PD-L1 expression in breast cancer brain metastases
Bibliographic record
Abstract
Abstract Background To evaluate the potential intracranial efficacy of immunotherapy among patients with breast cancer brain metastases (BrM), we analyzed the immunohistochemical expression of programmed death-ligand 1 (PD-L1), a predictive biomarker of response to immunotherapy. Methods In this single-center retrospective cohort study, consecutive patients with breast cancer BrM (immunotherapy naïve) who underwent surgery for BrM at Sunnybrook Health Sciences Center between July 1999 and June 2013 were identified. PD-L1 expression by immunohistochemistry (IHC) was assessed on BrM samples in triplicate; PD-L1 positive status was defined as PD-L1 expression ≥1% on tumor-infiltrating cells as a percentage of tumor area using the Ventana SP142 antibody. Estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor (HER2) status was determined using 2018 ASCO/CAP guidelines. Results The median patient age at the time of BrM diagnosis was 52 (range 32–85). PD-L1 expression using the SP42 antibody was identified in 9 out of 59 (15.3%) breast cancer BrM. The frequency of PD-L1 positive BrM by subtype is as follows: TNBC (n = 3/12, 25.0%), HER2+/HR- (n = 3/14, 21.4%), HR+/HER2- (n = 2/18, 11.1%), and HER2+/HR+ (n = 1/14, 7.1%). 24-month brain-specific progression-free survival was 66.7% (95% CI 37.9%–100%) among patients with PD-L1 positive BrM versus 42% (95% CI 26.6%–67.3%) among those with PD-L1 negative BrM (log-rank P-value .142). Conclusions One in 7 patients in our cohort had PD-L1 positive BrM; this proportion was highest (25%) among those with TNBC. Intracranial efficacy of immunotherapy warrants further study, particularly among patients with treatment-naïve metastatic TNBC, for whom extracranial efficacy has already been established.
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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 | 0.001 | 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".