Organoid Cultures as Preclinical Models of Non–Small Cell Lung Cancer
Bibliographic record
Abstract
PURPOSE: Non-small cell lung cancer (NSCLC) is the most common cause of cancer-related deaths worldwide. There is an unmet need to develop novel clinically relevant models of NSCLC to accelerate identification of drug targets and our understanding of the disease. EXPERIMENTAL DESIGN: Thirty surgically resected NSCLC primary patient tissue and 35 previously established patient-derived xenograft (PDX) models were processed for organoid culture establishment. Organoids were histologically and molecularly characterized by cytology and histology, exome sequencing, and RNA-sequencing analysis. Tumorigenicity was assessed through subcutaneous injection of organoids in NOD/SCID mice. Organoids were subjected to drug testing using EGFR, FGFR, and MEK-targeted therapies. RESULTS: We have identified cell culture conditions favoring the establishment of short-term and long-term expansion of NSCLC organoids derived from primary lung patient and PDX tumor tissue. The NSCLC organoids recapitulated the histology of the patient and PDX tumor. They also retained tumorigenicity, as evidenced by cytologic features of malignancy, xenograft formation, preservation of mutations, copy number aberrations, and gene expression profiles between the organoid and matched parental tumor tissue by whole-exome and RNA sequencing. NSCLC organoid models also preserved the sensitivity of the matched parental tumor to targeted therapeutics, and could be used to validate or discover biomarker-drug combinations. CONCLUSIONS: Our panel of NSCLC organoids closely recapitulates the genomics and biology of patient tumors, and is a potential platform for drug testing and biomarker validation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".