Taking aim with IAP antagonists at triple-negative breast cancer: a moving target no more?
Bibliographic record
Abstract
Triple-negative breast cancer (TN-BC) is a malignancy with the worst prognosis for all four corresponding stages compared with other receptor-positive BCs 1 . TN-BC account for 10–20% of all BCs, with BC affecting one in nine women over their lifespan. TN-BC is defined by immunohistochemistry as lacking the estrogen receptor (ER-negative), the progesterone receptor that is normally induced by functional estrogen receptor (PR-negative) and lacking the human epidermal growth factor 2 (Her2) receptor overexpression or amplification as well (Her2-negative). Hence, TN-BC is nonresponsive to targeted BC therapies such as small-molecule endocrine therapies typified by anti-estrogens (ER antagonists) or aromatase inhibitors (that block conversion of endogenous androgens to estrogens), and alternatively to biologics that target the Her2 plasma membrane receptor. These targeted therapies have become mainstays in BC treatment and adjuvant therapy resulting in improved survival for those receptor-positive BC patients. Traditional cytotoxic chemotherapy, consisting of taxanes and other agents, remains the only therapeutic option for metastatic TN-BC. The limited success with these drugs drives the need for new effective therapies for this “untargetable” BC.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".