Abstract 217: Establishment of a spatially defined co-culture model of non-small cell lung cancer organoids and cancer-associated fibroblasts to investigate phenotypic heterogeneity
Bibliographic record
Abstract
Abstract Background: Cancer-associated fibroblasts (CAFs) are heterogeneous, with CAF subtypes being localized in spatially defined areas of tumors. However, it remains unclear which CAF subsets specifically play a role in fundamental processes such as tumor growth, invasion, and response to therapy. This study aims to establish a novel in vitro model that recapitulates the in vivo tumor-stroma spatial relationship to investigate CAF heterogeneity and its impact on tumor cell growth in non-small cell lung cancer (NSCLC). Methods: The in vitro model was established using NSCLC patient-derived CAFs and tumor organoids as a 6:1 ratio. Tumor cells originating from xenograft-derived organoids (XDOs) were embedded in a hydrogel containing CAFs (Day 0) and co-cultured for 5 days. Confocal microscopy was utilized to characterize and measure the proliferation of the XDO tumor cells in the co-culture setup, and the proliferation rate was defined as differences in tumor spheroid area between Day 1 and 5. Single-cell RNA-sequencing (scRNAseq) and gene set enrichment analysis (GSEA) were then used to assess the gene expression and signaling pathways dominant in mono- and co-culture to characterize CAF heterogeneity in this model. Results: Initially, the culture media formulations and the matrix for the growth of both CAFs and tumor cells were screened to assess the compatibility of different culture elements. Both CAFs and XDO tumor cells proliferated well in reduced organoid media (M27RD). This reduced medium and the novel matrix composition (50% Matrigel/50% Rat Tail type I collagen) also allowed for maintaining CAF contractility and XDO proliferation. Utilizing these harmonized media and matrix conditions, a spatially defined co-culture model, which incorporated XDOs as a central cluster surrounded by CAFs, was generated. In confocal microscopy, the proliferation rate of tumor cells was significantly higher in co-cultures with CAFs (adjusted p-value < 0.01), while not significant in mono-culture of tumor cells. The GSEA using the Hallmark gene sets on scRNAseq data generated in three co-cultures identified the heterogeneity among CAFs: two CAFs showed significantly higher effect on tumor cell proliferation with the most highly differential expressed genes (DEGs) enriched with inflammation and mitosis and the top high DEGs associated with epithelial-mesenchymal transition, while the other CAF has no effect. Conclusion: The harmonized co-culture conditions for NSCLC organoids and CAFs enabled the characterization of both cell phenotypes and the building of an informed model architecture to study tumor-stroma communication. The presence of CAFs increased tumor cell growth with enrichment in secreted proteins involved in epithelial-mesenchymal transition. Citation Format: Cheol-Kyu Park, Roya Navab, Elisa D'Arcangelo, Quan Li, Hiroyuki Ogawa, Nikolina Radulovich, Nhu-An Pham, Ming-Sound Tsao. Establishment of a spatially defined co-culture model of non-small cell lung cancer organoids and cancer-associated fibroblasts to investigate phenotypic heterogeneity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 217.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".