 clinical feature (stage) and 9 imaging features (all with individual c-indices <0.5) representing nuclear shape, size, number and mitotic figure activity were selected.
Conclusions: Our innovative lab-based AI / deep-learning platform produced accurate BCA risk models to predict metastasis and OS through automated H&E BCA grading. Future analyses include a multi-site validation study to confirm these initial training results.
Citation Format: Marcel Prastawa, Abishek Sainath Madduri, Brandon Veremis, Alexander Shtabsky, Bahram Marami, Jack Zeineh, Michael Joseph Donovan, Gerardo Fernandez. The application of machine learning techniques to standardize breast cancer grading and develop multivariate risk outcome models [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P3-08-11.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.