Higher serum PD‐L1 level predicts increased overall survival with lapatinib versus trastuzumab in the CCTG MA.31 phase 3 trial
Bibliographic record
Abstract
BACKGROUND: The purpose of this retrospective biomarker study of the Canadian Cancer Trials Group (CCTG) MA.31 randomized phase 3 trial (lapatinib vs trastuzumab) of HER2-positive metastatic breast cancer (MBC) was to evaluate the prognostic and predictive biomarker utility of pretreatment serum programmed death ligand 1 (PD-L1) levels. METHODS: CCTG MA.31 accrued 652 HER2-positive patients; 387 had serum available (185 in the trastuzumab arm and 202 in the lapatinib arm). The Ella immunoassay platform (ProteinSimple, San Jose, California) was used to quantitate serum PD-L1 levels. Stepwise forward Cox multivariable analyses were performed for progression-free survival and overall survival (OS). RESULTS: In the whole trial population, continuous pretreatment serum PD-L1 levels were not associated with OS. However, within the trastuzumab arm, a higher continuous pretreatment serum PD-L1 level was significant for shorter OS (hazard ratio [HR], 3.85; P = .04), but within the lapatinib arm, pretreatment serum PD-L1 was not associated with OS (P = .37). In the whole trial, in a multivariable analysis for OS, serum PD-L1 (median cut point) remained a significant independent covariate (HR, 2.38; P = .001). There was a significant interaction between treatment arm and continuous serum PD-L1 (bootstrap method; P = .0025): at or above 214.2 pg/mL (the 89th percentile), serum PD-L1 was associated with significantly shorter OS with trastuzumab treatment versus lapatinib treatment. CONCLUSIONS: In the CCTG MA.31 trial, serum PD-L1 was a significant predictive factor: a higher pretreatment serum PD-L1 level was associated with shorter OS with trastuzumab treatment but with longer OS with lapatinib treatment. Immune evasion may decrease the effectiveness of trastuzumab therapy. Further evaluation of elevated serum PD-L1 in advanced breast cancer is warranted to identify patients with HER2-positive MBC who may benefit from novel immune-targeted therapies in addition to trastuzumab.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".