Making Sense of Intratumor Genetic Heterogeneity: Altered Frequency of Androgen Receptor CAG Repeat Length Variants in Breast Cancer Tissues
Bibliographic record
Abstract
To examine the significance of intratumor genetic heterogeneity (ITGH) of the androgen receptor (AR) gene in breast cancer, patient-matched samples of laser capture microdissected breast tumor cells, adjacent normal breast epithelia cells, and peripheral blood leukocytes were sequenced using a novel next generation sequencing protocol. This protocol measured the frequency of distribution of a variable AR CAG repeat length, a functional polymorphism associated with breast cancer risk. All samples exhibited some degree of ITGH with up to 30 CAG repeat length variants identified. Each type of tissue exhibited a different distribution profile of CAG repeat lengths with substantial differences in the frequencies of zero and 18-25 CAG AR variants. Tissue differences in the frequency of ARs with each of these CAG repeat lengths were significant as measured by paired, twin t-tests. These results suggest that preferential selection of 18-25 CAG repeat length variants in breast tumors may be associated with breast cancer, and support the observation that shorter CAG repeats may protect against breast cancer. They also suggest that merely identifying variant genes will be insufficient to determine the critical mutational events of oncogenesis, which will require measuring the frequency of distribution of mutations within cancerous and matching normal tissues.
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 | 0.000 |
| 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) | 0.001 | 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".