Brigatinib (BRG) in patients (pts) with crizotinib (CRZ)-refractory ALK+ non-small cell lung cancer (NSCLC) and brain metastases in the pivotal randomized phase 2 ALTA trial.
Bibliographic record
Abstract
e20502 Background: The CNS is often a site of first disease progression in CRZ-treated ALK+ NSCLC. The ALTA trial is assessing BRG, an investigational next-generation ALK inhibitor, in pts with CRZ-refractory advanced ALK+ NSCLC, including pts with baseline brain metastases. Methods: In ALTA (NCT02094573), pts were stratified by presence of baseline brain metastases and best response to prior CRZ and randomized 1:1 to receive BRG at 90 mg qd (arm A) or 180 mg qd with a 7-d lead-in at 90 mg (arm B). Here, we show data for pts with baseline brain metastases. An independent review committee (IRC) assessed intracranial efficacy. Results: Of 222 pts (112 in arm A; 110 in arm B), 80 (71%)/74 (67%) in A/B had baseline brain metastases per investigators, with median age 49/55 y; 74%/76% had received chemotherapy. As of May 31, 2016, 51%/59% of these pts continued to receive BRG in A/B; median follow-up was 9.6/11.4 mo. Intracranial efficacy is shown in the table. Among these pts, most common treatment-emergent adverse events were: nausea 35%/46% (A/B), headache 30%/31%, vomiting 29%/31%, diarrhea 21%/38%, cough 25%/32%; grade ≥3: increased blood CPK 1%/12%, hypertension 4%/7%, increased lipase 4%/3%. Conclusions: BRG yielded substantial intracranial responses with robust iPFS and acceptable safety in ALK+ NSCLC pts with baseline brain metastases in ALTA. 180 mg (with lead-in) showed consistently improved intracranial efficacy compared with 90 mg. Clinical trial information: NCT02094573. [Table: see text]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".