Quantitative immunohistochemical (IHC) assessment of ataxia-telangiectasia mutated (ATM) in estrogen receptor (ER) negative early breast cancer (BC).
Bibliographic record
Abstract
e21083 Background: ATM plays a key role in the cellular response to DNA damage and germline mutations are associated with a predisposition to BC. We evaluated the impact of ATM expression on outcomes in Stage I-III ER negative BC patients (pts). Methods: A tissue microarray was constructed from formalin-fixed paraffin-embedded specimens of ER negative Stage I-III BC pts from the Tom Baker Cancer Centre. ATM expression was assessed by quantitative fluorescence IHC using a rabbit anti-ATM monoclonal antibody (Epitomics) and the HistoRx AQUA platform. ATM expression index (EI) was calculated by the tumor cell to stroma expression ratio. Prognostic ability of ATM EI was explored univariately, and multivariately after adjusting for clinicopathological factors selected using algorithmic methods, with Cox regression methods. Results: Of 126 eligible pts treated from 1999-2004, 44.4% were HER2-neu + and 52.4% were triple negative. ATM EI was normalized using a logarithmic transformation. Univariately, higher ATM EI was associated with worse outcomes (Table). Multivariately, higher ATM EI remained significantly prognostic for recurrence-free survival (RFS) (HR=5.46, p=0.046), local RFS (HR=5.56, p=0.052), distant RFS (HR=4.38, p=0.080), but not overall survival (p=0.48). Conclusions: High ATM EI is associated with worse RFS in ER negative BC in this hypothesis-generating study. Validation of this finding is currently ongoing. [Table: see text]
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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) | 0.000 | 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) | 0.003 | 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".