Cancer and the breakdown of multicellularity: What <i>Dictyostelium discoideum</i>, a social amoeba, can teach us
Bibliographic record
Abstract
Ancient pathways promoting unicellularity and multicellularity are associated with cancer, the former being pro-oncogenic and the latter acting to suppress oncogenesis. However, there are only a limited number of non-vertebrate models for studying these pathways. Here, we review Dictyostelium discoideum and describe how it can be used to understand these gene networks. D. discoideum has a unicellular and multicellular life cycle, making it possible to study orthologs of cancer-associated genes in both phases. During development, differentiated amoebae form a fruiting body composed of a mass of spores that are supported atop a stalk. A portion of the cells sacrifice themselves to become non-reproductive stalk cells. Cheating disrupts the principles of multicellularity, as cheater cells alter their cell fate to preferentially become spores. Importantly, D. discoideum has gene networks and several strategies for maintaining multicellularity. Therefore, D. discoideum can help us better understand how conserved genes and pathways involved in multicellularity also influence cancer development, potentially identifying new therapeutic avenues.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".