Mitochondrial DNA mutations in breast cancer tissue and in matched nipple aspirate fluid
Bibliographic record
Abstract
Unlike nuclear (n)DNA, of which there is one paired copy per cell, there are many copies of mitochondrial (mt)DNA per cell, making PCR amplification of mtDNA easier in samples of limited cellularity. The aims of this study were to (i) determine the mutation patterns of breast cancers through a comprehensive screen of mtDNA mutations, and (ii) assess if mutations in the cancers are also detectable in breast nipple aspirate fluid (NAF), a physiologic fluid which contains shed ductal epithelial cells. Fifteen breast cancers, matched benign tissues and NAF were collected. Nine overlapping primer sets were used to sequence the entire mitochondrial genome from tissue samples. For NAF samples, we focused on the 19 nucleotide positions (np) where mutations were found in a 3701 bp region (np 15331 to 2463), which includes the displacement (D)-loop, a mtDNA mutation hot spot. Fourteen of the fifteen (93%) cancer samples had > or =1 somatic mtDNA mutation for a total of 45 at 35 np (9 np reported previously, 26 new). Nine of fifteen tumors had > or =2 mutations. The D-loop contained 17 of 45 (38%) and non-D-loop (coding) regions contained 28 (62%) mutations. Of the 28 mutations in the coding loci, 11 led to an amino acid change. The frequency of mtDNA mutations was higher in the D-loop region (1.5 versus 0.18% of loci). 155 polymorphisms were identified (98 reported previously, 57 new). Sixteen of forty-five (36%) mutations were located at polymorphism sites. Four of nineteen mtDNA mutations in 10 cancers located between np 15331 and 2463 were found in matched NAF (two of eleven mutations in the D-loop and two of eight in non-D-loop regions). No mutations were found in five matched NAF samples from women whose cancers lacked a mutation in the same region. In conclusion, mtDNA mutations in breast cancer occur both within and outside of the D-loop, though the mutation rate in the D-loop is over 7-fold higher than in coding areas. We identified 26 new mutation loci (25 in regions sequenced by others, one in an area not). The high frequency of mtDNA mutations at polymorphic loci requires further investigation. Specific mtDNA mutations can be detected in a subset of NAF samples from women with breast cancer.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".