Mutator phenotype in cancer: Timing and perspectives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it