NSABP B-41, a Randomized Neoadjuvant Trial: Genes and Signatures Associated with Pathologic Complete Response
Bibliographic record
Abstract
PURPOSE: In NSABP B-41, pathologic complete response (pCR) was associated with prolonged survival among women with HER2-positive operable breast cancer treated with neoadjuvant chemotherapy and lapatinib, trastuzumab, or the combination. We used a large human breast cancer gene expression panel to select candidate prognostic biomarkers for pCR among women treated with trastuzumab in NSABP B-41. PATIENTS AND METHODS: Eligible patients had a baseline preadjuvant treatment core biopsy sample, known pCR status, and no withdrawal of consent. We analyzed extracted RNA using the human nCounter Breast Cancer 360 gene expression panel. Gene counts were normalized to housekeeping genes and transformed into logarithmic scale with base 2. To screen for candidate genes and metagene signatures prognostic of pCR, we used univariate logistic regression. Variable selection was done by multivariable logistic regression with lasso regularization. RESULTS: Analyses of data from 130 patients revealed that a composite of gene expression from 19 genes and one gene signature appeared to predict pCR in women with HER2-positive early-stage breast cancer undergoing neoadjuvant chemotherapy with trastuzumab-containing regimens. The identified genes are involved in important pathways such as epithelial-mesenchymal transition, adhesion and migration, estrogen receptor signaling, DNA damage and repair, apoptosis, and proliferation. The AUC from a 10-fold cross-validation on predicting pCR, with these 20 genomic markers in a logistic regression model, was 0.73. CONCLUSIONS: , and a few other genomic markers was highly predictive of pCR after trastuzumab-containing regimens. These findings need to be validated and calibrated in future studies.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".