Biomarker Alteration to Neoadjuvant Chemotherapy Predict Pathological Response and Prognosis in Breast Cancer Patients
Bibliographic record
Abstract
Background: The values of biomarkers expression might be changed following neoadjuvant chemotherapy (NACT), but little is known about the change range and its relationship to prognosis. This study aimed to investigate the potential changes of biomarkers expression before and after neoadjuvant chemotherapy, then predicting the pathological response and prognosis to NACT. Methods: A total of 119 patients who were initially diagnosed of breast cancer and underwent neoadjuvant chemotherapy were included in the study. Miller-Payne grading system was used to evaluate the pathologic response after neoadjuvant chemotherapy. Survival curves were estimated using the Kaplan-Meier method, and the log-rank test was used to test for differences between groups. Results: The high expression of ER, PR and Ki67 pre-NACT, the biomarkers expression post-NACT is also high (All P values <0.05). We found that the change of biomarkers expression before and after chemotherapy were all considered as medium changes (range between 10 to 30), while only PR expression change after NACT were associated with distant disease-free survival ( P <0.001) and overall survival ( P =0.031,6). PR expression also related to pathologic response ( P =0.028) but not ER, HER2 and Ki-67. Furthermore, a total of 67 down regulated of Ki67 expression compared with 37 up regulated expression, the results showed that decreasing expression of Ki67 had fewer local recurrence compared with Ki67 increasing expression after NACT. Conclusions: Our research have provided the prognostic value of biomarkers expression change following the neoadjuvant chemotherapy. These findings might help optimize the choice of targeted therapy and improve the predictive effect to patient survival.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".