Abstract B74: Identifying conserved properties of the epithelial-mesenchymal transition in cancer using highly multiplexed single-cell RNA sequencing
Bibliographic record
Abstract
Abstract Epithelial-mesenchymal heterogeneity is ubiquitous among epithelial tissues and the reversible transition (EMT) between these two states provides ovarian cancer cells with plasticity to promote metastasis and resistance to chemotherapy. While extensively studied, no common, EMT-defining expression program has been identified. Leveraging multiplexed single-cell RNA sequencing (scRNA-seq), we compared transcriptional dynamics of the EMT from twelve distinct time course experiments comprising four different human cancer cell types and three different EMT inducers. From this, we identified many context-specific changes, cell type-specific patterns, and a core set of genes whose dynamics are largely conserved, including activation of the canonical EMT genes vimentin and fibronectin, and other less-documented genes, such as the immunoregulatory factors IL32 and CXCL1. Integrating scRNA-seq data from a variety of tumors and healthy epithelium revealed consistent patterns. In general, progression through the EMT was associated with increased expression of cytokines and growth factors. To assess the dependence of the EMT on this paracrine signaling, we perturbed each time course experiment with 22 kinase inhibitors and used scRNA-seq to assess the dependence of the transition on a variety of signaling pathways. This revealed multiple pathways whose perturbation affects the EMT, e.g., inhibition of RIPK1 or triple inhibition of VEGFR, FGFR, and PDGFR strikingly blocks cells at an intermediate state along the EMT continuum. Interestingly, the human ovarian cancer cell line OVCA420 had the strongest association between cytokine expression and EMT progression. While the EMT has been loosely associated with immunoregulation in several cancers, this has yet to be studied in ovarian cancer. To determine if the EMT signature exists in tumors, we generated scRNA-seq data from tumors derived from the syngeneic STOSE and ID8 mouse ovarian cancer cells. Both tumors exhibited significant intratumoral heterogeneity of the EMT signature we had identified, and expression of this signature correlated with many of the same secreted factors, including several chemokines and interleukins. Further, myeloid and lymphocyte populations within the same tumors expressed receptors for several of these factors, suggesting EMT-associated cytokines are not only important for paracrine coordination of the EMT, but they may also modulate immune cell activity within the microenvironment. These results highlight the complexity and diversity of the EMT, but also reveal core genes whose dynamics are conserved across contexts. Coordinated progression through the EMT depends on paracrine signaling from EMT-associated cytokines that likely also affect many cell types in the tumor microenvironment. Targeting their signaling networks may present opportunities to not only reduce metastasis and chemoresistance, but also to reprogram the tumor microenvironment to promote immune cell activation and tumor clearance. Citation Format: Barbara C. Vanderhyden, David P. Cook. Identifying conserved properties of the epithelial-mesenchymal transition in cancer using highly multiplexed single-cell RNA sequencing [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research; 2019 Sep 13-16, 2019; Atlanta, GA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(13_Suppl):Abstract nr B74.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".