Mitochondrial genome analysis in biofluids for early cancer detection and monitoring
Bibliographic record
Abstract
BACKGROUND: Biofluids collected in a non-invasive fashion are potentially valuable samples for assaying genomic alterations for early detection and monitoring of cancer. The low cellularity and nucleic acid content in biofluids, the high copy number of the mitochondrial genome (mtgenome) and its noted early imprints in cancer make this molecule theoretically more sensitive than nuclear targets to measure for early cancer detection. OBJECTIVE: This review explores mtgenome analysis in biofluids and addresses the question of whether targeting the mtgenome in biofluids is superior or equivalent to analysis of nuclear genomic alterations. METHODS: The literature was retrieved from PubMed using a combination of the following keywords: mtDNA, mutation, deletion, content, copy number, cancer, biofluids, bodily fluids and the specific cancers described here. Studies that analyzed mtgenome alterations in biofluids were included. Analytical methods available for assaying mtgenome changes in biofluids are discussed. RESULTS: Despite the limited data available, mtgenome changes in biofluids have been demonstrated in a wide variety of cancer patients. CONCLUSION: Mtgenome analysis in biofluids is feasible and relatively easy. Despite the paucity of data, tumor-specific mtgenome changes are observed in biofluids of cancer patients. Given the multiple copies per cell of the mtgenome, future cancer detection efforts should consider complementary analysis of mtgenome changes in biofluids.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".