O082 Patient derived renal cell carcinoma tumouroids for personalised renal cancer medicine
Bibliographic record
Abstract
Abstract Introduction Despite the significant advancements in the therapeutic management of renal cell carcinoma (RCC), there is still a pressing need for patient-specific platforms that can predict personalised treatment response. This study aims to optimise renal cancer tumouroids: 3D in vitro models that mimic the tumour density, and incorporate primary cells isolated from RCC surgical specimens and extracellular matrix components to recapitulate the tumour microenvironment. Methods RCC samples were collected post-surgery (n=16), disaggregated using different methods and the isolated cells were grown in 2D and tumouroids. Cell proliferation was compared among different culture conditions by measuring ATP production. Tumouroids were also challenged with a clinically used tyrosine kinase inhibitor, pazopanib. Immunofluorescence and confocal microscopy were used to detect expression of RCC markers and whole-exome sequencing was performed to investigate tumouroids’ resemblance to the parental tumour. Results Patient-derived RCC tumouroids retain the expression of renal cancer associated gene markers, including mutant VHL, and protein markers, including cytokeratins and epithelial-tomesenchymal markers. A non-linear increase in proliferation was observed in the tumouroids which may be indicative of 3D organization and other cellular functions. Tumouroids’ response to pazopanib ranged from none to strong (40%) and tumouroids of higher complexity, that incorporated a stromal compartment, responded stronger than simpler tumouroids (n=3). Conclusion Our results indicate that RCC cells maintain their phenotype and genotype when grown as tumouroids. Future work will compare the response from tumouroids to the responses from both xenografts and the actual patients to determine the suitability of tumouroids as personalised cancer treatment platforms. Take-home message Renal Cell Carcinoma cells isolated from human patients maintain their phenotype and genotype when grown in biomimetic tumouroids and respond to treatment.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".