Human-specific GAPDH RT-qPCR is an accurate and sensitive method of xenograft metastasis quantification
Bibliographic record
Abstract
Abstract Metastasis is the primary cause of cancer-related mortality. Having experimental models that accurately reflect changes in the metastatic burden is imperative for developing improved treatments and a better understanding of the disease. The murine xenograft tumor model mimics the human scenario and provides a platform for in vivo and ex vivo metastasis quantification analyses. Histological analysis of hematoxylin and eosin (H&E) stained thin sections has been the gold standard for quantifying metastasis ex vivo but gaining favor for its ease and accuracy is reverse transcription-qualitative polymerase chain reaction (RT-qPCR). Herein we directly compare histological and RT-qPCR-based methods for quantifying lung metastasis in a murine xenograft tumor model. Furthermore, we have introduced a variation of the RT-qPCR method; human-specific glyceraldehyde 3-phosphate dehydrogenase (GAPDH) RT-qPCR, which allows quantification of metastasis in xenograft models, without the requirement of overexpression of exogenous genes. Human-specific GAPDH RT-qPCR detected increased lung metastasis resulting from aldehyde dehydrogenase 1A3 (ALDH1A3) expression in MDA-MB-231 breast cancer cells orthotopically implanted in NOD/SCID mice. Further, in the xenograft tumor model, human-specific GAPDH RT-qPCR was more sensitive and cost-effective than quantification of lung metastasis by histological analysis of H&E stained fixed thin sections. The two assays were highly correlative in terms of determining relative metastatic burden, suggesting that the human-specific GAPDH RT-qPCR method could be used as a standard method for quantification of disseminated human cells in murine xenograft models.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".