The role of the phosphatase and tensin homolog status in predicting pathological complete response to neoadjuvant anti-HER2 therapies in HER2-positive primary breast cancer
Bibliographic record
Abstract
PURPOSE: The addition of anti-HER2 therapies to neoadjuvant treatment significantly enhances pathological complete response (PCR) rate in patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer. Selecting patients unlikely to benefit from neoadjuvant anti-HER2 therapies is increasingly important. In this study, we proposed to assess the role of the phosphatase and tensin homolog (PTEN) as a biomarker in predicting PCR to neoadjuvant anti-HER2 therapies by conducting meta-analysis. METHODS: Our team searched Embase, Medline, and the Cochrane Library by the end of September 16, 2018, for trials on patients with HER2-positive breast cancer treated with neoadjuvant anti-HER2 therapies. The associations between PTEN expression and PCR rate were then assessed. Odds ratio (ORs) and hazard ratio (HRs) with 95% confidence intervals (CIs) with 2-sided P values were calculated. The Newcastle-Ottawa scale (NOS) was used to estimate the quality of the involved trials. RESULTS: A total of 820 patients from 8 trials were included in this meta-analysis. Overall, the PTEN normal tumors was related to a significant increase in PCR rate (OR 0.55; 95% CI = 0.31-0.96; P = .04; I = 54%). In different anti-HER2 agents analysis, the PTEN normal tumors was related to a significant increase in PCR rate in patients treated with trastuzumab alone (OR 0.40; 95% CI = 0.24-0.67; P = .0005; I = 15%). Besides, no significant association between PTEN status and PCR rate was detected in patients treated with lapatinib alone (OR 1.90; 95% CI = 0.78-4.60; P = .16; I = 0%) or trastuzumab plus lapatinib (OR 1.27; 95% CI = 0.27-5.97; P = .76; I = 73%). CONCLUSION: Based on current evidence, PTEN status could be n suitable biomarker in predicting PCR rate to neoadjuvant anti-HER2 therapies, especially in trastuzumab-treated patients.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.024 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".