Intratumor Heterogeneity and Treatment Resistance of Solid Tumors with a Focus on Polyploid/Senescent Giant Cancer Cells (PGCCs)
Bibliographic record
Abstract
Single cell biology has revealed that solid tumors and tumor-derived cell lines typically contain subpopulations of cancer cells that are readily distinguishable from the bulk of cancer cells by virtue of their enormous size. Such cells with a highly enlarged nucleus, multiple nuclei, and/or multiple micronuclei are often referred to as polyploid giant cancer cells (PGCCs), and may exhibit features of senescence. PGCCs may enter a dormant phase (active sleep) after they are formed, but a subset remain viable, secrete growth promoting factors, and can give rise to therapy resistant and tumor repopulating progeny. Here we will briefly discuss the prevalence and prognostic value of PGCCs across different cancer types, the current understanding of the mechanisms of their formation and fate, and possible reasons why these tumor repopulating "monsters" continue to be ignored in most cancer therapy-related preclinical studies. In addition to PGCCs, other subpopulations of cancer cells within a solid tumor (such as oncogenic caspase 3-activated cancer cells and drug-tolerant persister cancer cells) can also contribute to therapy resistance and pose major challenges to the delivery of cancer therapy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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".