Zanidatamab monotherapy or combined with chemotherapy in HER2-expressing gastroesophageal adenocarcinoma: a phase 1 trial
Bibliographic record
Abstract
There is a need for novel therapies for patients with previously treated HER2-positive gastroesophageal adenocarcinoma (GEA). This phase 1 (NCT02892123) dose-escalation and expansion trial evaluated zanidatamab (a dual HER2-targeted bispecific antibody) ± chemotherapy in previously treated patients with HER2-expressing, locally advanced/metastatic cancers. Here, we report the outcomes for GEA cohorts receiving zanidatamab monotherapy or with chemotherapy (paclitaxel or capecitabine). The primary endpoint was safety and tolerability. Secondary endpoints were objective response rate (ORR), disease control rate, progression-free survival, pharmacokinetics, and immunogenicity. Seventy patients were enrolled (n = 29 monotherapy; n = 41 combination therapy); most received prior HER2-targeted agents (monotherapy, 93%; combination therapy, 95%). With monotherapy, 69% of patients had any-grade treatment-related AEs (TRAEs); 17% had grade ≥ 3 TRAEs. The most common any-grade TRAEs were diarrhea (41%) and infusion-related reactions (24%). With combination therapy, 98% of patients had any-grade TRAEs; 51% had grade ≥ 3 TRAEs. The most common any-grade TRAEs were diarrhea (68%) and fatigue (44%). Confirmed ORR was 32.1% (95% confidence interval [CI] 15.9–52.4) with monotherapy and 48.6% (95% CI 31.9–65.6) with combination therapy. In heavily pre-treated patients with HER2-expressing GEA, zanidatamab ± chemotherapy had a manageable safety profile and promising antitumor activity. Following progression on HER2-targeted first-line regimens, there are limited HER2-targeted therapies that have demonstrated efficacy in patients with gastroesophageal adenocarcinoma (GEA). Here, the authors report the results of a phase 1 clinical trial investigating zanidatamab (a HER2-targeted bispecific antibody) in heavily pre-treated patients with advanced or metastatic, HER2-expressing GEA.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".