Abstract PO-077: High-throughput 3D-spheroid invasion assay: A powerful tool to identify novel drugs targeting tumor micro-environment in HNSCC
Bibliographic record
Abstract
Abstract Cancer metastasis is a complex cascade that involves activation of cancer cell invasion and migration. This activation is greatly attributed by multiple factors, including the tumor-microenvironment (TME). Cancer-associated fibroblasts (CAF), a prominent cell type within the TME, have been shown to promote cancer cell invasion and migration, causing metastasis in various cancers including head and neck squamous cell carcinoma (HNSCC). The molecular mechanisms of how CAFs promote HNSCC invasion remain elusive. Our research goal is to understand how CAFs influence HNSCC cells to become invasive and potentially metastatic. Using a top-down research approach, our aim is to identify novel therapeutic chemical probe/drug regimens that potentially target CAF-dependent HNSCC cancer cell-invasion. In-order to perform high-throughput small molecule screens, we have established a 384-well format, three-dimensional (3D) spheroid invasion assay, as a powerful tool to study CAF-dependent HNSCC cancer cell invasion. This platform is currently being used to screen small molecule libraries and identify putative molecular targets, providing insights into underlying mechanisms of CAF-induced cancer cell invasion and candidate therapeutic strategies. Citation Format: Kunal Karve, Stephanie Poon, Panagiotis Prinos, Laurie Ailles. High-throughput 3D-spheroid invasion assay: A powerful tool to identify novel drugs targeting tumor micro-environment in HNSCC [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Innovating through Basic, Clinical, and Translational Research; 2023 Jul 7-8; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2023;29(18_Suppl):Abstract nr PO-077.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.004 | 0.002 |
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".