Neurotrophic receptor tyrosine kinase family members in secretory and non-secretory breast carcinomas
Bibliographic record
Abstract
BACKGROUNDBreast cancer is the most common female cancer and a major cause of morbidity and mortality.Progress in breast cancer therapeutics has been attained with the introduction of targeted therapies for specific sub-sets.However, other subsets lack targeted interventions and thus there is persisting need for identification and characterization of molecular targets in order to advance breast cancer therapeutics. AIMTo analyze the role of lesions in neurotrophic receptor tyrosine kinase (NTRK) genes in breast cancers. METHODSAnalysis of publicly available genomic breast cancer datasets was performed for identification and characterization of cases with fusions and other molecular abnormalities involving NTRK1, NTRK2 and NTRK3 genes. RESULTSNTRK fusions are present in a small number of breast cancers at the extensive GENIE project data set which contains more than 10000 breast cancers.These cases are not identified as secretory in the database, suggesting that the histologic characterization is not always evident.In the breast cancer The Cancer Genome Atlas (TCGA) cohort the more common molecular lesion in NTRK genes is amplification of NTRK1 observed in 7.9% of breast cancers. CONCLUSIONNeurotrophin receptors molecular lesions other than fusions are observed more often than fusions.However, currently available NTRK inhibitors are effective Stravodimou A et al.NTRK in breast cancer WJCO https://www.wjgnet.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".