Clonal Changes in Tumours During Growth and Progression Evaluated by Southern Gel Analysis of Random Integrations of Foreign DNA
Bibliographic record
Abstract
We have exploited random integrations of foreign DNA as a means of genetically tagging tumour cell populations with which to analyse the clonal evolution of tumour growth in vivo. Transfection of a non-metastatic mouse mammary carcinoma called SP1 (or a metastatic variant, SP1HU9L) with the pSV2neo plasmid or retrovirus vector infection with a "clipped-wing' vector (delta p delta eMoTN) was used to generate large numbers of uniquely marked tumour cell clones in single-step selections. The basic approach was to pool large numbers of independently marked transfectants or infectants, inject these cells into mice and analyse the resulting primary tumours and/or metastases later. Overgrowth or derivation of tumour masses by a limited number of clones could be detected by Southern gel analysis. The main findings were: (i) injection of pooled populations containing large numbers of uniquely marked cell clones (up to several thousand) invariably resulted in advanced primary tumours that contained a very limited number of clones, and in some cases only one easily detectable clone; (ii) primary tumours could be overgrown within six weeks by the progeny of the same single metastatic clone when the inoculum contained 1-10% metastatic cells, which suggests that metastatic SP1 cells have a selective growth advantage in primary tumours as well as for metastatic spread; and (iii) spontaneous lung metastases were clonal or biclonal at the time of analysis. The results show that spontaneous metastases can develop from a genetically distinct subpopulation of cells in a non-random (i.e. selective) manner. Because primary tumours can become overgrown by the progeny of a metastatic clone, results of any comparison of the properties of a primary tumour with a distant metastasis could be affected by the stage at which the primary tumour is removed and analysed.
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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 | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".