A Genome-Wide Screen in Mice To Identify Cell-Extrinsic Regulators of Pulmonary Metastatic Colonisation
Bibliographic record
Abstract
Abstract Metastatic colonization, whereby a disseminated tumor cell is able to survive and proliferate at a secondary site, involves both tumor cell-intrinsic and -extrinsic factors. To identify tumor cell-extrinsic (microenvironmental) factors that regulate the ability of metastatic tumor cells to effectively colonize a tissue, we performed a genome-wide screen utilizing the experimental metastasis assay on mutant mice. Mutant and wildtype (control) mice were tail vein-dosed with murine metastatic melanoma B16-F10 cells and 10 days later the number of pulmonary metastatic colonies were counted. Of the 1,300 genes/genetic locations (1,344 alleles) assessed in the screen 34 genes were determined to significantly regulate pulmonary metastatic colonization (15 increased and 19 decreased; P < 0.005 and genotype effect <-55 or >+55). While several of these genes have known roles in immune system regulation (Bach2, Cyba, Cybb, Cybc1, Id2, Igh-6, Irf1, Irf7, Ncf1, Ncf2, Ncf4 and Pik3cg) most are involved in a disparate range of biological processes, ranging from ubiquitination (Herc1) to diphthamide synthesis (Dph6) to Rho GTPase-activation (Arhgap30 and Fgd4), with no previous reports of a role in the regulation of metastasis. Thus, we have identified numerous novel regulators of pulmonary metastatic colonization, which may represent potential therapeutic targets.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".