Mutator phenotype in cancer: Timing and perspectives
Bibliographic record
Abstract
Normal human cells replicate their DNA with exceptional accuracy. During every division cycle, each daughter cell receives a full and accurate complement of genetic information. It has been estimated that approximately one error occurs during DNA replication for each 10(9) to 10(10) nucleotides polymerized. Stem cells, the cells that are progenitors of cancer, may replicate their genes even more accurately. In contrast, the malignant cells that constitute a tumor are markedly heterogeneous and exhibit multiple chromosomal abnormalities and alterations in the nucleotide sequence of DNA. To account for the disparity between the rarity of mutations in normal cells and the large numbers of mutations present in cancer, we initially hypothesized that during tumor progression, cancer cells must exhibit a mutator phenotype. In this perspective, we summarize the evidence supporting a mutator phenotype in human cancer, analyze recent measurements of mutations in human cancer, consider the timing for the expression of a mutator phenotype, and focus on the important consequences of large numbers of random mutations in human tumors.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".