The interesting relationship between APOBEC3 deoxycytidine deaminases and cancer: a long road ahead
Bibliographic record
Abstract
Cancer is considered a group of diseases characterized by uncontrolled growth and spread of abnormal cells and is propelled by somatic mutations. Apolipoprotein B mRNA-editing enzyme catalytic polypeptide-like 3 (APOBEC3) family of enzymes are endogenous sources of somatic mutations found in multiple human cancers. While these enzymes normally act as an intrinsic immune defence against viruses, they can also catalyse 'off-target' cytidine deamination in genomic single-stranded DNA intermediates. The deamination of cytosine forms uracil, which is promutagenic in DNA. Key factors to trigger the APOBEC 'off-target' activity are overexpression in a non-normal cell type, nuclear localization and replication stress. The resulting uracil-induced mutations contribute to genomic variation, which may result in neutral, beneficial or harmful consequences for the cancer. This review summarizes the functional and biochemical basis of the APOBEC3 enzyme activity and highlights their relationship with the most well-studied cancers in this particular context such as breast, lung, bladder, and human papillomavirus-associated cancers. We focus on APOBEC3A, APOBEC3B and APOBEC3H haplotype I because they are the leading candidates as sources of somatic mutations in these and other cancers. Also, we discuss the prognostic value of the APOBEC3 expression in drug resistance and response to therapies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".