BRCA1 protein levels and PIK3CA mutations as predictive biomarkers for response to neoadjuvant chemotherapy in locally advanced breast cancer: An exploratory analysis
Bibliographic record
Abstract
BRCA1 overexpression and phosphoinositide 3-kinase (PIK3CA) pathway activation are involved in the resistance to DNA damaging agents. Thus, we hypothesized that BRCA1 protein expression and activating PIK3CA mutations are potential tumor biomarkers for the chemotherapeutic response to doxorubicin/cyclophosphamide plus docetaxel in locally advanced breast cancer. Informed consent was obtained and clinical, pathological and response data were collected. BRCA1 protein expression levels were assessed by immunohistochemistry of the archived tissue by two independent pathologists. The PIK3CA mutation status was assessed by nested PCR amplification and DNA sequencing. BRCA1 protein levels and the PIK3CA mutation status were correlated with pathological complete response and a partial response or better using the Chi-square test, Fisher's exact test and logistic regression. Of the 136 eligible participants, 59 samples could be analyzed. There was a trend of relatively low levels of BRCA1 protein achieving a pathological complete response (pCR), although this was not statistically significant [odds ratio (OR)=1.74; p=0.437]. Twenty-eight percent of patients had PIK3CA mutations, but no statistically significant association with pCR (OR=0.977; p=0.971) was noted. Neither BRCA1 protein levels (OR=1.18; p=0.818) nor PIK3CA mutations (OR=1.03; p=0.971) appeared to be associated with the likelihood of achieving a partial response or better from neoadjuvant chemotherapy. PIK3CA wild-type mutation status showed a trend towards an increased likelihood of not presenting with inflammatory disease (OR=5.34; p=0.101). In this exploratory study, neither BRCA1 protein expression levels nor the presence of PIK3CA mutations were significantly associated with chemotherapy response in locally advanced breast cancer. However, the relatively small sample size limits the overall interpretation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".